DNA methylation of ACADS promotes immunogenic cell death in hepatocellular carcinoma by Ze Qian, Yifan Jiang, Yacong Wang, Yu Li, Lin Zhang, Xiaofeng Xu, Diyu Chen

“…Methods and results Using RNA sequencing data from different tumours in The Cancer Genome Atlas database, we observed that ACADS was downregulated and hypermethylated in HCC. …”
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The immune-related gene CD5 is a prognostic biomarker associated with the tumor microenvironment of breast cancer by Yi Zhao, Hengheng Zhang, Wenwen Wang, Guoshuang Shen, Miaozhou Wang, Zhen Liu, Jiuda Zhao, Jinming Li

“…In this study, we obtained the RNA-seq data of 1086 patients from The Cancer Genome Atlas (TCGA) database. We calculated the proportions of tumor-infiltrating immune cells (TICs) and immune and stromal components using the CIBERSORT and ESTIMATE methods, and we screened differentially expressed genes (DEGs). …”
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Analysis of diagnostic genes and molecular mechanisms of Crohn’s disease and colon cancer based on machine learning algorithms by Jie Xiao, Junyao Liang, Tao Zhou, Man Zhou, Dexu Zhang, Hui Feng, Chusen Tang, Qian Zhou, Weiqing Yang, Xiaoqin Tan, Wanjia Zhang, Yin Xu

“…In this study, two data series related to CD were identified from the Gene Expression Omnibus (GEO) database under specific criteria, and relevant COAD gene data were obtained from The Cancer Genome Atlas (TCGA). Weighted Gene Co-expression Network Analysis (WGCNA), differentially expressed genes (DEGs), and protein-protein interaction (PPI) network analysis were conducted. …”
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Sulfatase modifying factor 2 as a predictive biomarker for urothelial carcinoma by Wei-Ting Kuo, Yi-Chen Lee, Jia-Bin Liao, Ching-Jiunn Tseng, Yi-Fang Yang

“…High SUMF2 mRNA levels were associated with poor overall survival (OS) and disease-free survival (DFS) in bladder UC (BLCA) from The Cancer Genome Atlas (TCGA) dataset. High SUMF2 protein levels were associated with grade (P < 0.001), T status (P = 0.01), and stage (P = 0.006) in patients with BLCA. …”
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Identified VCAM1 as prognostic gene in gastric cancer by co-expression network analysis by Wenjuan Li, Hong Gao, Jianjun Liu

“…Data from RNA sequencing and clinical pathological details were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) dataset. …”
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Knockdown of NDUFAF6 inhibits breast cancer progression via promoting mitophagy and apoptosis by Shang Wu, Xindi Ma, Xiangmei Zhang, Kaiye Du, Chao Shi, Ahmed Ali Almaamari, Boye Han, Suwen Su, Yunjiang Liu

“…Background While NDUFAF6 is implicated in breast cancer, its specific role remains unclear.Methods The expression levels and prognostic significance of NDUFAF6 in breast cancer were assessed using The Cancer Genome Atlas, Gene Expression Omnibus, Kaplan-Meier plotter and cBio-Portal databases. …”
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Programmed cell death-related gene IL20RA facilitates tumor progression and remodels tumor microenvironment in thyroid cancer by Yuqi Wang, Yunlong Zhang, Min Liu, Yu Liu, Yu Zeng, Wei Zhang, Shuping Wu, Linfei Hu, Xianhui Ruan, Xiangqian Zheng, Ming Gao, Jingzhu Zhao

“…This study aimed to explore the relationship between PCD-related genes expression and prognosis in thyroid cancer (THCA), especially IL20RA, as a potential prognostic marker for THCA. Data from The Cancer Genome Atlas (TCGA) database was utilized to develop a PCD-related risk prediction model based on LASSO regression along with univariate Cox regression. …”
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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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PPIH is a novel diagnostic biomarker associated with immune infiltration in cholangiocarcinoma by Jun Ye, Zhitao Chen, Chuan Zhang, Rui Xie, Haini Chen, Peng Ren

“…Methods We analyzed the expression levels, prognostic significance, and diagnostic efficiency of PPIH in CHOL using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, coupled with gene enrichment analyses. …”
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Prognostic Significance and Therapeutic Potential of SERPINE1 in Head and Neck Squamous Cell Carcinoma by Changyu Zhu, Heshu Liu, Zhixin Li, Yijun Shi, Jingyang Zhao, Yuping Bai, Qian Chen, Wei Li

“…Methods and Results In this study, we delved into the variations in gene mutation, methylation patterns, and expression levels of SERPINE1 in head and neck squamous cell carcinoma (HNSCC) and normal tissues, leveraging comprehensive analyses of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. …”
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Targeting AURKA with multifunctional nanoparticles in CRPC therapy by Bin Deng, Binghu Ke, Qixing Tian, Yukui Gao, Qiliang Zhai, Wenqiang Zhang

“…Bioinformatics analysis of the Cancer Genome Atlas-prostate adenocarcinoma (TCGA-PRAD) dataset revealed overexpression of AURKA in PCa, correlating with poor clinical outcomes. …”
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Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-po... by Kaimin Hu, Yinan Wu, Yajing Huang, Meiqi Zhou, Yanyan Wang, Xingru Huang

“…To confirm the presence of morphological discrepancies in tumor tissues of ER+ breast cancer classified as epithelial- and mesenchymal-phenotypes according to EMT-related transcriptional features, we trained deep learning algorithms based on EfficientNetV2 architecture to assign the phenotypic status for each patient utilizing hematoxylin & eosin (H&E)-stained slides from The Cancer Genome Atlas database. Our classifier model accurately identified the precise phenotypic status, achieving an area under the curve (AUC) of 0.886 at the tile-level and an AUC of 0.910 at the slide-level. …”
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Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis by Chun Wai Ng, Kwong-Kwok Wong, Barrett C. Lawson, Sammy Ferri-Borgogno, Samuel C. Mok

“…Methods In this study, 773 WSIs of H&E-stained tumor sections from 335 patients with treatment naïve high-grade serous ovarian cancer who were included in The Cancer Genome Atlas (TCGA) Pan-Cancer study were used to train, and validate, and to test a ResNet101 CNN model modified with attention mechanism. …”
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The Prognostic Significance of TRs in Hepatocellular Carcinoma: Insights from TCGA and GEO Databases by Hao Zhou, Weijie Wang, Ruopeng Liang, Rongtao Zhu, Jiahui Cao, Chenguang Sun, Yuling Sun

“…Methods: This study utilized Kaplan-Meier analysis of TR expression profiles from The Cancer Genome Atlas (TCGA). Expression levels of TRs in HCC and immune single cells were assessed using datasets from the Gene Expression Omnibus (GEO) and TCGA, analyzed with R software. …”
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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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Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models by Matheus Dalmolin, Karolayne S. Azevedo, Luísa C. de Souza, Caroline B. de Farias, Martina Lichtenfels, Marcelo A. C. Fernandes

“…Gene expression data from RNA-seq, extracted from The Cancer Genome Atlas (TCGA), were used to train ML models, including decision trees (DTs), random forest (RF), and XGBoost (XGB), which achieved accuracies of 98.69%, 99.82%, and 99.37%, respectively. …”
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Transcription factor FOXD1 and miRNA-204-5p play a major role in B4GALNT2 downregulation in colon cancer by Martina Duca, Nadia Malagolini, Michela Pucci, Virginie Cogez, Anne Harduin-Lepers, Fabio Dall’Olio

“…Through an in silico analysis of The Cancer Genome Atlas and of the Cancer Cell Line Encyclopedia, we identified the transcription factors FOXD1, FOXF2 and PGR as well as mir-204-5p as potential inhibitory agents. …”
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Simultaneous detection of eight cancer types using a multiplex droplet digital PCR assay by Isabelle Neefs, Nele De Meulenaere, Thomas Vanpoucke, Janah Vandenhoeck, Dieter Peeters, Marc Peeters, Guy Van Camp, Ken Op de Beeck

“…Based on previous data analyses using The Cancer Genome Atlas (TCGA), we selected differentially methylated targets for eight frequent tumor types (lung, breast, colorectal, prostate, pancreatic, head and neck, liver, and esophageal cancer). …”
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Serum Cystatin S (CST4): A Novel Prognostic Marker for Gastric Cancer by Chao Gu, Shan Chen, Lining Huang, Chenliang Cao, Renshun Yuan, Zhongyang Kou, Weiwei Chen, Haihua Shi, Xiaodong Gu

“…In addition, CST4 expression was correlated with immune cell infiltration using data from The Cancer Genome Atlas (TCGA). Patients were stratified by median CST4 levels, and Kaplan-Meier curves for OS and DFS were plotted. …”
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An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma by Michele Massimino, Elena Tirrò, Stefania Stella, Cristina Tomarchio, Sebastiano Di Bella, Silvia Rita Vitale, Chiara Conti, Marialuisa Puglisi, Rosa Maria Di Crescenzo, Silvia Varricchio, Francesco Merolla, Giuseppe Broggi, Federica Martorana, Alice Turdo, Miriam Gaggianesi, Livia Manzella, Andrea Russo, Giorgio Stassi, Rosario Caltabiano, Stefania Staibano, Paolo Vigneri

“…Methods: Following the findings published by “The Cancer Genome Atlas–UM” (TCGA-UM) study, we developed an NGS-based gene panel (called the UMpanel) that classifies mutation sets in four categories: initiating alterations (<i>CYSLTR2</i>, <i>GNA11</i>, <i>GNAQ</i> and <i>PLCB4</i>), prognostic alterations (<i>BAP1</i>, <i>EIF1AX</i>, <i>SF3B1</i> and <i>SRSF2</i>), emergent biomarkers (<i>CDKN2A</i>, <i>CENPE</i>, <i>FOXO1</i>, <i>HIF1A</i>, <i>RPL5</i> and <i>TP53</i>) and chromosomal abnormalities (imbalances in chromosomes 1, 3 and 8). …”
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