Single-cell sequencing uncovers the mechanistic role of DAPK1 in glioma and its diagnostic and prognostic implications by Tian-Hang Yu, Yan-Yu Ding, Yan-Yu Ding, Si-Guo Zhao, Jie-Hui Zhao, Yu Gu, Dong-Hui Chen, Fang Zhang, Wen-Ming Hong, Wen-Ming Hong

“…However, the precise role and underlying mechanisms of DAPK1 in gliomas remain inadequately understood.MethodsWe performed analyses on RNA-seq and microarray datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to single-cell RNA sequencing (scRNA-seq) data from glioma patients available in GEO. …”
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Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma by Dongqian Li, Qian Bao, Shiqi Ren, Haoxiang Ding, Chengfeng Guo, Kai Gao, Jian Wan, Yao Wang, MingYan Zhu, Yicheng Xiong

“…This paper’s data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. …”
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Activin levels correlate with lymphocytic infiltration in epithelial ovarian cancer by Elizabeth T. Evans, Emily F. Page, Alex Seok Choi, Zainab Shonibare, Andrea G. Kahn, Rebecca C. Arend, Karthikeyan Mythreye

“…Immune gene expression profile was further explored within the TCGA‐OV cohort derived from The Cancer Genome Atlas (TCGA). Immunohistochemical analysis was performed to evaluate activin A and T‐cell markers CD8 and FoxP3 at the protein level. …”
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Expression of CSTF2 in oral squamous cell carcinoma and its relationship with immune infiltration and poor prognosis by Zumulaiti Aierken, Muertiza Muhetaer, Zhang Lei, Ainiwaerjiang Abudourousuli, Ainiwaerjiang Abudourousuli

“…However, its specific association with patient prognosis and immune cell infiltration in OSCC remains insufficiently understood.MethodsTo assess the expression levels and prognostic implications of CSTF2 in OSCC, comprehensive data were acquired from The Cancer Genome Atlas (TCGA) and subsequently normalized. …”
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Identification of Survival Risk and Immune-Related Characteristics of Kidney Renal Clear Cell Carcinoma by Xiaobin Wu, Yonghui Liang, Xian Chen, Xiangyang Long, Wujun Xu, Li Liu, Binhui Wang, Xiong Zou

“…A series of information such as RNA sequence, clinical data, and tumor mutation burden (TMB) of KIRC patients were downloaded through The Cancer Genome Atlas (TCGA). Next, combining the survival information and gene expression data of TCGA and Gene Expression Omnibus (GEO), we established an immune gene-related prognosis model (IGRPM) and analyzed it. …”
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Predicting Outcomes in Esophageal Squamous Cell Carcinoma Using scRNA‐Seq and Bulk RNA‐Seq: A Model Development and Validation Study by Jiaqi Zhang, Shunzhe Song, Yuqing Li, Aixia Gong

“…Methods A total of 8, 99, and 140 individuals from The Gene Expression Omnibus database, The Cancer Genome Atlas database, and the Memorial Sloan Kettering Cancer Center, respectively, were encompassed in the investigation. …”
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Tumour DNA methylation markers associated with breast cancer survival: a replication study by Elaheh Zarean, Shuai Li, Ee Ming Wong, Enes Makalic, Roger L. Milne, Graham G. Giles, Catriona McLean, Melissa C. Southey, Pierre-Antoine Dugué

“…Candidate methylation sites (N = 22) and signatures (N = 3) potentially associated with breast cancer survival were identified from five prior studies that used The Cancer Genome Atlas (TCGA) methylation dataset, which shares key characteristics with the MCCS: comparable sample size, tissue type (formalin-fixed paraffin-embedded; FFPE), technology (Illumina HumanMethylation450 array), and participant characteristics (age, ancestry, and disease subtype and severity). …”
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Apoptosis antagonizing transcription factor expression and its validation as a potential diagnostic and prognostic biomarker in oral squamous cell carcinoma by Ainiwaerjiang Abudourousuli, Zumulaiti Aierken, Hasiyati Mamuti, Tuxunayi Yimamu, Chengli Da

“…This study aimed to investigate the expression of apoptosis antagonizing transcription factor (AATF) in OSCC, examine its correlation with clinicopathological features, assess its prognostic implications, and explore its potential role in OSCC progression.MethodsExpression profiles and clinical data of OSCC patients were obtained from The Cancer Genome Atlas (TCGA). Immunohistochemical analysis on tissue microarrays was performed to assess AATF expression in OSCC. …”
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FOSL1 transcriptionally dictates the Warburg effect and enhances chemoresistance in triple-negative breast cancer by Gang Zhao, Yutong Liu, Shiqi Yin, Runxiang Cao, Qian Zhao, Yifan Fu, Ye Du

“…However, the transcriptional mechanisms of aerobic glycolysis in TNBC remains poorly understood. Methods The Cancer Genome Atlas (TCGA) cohort was utilized to identify genes associated with glycolysis. …”
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Intersection of rare pathogenic variants from TCGA in the All of Us Research Program v6 by Blaine A. Bates, Kylee E. Bates, Spencer A. Boris, Colin Wessman, David Stone, Justin Bryan, Mary F. Davis, Matthew H. Bailey

“…Summary: Using rare cancer predisposition alleles derived from The Cancer Genome Atlas (TCGA) and high cancer prevalence (14% of participants) in All of Us (version 6), we assessed the impact of these rare alleles on cancer occurrence in six broad groups of genetic similarity provided by All of Us: African/African American (AFR), Admixed American/Latino (AMR), East Asian (EAS), European (EUR), Middle Eastern (MID), or South Asian (SAS). …”
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Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer by Shuo-Fu Chen, Liang-Yun Wang, Yi-Sian Lin, Cho-Yi Chen

“…Methods The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. …”
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Immune gene features and prognosis in colorectal cancer: insights from ssGSEA typing by Anwen Huang, Jinxiu Wu, Jiakuan Wang, Chengwen Jiao, Yunfei Yang, Huaiwen Xiao, Li Yao

“…Methods We obtained and merged RNA-Seq data along with clinical details for colorectal cancer (CRC) from The Cancer Genome Atlas (TCGA) repository, and then performed immunocluster typing on all CRC specimens. …”
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Single-cell RNA-seq analysis reveals microenvironmental infiltration of myeloid cells and pancreatic prognostic markers in PDAC by Yanying Fan, Lili Wu, Xinyu Qiu, Han Shi, Longhang Wu, Juan Lin, Jie Lin, Tianhong Teng

“…Methods Single-cell RNA sequencing (scRNA-seq) data were downloaded from t the Tumor Immune Single-cell Hub and gene expression data were retrieved from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. …”
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Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience by Mingrong Zuo, Xiang Xing, Linmao Zheng, Hao Wang, Yunbo Yuan, Siliang Chen, Tianping Yu, ShuXin Zhang, Yuan Yang, Qing Mao, Yongbin Yu, Ni Chen, Yanhui Liu

“…We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA). We utilized the OpenSlide library to load WSIs, segmented them into small patches using the DeepZoom module, and then normalized the color using the Reinhard method. …”
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UBE2J1 is identified as a novel plasma cell-related gene involved in the prognosis of high-grade serous ovarian cancer by Yunjie Tian, Ruoyu Dong, Yingxia Guan, Ying Wang, Wei Zhao, Jun Zhang, Shan Kang

“…The effects of immune cell markers on prognosis were analyzed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and gene set variation analysis (GSVA) of bulk sequencing data from The Cancer Genome Atlas (TCGA)-HGSOC cohort. Finally, the effects of key markers on HGSOC cells were evaluated via Cell Counting Kit-8 (CCK-8), Transwell, colony formation, wound healing, immunofluorescence and in vivo tumor growth assays. …”
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Screening and validation of tsRNAs associated with lung adenocarcinoma by LU Chunli, SHAN Yifan, XIE Weijia

“…The effects of tsRNAs expression levels on the prognosis of lung adenocarcinoma patients were analyzed based on the Cancer Genome Atlas （TCGA） database （TCGA-LUAD）. The target genes were predicted based on TRFtarget2.0 and tRFTar databases. …”
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Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk by Fahad M. Aldakheel, Hadeel Alnajran, Shatha A. Alduraywish, Ayesha Mateen, Mohammed S. Alqahtani, Rabbani Syed

“…This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. …”
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Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma by Ai-lan Li, Yong-mei Zhu, Lai-qiang Gao, Shu-yue Wei, Ming-tao Wang, Qiang Ma, You-you Zheng, Jian-hua Li, Qing-feng Wang

“…The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. …”
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Deciphering the heterogeneity and plasticity of the tumor microenvironment in liver cancer provides insights for prognosis by Yihao Sun, Yihao Sun, Guojuan Shi, Jian Yang, Chun-Zhong Zhou, Chuhan Peng, Yu-Hong Luo, Ying Pan, Rui-Qi Wang

“…Using genomic and clinical data from The Cancer Genome Atlas, we identified specific cell components linked to tumor characteristics and genetics. …”
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Development and experimental validation of dephosphorylation-related biomarkers to assess prognosis and immunotherapeutic response in gliomas by Hui Tang, Xuping Yang, Xuping Yang, Guoqian Li, Ke Peng, Yang Sun, Longyang Jiang, Longyang Jiang, Yilan Huang, Yilan Huang

“…This study aims to construct a validated prognostic risk model for dephosphorylation, which will provide new directions for clinical treatment, prognostic assessment, and temozolomide (TMZ) resistance in glioma patients.MethodsScreening dephosphorylation-related genes (DRGs) and transcriptome expression data from The Cancer Genome Atlas (TCGA), Molecular signatures database (MSigDB) and constructing risk scoring models. …”
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