dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical Data Ethnic origin, family history of POI (at least 1 first-degree female relative with POI according to the patient), pubertal development, menstrual history (primary amenorrhea or secondary menstrual disturbance), and prior spontaneous pregnancies were recorded during the medical consultations at diagnosis. Clinical symptoms suggesting syndromic POI were evaluated and any personal history of autoimmune disorders was noted. Furthermore, tests were done for 21-hydroxylase antibodies, ovarian antibodies, thyroid peroxidase antibodies, thyroglobulin antibodies, glutamate decarboxylase antibodies, antibodies common in celiac disease and lupus antibodies.
DNA Sequencing Genomic DNA was extracted either from the patient's blood cells or from a derived lymphoblastoid. The 18 genes studied were selected because of their potential implication in POI according to previous studies (Table  1 ). We tested for mutations in the coding exons and abutting splice sites of BMP15 (NM_005448. 2), DMC1 (NM-007068. 3), EIF2S2 (NM_003908. 2), FIGLA (NM_001004311. 3), FOXL2 (NM-023067. 3), FSHR (NM_000145. 3), GDF9 (NM_005260. 5), GPR3 (NM_005281. 3), HFM1 (NM_001017975. 4), LHX8 (NM_00100933. 1), MSH5 (NM-172165. 3), NOBOX (NM_001080413. 3), NR5A1 (NM_004959. 4), PGRMC1 (NM_006667. 4), STAG3 (NM_001282717. 1), BHLHB9 (NM_001142528), FSHB (NM_001018080), and XPNPEP2 (NM_003399. 5) using the Ion Torrent semiconductor sequencing technique. The primers were designed using the Ampliseq Designer software.
The libraries were prepared from 50 ng of genomic DNA using the Ion Plus fragment library kit. Adapter ligation, nick repair, and amplification were performed according to the Ion Torrent protocol (Life Technologies). The Ion One Touch template kit was used for the emulsion polymerase chain reaction and enrichment steps. Sequencing of the amplicon libraries was done on the Ion Torrent PGM system with 316 chips, and the Ion Xpress barcode adapters kit was used for the barcoding. Version 2 of the Ion sequencing kit was used for all sequencing reactions, according to the recommended protocol. After sequencing, reads were mapped to the human genome 19 assembly with the Torrent mapping alignment program. Single-nucleotide variants and small insertions/deletions (indels) were identified using Torrent Variant Caller (Life Technologies) and Nextgene software  [27].
Description of the Population We included 269 consecutive patients diagnosed with POI. As shown in Table  2 , 126 patients (52%) were of Caucasian origin, 57 (23%) came from sub-Saharan Africa, 50 (20%) were from North Africa, and 13 (5%) came from Asia (missing data N = 23). Forty-three patients (16%) had a family history of POI (missing data N = 5). Primary amenorrhea (PA) was observed in 34 patients (13%) and 229 patients (87%) (missing data N = 6) had a secondary menstrual disturbance (SMD) (amenorrhea or oligomenorrhea).
Methods: In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. Results: Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation.
We took advantage of a previously published study, where a set of upregulated gene with clinical impact has been identified in VHLknockout RENCA cells  [12]. To assess the degree of VHL cascade activation in our cell lines, we compared the expression of this VHL knockout related genes to our transcriptomic dataset. The heatmap of T-LM and K-LM late passages cells (including P4-P5-P6) revealed clustering and increased The z-score indicates how genes are differentially expressed according to GO terms: a positive z-score indicates that the number of up-regulated genes is higher than the number of down-regulated genes in the respective GO term. Counts represent the number of genes which are differentially expressed expression of the VHL knockout related genes when compared to early passaged cells (Supplementary Fig.
Clinically relevant signatures and biomarker discovery We next investigated whether transcriptional signatures derived from the analysis of differentially expressed genes in the KPT, T-LM and K-LM-groups could predict clinical outcome of patients. To this aim we used the Clear Cell Renal Cell Carcinoma dataset (KIRC) from The Cancer Genome Atlas (TCGA). The general strategy is outlined in Fig. 4. To select biomarkers from our mouse model, we compared genes that changed their expression between the parental P0 and P6 cell line and exhibited a progressive expression pattern (KPT, T-LM, K-LM; Supplementary Table  S1 ). To determine the list of clinically relevant genes, we next investigated the predictive value of the 3 gene lists using TCGA (KIRC cohort). For each gene, we fitted a Cox-proportional hazard regression model based on overall survival (OS) or disease-free survival (DFS).
5g, h ). For CFB, DFS or OS were not significantly changed, albeit a tendency for DFS was recorded. We next used plasma samples from metastatic patients before receiving a first cycle of sunitinib or bevacizumab (SUVEGIL, NCT00943839 and TORAVA, NCT00619268 clinical trials). The analyses were performed at the third quartile. Patients treated with anti-angiogenic drugs (sunitinib and bevacizumab) and stratified according to low and high SAA2 CFB levels, had a spectacular better PFS when belonging to the SAA2 / CFB low group (cut-off: SAA2 272. 49 μg/ml; CFB 311. 96 μg/ml). When patients treated with sunitinib alone were analyzed, the PFS and OS was significantly shorter in the high expression group (Fig. 6a, b ). When SAA2 and CFB were combined, we can subdivide the cohort into 3 different groups and rank PFS and OS accordingly: CFB low/SAA2 low (PFS 13.
23 months, OS: 20. 8 months), CFB low/SAA2 high or CFB high/ SAA2 low (PFS 9. 87 months, OS 16. 52 months), and CFB high/SAA2 (PFS 2. 8 months, OS 8. 33 months) (Fig. 6c ). Thus, the combined analysis of these two markers is a powerful predictor of patient outcome following antiangiogenic treatment with sunitinib or bevacizumab. We also investigated CFB and SAA2 in the cohort of the published dataset treated with immunotherapy   (Supplementary Fig. S9 ). For immunotherapy with anti-PD1, CFB and SSA2 alone were not significant predictor. However, the combination of CFB and SAA2 was found to be predictive for OS.
From personalized DMFS curves, we then took the median of the 10,000 TTRs and defined it as the predicted TTR. With the predicted TTR for all the patients in the dataset, together with the real TTR, we calculated the C-Index of the models with different combination of these three covariates. The best C-Index was achieved with the model combining Fuhrman grade and CFB (C-Index = 0. 7273), followed by the three covariates together (C-Index = 0. 7163) and Fuhrman grade alone (C-Index = 0. 6948). To compare, we also computed the C-Index using multivariable Cox regression. In all cases, the C-Index was similar to the mathematical model, see Table  2.
Importantly, the signatures, especially the K-LM signature, were stronger predictors than current predictors in clinical use such as Fuhrman grade or clinical stage. Our signature is different from the signature previously published such as ccrcc1-4 signatures  [33] , 16-gene assay  [34]  and ClearCode34  [35]. Ricketts et al. (29)  reported a comprehensive molecular characterization using the TCGA database where they compared the three types of RCC (ccRCC, papillary RCC and chromophobe RCC). For ccRCC, the signatures were related to increased ribose metabolism pathway and to Th2 immune profile. However, our study is very different because starting from an animal model, we specifically focused our comparative translational analysis on the ccRCC subtype. Thus, the results cannot be compared, albeit their study also revealed immunology-related genes.
Furthermore, a recent study reported tracking of ccRCC evolution at the genomic level and demonstrated that metastatic competence was afforded by chromosomal complexity with loss of 9p as a selective event for metastasis and patient survival  [36]. However, our study did not reveal chromosomal alterations and, thus, we specifically focused on modifications in gene expression. Our signatures were also found to be predictive when analyzed in an immunotherapy data set which could of use in stratifying patients undergoing targeted therapy. For a more detailed investigation, we selected SAA2 and CFB, because they represent soluble and measurable proteins in the blood, that have never described in RCC. SAA2, an acute phase protein related to SAA1, was found to be a strong predictor of OS and DFS in the TCGA-KIRC database globally and also for the M0 and M1 subgroups in UroCCR cohort.
Patients whose CFB levels were increased following treatment had faster disease progression and shortened survival compared to patients whose levels were decreased. The combination of SAA2 and CFB plasma level measurements before treatment produced a more powerful analysis than either marker alone. This indicates that a combined analysis of these two markers may represent a tool for predicting patient outcome following targeted treatment and for therapy decision. Noteworthy, CFB is a prognostic preoperative marker in pancreatic carcinoma which outperformed CA19-9 and CEA  [40]. However, the combination of CFB and SAA2 was not tested in this study. The International Metastatic RCC Database Consortium Risk Model for metastatic RCC (IMDC) is used to predict survival in patients with metastatic RCC, treated with systemic therapy.
Transcriptome data generation and analysis Counts of samples by group and passages: From the log2 scale normalized data set, Principal Component Analysis (PCA, function prcomp of stats R package (v3. 6. 2) with the parameter center = T) was performed on parental cell line (P0) and each passage resulting in P0-P3, P0-P5 and P0-P6 PCA. For the P0-P6 PCA, genes with the most important association were selected by keeping genes whose contributions were above the mean of all contributions for PC1 and PC2. This resulted in a set of 5194 genes. The mean value was computed for each passage P0 to P6 and used for the heatmap using the pheatmap R package (v1. 0. 12, clustering_method = "ward. D2", scale = "row"). Differential Expression Analysis (DEA) was performed between the passage 3 and 6 for each group separately (KPT, T-LM, K-LM) with the limma R package (v.
3. 42. 2  [41] ). A gene was considerate as a Differentially Expressed Gene (DEG) if its adjusted p-value was ≤0. 01 (Benjamini & Hochberg (BH) method (CIT4)). The results are summarized in the following table. Then, enrichment analysis was done for DEG sets of each group separately. To perform enrichment analyses we used hypergeometric test (enricher function of clusterProfiler R package  [42] ; v3. 10. 1) with go_terms. mgi downloaded on Mouse Genome Database (MGD) at the Mouse Genome Informatics website (URL: http:// www. infor matics. jax. org  [43]  (04/2019). A GO term was considered significantly enriched if its adjusted p-value was ≤0. 05 (BH method). Then, we computed a z-score value as an additional indicator of the direction of the dis-regulation of the GO term as: z-score = (up-down)/ sqrt(count) where up / down are the number of assigned genes up-or down-regulated, respectively, in the GO term  [44].
TCGA KIRC cohort TCGA Kidney Renal Clear Cell Carcinoma (KIRC) HiSeqV2 data were downloaded from XenaBrowser  [47]. We chose log2(x + 1) transformed RSEM normalized count (version 2017-10-13) as recommended on its web site (https:// xenab rowser. net/; page: "dataset:gene expression RNAseq-IlluminaHiSeq percentile"). We removed genes whose expression had null values in more than 2 third of all samples (2360 genes). Complementary associated clinical data were downloaded from cbioportal (www. cbiop ortal. org/,  [48] ). Only primary tumor samples were used remaining to 533 patients whose 352 had a M0 status.
Human patient samples Patient samples (tumor tissue and plasma) from the UroCCR cohort were used with associated clinical data (clini caltr ial. gov, NCT03293563). Eligible patients for SUVEGIL and TORAVA trials were at least 18 years of age and had metastatic ccRCC histologically confirmed, with the presence of measurable disease according to Response Evaluation Criteria in Solid Tumors v1. 1. For SUVEGIL and TORAVA cohorts, patients did not received previous systemic therapy for RCC, and were eligible for sunitinib or bevacizumab treatment in the first-line setting. Patients were ineligible if they had symptomatic or uncontrolled brain metastases, an estimated lifetime less than 3 months, uncontrolled hypertension or clinically significant cardiovascular events (heart failure, prolongation of the QT interval), history of other primary cancer.
All patients gave written informed consent. Tumors were assessed at baseline and then every 12 weeks by thoracic, abdominal, pelvic and bone CT scans. Brain CT scans were performed in case of symptoms. This cohort includes patients from the SUVEGIL (24 patients) and TORAVA (35 patients) trials. The SUVEGIL trial (clini caltr ial. gov, NCT00943839) was a multi-center prospective singlearm study. The goal of the trial is to determine whether a link exists between the effectiveness of therapy with sunitinib malate and development of blood biomarkers in patients with kidney cancer. Patients received oral sunitinib (50 mg per day) once daily for 4 weeks (on days 1 to 28), followed by 2 weeks without treatment. Courses repeat every 6 weeks in the absence of disease progression or unacceptable toxicity.
The TORAVA trial (clini caltr ial. gov, NCT00619268) was a randomized prospective study. Patient characteristics and results have been previously described  [43]. Briefly, patients aged 18 years or older with untreated metastatic ccRCC were randomly assigned (2: 1: 1) to receive the combination of bevacizumab (10 mg kg -1 iv every 2 weeks) and temsirolimus (25 mg iv weekly) IFN-α (9 mIU i. v. three times per week), or one of the standard treatments: sunitinib (50 mg per day orally for 4 weeks followed by 2 weeks off )  [49]. These studies were approved by the ethic committee at each participating center and run in agreement with the International Conference on Harmonization of Good Clinical Practice Guideline. Blood samples were collected during the inclusion visit (baseline).
Validation of signatures in a cohort treated with immunotherapy The relevance of the KPT, K-LM and T-LM signatures, as well as SAA2, CFB and SAA2-CFB combination were also tested in a two cohorts treated with either everolimus (mTOR inhibitor, 130 patients) or nivolumab (anti-PD-1, 181 patients) immunotherapies  [13]. We used. SigCheck R package (v2. 22, [46]  with sigCheck function (scoreMethod = "High"). Signatures were tested for OS and Progression-Free Survival (PFS). To show that the signatures were significantly more associated with OS and PFS outcomes than random predictors, we compared the performance of each signature with 1000 signatures composed of the same number of randomly-selected genes (sigCheckRandom function).
Computational analysis Machine learning analysis for right-censored data was done using the Random Survival Forest (RSF) algorithm, using the RSF implementation of the random-ForestSRC R package. All RSF models were fitted using 1000 trees with the log-rank splitting rule. The impact of covariates on the DMFS curves was assessed by using the forest-averaged minimal depth (similar to the analysis done in Ref A3). To analyze clinical DMFS data we developed a mathematical model that is fully described in Ref A4. First, we developed a mechanistic model of primary tumor growth and metastasis dissemination assuming Gompertzian kinetics for tumor growth and metastasis dissemination being proportional to the primary tumor growth. The full metastatic process was described by the following transport equation where the function ρ i (t,ν) represents the distribution of metastatic tumors with size ν at time t for the individual patient i.
Funding This work was supported by grants from the PlanCancer (" Systems Biology of Renal Cell Carcinoma using an Experimental RCC model" (C18005GS, SystemsRCC )), from the  university Bordeaux  ( G2P  project) and from the  SIRIC BRIO project (Commucan)  to AB. LC and AE were supported by post-doctoral fellowships from the  Region "Nouvelle Aquitaine". A. A. -A. thanks a postdoctoral contract funded by  Junta de Comunidades de Castilla-La Mancha  (grant number  SBPLY/19/180501/000211 ).
Availability of data and materials Microarray gene expression data is available via Gene Expression Omnibus using the accession GSE142109 (Reviewer Access Token: klwrusmcbxkpjkz). DNA sequencing was deposited in ArrayExpress: http:// www. ebi. ac. uk/ array expre ss/ exper iments/ E-MTAB-8645, Access for reviewers: Username:Reviewer_E-MTAB-8645 Password: Eaa1yr9I. Methylation data is available via Gene Expression Omnibus using the accession GSE139338 (https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE13 9338; Reviewer Access Token: mxebousmdlyzbcp. Other data and material are available upon request.
At the end of the LC-MS/MS analysis, raw data processing, MS/MS ion search was performed with Mascot v2. 5. 1 (http://www. matrixsc ience. com) against Bos taurus database (i. e. ref_bos_taurus, 23,970 sequences). The following parameters were used during the request: precursor mass tolerance of 10 ppm and fragment mass tolerance of 0. 02 Da, a maximum of two missed cleavage sites of trypsin, carbamidomethylation (C), oxidation (M) and deamidation (NQ) set as variable modifications. Protein identification was validated when at least two peptides originating from one protein showed statistically significant identity above Mascot scores with a False Dicovery Rate of 1%. Ions score is -10 log(P), where P is the probability that the observed match is a random event. For plasma proteome, the Mascot score was 37 with a False Discovery Rate (FDR) of 1% and the adjusted p-value was 0.
0088. For muscle, the Mascot score was 36 with and at an FRD of 1% the adjusted p-value was 0. 0098. For label-free protein quantitation analysis, LC-Progenesis was used with the same identification parameters described above with the phenotypic data among the high and low quality beef texture. All unique validated peptides of an identified protein were included and the total cumulative abundance was calculated by summing the abundances of all peptides allocated to the respective protein. Statistical analysis was performed using the "between subject design" and p-values were calculated by a repeated measures analysis of variance using the normalized abundances across all runs. LC-Progenesis analysis yielded 546 unique gene name proteins for muscle and 188 proteins for plasma.
Statistical analyses The statistical analyses were performed using a range of software tools, mainly XLSTAT 2018. 2 (AddinSoft, Paris, France) as well as the online tools NormalyzerDE and MetaOmGraph. Raw data means were scrutinized for data entry errors, and any missing data and outliers. Log2 transformation and mean normalization gave the best performance in reducing variability among replicate samples and was applied to protein abundance in both plasma and muscle datasets. A one-way ANOVA was used for the comparison of protein abundances between the high-quality meat versus low-quality on each proteome source separately. Differences in protein abundances between the high and low groups for each beef sensory quality trait were considered significant at P < 0. 05 and tendencies towards significance were defined at P < 0.
Bioinformatics analyses Protein-protein interactions among the differential proteins were analysed separately for muscle and plasma using the STRING web service database (https://string-db. org/). In addition, the differential proteins among the two quality groups and for both sources (plasma and muscle) were compared for the enriched Gene Ontology (GO) terms using the web tool Metascape® (https://metascape. org/) as described by  Gagaoua, Terlouw, et al. , 2021  Furthermore, the web-based Pro-teINSIDE tool (http://www. proteinside. org/) was used to assign GO terms for the categories Biological Process (BP), Molecular Function (MF) and Cellular Component (CC) at Benjamini-Hochberg P < 0. 05 against human orthologs.
Protein-protein interactions and correlation networks on the putative plasma biomarkers Protein-Protein interactions were assessed using the bioinformatics STRING database (https://string-db. org/) by the 16 differentially abundant proteins in the plasma (Fig. 5A ). Furthermore, to visualize significant associations among the proteins in the dataset, another network based on the Spearman's correlation coefficients (P < 0. 05) was constructed (Fig. 5B ; Supplementary Table  S2. ). In this correlation network, the solid and dash lines represent the positive and negative correlations, respectively. The network in Fig. 5B  demonstrates that B2M interacts with the highest number of proteins (9 interactions) and relates the two distinct hubs together via HSPA2. APOA4, HP-20 and AHSG were all correlated significantly 8 times and contribute strongly to the correlation network.
, 2005). The SERPINA3 gene was recently suggested to affect bovine muscle development, beef quality and yield Fig. 9. Protein -Protein interactions networks among the 15 differentially abundant proteins in the Longissimus thoracis muscle. A) A protein network built using the STRING database (https://string-db. org/). B) A protein correlation network built using the significant (P < 0. 05) correlations among the 15 proteins. The solid and dash lines in this network represent the positive and negative correlations, respectively. The proteins are colored following the biological pathway at which they belong: cytoskeletal; proteolysis. metabolism; hydrolases; glutathione metabolic process; and protein biosynthesis and calcium binding. Fig. 10. Heat map showing the significant enriched Gene Ontology (GO) terms comparing based on the proteins identified to be significantly different among the two texture groups from both plasma and muscle.
LC-MS/MS Analysis A total of 4 µl of each sample (1 µg of peptides) were injected into a Dionex Ultimate 3000 RSLC system coupled to an Orbitrap Fusion TM Lumos TM Tribrid TM mass spectrometer (Thermo). Peptides were separated in an Acclaim PepMap 75 µm (diameter) × 500 mm (length) column packed with 3 µm diameter superficially porous particles (Thermo Scientific). The separation was performed at the flow of 0. 3 µl/min with a linear gradient 1-35% (0. 1% formic acid and 80% ACN) for 160 min and 35-50% for 10 min. A complete run including regeneration (98% buffer) was of 215 min. Nanospray ionization was performed by applying 1. 6 kV in a positive mode. Capillary transfer was performed at 275 • C using a capillary probe SilicaTip Emitter 10 µm. The mass spectrometer was operated in data dependent acquisition mode.