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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Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database by Wenqing Zhou, Yongkui Pang, Yunmin Yao, Huiying Qiao

“…We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. …”
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A Metabolic Gene Signature to Predict Overall Survival in Head and Neck Squamous Cell Carcinoma by Zeng-Hong Wu, Yun Tang, Yue Zhou

“…In this study, we used RNA sequencing (RNA-seq) data from the cancer genome atlas (TCGA), with validation in the GEO dataset to profile the metabolic microenvironment and define potential biomarkers for metabolic therapy. …”
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Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma by Qianru Li, Qianru Li, Heli Li

“…However, the specific downstream targets and mechanisms by which AhR influences melanoma remain insufficiently understood.MethodsMelanoma samples from The Cancer Genome Atlas (TCGA) and normal skin tissues from the Genotype-Tissue Expression (GTEx) database were analyzed to identify differentially expressed genes, which were intersected with a curated list of AhR-related pathway genes. …”
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Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis by Guanyou Huang, Yong Yu, Heng Su, Hongchuan Gan, Liangzhao Chu

“…Objective This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.Methods Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. …”
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Aberrant DNA Methylation of FTH-1 and SHOX2 Contributes to Lung Cancer Progression by Salar Tahmasbi, Armin Sadeghi, Habib Zarredar, Milad Asadi, Shahryar Hashemzadeh, Venus Zafari, Hamed Sabbagh-Jadid, Mortaza Raeisi

“…Ferritin heavy chain 1 (FTH-1) and SHOX homeobox 2 (SHOX2) DNA methylation were investigated in non-small cell lung cancer (NSCLC) as novel epigenetic biomarkers.Method: In this case-control study, we initially evaluated the diagnostic value of FTH-1 and SHOX2 DNA methylation, and the Cancer Genome Atlas (TCGA) data on the methylation profile of NSCLC was analyzed. …”
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The heterogeneity of NOTCH1 to tumor immune infiltration in pan-cancer by XiaoJun Duan, Rihan Wu, Mingyang Zhang, Kexin Li, Lei Yu, Huirong Sun, Xingxia Hao, Changshan Wang

“…In this study, the data originated from the Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) databases were input into multiple online bioinformatic tools to study the characteristics of NOTCH1 in pan-cancer. …”
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NIPAL1 as a prognostic biomarker associated with pancreatic adenocarcinoma progression and immune infiltration by Youlong Zhu, Zongze Qi, Shaoqi Zu, Fangchao Yang, Yanming Wang, Lei Zhu, Xintong Li, Ruixue Li, Hong Zhu

“…Primitive RNA sequencing (RNA-seq) data of PAAD from The Cancer Genome Atlas (TCGA) was utilized for bioinformatics analysis to characterize the expression levels of NIPAL1 in tumor and normal tissues. …”
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The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis by Samer Tohme, Hamza O Yazdani, Amaan Rahman, Sanah Handu, Sidrah Khan, Tanner Wilson, David A Geller, Richard L Simmons, Michele Molinari, Christof Kaltenmeier

“…The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. Methods. The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. …”
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Elevated POSTN expression predicts poor prognosis and is associated with radioresistance in cervical cancer patients treated with radical radiotherapy by Cui-qin Huang, Wen-tao Xiao, Xiang-rong Yao, Zhi-min Li, Jun-yan He

“…We analyzed data from 92 CC patients in The Cancer Genome Atlas (TCGA) and 153 patients from our institution, assessing POSTN expression levels through mRNA analysis and immunohistochemistry (IHC). …”
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Elucidating the expression and role of cGAS in pan-cancer using integrated bioinformatics and experimental approaches by Zhen Lian, Xue Liu, Xue Li

“…In this study, The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) data were used to analyze the mRNA expression and genomic alterations of cGAS in pan-cancer. …”
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Construction and Investigation of MicroRNA-mRNA Regulatory Network of Gastric Cancer with Helicobacter pylori Infection by Ping Yang, Junjie Liu, Tianci Yang, Lei Zhang, Peiyou Gong, Boqing Li, Xiuzhi Zhou

“…We identified differentially expressed microRNAs (DEMs) and genes (DEGs) from the Gene Expression Omnibus (GEO) dataset, constructed microRNA-(miRNA-)mRNA expression networks, analyzed the function and signal pathway of cross-genes, analyzed the relations between cross-genes and GC prognosis with the Cancer Genome Atlas (TCGA) data, and verified the expression of cross-genes in patients with H. pylori infection. …”
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Prognostic values of intracellular cell-related genes in esophageal cancer and their regulatory mechanisms by Wei Cao, Dacheng Jin, Weirun Min, Haochi Li, Rong Wang, Jinlong Zhang, Yunjiu Gou

“…Hence, investigating the prognostic significance and regulatory mechanisms of genes related to these intracellular structures in esophageal cancer is imperative. The Cancer Genome Atlas (TCGA) Esophageal Cancer (ESCA) dataset served as the training set for the analysis. …”
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Promising markers of CIMP+ colon tumors identified on the basis of TCGA data analysis by G. S. Krasnov, A. D. Beniaminov, R. A. Tychko, G. A. Puzanov, R. O. Novakovskiy, A. V. Kudryavtseva, A. A. Dmitriev

“…For the identification of CpG sites, the methylation level of which could be used to detect CIMP+ tumors, an analysis of expression and methylation profiles of 297 primary colon tumors and 38 histologically normal tissues paired to them, which are presented in the TCGA (The Cancer Genome Atlas) project database, was performed by us using the CrossHub tool created previously. …”
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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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SUGT1 is a prognostic biomarker and is associated with immune infiltrates in ovarian cancer by Linyan Ge, Xiu Liu, Lingyan Zhang, Jiaren Zhang, Guanghui Song

“…Methods We conducted a comprehensive bioinformatics analysis of SUGT1 expression in patients with OC compared with their normal controls, including the data from the cancer genome atlas (TCGA), genotype–tissue expression (GTEx) databases, gene ontology (GO) analysis, Kyoto Encylopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), single sample gene set enrichment analysis (ssGSEA). …”
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Identification of critical biomarkers and immune landscape patterns in glioma based on multi-database by Hanzhang Yuan, Jingsheng Cheng, Jun Xia, Zeng Yang, Lixin Xu

“…Patients and methods Differentially expressed genes (DEGs) of glioma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. …”
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Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint by Yan Wang, Xiaoye Jin, Rui Qiu, Bo Ma, Sheng Zhang, Xuyang Song, Jinxi He

“…Clinical application was assessed using The Cancer Genome Atlas (TCGA) dataset, with Best Overall Response (BOR) serving as the clinical efficacy measure. …”
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Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning by Jianhui Chen, Qun Li, Xiaofang Liu, Fang Lin, Yaling Jing, Jiayan Yang, Lianfang Zhao

“…Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC).Results26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. …”
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