Bioinformatics approaches to multi-omics analysis of the potential of CDKN2A as a biomarker and therapeutic target for uterine corpus endometrial carcinoma by Liang Ma, Yuling Li, Jingxian Wu, Yanfei Gao

“…Analysis of somatic copy number alterations in the TCGA (The Cancer Genome Atlas)-UCEC dataset revealed a connection between CDKN2A and drug metabolism in UCEC. …”
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The Underlying Mechanism Involved in Gefitinib Resistance and Corresponding Experiment Validation in Lung Cancer by Puwei Song, Jianghui Zhou, Kaiqin Wu, Wenli Wang, Shaorui Gu

“…Open-accessed data of lung cancer patients were downloaded from The Cancer Genome Atlas Program and Gene Expression Omnibus databases. …”
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Pan-cancer analysis and single-cell analysis identifies the CENPN as a biomarker for survival prognosis and immunotherapy by Nie Zhang, Zhuoying He, Xuejin Qin, Ke Han, Zhengchun Zhu, Fei Zhong

“…Methods Investigate the role of CENPN in various malignancies, we leveraged data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. …”
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Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer by Fang Fang, Yuanshan Yao, Zhe Ma

“…This study aimed to identify the lncRNAs involved in the crosstalk between tumor metabolism and M2 macrophages. Methods. The Cancer Genome Atlas was responsible for the public data. …”
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Solid Predominant Histology and High Podoplanin Expression in Cancer-Associated Fibroblast Predict Primary Resistance to Osimertinib in EGFR-Mutated Lung Adenocarcinoma by Yuji Uehara, MD, Hiroki Izumi, MD, PhD, Tetsuro Taki, MD, PhD, Tetsuya Sakai, MD, PhD, Hibiki Udagawa, MD, PhD, Eri Sugiyama, MD, PhD, Shigeki Umemura, MD, PhD, Yoshitaka Zenke, MD, PhD, Shingo Matsumoto, MD, PhD, Kiyotaka Yoh, MD, PhD, Shoko Kubota, MD, PhD, Keiju Aokage, MD, PhD, Naoya Sakamoto, MD, PhD, Shingo Sakashita, MD, PhD, Motohiro Kojima, MD, PhD, Michiko Nagamine, MD, PhD, Yukio Hosomi, MD, PhD, Masahiro Tsuboi, MD, PhD, Koichi Goto, MD, PhD, Genichiro Ishii, MD, PhD

“…We also investigated HGF mRNA expression levels, using The Cancer Genome Atlas Program and Singapore Oncology Data Portal cohorts. …”
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ENTPD8 overexpression enhances anti-PD-L1 therapy in hepatocellular carcinoma via miR-214-5p inhibition by Si-qi Zhao, Min-jie Chen, Fei Chen, Zhao-feng Gao, Xiao-ping Li, Ling-yu Hu, Hai-ying Cheng, Jin-yan Xuan, Jian-guo Fei, Zheng-wei Song

“…Immunohistochemistry, The Cancer Genome Atlas (TCGA) data, and single-cell expression analysis revealed reduced ENTPD8 levels in liver cancer compared to adjacent tissues, with ENTPD8 primarily expressed in tumor cells within the tumor tissue. …”
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Immunoinfiltration Analysis of Mitochondrial Damage‐Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine... by Jirong Zhang, Lin Lin

“…Materials & Methods Using GeneCards and The Cancer Genome Atlas (TCGA) databases, we screened differentially expressed mitochondrial damage‐related genes in LUAD. …”
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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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Development of a Novel Prognostic Model for Lung Adenocarcinoma Utilizing Pyroptosis-Associated LncRNAs by Hong-Yan Bai, Tian-Tian Li, Li-Na Sun, Jing-Hong Zhang, Xiu-He Kang, Yi-Qing Qu

“…This study used genotype-tissue expression (GTEx) and the cancer genome atlas (TCGA) databases to obtain 477 lung adenocarcinoma (LUAD) and 347 healthy individuals’ samples as research subjects and divided LUAD patients into low-risk and high-risk groups based on prognostic risk scores. …”
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Autophagy-related long non-coding RNA MIR210HG plays a therapeutic role in hepatocellular carcinoma by Chaoqun Zhang, Dianxing Sun, Huifang Zhou, Chao Liu, Jie Ruan, Jiwen Kang, Ying Xie

“…Methods LncRNA MIR210HG expression and its correlation with survival outcomes in hepatocellular carcinoma patients were analyzed using data from The Cancer Genome Atlas (TCGA). Kaplan–Meier and Cox regression analyses were conducted to assess survival correlations. …”
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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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Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis by Yutao Wang, Jiaxing Lin, Kexin Yan, Jianfeng Wang

“…We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P<0.05 were selected using Univariable Cox regression analysis. …”
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MAIP1-Related Tumor Immune Infiltration: As a Potential Prognostic Biomarker for Esophageal Cancer by Xiaoyu He, Fu Gan, Yongjian Lin, Guoqing Liu, Yunhua Lin, Dongxu Chen

“…We obtained RNA-seq datasets and corresponding clinical data for EC patients from the Cancer Genome Atlas (TCGA) database via the UCSC Xena browser to extract MAIP1 expression and plot survival curves to determine their prognosis. …”
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Identification of an Inflammatory Response-Related Gene Signature to Predict Survival and Immune Status in Glioma Patients by Zhaoyue Yan, Yushuai Gao, Jinliang Yu, Zhiyuan Shen, Xingyao Bu

“…Further, univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted to construct an IRRG signature using The Cancer Genome Atlas (TCGA) cohort. Gliomas from the Chinese Glioma Genome Atlas (CGGA) cohort were employed for independent validation. …”
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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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TREMSUCS-TCGA – an integrated workflow for the identification of biomarkers for treatment success by Balogh Gabor, Jorge Natasha, Dupain Célia, Kamal Maud, Servant Nicolas, Le Tourneau Christophe, Stadler Peter F., Bernhart Stephan H.

“…Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. …”
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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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Expression and clinical significance of disulfidptosis-related LncRNA in gastric cancer by CHU Yudan, SUN Haidong, CUI Ran, ZHENG Hong

“…Methods The multi-omics features of disulfidptosis genes in gastric cancer were first analyzed based on The Cancer Genome Atlas(TCGA) database. DRLncs were further screened, and disulfidptosis related gene recurrence score (DRG-RS) model was constructed to predict the overall survival(OS) and correlation with the clinical characteristics of gastric cancer patients. …”
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Identification of KRT16 and ANXA10 as cell cycle regulation genes for lung adenocarcinoma based on self-transcriptome sequencing of surgical samples and TCGA public data mining by Wen-jian Liu, Jia-pan Shen, Ren-quan Zhang, Xiao-yun Fan

“…Methods Differentially expressed genes (DEGs) were identified by self-transcriptome sequencing of tumor tissues and paracancerous tissues resected during surgery and combined with The Cancer Genome Atlas (TCGA) data to screen for the genes associated with LUAD prognosis. …”
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Exosome-Associated Gene Signature for Predicting the Prognosis of Ovarian Cancer Patients by Zihan Zhu, Rui Geng, Yixin Zhang, Jinhui Liu, Jianling Bai

“…First, 117 ERGs in The Cancer Genome Atlas (TCGA) dataset were recognized. Afterwards, the risk signature consisting of four ERGs with prognostic significance was built by univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. …”
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