cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15253747.v1
title: Additional file 6 of Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

description: Additional file 6. Co-expression network statistics of Jatropha curcas.
DOI: 10.25582/data.2021-09.2326493/2286006
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-11.2387640/2286007
title: Data for EMSL Project 51834 from November 2021
DOI: 10.6084/m9.figshare.21545521.v1
title: Histopathology-based diagnosis of Mooren’s ulcer concealed beneath the pterygium on eye

description: Mooren’s ulcer (MU) is a chronic and painful ulcerative keratitis that is difficult to diagnose, especially when concealed beneath the pterygium, which is a common, benign, wedge-shaped, fleshy tissue growth of the conjunctiva extending onto the cornea. The coexistence of MU and pterygium is extremely rare. A 41-year-old man presented with a 2-month history of unprovoked redness, pain, and blurred vision in the right eye. Corneal epithelial defects around the pterygium head were noted upon slit-lamp examination and fluorescein staining. The patient was initially misdiagnosed with a corneal epithelial defect and pterygium. The initial treatments with anti-inflammatory and corneal epithelial growth promotion tear agents failed. Anterior segment optical coherence tomography (AS-OCT) showed corneal stromal lysis thinning, and in vivo confocal microscopy (IVCM) revealed marked inflammatory cell infiltration and stromal degeneration. We suspected the pathology was an immune-related or tumor-related corneal ulcer. The MU concealed beneath the pterygium was diagnosed by histopathological examination of a biopsy specimen that presented typical localized loss of the corneal epithelium and Bowman’s layer, stromal degeneration, and inflammatory cell infiltration. Finally, we performed lamellar keratoplasty (LKP) combined with pterygium excision surgery. The patient recovered with no complications or recurrence during the 1-year follow-up period. Few cases of MU concealed beneath the pterygium have been reported. It is beneficial to rule out the pathological changes concealed beneath the pterygium, combined with multiple means of examination such as slit-lamp examination, AS-OCT, and IVCM. A histopathological examination should be performed to establish a diagnosis.
DOI: 10.6084/m9.figshare.15253771
title: Additional file 8 of Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

description: Additional file 8. Oil-biosynthesis-related genes are closely linked relative to random background.
DOI: 10.25582/data.2021-11.2389437/2286008
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-03.2461250/2286009
title: Data for EMSL Project 51834 from March 2022
DOI: 10.25582/data.2021-11.2387630/2286010
title: Data for EMSL Project 51834 from November 2021
DOI: 10.6084/m9.figshare.15253714
title: Additional file 12 of Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

description: Additional file 12. Predicited oil-biosynthesis-related genes in different sub-pathways.
DOI: 10.25582/data.2022-04.2479044/2286011
title: Data for EMSL Project 51834 from April 2022
DOI: 10.25582/data.2021-12.2389924/2286012
title: Data for EMSL Project 51834 from December 2021
DOI: 10.6084/m9.figshare.15253726.v1
title: Additional file 2 of Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

description: Additional file 2. Jatropha curcas oil-biosynthesis-related genes based on homology search.
DOI: 10.6084/m9.figshare.15253735.v1
title: Additional file 4 of Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

description: Additional file 4. PPI network statistics of Jatropha curcas.
DOI: 10.25582/data.2021-10.2361082/2286013
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-11.2388317/2286014
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-09.2607242/2286015
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-11.2380535/2286016
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-11.2387062/2286017
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-10.2352787/2286018
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2022-09.2606361/2286019
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-09.2327237/2286020
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-09.2327126/2286021
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-11.2387648/2286022
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-09.2330925/2286023
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-10.2367348/2286024
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-10.2363872/2286025
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-10.2372854/2286026
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-10.2369652/2286027
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-09.2329612/2286028
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2022-10.2615301/2286029
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2021-09.2322339/2286030
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2022-09.2610792/2286031
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-09.2314733/2286032
title: Data for EMSL Project 51834 from September 2021
DOI: 10.6084/m9.figshare.15253738.v1
title: Additional file 5 of Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

description: Additional file 5. The results of NBD algorithm on co-expression networks with different SCC cutoffs.
DOI: 10.25582/data.2021-10.2368801/2286033
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-11.2385088/2286034
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-09.2609196/2286035
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2022-10.2613547/2286036
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-09.2607839/2286037
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-11.2387291/2286038
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-12.2411244/2286039
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2021-09.2328646/2286040
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-09.2326490/2286041
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2022-10.2616418/2286042
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-04.2478406/2286043
title: Data for EMSL Project 51834 from April 2022
DOI: 10.6084/m9.figshare.15253771.v1
title: Additional file 8 of Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

description: Additional file 8. Oil-biosynthesis-related genes are closely linked relative to random background.
DOI: 10.25582/data.2021-11.2389177/2286044
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-09.2610903/2286045
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-11.2384664/2286046
title: Data for EMSL Project 51834 from November 2021
DOI: 10.6084/m9.figshare.14205560.v1
title: Additional file 3 of Metagenomic sequencing revealed the potential of banknotes as a repository of microbial genes

description: Additional file 3: Supplemental Table S1. Primer sequence. Primer sequences used in this paper.
DOI: 10.25582/data.2022-01.2417408/2286047
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2021-12.2397081/2286048
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2021-09.2322415/2286049
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2022-10.2614953/2286050
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-09.2607909/2286051
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2022-09.2606227/2286052
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2022-10.2621966/2286053
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-09.2607463/2286054
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-11.2388160/2286055
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-08.2304763/2286056
title: Data for EMSL Project 51834 from August 2021
DOI: 10.25582/data.2021-09.2318197/2286057
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-09.2327409/2286058
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-10.2372883/2286059
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-11.2388154/2286060
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-09.2606066/2286061
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-08.2304581/2286062
title: Data for EMSL Project 51834 from August 2021
DOI: 10.25582/data.2022-04.2479133/2286063
title: Data for EMSL Project 51834 from April 2022
DOI: 10.25582/data.2022-10.2617492/2286064
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2021-10.2368071/2286065
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-09.2329641/2286066
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2022-09.2609313/2286067
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2022-09.2609223/2286068
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2022-09.2609675/2286069
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2022-01.2417180/2286070
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2021-11.2387655/2286071
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-09.2319275/2286072
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-09.2329584/2286073
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-09.2327062/2286074
title: Data for EMSL Project 51834 from September 2021
DOI: 10.6084/m9.figshare.21545642.v1
title: Additional file 5 of Integrated analysis to identify the AC005154.6/hsa-miR-29c-3p/CCNL2 axis as a novel prognostic biomarker associated with immune infiltration in prostate cancer

description: Additional file 5. Thirteen ceRNA networks correlated with patient survival.
DOI: 10.6084/m9.figshare.21545645
title: Additional file 6 of Integrated analysis to identify the AC005154.6/hsa-miR-29c-3p/CCNL2 axis as a novel prognostic biomarker associated with immune infiltration in prostate cancer

description: Additional file 6. Prognosis model constructed by AC005154.6/hsa-miR-29c-3p/CCNL2 axis.
DOI: 10.25582/data.2021-12.2390494/2286075
title: Data for EMSL Project 51834 from December 2021
DOI: 10.6084/m9.figshare.21545639
title: Additional file 4 of Integrated analysis to identify the AC005154.6/hsa-miR-29c-3p/CCNL2 axis as a novel prognostic biomarker associated with immune infiltration in prostate cancer

description: Additional file 4. Sixty-one ceRNA networks constructed by identified DEmRNAs, DEmiRNAs, and DElncRNAs.
DOI: 10.25582/data.2021-12.2404797/2286076
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2022-09.2606301/2286077
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-08.2303216/2286078
title: Data for EMSL Project 51834 from August 2021
DOI: 10.25582/data.2022-09.2609864/2286079
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2022-04.2478895/2286080
title: Data for EMSL Project 51834 from April 2022
DOI: 10.25582/data.2021-08.2305330/2286081
title: Data for EMSL Project 51834 from August 2021
DOI: 10.25582/data.2021-09.2330086/2286082
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-11.2387657/2286083
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-01.2417375/2286084
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2022-04.2478871/2286085
title: Data for EMSL Project 51834 from April 2022
DOI: 10.25582/data.2022-09.2607487/2286086
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-09.2322558/2286087
title: Data for EMSL Project 51834 from September 2021
DOI: 10.6084/m9.figshare.21545651.v1
title: Additional file 8 of Integrated analysis to identify the AC005154.6/hsa-miR-29c-3p/CCNL2 axis as a novel prognostic biomarker associated with immune infiltration in prostate cancer

description: Additional file 8. Twenty-nine small molecules identified from CMap.
DOI: 10.25582/data.2021-12.2390392/2286088
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2021-08.2305284/2286089
title: Data for EMSL Project 51834 from August 2021
DOI: 10.6084/m9.figshare.7915706
title: Additional file 6: of Genomic signature of parity in the breast of premenopausal women

description: Genes differentially expressed between epithelial and stromal tissues. (XLSX 189 kb)
DOI: 10.25582/data.2021-08.2305316/2286090
title: Data for EMSL Project 51834 from August 2021
DOI: 10.6084/m9.figshare.21545639.v1
title: Additional file 4 of Integrated analysis to identify the AC005154.6/hsa-miR-29c-3p/CCNL2 axis as a novel prognostic biomarker associated with immune infiltration in prostate cancer

description: Additional file 4. Sixty-one ceRNA networks constructed by identified DEmRNAs, DEmiRNAs, and DElncRNAs.
DOI: 10.25582/data.2021-09.2327285/2286091
title: Data for EMSL Project 51834 from September 2021
DOI: 10.6084/m9.figshare.19448466
title: Analyzing and validating the prognostic value and immune microenvironment of clear cell renal cell carcinoma

description: Tumor immune microenvironment (TIME) plays an important role in tumor diagnosis, prevention, treatment and prognosis. However, the correlation and potential mechanism between clear cell renal cell carcinoma <b>(</b>ccRCC) and its TIME are not clear. Therefore, we aimed to identify potential prognostic biomarkers related to TIME of ccRCC. Unsupervised consensus clustering analysis was performed to divide patients into different immune subgroups according to their single-sample gene set enrichment analysis (ssGSEA) scores. Then, we validated the differences in immune cell infiltration, prognosis, clinical characteristics and expression levels of HLA and immune checkpoint genes between different immune subgroups. Weighted gene coexpression network analysis (WGCNA) was used to identify the significant modules and hub genes that were related to the immune subgroups. A nomogram was established to predict the overall survival (OS) outcomes after independent prognostic factors were identified by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses. Five clusters (immune subgroups) were identified. There was no significant difference in age, sex or N stage. And there were significant differences in race, T stage, M stage, grade, prognosis and tumor microenvironment. WGCNA revealed that the red module has an important relationship with TIME, and obtained 14 hub genes. In addition, the nomogram containing <i>LAG3</i> and <i>GZMK</i> accurately predicted OS outcomes of ccRCC patients. <i>LAG3</i> and <i>GZMK</i> have a certain correlation with the prognosis of ccRCC patients, and play an important role in the TIME. These two hub genes deserve further study as biomarkers of the TIME.
DOI: 10.6084/m9.figshare.19448466.v1
title: Analyzing and validating the prognostic value and immune microenvironment of clear cell renal cell carcinoma

description: Tumor immune microenvironment (TIME) plays an important role in tumor diagnosis, prevention, treatment and prognosis. However, the correlation and potential mechanism between clear cell renal cell carcinoma <b>(</b>ccRCC) and its TIME are not clear. Therefore, we aimed to identify potential prognostic biomarkers related to TIME of ccRCC. Unsupervised consensus clustering analysis was performed to divide patients into different immune subgroups according to their single-sample gene set enrichment analysis (ssGSEA) scores. Then, we validated the differences in immune cell infiltration, prognosis, clinical characteristics and expression levels of HLA and immune checkpoint genes between different immune subgroups. Weighted gene coexpression network analysis (WGCNA) was used to identify the significant modules and hub genes that were related to the immune subgroups. A nomogram was established to predict the overall survival (OS) outcomes after independent prognostic factors were identified by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses. Five clusters (immune subgroups) were identified. There was no significant difference in age, sex or N stage. And there were significant differences in race, T stage, M stage, grade, prognosis and tumor microenvironment. WGCNA revealed that the red module has an important relationship with TIME, and obtained 14 hub genes. In addition, the nomogram containing <i>LAG3</i> and <i>GZMK</i> accurately predicted OS outcomes of ccRCC patients. <i>LAG3</i> and <i>GZMK</i> have a certain correlation with the prognosis of ccRCC patients, and play an important role in the TIME. These two hub genes deserve further study as biomarkers of the TIME.
DOI: 10.25582/data.2021-08.2304815/2286092
title: Data for EMSL Project 51834 from August 2021
DOI: 10.6084/m9.figshare.21545651
title: Additional file 8 of Integrated analysis to identify the AC005154.6/hsa-miR-29c-3p/CCNL2 axis as a novel prognostic biomarker associated with immune infiltration in prostate cancer

description: Additional file 8. Twenty-nine small molecules identified from CMap.
DOI: 10.6084/m9.figshare.21545521
title: Histopathology-based diagnosis of Mooren’s ulcer concealed beneath the pterygium on eye

description: Mooren’s ulcer (MU) is a chronic and painful ulcerative keratitis that is difficult to diagnose, especially when concealed beneath the pterygium, which is a common, benign, wedge-shaped, fleshy tissue growth of the conjunctiva extending onto the cornea. The coexistence of MU and pterygium is extremely rare. A 41-year-old man presented with a 2-month history of unprovoked redness, pain, and blurred vision in the right eye. Corneal epithelial defects around the pterygium head were noted upon slit-lamp examination and fluorescein staining. The patient was initially misdiagnosed with a corneal epithelial defect and pterygium. The initial treatments with anti-inflammatory and corneal epithelial growth promotion tear agents failed. Anterior segment optical coherence tomography (AS-OCT) showed corneal stromal lysis thinning, and in vivo confocal microscopy (IVCM) revealed marked inflammatory cell infiltration and stromal degeneration. We suspected the pathology was an immune-related or tumor-related corneal ulcer. The MU concealed beneath the pterygium was diagnosed by histopathological examination of a biopsy specimen that presented typical localized loss of the corneal epithelium and Bowman’s layer, stromal degeneration, and inflammatory cell infiltration. Finally, we performed lamellar keratoplasty (LKP) combined with pterygium excision surgery. The patient recovered with no complications or recurrence during the 1-year follow-up period. Few cases of MU concealed beneath the pterygium have been reported. It is beneficial to rule out the pathological changes concealed beneath the pterygium, combined with multiple means of examination such as slit-lamp examination, AS-OCT, and IVCM. A histopathological examination should be performed to establish a diagnosis.
DOI: 10.25582/data.2022-09.2609430/2286093
title: Data for EMSL Project 51834 from September 2022
DOI: 10.6084/m9.figshare.14205560
title: Additional file 3 of Metagenomic sequencing revealed the potential of banknotes as a repository of microbial genes

description: Additional file 3: Supplemental Table S1. Primer sequence. Primer sequences used in this paper.
DOI: 10.25582/data.2021-08.2304547/2286094
title: Data for EMSL Project 51834 from August 2021
DOI: 10.6084/m9.figshare.7915745.v1
title: Additional file 2: of Genome-wide association study of four yield-related traits at the R6 stage in soybean

description: Table S2. qRT-PCR primers. (XLSX 9 kb)
DOI: 10.6084/m9.figshare.14731490
title: Widely targeted metabolomic analyses of albino tea germplasm ‘Huabai 1’ and ‘Baiye 1’

description: ‘Huabai 1’ is a novel albino tea germplasm that has a stable albino phenotype and prolonged albino period. In this study, widely targeted metabolomics was employed to analyze the metabolic profile of ‘Huabai 1’, and compare metabolites between albino shoots of ‘Huabai 1’ (HBW) and albino shoots of the broadest cultivated cultivar ‘Baiye 1’ (BYW). A total of 359 differential metabolites were identified in ‘Huabai 1’. Comparative analysis of differential metabolites revealed that 83 and 161 differential metabolites were specific for HBW and BYW, respectively. The phenylpropanoid and flavonoids biosynthesis are the key metabolic pathways, contributing to the color change in ‘Huabai 1’, while the purine metabolism and lipid-related metabolites may play important roles in regulating albinism in ‘Baiye 1’. Comparative analysis of amino acids and catechins revealed that the umami taste of albino tea may be due to the internal balance of amino acid components and the decrease in phenol-ammonia ratio. In addition, lipid-related metabolites, the main contributors to the aroma of tea, were significantly enriched in BYW, while, HBW possess higher antioxidative components than that of BYW, such as apigenin and genistein etc., suggesting that the ‘Huabai 1’ may have high prospects in commercialization and future breeding programs.
DOI: 10.25582/data.2022-10.2613941/2286095
title: Data for EMSL Project 51834 from October 2022
DOI: 10.6084/m9.figshare.14731493
title: UBE2T And CYP3A4: hub genes regulating the transformation of cirrhosis into hepatocellular carcinoma

description: Cirrhosis is the primary driver of hepatocellular carcinoma (HCC). Blocking the deterioration of cirrhosis into HCC would be of benefit for long–term survival. We applied a series of bioinformatic online databases to select and analyze the hub genes concerning cirrhosis and HCC, and identified UBE2T and CYP3A4 as hub genes for cirrhosis-HCC transformation. An elevated UBE2T and a decreased CYP3A4 were expressed in HCC compared with cirrhosis, which have been confirmed by real-time polymerase chain reaction (RT–PCR), Western Blotting (WB) and immunohistochemistry (IHC). The specificity (89.9%) and sensitivity (74.1%) on the combination of UBE2T and CYP3A4 for predicting HCC development in cirrhosis patients were better than that of UBE2T or CYP3A4 alone, or alpha-fetoprotein. The effectiveness of the combination of UBE2T and CYP3A4 was further verified by 15 paired HCC and paracancerous cirrhosis samples using RT–PCR, with a specificity of 100% and a sensitivity of 80%. HCC patients with elevated UBE2T and decreased CYP3A4 expression demonstrated a poorer overall survival rate (<i>P </i>= 0.0016, 0.019) and disease-free survival rate (<i>P </i>= 0.0013, 0.041). In conclusion, UBE2T and CYP3A4 could be a new combination of biomarkers for the carcinogenesis and progression of cirrhosis into HCC.