However, the role of SM22α in VSMC apoptosis is controversial. Here, we identified that SM22α loss contributed to apoptosis of VSMCs via activation of macrophages. Firstly, deficiency of SM22α enhanced the interaction of VSMCs with macrophages. Macrophages were retained and activated by Sm22α−/− VSMCs via upregulating VCAM-1 expression. The ratio of apoptosis was increased by 1.62-fold in VSMCs treated with the conditional media (CM) from activated RAW264.7 cells, compared to that of the control CM (P < 0.01), and apoptosis of Sm22α−/− VSMCs was higher than that of WT VSMCs (P < 0.001). Next, circRasGEF1B from activated macrophages was delivered into VSMCs promoting ZFP36 expression via stabilization of ZFP36 mRNA. Importantly, circRasGEF1B, as a scaffold, guided ZFP36 to preferentially bind to and decay Bcl-2 mRNA in a sequence-specific manner and triggered apoptosis of VSMCs, especially in Sm22α−/− VSMCs. These findings reveal a novel mechanism by which the circRasGEF1B-ZFP36 axis mediates macrophage-induced VSMC apoptosis via decay of Bcl-2 mRNA, whereas Sm22α−/− VSMCs have a higher sensitivity to apoptosis.Vascular smooth muscle cell (VSMC) apoptosis is a major defining feature of abdominal aortic aneurysm (AAA) and mainly caused by inflammatory cell infiltration. Smooth muscle (SM) 22 Vascular smooth muscle cells (VSMCs) are the main structural cells of blood vessels, and damage or death of VSMCs contributes to multiple vascular pathologies. VSMC phenotypic switching after injury is extensive and has been ascribed to the injury stimulus; in many cases, VSMC phenotypic switching is accompanied by changes in cell proliferation, cell migration, inflammation, and apoptosis. In atherosclerosis, apoptosis of VSMCs has been associated with plaque rupture and aneurysm formation, which are thought to be a result of chronic inflammation . Furtherα and SM α-actin, that is a prominent feature of VSMC phenotypic switching, has been demonstrated in an advanced human abdominal aortic aneurysm (AAA) and mouse model [Sm22α−/− mice develop enhanced inflammatory response and ROS production, which was involved in neointimal hyperplasia through different signaling mechanisms [Sm22α−/− VSMCs have transited to an inflammatory phenotype; however, it is not clear if the modulated VSMCs signal to induce apoptosis. A more recent study using AAV carrying SM22α siRNA or SM22α overexpression plasmid in Ang II-perfused −/−ApoE mice confirmed that the causative role of SM22α deficiency in AAA formation occurs partly through enhancing vascular inflammation rather than increasing cell apoptosis [ApoE−/− mice with AAV-mediated knockdown or overexpression of SM22α in vivo are not enough to exclude a potential causative link between disturbed SM22α expression and VSMC apoptosis.A decrease in VSMC marker genes, including smooth muscle (SM) 22se model , 5. Our chanisms –9, suggepoptosis . HoweverSm22α−/− mice signaled to macrophages and displayed higher sensitivity to apoptosis induced by macrophages. Macrophage-derived circRasGEF1B reprograms VSMCs to apoptosis via directing ZFP36 to selectively bind to and decay Bcl-2 mRNA in vitro and in vivo. Our findings suggest that inflammatory VSMCs favor interacting with macrophages and the resulting activation of the circRasGEF1B-ZFP36 axis is a novel mechanism underlying macrophages inducing VSMC apoptosis.In the present study, we demonstrate that VSMCs of −/−Sm22α mouse line (B6.129S6-Taglntm2(cre)Yec/J) carrying a Cre-recombinase gene inserted into the endogenous SM22α locus was purchased from the Jackson Laboratory. All animal procedures conformed to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health and were approved by the Institutional Animal Care and Use Committee of Hebei Medical University.The −/−Sm22α VSMCs were collected at 4°C, and protease inhibitor cocktail tablet (Roche Inc.) was added to total conditioned media to prevent protein degradation. The protein concentration of each sample was determined using the Bradford assay (Bio-Rad). 200 μg of protein sample was used for mass spectrometry analysis. A filter-aided sample preparation (FASP) protein digestion protocol was used for sample preparation [−/−Sm22α, significantly enriched proteins were identified by Gene Ontology (GO) analysis.Total serum-free media obtained from WT and paration , and reaparation . To screRaw sequencing reads are available from the Gene Expression Omnibus under GEO accession number GSE99811, and data analysis was performed as described previously . To scre−/−Sm22α and WT mice were used for the experiments. Osmotic minipumps  were filled with saline or angiotensin (Ang) II  at a dosage of 1000 ng/kg/min dissolved in saline. The pumps were placed into the subcutaneous space for 4 weeks. After the mice were sacrificed, the aorta was dissected free from the surrounding connective tissue. Pictures were taken with a digital camera and used to measure the outer diameter of the suprarenal aorta as described previously [Ten- to 12-week-old male eviously . Suprareeviously .α-actin  at 4°C overnight, followed by a secondary antibody before staining with the DAB Kit . Nuclei were counterstained with hematoxylin. Sections incubated with species-matched IgG alone were used as negative controls.For immunohistochemical staining, frozen sections were incubated with 3% hydrogen peroxide, followed by blocking with 3% normal blocking serum. The sections were incubated with primary antibodies against CD14  or SM For immunofluorescence staining, frozen aortic sections were incubated with antibodies against CD68 , followed by TRITC-conjugated secondary antibodies. The fluorescence signal was monitored by confocal laser scanning microscopy .−/−Sm22α mice  were isolated by collagenase digestion. The isolated cells were maintained in low-glucose Dulbecco's modified Eagle's medium  containing 10% fetal bovine serum , 100 U/mL penicillin, and 100 μg/mL streptomycin [α-actin. Cells from passages 3 to 5 only were used for further experiments. The mouse VSMC line MOVAS was purchased from ATCC (CRL-2797) and cultured in high-glucose DMEM containing 10% FBS and 0.2 mg/mL G-418. Before being treated with different stimulations, all of the VSMCs were incubated in serum-free medium for 24 h. RAW264.7 cells were purchased from ATCC (TIB-71™) and cultured in low-glucose DMEM containing 10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin.Primary VSMCs from the aortas of WT or ptomycin , 17. Theα was cloned into pCMV-FLAG®-MAT-Tag™-1 Expression Vector . The pAdeno-MCMV-HA-P2A-EGFP was used to pack green fluorescent protein- (GFP-) tagged adenovirus . The VSMCs were infected with 1010 pfu/mL adenovirus for 24 h, washed, maintained in serum-starved medium for 24 h, and then treated with indicated stimulations.Full-length cDNA of SM22The siRNA duplexes targeting mouse circRasGEF1B (si-circRasGEF1B), ZFP36 (si-ZFP36), and scrambled siRNA (si-Con) were designed and obtained from RiboBio ; the siRNAs were transiently transfected into VSMCs using Lipofectamine® RNAiMAX Transfection Reagent (Invitrogen) according to the manufacturer's protocol.The circRasGEF1B sequence was amplified by PCR and constructed into a pLCDH-ciR vector . The different sequences of circRasGEF1B deletion mutants were synthesized and inserted into the pLCDH-ciR vector to overexpress mutant circRasGEF1B. The pLCDH empty vector and pLCDH-circRasGEF1B or pLCDH-circRasGEF1B mutants were transfected into VSMCs with the X-tremeGENE HP DNA transfection reagent  for 24 h according to the manufacturer's protocol and then treated with indicated stimulations.μm pore filter (Corning). VSMCs were pre-seeded in the lower chamber. After achieving confluence, serum-starved VSMCs were stimulated by Ang II (10−5 M) for 24 h. Thereafter, RAW264.7 cells were placed in the upper chamber. After incubating for 6 h, nonmigratory cells on the upper membrane surface were removed, and the cells that traversed and spread on the lower membrane surface were fixed with 4% paraformaldehyde and stained with gentian violet. By utilizing a microscope with a 40x objective, the number of migratory cells per membrane was enumerated. At least three random fields in each filter were examined. Each experiment was performed in triplicate, and migration was expressed as the mean ± SD of total cells counted per field.Macrophage migratory activity was performed using 24-well transwell plates with a 5 −5 M) or indicated treatment, and then, RAW264.7 cells  were added to each well. After 30 min incubation, nonadherent cells were removed carefully by washing with cold phosphate-buffered saline (PBS). The fluorescent intensities were determined by excitation and emission at 490 and 535 nm, respectively. For adenovirus-infected VSMCs, RAW264.7 cells were added to each well, and the adherent cells were counted.VSMCs plated in 96-well culture plates were stimulated with Ang II  according to the manufacturer's instructions.Apoptosis of frozen aortic sections was determined by using the ApopTag Peroxidase In Situ Apoptosis Detection Kit (Millipore), VSMC apoptosis transfected with plasmids was determined by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining, and the fluorescence signal was monitored by confocal laser scanning microscopy .Apoptosis of VSMCs was determined by the Annexin V-FITC/PI Apoptosis Detection Kit  according to the manufacturer's instructions, and the BD LSRFortessa™ flow cytometer (BD Biosciences) was used to analyze the apoptotic index. Alternatively, using the ApopTag Red β-actin/GAPDH or U6 snRNA levels, using the 2−ΔΔCt method, respectively. The sequence for each primer is listed in Supplementary Table Total RNAs were extracted using TRIzol Reagent (Life Technologies), following the manufacturer's instructions. To quantify the amount of mRNA or circRNA, cDNAs were synthesized using the M-MLV First Strand Kit (Life Technologies), and quantitative PCR was performed using SYBR Green qPCR SuperMix-UDG (Life Technologies). For microRNA, total RNA was extracted by using the QIAzol Lysis Reagent. Reverse transcription and quantitative reverse transcription PCR were performed with the miRNA Detection Kit by Sangon Biotech . Relative circRNA, mRNA, or miRNA expression was normalized to α , anti-ICAM-1 , anti-IGFBP7 , and anti-TNC  at 4°C overnight. GAPDH  was used as an internal control. This was followed by incubation with an IRDye800®-conjugated secondary antibody  for 1 h at room temperature and subsequent scanning with the Odyssey Infrared Imaging System (LI-COR Biosciences). The integrated intensity for each detected band was determined using Odyssey Imager software. Data are presented as mean ± SD from at least three independent experiments.Lysates from cells or tissues were prepared with RIPA lysis. Equal amounts of protein were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and electrotransferred to a polyvinylidene fluoride (PVDF) membrane. Membranes were blocked with 5% nonfat dairy milk and incubated with primary antibodies against anti-VCAM-1 , anti-ZFP36 , anti-Bcl-2 , anti-Bax , anti-cleaved caspase-3 , anti-pro-caspase-3 , anti-SM22μL RNase inhibitor), and then incubated with 5 μg ZPF36 primary antibody  at 4°C for 2 h. 50 μL Protein A/G PLUS-Agarose (Santa Cruz) was added to each sample, and the mixtures were incubated at 4°C for 4 h. The pellets were washed with PBS and resuspended in 1 mL TRizol Reagent (Invitrogen). The precipitated RNAs in the aqueous solution were subjected to qRT-PCR analysis to demonstrate the presence of the binding products using respective primers. The experiment was replicated at least three times.VSMCs were washed in ice-cold PBS, lysed in lysis buffer , and then incubated with 3 μg biotinylated DNA oligo probes  at 4°C for 2 h. A total of 50 μL Dynabeads™ MyOne™ Streptavidin C1 magnetic beads (Invitrogen) were added to each binding reaction and further incubated at 4°C for 4 h. The beads were washed briefly with lysis buffer for three times; then, the enriched proteins were identified by immunoblotting, and enriched RNAs were identified by qRT-PCR. The experiment was replicated at least three times.Eight to ten dishes of 15 cm in diameter of VSMCs were used per RNA pull-down experiment. RNA pull-down assays were performed as described . BrieflyIn Situ Hybridization Kit . Hybridization was performed using fluorescence-labeled probes in hybridization buffer by incubation at 37°C for overnight. After stringent washing with SSC buffer, cell nuclei were counterstained with DAPI (Invitrogen). Images were acquired using confocal laser scanning microscopy .The VSMCs were washed in PBS, fixed in 4% paraformaldehyde for 30 min, and permeabilized for 15 min. For FISH, the cells were incubated using specific probes of circRasGEF1B according to user manual of the Fluorescent μg/mL ActD . Total RNA was harvested at indicated time points, and mRNA expression was detected by qRT-PCR. The half-life of ZFP36 mRNA was determined by comparing to the mRNA level before adding ActD.VSMCs overexpressed circRasGEF1B following transfecting with the plasmid for 24 h. Then, de novo RNA synthesis was blocked with 10 t-test for 2 independent groups or paired t-test for 2 dependent groups and the one-way analysis of variance (ANOVA) followed by the post-Bonferroni's multiple comparison test for ≥3 groups. For all statistical comparisons, a value of P < 0.05 was considered statistically significant and denoted with one, two, and three asterisks when lower than 0.05, 0.01, and 0.001, respectively.Data analysis was performed using SPSS version 16.0 or GraphPad Prism 6 software. Data are presented as the means ± SD from at least three independent experiments, and each independent experiment was repeated three times to obtain the mean. Normally distributed datasets were analyzed by the unpaired Student's −/−Sm22α mice infused with Ang II compared to WT mice  of Ang II-induced −/−Sm22α VSMCs .To assess a potential causative link between SM22.7 cells  and enha.7 cells . Further/− VSMCs . Rescue rophages , which drophages  and exprrophages , suggestSm22α−/− VSMCs recruit and activate macrophages, we analyzed the complete secretome for the conditional media (CM) of VSMCs from wild-type (WT) and −/−Sm22α mice. Putative differentially expressed proteins generated by iTRAQ were identified (1.5-fold change). Using these criteria, there were a total of 267 proteins differentially expressed between Sm22α−/− and WT mice. GO biological process (BP) revealed that the molecules related to cell adhesion, TN-C, VCAM-1, and NID-2 were significantly upregulated more than 20-fold in −/−Sm22α VSMCs  assay. Fluorescence-stained circRasGEF1B was observed only in the cytoplasm of VSMCs treated with the CM of activated RAW264.7 cells  . Based oIt has been reported that the ZFP36 family promotes mRNA decay via binding to the 3′-UTRs of their target mRNAs with AU-rich element (ARE) to maintain appropriate target transcript and protein levels, including Bcl-2 and ZFP36 itself . As mentG values for RNA-RNA pairs by RNAup Server [G values between circRasGEF1B and Bcl-2 mRNA, compared with binding to ZFP36 mRNA  macrophage-derived circRasGEF1B reprograms VSMC apoptosis via recruiting ZFP36 to selectively bind to and decay Bcl-2 mRNA; and (3) the circRasGEF1B-ZFP36 axis is a novel pathway for communication between macrophages and VSMCs and a new mechanism by which macrophages determine VSMC fate. Thus, perhaps modulating VSMC phenotypes to a differentiated or reparative state by targeting SM22α or circRasGEF1B reduces the harmful communication between macrophages and VSMCs and may be beneficial for therapies of aortic aneurysm and its clinical complications.In the current study, we demonstrated that poptosis . Our finα has been considered to be one of the hallmarks of SMC phenotypic switching [α is vital in maintaining VSMC contractile phenotype and vascular homeostasis [α may provide a vascular environment susceptible to inflammation and predispose the aorta to aneurismal formation [α in VSMC apoptosis remains to be not fully studied. Herein, we found that disruption of SM22α significantly increased the expression and secretion of VCAM-1 and led to macrophage recruitment in vivo and in vitro under Ang II treatment, which was abolished by rescued expression of SM22α or by VCAM-1 neutralizing antibody. Decrease in SM22α expression has been well defined in a variety of VSMC-driven vascular diseases. Our and other studies provide further evidence of the key role of SM22α in maintaining vascular structural integrity and the pathophysiology of multiple vascular diseases not just as a biomarker of contractile SMC. Although the recent study considered that the effect of SM22α deficiency on AAA formation was not mediated by increasing cell apoptosis [ApoE−/− mouse in vivo study and ignored the effect of ApoE deficiency on VSMC apoptosis. Ang II-infused ApoE−/−mice, as a popular mouse model for aneurysm research, displaying vascular matrix degradation and inflammation can be far more than the changes observed in Sm22α−/− mice under the same conditions, and the effect of AAV-SM22α in vivo could be not enough to ameliorate these lesions in ApoE−/−mice.SM22witching , 36. Oureostasis  and is deostasis . VSMCs mormation . Howeverpoptosis , this coα that is a proapoptotic cytokine was induced in the activated macrophages, we showed that the apoptosis of VSMCs was still higher upon treatment with the macrophage CM that was treated with the TNF-α neutralizing antibody. In contrast, the activity of apoptosis reduced in si-circRasGEF1B-treated cells that exhibited increase in Bcl-2 expression. Thus, circRasGEF1B is a novel mediator by which macrophages induce VSMC apoptosis.Involvement of macrophages in the pathogenesis of unstable plaque and aortic aneurysm has been well defined in the past decade. Human macrophages potently induce VSMC apoptosis via direct cell-cell interactions mediated by Fas/Fas-L, promoting plaque rupture . Our preCytoplasmic mRNA decay constitutes an important posttranscriptional mechanism in mammalian cells. The regulation of cytoplasmic mRNA half-life is mediated by mRNA-binding proteins and noncoding RNAs (ncRNAs), such as microRNAs and long noncoding RNAs . The AU-There are several limitations to this study. It has been known that activated macrophages release inflammatory factors to induce VSMC apoptosis via the membrane receptor pathway. Now, it is unknown how macrophage-derived circRasGEF1B and proapoptosis factor-activated pathways interact and converge to regulate Bcl-2 mRNA stability in VSMCs. Is it the same in macrophages? It is necessary to further investigate the crosstalking between this apoptosis pathway and other functions of circRasGEF1B.−/−Sm22α VSMCs favor interacting with macrophages and the resulting activation of the circRasGEF1B-ZFP36 axis is a novel mechanism underlying macrophages inducing VSMC apoptosis.In summary, we provide evidence that


Background: Tumor stemness is the stem-like phenotype of cancer cells, as a hallmark for multiple processes in the development of hepatocellular carcinoma (HCC). However, comprehensive functions of the regulators of tumor cell’s stemness in HCC remain unclear.Methods: Gene expression data and clinical information of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) dataset as the training set, and three validation datasets were derived from Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). Patients were dichotomized according to median mRNA expression–based stemness index (mRNAsi) scores, and differentially expressed genes were further screened out. Functional enrichment analysis of these DEGs was performed to identify candidate extracellular matrix (ECM)–related genes in key pathways. A prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) to the candidate ECM genes. The Kaplan–Meier curve and receiver operating characteristic (ROC) curve were used to evaluate the prognostic value of the signature. Correlations between signatures and genomic profiles, tumor immune microenvironment, and treatment response were also explored using multiple bioinformatic methods.Results: A prognostic prediction signature was established based on 10 ECM genes, including TRAPPC4, RSU1, ILK, LAMA1, LAMB1, FLNC, ITGAV, AGRN, ARHGEF6, and LIMS2, which could effectively distinguish patients with different outcomes in the training and validation sets, showing a good prognostic prediction ability. Across different clinicopathological parameter stratifications, the ECMs signature still retains its robust efficacy in discriminating patient with different outcomes. Based on the risk score, vascular invasion, α-fetoprotein (AFP), T stage, and N stage, we further constructed a nomogram , which is more practical for clinical prognostic risk stratification. The infiltration abundance of macrophages M0, mast cells, and Treg cells was significantly higher in the high-risk group, which also had upregulated levels of immune checkpoints PD-1 and CTLA-4. More importantly, the ECMs signature was able to distinguish patients with superior responses to immunotherapy, transarterial chemoembolization, and sorafenib.Conclusion: In this study, we constructed an ECM signature, which is an independent prognostic biomarker for HCC patients and has a potential guiding role in treatment selection. Liver cancer (LC) is one of the most prevalent tumors in the world, ranking as the sixth most common cancer and the third cause of cancer-related deaths worldwide . The WorCancer stem cells (CSCs) are a small group of undifferentiated cells in tumor tissue and can induce unlimited self-renewal of heterogeneous tumor cells . A previIn this study, we developed a novel ECM-related signature for predicting the prognosis of HCC using data from the TCGA database. Then we validated its prognostic prediction capacity using data from the GEO database and evaluated its correlation with clinicopathological features, genetic alterations, immune microenvironment, and therapeutic response. This signature is expected to provide potential guidance for personalized treatment of HCC patients.A schematic workflow of this study is present in https://portal.gdc.cancer.gov/). GSE54236 with 161 samples and ICGC-LIRI-JP with 260 samples were retrieved from the GEO (https://www.ncbi.nlm.nih.gov/geo/) or ICGC (https://dcc.icgc.org/) as independent validation datasets. In addition, transcriptome data and calculated tumor volume doubling times of the GSE54236 cohort were used to explore the relationship between the risk score and rapid tumor growth. The tumor volume doubling time was calculated based on imaging data (https://www.proteinatlas.org/).In total, 355 patients with expression data of HCC were obtained from the TCGA database | > 1 as the DEG. Next, we performed a gene set enrichment analysis (GSEA) on the DEGs using GSEA software  and selected key pathways, and genes contained in key pathways were selected as candidate ones.After classifying TCGA HCC patients into two groups based on the median mRNAsi score, the differences in OS and DFS were analyzed by using the “survminer” R package (v.4.0.3). We compared the gene expression feature of the two groups and selected genes with a false discovery rate (FDR) < 0.05 and |logrisk score =Σ Coefficient (ECMs i) * Expression (ECMs i). Coefficient of gene (i) is the regression coefficient of gene (i) in the LASSO–Cox regression model and Expression of gene (i) is the expression value of gene (i) for each patient.First, we used the LASSO analysis to screen variables from high dimensional data to construct an ECM signature , and theWe divided the HCC patients into high- and low-risk groups based on the median risk score and utilized the Kaplan–Meier (K-M) analysis to compare the survival differences between different risk groups; the prognostic capability of ECMs was then measured by the area under the curve (AUC) of a time-dependent receiver operating characteristic (ROC). Last, we utilized datasets from the validation cohorts to confirm this prognostic prediction capacity.We analyzed the relationship between the risk score and the clinical features of HCC including AFP, vascular invasion, primary tumor (T) stage, regional lymph nodes (N) stage, and distant metastasis (M) stage. To evaluate the stability of the robustness and accuracy of prognostic models, we compared the survival difference between high- and low-risk groups under clinical characteristic stratification.We explored the correlation and independence of ECMs signature and clinical characteristics using a multivariate Cox risk regression analysis. In order to be applicable to the clinic, we integrated the risk score with vascular invasion, AFP, T stage, and N stage to construct a nomogram using the “rms” R package. The prognostic predictive ability of nomogram was evaluated by the calibration curve and time-dependent ROC curves. The ROC curves were plotted by using the “timeROC” R package. The survival net benefits of each variable were estimated by a decision curve analysis (DCA) using the “stdca” R package.https://reactome.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (https://www.genome.jp/kegg/) analyses were performed by using the “clusterProfiler” R package.To explore biological processes related to the risk score, Reactome pathway (http://tide.dfci.harvard.edu/)  . Patientrd.edu/) .https://asia.ensembl.org/info/genome/variation/prediction/predicted_data.html) database. Subsequently, the data were read and the TMB values were calculated using the ‘‘maftools’’ R package.Genetic mutation data were analyzed and visualized by using the ‘‘maftools’’ R package. We compared the differences of somatic variants in most prevalent mutated genes between high- and low-risk groups. The copy number variant (CNV) data were downloaded from the Affymetrix Genome-Wide Human SNP Array 6.0 platform using the “TCGAbiolinks” package, and abnormal chromosomal regions were detected using the “R/Bioconductor GAIA” package . Non-synhttps://www.cancerrxgene.org/) for analysis. The half-maximal inhibitory concentration (IC50) is an important indicator for evaluating drug efficacy or sample response to treatment. The GDSC database helps in predicting each sample’s response to targeted therapy or chemotherapy based on the sample’s transcriptome. According to the cellular expression profiles in the GDSC database, a ridge regression model was constructed with the “pRRopheticl” R package to calculate the IC50 of the drugs in the two risk groups. Transcriptome data and treatment response information from the GSE104580 cohort were used to test the effectiveness of signature in discriminating TACE treatment responders from non-responders. Transcriptome data and treatment response information from the IMvigor210 cohort were used to examine the effectiveness of the signature in identifying complete/partial responses (CR/PR) or stable/progressive disease (SD/PD) with immunotherapy.In total, four classic drugs were selected from the Genomics of Drug Sensitivity in Cancer (GDSC) database . We used Fisher’s test to compare the categorical variables and the K-M curve to evaluate the differences in survival between different risk groups. The results of the multivariate Cox regression analysis were visualized using the nomogram. The concordance index, time-dependent ROC, and calibration were also important indicators used to assess the nomogram. All tests were two-sided, and p = 0.002; p = 0.0021; p < 0.05, p < 0.05, p < 0.001; According to the median mRNAsi score, we dichotomized HCC patients in the training set into mRNAsi-high and mRNAsi-low groups and identified that HCC patients with high mRNAsi had both significantly shorter OS  to construct a prognostic prediction model  and ARHGEF6  were related to a significantly superior OS; on the contrary, the upregulation of RSU1 was significantly associated with a worse survival  + (−0.008 * ExpARHGEF6) + (0.002 * ExpAGRN) + (0.004 * ExpITGAV) + (0.005 * ExpFLNC) + (0.007 * ExpLAMB1) + (0.015 * ExpLAMA1) + (0.043 * ExpILK) + (0.131 * ExpRSU1) + (0.132 * ExpTRAPPC4).Risk score= . In addition, the risk score was significantly higher in the HCC patients presented with macro-vascular invasion, followed by those with micro-vascular invasion  and/or M0  or not (T1+T2), patients in the high-risk group had significantly worse OS . Meanwhip < 0.001; HR = 1.96, 95% CI = 1.17–3.3, and p = 0.01; To better predict the 1-, 3-, and 5-year survival of HCC patients, we constructed a nomogram combining the risk score, vascular invasion, AFP, T stage, and N stage. The C-index of nomogram was 0.70 (95% CI: 0.63–0.77) . Meanwhip < 0.05, p < 0.05, We performed a pathway enrichment analysis using DEGs between high- and low-risk groups in the TCGA-LIHC cohort to explore the biological processes associated with the risk scores. A Reactome pathway enrichment results showed that DEGs were significantly enriched in the cell cycle and metabolism-related pathways, such as cell cycle checkpoints, cell cycle mitosis, DNA replication, fatty acid metabolism, and peroxisome lipid metabolism pathways (+ memory T cells, resting NK cells, monocytes, macrophage M1, activated myeloid dendritic cells, and activated mast cells (p < 0.0001, p = 0.031; p = 0.026; p = 0.03, We evaluated the tumor-infiltrated immune cell contents and immune checkpoint profile and predicted immunotherapy responses in different risk groups. In total, the high-risk group had a significantly higher abundance of macrophage M0, resting mast cells, and Treg cells and lower abundance of activated CD4st cells . Common st cells  and CTLAst cells  were sigst cells . In addist cells . In addiTP53, MYO18B, JARID2, and HUWE1 alterations in the high-risk group was significantly higher than those in the low-risk group; on the contrary, HTT, PIK3CA, and LRRC7 mutations were more enriched in the low-risk group (p < 0.01, TP53 alterations were mainly located in the P53 domain, and the high-risk group had more mutations than the low-risk group in this domain. In addition, the low-risk group had significantly high frequencies of amplifications and deletions in chromosome 11 and 13, respectively (FDR<0.01, p = 1, There are two figures presenting the top 20 frequently mutated genes in the high-risk and low-risk groups . The prep < 0.0001, p < 0.01, The results of drug sensitivity analysis indicated that the patients in the high-risk group were more sensitive to commonly used drugs such as sorafenib and cisplatin (TRAPPC4, RSU1, ILK, LAMA1, LAMB1, FLNC, ITGAV, AGRN, ARHGEF6, and LIMS2. Previous studies have demonstrated that these 10 ECM-related genes are associated with the prognosis of various types of cancer, especially HCC. In published studies, AGRN (ITGAV (FLNC (ILK (RSU1 (TRAPPC4 is expressed at low levels in HCC tissues, and HCC patients with low TRAPPC4 expression have a shorter survival time than those with high expression (LIMS2 and ARHGEF6 were expressed at lower levels in tumors than in normal tissues in our study and associated with better clinical outcomes. The dysregulation of LIMS2, also known as PINCH2, caused liver enlargement and tumorigenesis (The resistance of HCC to traditional treatments and high tumor recurrence after therapy are pivotal causes of cancer-related deaths ; among tes, AGRN , ITGAV (N (ITGAV , FLNC (QAV (FLNC , ILK (ChLNC (ILK , and RSULK (RSU1  were oveLK (RSU1 . Contrarpression . This diigenesis .Subsequently, we explored the probability of the ECM signature in clinical application. By evaluating the relationship between clinical features and the risk score, we found that ECM signature was closely associated with AFP, vascular invasion, T stage, and N stage. AFP is the most prevalent clinically applied biomarker for the detection and treatment monitor of HCC, associated with HCC differentiation . Previou+ T cells, Treg cells, macrophages M0, and myeloid dendritic cells. It has been reported that the number of Tregs in tumor tissue or peripheral blood of HCC patients is increased compared with healthy individuals (In recent years, immune-based therapies have revolutionized the systematic management of advanced cancers . The appividuals , and Treividuals . Both maividuals . They prividuals . These fividuals . Previouividuals . InteresIn conclusion, this study developed a prognostic signature composed of 10 ECM-related genes that can accurately and robustly predict the prognosis of HCC. These genes have the potential to be developed as therapeutic targets for HCC. Also, this ECMs signature has an important value for HCC in the selection of treatment.


Hepatocellular carcinoma (HCC) is a serious malignant disease with high incidence, high mortality and poor prognosis. This study aimed to establish a novel signature based on apoptosis-related genes (ARGs) to predict the prognosis of HCC.Expression data of HCC from TCGA database and the list of 160 ARGs from MSigDB were downloaded. The genes included in apoptosis-related signature were selected by univariate Cox regression analysis and lasso Cox regression analysis. Subsequently, a prognostic risk model for scoring patients was developed, and then separates patients into two groups. Kaplan–Meier and receiver operating characteristic analysis were performed to evaluate the prognostic value of the model in TCGA, GEO and ICGC databases. The characteristics of immune cell infiltration between two groups of HCC were investigated. Finally, a nomogram was plotted to visualize the prognosis prediction.Nine genes  were included in the prognostic risk model. Survival was lower in the high-risk group. Surprisingly, the high-risk group was significantly more in immune cell infiltration and with higher immunoscore and stromalscore than in the low-risk group. In addition, the risk score was an independent prognostic factor for HCC.Prognostic signature comprising nine ARGs could be used as a potential prognostic factor for HCC. It also provides an important idea for further understanding the immunotherapy of HCC.The online version contains supplementary material available at 10.1186/s12876-022-02481-w. Primary liver cancer is one of the six most common cancer and the third leading cause of cancer death . In patiApoptosis, also known as programmed cell death, is finely regulated at the gene level resulting in the orderly and efficient removal of damaged cells . Apoptoshttps://gdc.xenahubs.net) [TCGA mRNA-seq data and corresponding clinical data of 368 HCC patients were accessed from the UCSC Xena (ubs.net)  and usedp < 0.01 & |log2 (Fold Change)|> 1. David was employed to perform Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (p < 0.05) [Based on the similarity displayed by the expression levels of ARGs, the “ConsensusClusterPlus” package was used to classify patients with HCC into different subtypes  . Princip < 0.05) .In the discovery cohort, the univariate Cox regression analysis was used to screen for prognostic ARGs. Then, the LASSO Cox regression using “glmnet” package in R was applied to develop the ARGs prognostic signature for the HCC patients. The optimal value of the lambda penalty parameter was defined by performing 10 cross-validations. The risk score calculating formula is:i means the expression levels of each ARGs, βi is the corresponding regression coefficients [The expficients . The patficients . Univarificients . Further2 (Fold Change)|> 0.1 was considered as statistically significance.The ssGSEA algorithm was applied to quantify the relative infiltration levels of various immune cells in the TIME of HCC. The gene set for marking each TME infiltration immune cell type was obtained from the study of Charoentong . The immNomograms were widely used for cancer prognosis. Based on age, gender, tumor (T), node (N), metastasis (M) and risk score, a nomogram was constructed using “RMS” package in R. Calibration curves was used to visually predict deviations between actual and predicted survival .p value < 0.05 was set as statistically significant for all the analyses.All statistics were carried out using the R software (version 3.6.3). Wilcox test was used to screen for statistically differentially genes and immune cells . In KaplThe k = 2 had higher intra-group correlation and lower inter-group correlation Fig. A–B. ThenAll 160 ARGs were subjected to univariate Cox regression analysis. Then, 18 ARGs significantly associated with the prognosis of HCC were identified Fig. A. LASSO To explore the biological behavior between high- and low-risk groups, we conducted ssGSEA analysis. The results indicated that high-risk group was remarkably rich in immune cell infiltration including activated CD4 T cell, activated dendritic cell and natural killer cell Fig. E. We hypThen, we investigated the expression of chemokine and cytokine in both two groups. TGRB1, SMAD9, TWIST1, CLDN3, TGFBR2, ACTA2, COL4A1, ZEB1 and VIM were considered to be associated with the transcripts of transforming growth factor (TGF)-β/epithelial mesenchymal transformation (EMT) pathway. The results indicated that these cytokine and chemokine were significantly up-regulated in high-risk group , M (p = 0.025), stage (p < 0.001) and risk score (p < 0.001) were considerably associated with the OS  and risk score (p < 0.001) remained closely correlated with the OS  is associated with mitosis and necessary for G2/M checkpoint initiation . A case–The ssGSEA algorithm was applied to quantify the relative infiltration levels of various immune cells in the TIME of HCC. The results indicated that high-risk group was remarkably rich in immune cell infiltration. Significantly higher immunescore and stromalscore were observed in the high-risk group than in the low-risk group in the ESTIMATE analysis. However, patients in the high-risk group did not show a matched survival advantage. Tumors with an immune rejection phenotype also displayed a large number of immune cells, and these immune cells stay in the stroma surrounding tumor cell nests rather than penetrating the parenchyma . The actIn order to explore the differences in biological processes between high and low risk groups, GSVA enrichment analysis was performed using “GSVA” R packages. The results showed that the activities of pathways associated with cell proliferation, such as cell cycle, mismatch repair and homologous recombination, etc., were significantly increased in the high-risk group. During cell cycle progression, many regulators may be closely related to the early steps of carcinogenesis . RadiatiNomogram can be used to predict disease risk or prognosis by combining multiple indicators , 46. A sHowever, this experiment also has some limitations. First, the results of this experiment are obtained based on public datasets. Therefore, a large number of clinical samples need to be collected for validation. Second, the underlying molecular mechanisms revealed by this experiment need further study.In conclusion, we constructed a prognostic signature comprising nine ARGs that could be used as a potential prognostic factor for HCC and help in clinical decision making for individualized treatment. The characteristics of TIME cell infiltration, the expression of immune checkpoint expression, and the relationship between immune cells and risk score were also analyzed, which provided important ideas for further understanding the immunotherapy for HCC.Additional file 1. Figure S1. Survival verification and comparison of prediction accuracy of risk score, cluster and AFP. (A) Comparison of survival between high and low risk groups in ICGC database. (B) The ROC curve analysis was used to analyze the 1-year prognosis prediction accuracy of risk score, cluster and AFP. (C) The ROC curve analysis was used to analyze the 3-year prognosis prediction accuracy of risk score, cluster and AFP. (D) The ROC curve analysis was used to analyze the 5-year prognosis prediction accuracy of risk score, cluster and AFP. (E) The DCA analysis was used to analyze 1-, 3- and 5-year prognosis prediction accuracy of risk score, cluster and AFP.Additional file 2. Figure S2. Correlation analysis between immune-checkpoint related gene expression, immune cells and risk score. (A) Difference in the immune-checkpoint related gene expression between low and high risk group. (B) Pearson correlation analysis between risk score and immune cells.Additional file 3. Figure S3. Univariate (A) and multivariate (B) analyses and nomograms (C) and calibration curves (D) in the ICGC database.


Several limitations are expected to overshadow their use as accurate markers for age prediction. The current study was conducted to determine the influence of immunologic disorders, such as autoimmune diseases and COVID-19, on the accuracy of sjTRECs as molecular markers for age estimation and immunosenescence among living Egyptians. Peripheral blood sjTRECs level was measured by qPCR in 90 autoimmune patients, 58 COVID-19 patients, and 85 healthy controls. The mean dCt values were significantly (p = 0.0002) different between the three groups, with the highest values in healthy subjects, followed by autoimmune and COVID-19 patients. A significant negative correlation was identified between the sjTRECs levels and ages in all studied cases. There were significant positive correlations between chronological age and predicted age for healthy individuals, autoimmune, and COVID-19 patients with mean absolute deviations (MAD) of 9.40, 11.04, and 9.71, respectively. The two patients’ groups exhibited early immunosenescence, which was more noticeable among the young adults with COVID-19 and autoimmune patients of age range (18–49 years). Autoimmunity may represent a critical factor impacting the accuracy of sjTRECs quantitation for age prediction.Signal joint T cell receptor excision circles ( Forensic age inference was primarily relied on morphological examination or radiography, and molecular methods were adopted later . Ethnic he blood . Additioimmunity .Immunosenescence is a term used to describe age related involution of the thymus and gradual reduction in T cell number and function. With immunosenescence, people become at higher risk of developing autoimmune diseases, infections, and malignancies ,6.sjTRECs. Hence, various immunologic disorders should be considered during sjTRECs’ quantification. The sjTRECs concentration in the human body can be affected by the thymic functionality, as well as T cell homeostasis, in the peripheral blood [Multiple genetic, pathologic, and environmental factors are known to modulate an individual’s immunologic condition, which is closely related to the levels of al blood ,8.sjTRECs as an age biomarker in healthy, as well as diseased persons of a wide age range, are also required to validate and understand the association between biological and chronological age indicated by this marker [During the search for biomarkers to estimate the chronological age independently from the biological age, the dynamics of the immune system should be considered. It is essential to assess the possible impact of different disease conditions on the process of age estimation in order to expand the approach for clinical applications ,9. Addits marker .The novel pandemic, COVID-19, has been related to significant health consequences such as those caused by systemic autoimmune diseases. Organ damage in COVID-19 has been shown to be mainly immune-mediated. Moreover, disturbance of self-tolerance to host antigens and development of autoantibodies has been detected in COVID-19 .sjTRECs gene expression level, as a molecular marker for age estimation and immunosenescence, in the peripheral blood of Egyptians with COVID-19 and different autoimmune disorders versus age/sex-matched healthy controls.Accumulating evidence from previous studies suggested that the immunologic disorders associated with acute SARS-CoV-2 infection, and the chronic autoimmune disorders, may impact the pace of ageing in these patients’ groups. Thus, the current study was performed to evaluate utilizing Study Design: this comparative cross-sectional study was conducted between 2020 and 2022 on a convenient sample of 90 patients with autoimmune diseases, 58 confirmed COVID-19 patients, and 85 healthy controls. Pregnant or lactating women, and patients with chronic illnesses other than autoimmune diseases, were excluded from the study. Cases were randomly selected. A single peripheral blood sample was collected from each participant. In COVID-19 disease, an example of acute illness, the sample was collected 24 h after confirmed positive SARS-CoV-2 PCR test. In autoimmune diseases, an example of chronic illness, it was collected from previously diagnosed cases (disease duration ≥ 6 month) during follow up visits, regardless of the stage of the disease activity. The current study was approved by the Benha University Research Ethics Committee, and written informed ethical consents were obtained from the participants or their guardians before enrollment in the studyn = 35), and severe (n = 23). The severity assessment was conducted in all cases according to the Chinese Center of Disease Control (CDC) [2 > 93%; PaO2/FiO2 ratio ≥ 300 mmHg) and severe cases .Study Participants: COVID-19 patients were recruited from the Contagious Disease Control Center (CDCC), Internal Medicine and Pediatric Departments, Benha University Hospital, Egypt. All COVID-19 patients were evaluated using complete medical history taking and full clinical examination. Inclusion criteria: according to the triage protocol of the Ministry of Health and Population, a confirmed COVID-19 case is defined as a patient with COVID-19 infection that has been confirmed by PCR test, regardless of the clinical manifestations. Patients were subdivided into mild (ol (CDC) . These in = 30), RA (n = 21), psoriasis (n = 20), Behcet’s Disease (BD) (n = 6), and JIA (n = 13). Inclusion criteria: patients with rheumatoid arthritis (RA) were selected using the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for RA published in 2010 [Autoimmune patients were recruited from the inpatients’ ward and the outpatient clinic of the Rheumatology, Rehabilitation, and Physical Medicine Department. All patients were subjected to full medical history taking, as well as general and systemic clinical examination. Patients with different autoimmune disorders include SLE . Following extraction, the DNA concentration was determined, in each sample, by a NanoDrop One Spectrophotometer .sjTRECs by qPCR peripheral sjTRECs levels was assessed by qPCR, using QuantiTect SYBR Green PCR kit , by the StepOnePlus Real-Time PCR System , according to the manufacturer’s instructions. Real-time PCR was performed using 40 ng DNA in 25μL reaction volumes, containing 400 nM of each primer. PCR conditions were 95 °C for 15 min , then 94 °C for 15 sec (denaturation), 54 °C for 30 sec , and 72 °C for 20 sec (extension), for 45 cycles. Specific primer pairs were used to amplify the sjTREC (accession no. NT_026437) and TATA box Binding Protein (TBP) (accession no. NG_008165). Primer sequences were as follows: sjTREC forward: 5′-CCA TGC TGA CAC CTC TGG TT-3′, sjTREC reverse: 5′-TCG TGA GAA CGG TGA ATG AAG-3′ [TBP reverse: 5′-AGCTGAAAACCCAACTTCTGT-3′ [sjTRECs in each sample was determined after correction by TBP using the dCT method  [sjTRECs and TBP melting temperatures were around 83 °C and 76.5 °C, respectively. PCR reactions were conducted in duplicate for each sample, and average values were used for data analysis. Non-template controls were used in each run to ensure the absence of non-specific amplification for both genes throughout the entire work.Measuring TCTGT-3′ . The lev sjTREC) . The anat-test (t) and the One-Way Analysis of Variance  to detect differences in dCt (CtTBP−CtsjTREC) values between the study groups, as appropriate. The Pearson correlation coefficient (r) was used to evaluate the correlation between dCt values and chronological age. Linear prediction models of chronological age conditioned on dCt values were developed for the different groups. The correlation between predicted ages and chronological ages were tested using the Pearson correlation coefficient (r), and the mean absolute deviation (MAD) of predicted ages from chronological ages were calculated. Statistical significance was accepted at p < 0.05.Statistical analysis via the computerized statistical package STATA/SE version 11.2 for Windows , and MS Excel were used for data entry, presentation, and analysis. Descriptive statistics, mean ± standard deviation (SD), range, frequency, and percentage were used to summarize data as appropriate. The Shapiro-Wilk W test was used to examine the distribution of numerical data. Categorical data were compared using the Chi-square test and the Student’s p > 0.05).p = 0.0002), with the highest values in healthy subjects, followed by autoimmune and COVID-19 patients , as demonstrated in 2: Chi-square test; F: One Way Analysis of Variance (ANOVA)).There were significant differences in the mean dCt values between the three groups (p < 0.001). There were significant differences in dCt values between healthy individuals, autoimmune patients, and COVID-19 patients for those aged 18–34 and 35–49 years . There were no significant differences in dCt values between male and female subjects, except for autoimmune patients (p = 0.03).Comparisons of dCt (CtTBP−CtsjTREC) values between the studied healthy individuals, autoimmune patients, and COVID-19 patients of different gender and age group are shown in p = 0.20). Similarly, patients with active autoimmune diseases showed non-significant lower dCt values  when compared to inactive cases, as shown in The average dCt values were lower in patients with severe COVID-19 compared to mild disease. However, the difference was not statistically significant . Significant moderate negative correlations were detected in patients with both active and inactive autoimmune diseases . The correlation between chronological age and dCt values was negative, and high correlation was observed in patients with mild and severe COVID-19 .2) value ranged from 0.26 in autoimmune patients to 0.61 and 0.64 for healthy individuals and COVID-19 patients, respectively.p < 0.001; MAD = 8.79) and severe disease .Correlations between chronological age (in years) and predicted age (in years) in the different study groups are shown in sjTRECs dCt values between RA, SLE, and healthy individuals revealed that there were significant differences as regards age groups (p < 0.001). Additionally, significant differences in dCt values were noticed between healthy individuals and RA and SLE patients, with the highest values in healthy individuals and the least values in RA patients . Data are shown in Comparisons of the age, sex distribution, and the sjTRECs levels with laboratory data of autoimmune patients was studied, and a significant negative correlation between dCt values and anti dsDNA  was detected. Disease duration in patients with autoimmune disorders ranges between six months and 16 years. Insignificant correlation (p = 0.17) was found between dCt values and the autoimmune disease duration, as seen in The correlation of Age markers can be evaluated by looking at how they react to different diseases and environmental conditions that might impair the accuracy of calendar age prediction. In prediction models intended to estimate chronological age, it is not recommended to use such markers, which are altered by the environment sjTRECs expression was significantly decreased due to age progression across the older age groups in cases and controls (p < 0.001). Data from a previous report [Our study population  has been categorized into four age groups . Such stratification revealed that s report  supportesjTRECs levels between the three studied subadults’ groups < 18 years (p = 0.93). The ability of the thymus to replace the apoptotic T lymphocytes is not consistent across the older patients. In children, the effective thymic activity and T cell functions defend them against viral infections, autoimmune disorders, and malignancies. They also play critical roles in SARS-CoV-2 pathogenesis. It was hypothesized that the thymus’s and T cells’ functions defend the children from the SARS-CoV-2 effects [In the current study, all the sub-adults with autoimmune diseases were diagnosed as JIA patients. It was previously reported that the thymic function in children with JIA is equivalent to that of the healthy control children . A findi effects .The latest three coronavirus epidemics were reported with an increased mortality in adults, but, SARS-CoV and MERS-CoV have not caused deaths in infected children, and it was concluded that children usually presented with mild coronavirus infections, including SARS-CoV-2 infection .sjTRECs in young adults and middle age groups with autoimmune disorders and COVID 19 were found to be highly significantly lower than the corresponding control groups . This can be explained by different studies on adults with autoimmune disorders that revealed a reduction in sjTRECs counts, suggesting that the disturbances in sjTRECs dynamics represent a pivotal component in autoimmune disease pathophysiology [The dCT levels of ysiology ,30,31. Oysiology .sjTRECs level when compared to the age matched control group, a relationship which was only significant among the young adults. Such a result may be explained by the peripheral lymphopenia reported in the peripheral circulation of COVID-19 patients [Notably, COVID-19 patients ≥ 18 years included in our study showed reduced patients . Recent patients  and redupatients . Periphepatients . Yet, a patients .sjTRECs levels were nearly matching each other’s in the three groups (cases and control) (p = 0.88). Immunosenescence due to the ageing process may explain the extreme reduction in the thymic function that results in very low sjTRECs level at older age in the three studied groups. Accordingly, inhomogeneity of the thymic function reported in elderly individuals can compromise the accuracy of using sjTRECs quantitation for age estimation [The clinical manifestations seen in COVID-19 patients above the age of 50 years can be attributed to the uncontrollable, weak antiviral response due to immunosenescence and thymic involution . Elderlytimation .sjTRECs showed significant negative correlation with chronological age in all the studied groups, including healthy (r = −0.78), COVID-19 (r = −0.80), and autoimmune patients (r = −0.51). Regarding the control group, our results (R2 = 0.61) are consistent with those of Ibrahim et al. [sjTRECs in peripheral blood as an efficient method for estimating age. Their research also found a highly significant negative correlation between sjTRECs level and individual age (R2 = 0.87). Similar results have been reported by Ou et al. [sjTRECs levels in old bloodstain and blood samples were decreased in an age-dependent manner, with R2 = 0.759 and R2 = 0.835, respectively. Cho et al. [sjTRECs levels and age, with R2 = 0.807. In 2018, Yamanoi et al. [sjTRECs level using SYBR green PCR, and their results indicated that sjTRECs level has declined with age in bloodstains from the Japanese population, with r = −0.786 (R2 = 0.617). The minor discrepancies between the results of the diverse studies may be due to the differences in the sample size, genetic, medical, as well as environmental factors [sjTRECs level include variable ethnic groups, using different age groups, storing the samples for a long period, and the detection procedures [The dCT level of u et al.  and otheu et al. , who fouo et al.  confirmei et al.  studied  factors . Other cocedures .sjTRECs between the two genders in both healthy and COVID-19 subjects . This result is consistent with the results of Ibrahim et al. [sjTRECs levels between males and females, in healthy Egyptian and Chinese populations, respectively. Data collected from other studies [sjTRECs than women. Other authors [sjTRECs levels, and they suggested ignoring this effect, since its impact on the predicted age values is minor. Interestingly, our study has identified a significant reduction (p = 0.03) in the mean levels of sjTRECs in female patients with autoimmune disorders when compared to the males of the same group. It is unclear why women have reduced sjTRECs levels, although the majority of the published studies showed either no sex differences or reduced sjTRECs in men. Yet, it may be attributed to differences in patients’ characteristics. Moreover, women are known to be at higher risk of developing autoimmune disorders [In our study, insignificant differences were detected in the mean levels of m et al. , who had studies  have pub authors  have alsisorders .TREC levels are always higher in the CD8+ T-cell subpopulation compared to CD4+ T cells. Besides, previous analysis of data extracted from patients with RA, SLE, psoriatic arthritis, and JIA suggested enrichment and activation of CD4+ T cell subpopulation during disease pathogenesis [sjTRECs level detected could also be influenced by the different effects of sex hormones and sex chromosomes on the immunopathogenesis of such diseases. Whether the course of the autoimmune disease is influenced by sex difference or not is still controversial and needs further investigations to identify the distinct influence of gender on the pathogenesis of each autoimmune disease.It was also reported that ogenesis ,44,45,46ogenesis . Our stusjTRECs significantly, and the sjTRECs expression didn’t show any significant correlation with disease activity among the studied cases. Similarly, Kurosaka et al.’s [sjTRECs level and SLE disease activity. However, our results revealed lower sjTRECs levels in the peripheral blood of patients with active disease, which is consistent with Vieira et al. [sjTRECs counts in CD8+, but not in CD4+ T lymphocytes. Fascinatingly, patients with inactive SLE displayed similar sjTRECs level to healthy controls in CD4+ and CD8+ T cells [sjTRECs level in patients with active autoimmune diseases, such as SLE, to many factors; one of them is the extremely high doses of immunosuppressive drugs and glucocorticosteroids which are well-known to persuade thymus atrophy and to suppress lymphopoiesis [sjTRECs were inversely correlated with the activity of RA. However, the correlation was absent with subsequent follow-up and disease progression.Disease activity in patients with autoimmune disorders didn’t affect the levels of et al.’s  results a et al. , whose w T cells . We may opoiesis . It has opoiesis . It is aopoiesis  revealedp = 0.20). The chronological age and dCt values were correlated in COVID-19 patients, and we found a high negative correlation with R2 = −0.84; 0.80 in mild and severe cases, respectively (p < 0.001). Patients with severe COVID-19 are manifested by lymphopenia, particularly T cell loss [The average dCt values were insignificantly lower in patients with severe COVID-19 compared to mild disease , R2 = 0.26. While for COVID-19 patients, the equation was more or less near to the healthy group (Y = −41.63–6.68 X ± 12.60), R2 = 0.64. The current results indicate that COVID-19 may affect the immune age and thymic function significantly in young adult patients, though the age prediction model does not show such a relationship. The MADs from the chronological age were 9.4, 11.04, and 9.71 years in healthy, autoimmune, and COVID-19 subjects, respectively, with a higher deviation in autoimmune patients when compared to the other two groups. Therefore, it was found that the accuracy of age prediction decreases with higher deviation as an impact of various autoimmune disorders. At older age groups, sjTRECs levels were very low and, thus, sjTRECs levels in older patients with COVID-19 and autoimmune diseases match the controls with the same predicted age. While in patients at the age of 50 and younger, sjTERCs levels in patients with autoimmune diseases match those of older age by around 11.04 years.Y = −14.89–4.85 X ± 11.42, where the 11.42 refers to the SE and RsjTREC-containing CD4+ T cells was not different in the RA patients compared to the healthy individuals; however, the curves were shifted by approximately 20 years towards a younger age. Therefore, RA patients aged 20–30 years old have sjTRECs level equivalent to healthy individuals of 50–60 years old. Similarly, sjTRECs level was significantly lower in SLE patients, which supports the idea that T cell subset imbalance and aberrant expression of the key signaling molecules on their surfaces contribute significantly to SLE disease pathogenesis [sjTRECs levels. The follow up process of the recovered COVID-19 patients during the peak of the pandemic and the economic burden was another limitation to study its effect during the convalescence stage.This obvious age reduction in autoimmune patients could be attributed to the alteration of T cell dynamics in RA cases and, possibly, to increased T cells’ turnover as part of the RA disease pathogenesis. Koetz et al.  stated togenesis . A limitsjTRECs in the peripheral blood of Egyptian patients, particularly young adults and middle-aged individuals. Such a finding may affect the reliability of using sjTRECs alone as a marker for age estimation in patients with such chronic immunologic disorders. It is recommended to couple sjTRECs with other age estimate indicators for forensic purposes. Acute infection with SARS-CoV-2 in young adults has a substantial negative influence on the thymic function. Nevertheless, the MAD and the age prediction equations of all COVID-19 patients were more or less near to the healthy individuals, which may oppose the notion that COVID-19 may implicate the age prediction using sjTRECs. Measuring sjTRECs level is an indicator of thymic function and immunosenescence. Consequently, we can also infer that the immune system of the affected age groups also shows signs of an early immunosenescence due to the effect of the disease pathogenesis on the T cell function. Above the age of 50, the marked reduction in sjTRECs levels, and the noticeable similarity in the low thymic functions among the three studied groups, reveal that age-related involution of the thymus and immunosenescence starts around the age of 50 years in the healthy Egyptian population. Though autoimmune disease activity and COVID-19 severity showed significant negative correlation with age, they have slightly reduced the sjTRECs levels as compared to inactive autoimmune and mild COVID-19 cases.Autoimmune diseases have dramatically reduced the levels of sjTRECs and the thymic function in patients’ groups and subgroups by using larger sample size. Using other markers, in addition to sjTRECs such as epigenetic markers, e.g., microRNA (miRNA), may provide insights to comprehend how genes control and maintain the microenvironment of the thymus during the ageing process.Finally, we believe that further longitudinal study is required to assess the dynamic effects of the individual autoimmune diseases and COVID-19 on


Cognitive decline, the primary clinical phenotype of Alzheimer’s disease (AD), is currently attributed mainly to amyloid and tau protein deposits. However, a growing body of evidence is converging on brain lipids, and blood–brain barrier (BBB) dysfunction, as crucial players involved in AD development. The critical role of lipids metabolism in the brain and its vascular barrier, and its constant modifications particularly throughout AD development, warrants investigation of brain lipid metabolism as a high value therapeutic target. Yet, there is limited knowledge on the biochemical and structural roles of lipids in BBB functionality in AD. Within this framework, we hypothesize that the ApoE4 genotype, strongly linked to AD risk and progression, may be related to altered fatty acids composition in the BBB. Interestingly, alpha linolenic acid (ALA), the precursor of the majoritarian brain component docosahexaenoic acid (DHA), emerges as a potential novel brain savior, acting via BBB functional improvements, and this may be primarily relevant to ApoE4 carriers. Transport of lipids to the brain is mediated by the major facilitator superfamily domain containing 2A (Mfsd2a) lining the BBB .The BBB is a selective interphase between systemic blood and brain parenchyma with brain endothelial cells as its principal cellular component. Its structural and functional stability is critical to keep a healthy brain. The brain blood capillaries are different from peripheral blood vessels due to the presence of tight junctions, lack of fenestrations, high number of mitochondria, restricted endocytosis and presence of unique transporters . PericytThe capillaries in brain provide the largest endothelial surface for molecular interchange between blood and brain . Even thGrowing evidence points to brain DHA reduced levels ,32 in phNeuroimaging studies in individuals with early AD as well as histological images of postmortem tissue have shown BBB breakdown in different brain areas . IncreasEssential fatty acids (EFA) LA and ALA are precursors of the important brain components arachidonic acid  and DHA (n-3), respectively, through processes of desaturation and elongation. The genes encoding the enzymes involved in those pathways are highly expressed in liver and brain ,43. ImpoAA has been shown to have neurotoxic effects . AA is aDHA is notoriously decreased in the AD human brain , and so The essential ALA n-3 and LA n-6 FA, source of the metabolic PUFA DHA and AA, respectively, come from the diet. The brain is highly enriched in long-chain DHA and AA as structural components of neuronal membranes. These FA are precursors of lipid-derived prostaglandins and resolvins. When derived from DHA, they contribute to reverse inflammatory and other neurodegenerative processes . Neural The conversion of essential ALA and LA to PUFA occurs via the same elongase and Δ6 desaturase enzymes in the metabolic pathway. Higher ALA levels usually overcome the LA substrate competition, allowing for the production of higher n-3 than n-6 PUFA metabolites . ImportaDHA supplementation has shown inconclusive results of both positive and negative effects related to BBB integrity  and imprSaturated FA conversion into MUFA is regulated by ∆9 desaturase activity (SCD1 gene). Curiously, MUFA have been found, in some studies, to be elevated in AD patients brain, and to correlate with cognitive impairment, although the underlying mechanisms beyond this association are unknown . Our recIn addition to the peripheral hepatic EFA metabolism and consequent brain transport, astrocytes have been suggested to be the central neural system providers of PUFA ,65. SurpThe early detection of AD-related brain lipid changes should be considered as a critical point related to the role of nutritional lipids in the prevention/restoration of the BBB during AD development. Based on our previous studies in C57Bl6/J mice , early aDue to the BBB inherent impermeability, brain and peripheral cholesterol synthesis are separated . Brain cApolipoprotein E4 (ApoE4) allele carriers are at increased risk to develop AD compared with those carrying the ApoE3 or E2 alleles . The ApoWe predict that the link between ApoE4 genotype and AD evolution towards cognitive decline emerges, at least for some extent, from the BBB deterioration due to altered nutritional and biochemical FA processes leading to ineffective brain lipid accretion though the BBB.The strong impact of ALA dietary enrichment on hepatic n-3 FA metabolism, may lead to restoration of DHA brain levels, by rescuing BBB disruption through membrane remodeling in brain endothelial cells. We envision that further research will unravel the mechanisms through which ALA enrichment improves BBB functionality, not only by repairing lipid transport, but also by enhancing brain ALA metabolism. In doing so, n-3 PUFA enrichment would comprise whole brain lipid homeostasis supporting restored cognitive functioning. This innovative concept opens the path for exciting prospects on ALA properties related to BBB in various brain diseases. Understanding the role and evolution of lipid modifications from early to old age, specifically in high risk ApoE4 carriers, would promote the discovery of novel nutritional strategies for the prevention, delay or restoration of cognitive decline.


We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean.Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations,  Whether quantifying the agreement between the output of an ocean simulation model ,2 and insurement ,4, or mosurement , one neesurement –8; populin situ and using satellites. Further, small spatial mismatches can result in large pixel-wise differences—see §5.3.2 of [These issues are well known and have led to the development of various normalized differences or ‘cost functions’, which differentially weight differences arising from deviations in quantity or location, or from unresolved scales e.g. ,7,9). Fo,9. Fo7,95.3.2 of —which peIn this paper, we explore the use of the Wasserstein distance , which sTo convey the intuitive appeal of the Wasserstein distance over pixel-wise distance measures, consider the toy example in a shows the optimal transport pattern between the two maps on the left (see §3(a)(i)). These optimal transport patterns are not to be interpreted as ‘physical’ transport of the underlying quantity. Still, these optimal transport patterns are useful for understanding how the data differ. In this work, we consider two primary types of comparison: (i) comparing two different data sources measuring the same signal on a spatio-temporal region or gridpoints; and (ii) comparing the same data source at different times. In both cases, visualizing the optimal transport can provide a scenario to elucidate the nature of the difference. This can be particularly useful when spatio-temporal differences are related to shifts in patterns that may not be well captured by pixel-wise comparisons.In addition to its merit as a scalar distance, the Wasserstein distance also enables the visualization of the transport that would most efficiently (from the perspective of a person moving the dirt) transform the first ocean map into the second. For example, the rightmost panel of figure 2in situ data is framed in terms of chlorophyll shifts along the depth dimension. Our numerical experiments allowed us to investigate whether the Wasserstein distance can effectively capture deviations in the ‘deep chlorophyll maximum’ (DCM) between two chlorophyll depth profiles [With this paper, we aim to highlight the usefulness of studying ocean data using the Wasserstein distance, which we show is particularly well suited for evaluation of ocean biogeochemical models, among many other applications. We compare satellite chlorophyll observations from the Eastern North Pacific Ocean and depth profiles from the NPSG with their counterparts from a biogeochemical model coupled to a state estimate of the ocean currents, temperature, and salinity . We showprofiles –30. Thes2 (a)Consider two discrete probability distributions optimal transport [base distance between cell The Wasserstein distance, which is also sometimes called earth mover’s distance  as discuransport ,31,32, itransport R package [Throughout, we use the  package , which i package  in whichequation ) is proher space  and in ter space . Also poer space , which ier space .great circle distance between the  coordinates, which we compute using the geodist package in R [R package named omd (https://github.com/sangwon-hyun/omd), which could be used for other ocean studies.A distinctive feature of ocean applications  is that the base distance age in R . Our wor (b)In our analysis, multidimensional scaling (MDS) plots will be used to help us interpret distance matrices, often highlighting seasonality and other relationships across time. Using the Wasserstein distance as described in §2(a), we can take a collection of maps and form a distance matrix ical MDS ,40. This (c)in situ observations. We use a subdomain of the model and remote sensing datasets focused on a latitude–longitude rectangle in the Pacific Ocean directly above—and including—Hawaii. The region is centred around about a, left panels). We also focus on data directly from a fixed location near the south of this region, Station ALOHA  . Through (i)Model data are based on output from a coupled physical-biogeochemical-optical model, modified for the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES) project. The CBIOMES-global model simulates the period from 1992 to 2011 .in situ data. The end product is a global three-dimensional configuration state estimate, at a horizontal resolution of The model’s physical component is derived from the Estimating the Circulation and Climate of the Ocean project, v. 4 (ECCOv4) ,7,8. ECCpth . See  for full (ii)Remote sensing (or satellite-derived) data are based on v. 3.0 of the European Space Agency Ocean Colour Climate Change Initiative (OC-CCI) –47, a bl (iii)https://hahana.soest.hawaii.edu/hot/dataaccess.html) contains concentrations of chlorophyll collected using a CTD fluorescence sensor. There are 28 583 observations measured between 3 October 1988 and 27 November 2016 in the depth range between 0 and 200 m. This data were downloaded directly from [R package for accessing the CMAP database.We additionally consider shipboard-measured chlorophyll-a from Station ALOHA  of in situ data and model data using the Wasserstein distance. In situ data are sampled irregularly in time, while Darwin data is complete in space and time. In order to compile the two datasets at matching locations in space and time, we colocalize the model data, by taking averages of the chlorophyll measurements in a certain space-time vicinity , we compare . 3 (a)In this section, we show several different data applications of the Wasserstein distance to the ocean setting, each highlighting a different aspect of ocean data comparisons. First, in §3(a)(i), we consider the climatological seasonal changes in chlorophyll patterns in both satellite and model data, and we also perform direct model–satellite comparisons. Here, ‘climatological’ refers to being based on the 12 average monthly chlorophyll levels (averaging from 1998 to 2006). Next, in §(ii), we consider the full time series of monthly averages from 1998 to 2006 and focus on using the Wasserstein distance to explore changes in chlorophyll patterns over that time period. Finally, in §(iii), we use a smaller longitude–latitude rectangle in the NPTZ, and base comparisons on estimated boundaries between regions instead of on the original chlorophyll concentrations. (i)a shows the pixel-wise difference, and portrays both large positive deviations in the northern region and smaller ones in a wider region near the equator. The right-most panel shows an example of the optimal transport pattern from comparing climatology remote sensing data and model data in April. Optimal transport is visualized as blue transparent arrows, and those corresponding to the top 10% are highlighted in bold red. Both plots indicate that the model and remote sensing data differ the most in the northern region, while optimal transport additionally shows a southbound shift in patterns across the whole domain.Our first comparison is between the two climatology data sources—remote sensing and model data. The third panel in figure 2b, from the Next, we form a 24 × 24 distance matrix c), projecting the 24 chlorophyll maps onto a two-dimensional plot. This MDS plot again shows that model data has higher variability than the remote sensing data. It also shows a clear separation between the two data sources. The line connecting the data sources shows a closed loop within each source, which shows seasonality according to the time of year. A careful look reveals that the seasonality pattern is different for the two data sources—the distance between the three months (August through October) and (December through January) is smaller in the model data than in the remote sensing data.We further summarize the distance matrix MDS plot c, projec (ii)calendar months apart if one ignores the years, i.e. We expand the analysis by using time-resolved data based on monthly averages of model and remote sensing data in all months available from 1998 to 2006. An MDS analysis leads to similar conclusions as those from the climatology data . Next, a show the distance from model data in January 1998 to all other months of model data in our date range, measured in two ways (Wasserstein distance and RMSE). Both have regular seasonality, but the Wasserstein distance curve peaks in the summer (around August) of each year, while the RMSE curve peaks in the early spring (around April). We focus on three months—shown as January 1998 (I), April 2002 (II), and August 2002 (III) in panel a—and note that the domain of calculations has been extended further northward as compared with Lastly, b comparing (I) and (II), we see that the RMSE is relatively high due to a few large mismatches in the coastal region, while the Wasserstein distance in this comparison is relatively small because only local shifts exist in the north. On the other hand, panel c comparing (I) and (II) shows that the Wasserstein distance is appropriately large; the rightmost figure shows how optimal transport captures many global southbound shifts in probability mass to the equatorial region. Pixel-wise difference (third figure from the left) fails to capture this visibly large pattern difference, and RMSE is measured to be smaller than from the comparison in panel b. This demonstrates how the Wasserstein distance can be an improvement over RMSE in quantifying such differences between maps.In panel  (iii)Sometimes, rather than comparing the scalar fields directly, we may be more interested in comparing a scientifically relevant derived feature of the fields. For example, one may algorithmically segment the ocean into cohesive regions—‘provinces’—based on underlying differences in one or more fields  and blue (remote sensing) lines.We show here how the Wasserstein distance can be used to evaluate how different the boundaries are of such provinces when determined from different datasets or algorithms. Here, we apply a clustering algorithm (t (TZCF) ,53. We db) and the MDS plots (panels c) in It is interesting to compare the distance matrices (panels  (b)in situ and model). In the vertical profile of chlorophyll, a DCM  is obsemum, SCM  at the pmum, SCM ,57. At tmum, SCM –60 and tin situ and model—at 226 shared dates between October 1988 and November 2016. Panel (a) shows an example of a single chlorophyll depth profile for the two data sources (for 15 September 2014), while all 226 depth profiles for each data source are shown in (b). For each comparison (i.e. each common date), we also record an estimate of the DCM, measured by the depth at which the maximum concentration of chlorophyll occurs. Panel (c) shows linear regressions of Wasserstein distance and RMSE on the estimated difference in DCM between the two data sources. The higher c suggests that Wasserstein distance is more effective than RMSE at capturing the observed difference in DCM. Additionally, electronic supplementary material, figure S9, shows that the most prominent movement across depth—pooled across all comparisons made—is from approximately 96 m in the in situ data, to 140 m in the model data. This indicates that in aggregate, there is a depth-wise mismatch in the DCM between the two data sources. The Wasserstein distance uncovers the spatial mismatch without the additional step of isolating the DCM.. 4We have demonstrated through a series of examples how the Wasserstein distance can be a useful tool for oceanographers performing the common task of comparing scalar fields in the ocean. Our analyses focused on two time-varying chlorophyll datasets in the Pacific Ocean—a map defined over a longitude–latitude box in the North Pacific and a depth profile at Station ALOHA. In several examples, we found that the Wasserstein distance was able to capture differences in seasonality, distribution shifts, and other scientifically relevant factors in ways that a pixel-wise difference could not. For example, in the depth profile analysis, the Wasserstein distance could more closely track the changes in the DCM than RMSE. A further advantage over RMSE that we did not demonstrate in our examples is that the Wasserstein distance does not require the two sources to be defined on identical sets of spatial cells.patterns, which may help detect long-term trends efficiently and with less uncertainty.Our Wasserstein distance-based analysis also suggested that the differences in chlorophyll data from the model and remote sensing observations can sometimes be larger than the within-source seasonal variability. The optimal transport maps that are generated in the computation of Wasserstein distance allowed us to understand that this difference was driven by a seasonally varying set of global-scale probability mass shifts. We also found that a key feature of these two data sources—the estimated boundary between the subpolar zone and the subtropical gyre—are much more similar in this region than the original chlorophyll maps. Analysis of the Wasserstein distance on remote sensing data  also helped reveal a long-term change from 1998 to 2006 that is not present in the model data. This suggests the usefulness of the Wasserstein distance for examining spatial data over time within a single source. Current studies often establish long-term trend terms of changes in magnitude; the Wasserstein distance detects changes in The demonstrations within this paper are just a starting point for the potential uses of the Wasserstein distance. We envisage this metric being used by many oceanographic data scientists for a variety of comparisons, across a range of dimensions and variables. One particular future development of interest would build on our application of Wasserstein distance to province boundaries with exploration of this technique for more complex applications than the single horizontal TZCF boundary demonstrated here. Defining and testing provinces (biomes) in the ocean is an active area of research ,52, and As demonstrated in our examples, the Wasserstein distance is particularly useful for model-data comparison because models can struggle to get the physical location of some key features in the ocean, such as the Gulf Stream. A pixel-wise comparison will measure the magnitude of difference at rigid locations, while the Wasserstein distance will focus on the pattern change and appropriately measure this discrepancy in the longitude–latitude space.temporal trends in patterns rather than in magnitudes. This shows the Wasserstein distance goes far beyond simple model-data comparison, and can be useful for analysing spatial fields of ocean physical, biogeochemical, and optical quantities over time.Further, the regression analysis in §3(a)(ii) suggests the Wasserstein distance as a powerful tool to examine Developing computational improvements will be important to allow for full global ocean comparisons. One simple extension is to only allow local transports, by directly modifying the base distances. Handling this sparser structured base distance effectively—by building specialized software—may be an important practicality. Faster approximations to optimal transport are popular in computer science and machine-learning applications, and can also be adopted when analysing ocean data.Another methodological extension is to consider optimal transport with unequal masses ; a natur


Liver transplantation (LT) is considered the only curative therapeutic option for early, unresectable, and unablatable hepatocellular carcinoma (HCC), particularly in the setting of chronic liver disease. The criteria for selecting patients for LT for HCC have evolved since the description of the Milan Criteria by Professor Mazzaferro. In Australia and New Zealand (ANZ), the choice of criteria has expanded over the last 24 years from the Milan to the University of California San Francisco (UCSF) criteria and, more recently, to Metroticket 2.0 (MT2). This study analysed the overall and HCC-related deaths following LT in ANZ through the last 24 years to clarify the impact of the expansion of these criteria. Our data confirm that overall survival following LT for HCC has significantly improved over time despite expanding criteria from Milan to UCSF. Patients fulfilling the MT2 criteria have a survival comparable to the UCSF cohort. Thus, the expansion of criteria to MT2 is justifiable.Milan era (1997–2006) and the UCSF era (2007–July 2015). Results: The overall 5- and 10-year cumulative survival rates for the entire cohort of 691 patients were 78% and 69%, respectively. Patients transplanted in UCSF era had significantly higher 5- and 10-year survival rates than in the Milan era . In the UCSF era, the 5-year survival rate for patients transplanted within Milan criteria was significantly better than those transplanted outside Milan but within UCSF criteria . Patients transplanted within the MT2 criteria had a significantly better 5- and 10-year survival rate as compared to those outside the criteria . Conclusion: Overall survival following LT for HCC has significantly improved over time despite expanding criteria from Milan to UCSF. Patients fulfilling the MT2 criteria have a survival comparable to the UCSF cohort. Thus, expansion of criteria to MT2 is justifiable.Background: Expansion in liver transplantation (LT) criteria for HCC from Milan to UCSF has not adversely impacted overall survival, prompting further expansion towards Metroticket 2.0 (MT2). In this study, we compared patient survival post-transplant before and after 2007 and long-term outcomes for LT within Milan versus UCSF criteria (to determine the true benefit of the expansion of criteria) and retrospectively validated the MT2 criteria. Methods: Retrospective analysis of ANZLITR . The entire cohort was divided based on criteria used at the time of listing, namely,  The incidence of hepatocellular cancer is increasing worldwide ,2. It haFollowing the proposal of the Milan  criteriap < 0.001), and AFP French model (p = 0.044) to predict which patients will survive for five years after LT . Similarly, the cumulative incidence of HCC-related death was 58% lower  amongst patients transplanted within the MT2 criteria in the UCSF era compared to those outside MT2 criteria. The 5- and 10-year cumulative incidence of deaths were also significantly lower amongst patients transplanted within the MT2 criteria compared to those outside criteria  . PatienPatient survival has certainly improved over time following LT . This imp = 0.02) [The reduced survival in recipients over 55 years is intriguing. Stratifying age by decade resulted in significantly lower numbers of patients in some decades. Hence, the 55-year cut-off, with 312 patients below the cut-off, and 379 greater than and equal to the cut-off, served as a balanced comparator. Jain et al. noted an overall poor survival amongst LT recipients over 60 years of age  . A simil = 0.02) . Whether = 0.02) . Conside = 0.02)  and 81%  = 0.02) , this as = 0.02)  have recOur data indicate that the overall survival for patients being transplanted outside Milan but within UCSF was significantly lower than for patients transplanted within Milan criteria . DespiteExpansion of criteria for LT for HCC has not always met with success . HoweverThe ANZ Liver and Intestinal Transplant Registry is able to capture every patient who underwent an LT over the study period, and the information entered is valid. However, being a bi-national registry, the number of data entry parameters is restricted as compared to an individual institution, and thus, detailed analyses of subsets of patients are limited. This is why we do not have detailed pre-LT treatment and donor data that may impact on survival. Another shortcoming of the study is the lack of complete pathologic data from 247 patients (36%), leading to the exclusion of these patients from the overall analysis. However, we do not have reason to believe that this would skew the results in any way. Another limitation of our study is that the analysis is retrospective in nature and based on explant histology to stratify patients within the criteria for the manuscript.Overall survival following LT for HCC has significantly improved over time despite expanding the criteria from Milan to UCSF. It is likely that this survival advantage is due to the patients who are listed as UCSF but fall within the Milan criteria. Patients fulfilling the MT2 criteria have a survival comparable to the UCSF group, and further expansion to MT2 appeared justified.