Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma by Miaodong Wang, Qin He, Zeshan Chen, Yijue Qin

“…We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. …”
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GPX8 as a Novel Prognostic Factor and Potential Therapeutic Target in Primary Glioma by Zhao-shou Yang, Qin Yang, Xiao-xiao Sun, Kui Xiong, Xiao-ting Zhu, Yi-chen Wang, Qian-yao Ren, Guo-hui Wu, Shi-min Wang, Xu-qin Cao, Xiao-rong Yang, Wen-gong Jiang

“…Here, we leveraged Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) database to investigate the association between GPX8 and overall survival (OS) of patients with primary gliomas, and our results showed that GPX8 expression was negatively correlated with OS. …”
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In-depth characterization of miRNome in papillary thyroid cancer with BRAF V600E mutation by Azliana Mohamad Yusof, Francis Yew Fu Tieng, Rohaizak Muhammad, Shahrun Niza Abdullah Suhaimi, Isa Mohamed Rose, Sazuita Saidin, Rahman Jamal, Imilia Ismail, Nurul-Syakima Ab Mutalib

“…MiRNAs differentially expressed between BRAF V600E-positive and BRAF V600E-negative PTC tissues were validated in silico using The Cancer Genome Atlas (TCGA) THCA datasets containing 420 samples. …”
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Fatty acid metabolism-related signature suggests an oncogenic role of TEKT1 in endometrial cancer by Zhikai Xiahou, Hong Xiao, Yafeng Song, Xin Xu

“…Materials and methods: A total of 309 fatty acid metabolism relative genes were analyzed in the endometrial cancer cohort from The Cancer Genome Atlas. Dataset GSE216872 was applied for external validation. …”
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The Identification and Validation of a Robust Immune-Associated Gene Signature in Cutaneous Melanoma by Le-Bin Song, Jiao-Chen Luan, Qi-Jie Zhang, Lin Chen, Hao-Yang Wang, Xue-Chen Cao, Ning-Hong Song, Yan Lu

“…Methods. 434 melanoma patients were extracted from The Cancer Genome Atlas (TCGA) database, and 1811 IAGs were downloaded from the ImmPort database in our retrospective study. …”
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Identification of Ferroptosis-Associated Long Noncoding RNA Prognostic Model and Tumor Immune Microenvironment in Thyroid Cancer by Yongjian Lin, Fu Gan, Xiaoyu He, Huachu Deng, Yong Li

“…The TC lncRNA data were extracted from RNA sequencing files of The Cancer Genome Atlas (TCGA). Then, we performed a two-cluster analysis and grouped 502 patients with TC in a 7 : 3 ratio. …”
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Macrophages Phenotype Regulated by IL-6 Are Associated with the Prognosis of Platinum-Resistant Serous Ovarian Cancer: Integrated Analysis of Clinical Trial and Omics by Xiaoqing Wu, Wenping Lu, Chaojie Xu, Cuihong Jiang, Zhili Zhuo, Ruipeng Wang, Dongni Zhang, Yongjia Cui, Lei Chang, Xi Zuo, Ya’nan Wang, Heting Mei, Weixuan Zhang, Mengfan Zhang, Chen Li

“…We used ESTIMATE to calculate the number of immune and stromal components in high-grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas database. The differential expression genes (DEGs) were analyzed via protein–protein interaction network, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis to reveal major pathways of DEGs. …”
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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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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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Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning by Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat

“…Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC >0.5. …”
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Comprehensive analysis of heat shock protein 110, 90, 70, 60 families and tumor immune microenvironment characterization in clear cell renal cell carcinoma by Wenjing Liao, Mao Huang, Xiaoyi Du, Liangdan Tang, Junwu Li, Qin Tang

“…The data obtained from the Cancer Genome Atlas (TCGA) were applied to determine the differential expression of HSPs in normal tissues and ccRCC. …”
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A Narrative Review of Prognostic Gene Signatures in Oral Squamous Cell Carcinoma Using LASSO Cox Regression by Nur Fatinazwa Mohd Faizal, Saptarsi Shai, Bansi P. Savaliya, Lee Peng Karen-Ng, Rupa Kumari, Rahul Kumar, Vui King Vincent-Chong

“…It is essential to distinguish between cancers arising in the head and neck regions due to significant differences in their etiologies, treatment approaches, and prognoses. As the Cancer Genome Atlas (TCGA) dataset is available in HNSCC, the survival analysis prognosis of OSCC patients based on the TCGA dataset for discovering gene expression-based prognostic biomarkers is limited. …”
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A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma by Xing Jin, Zhengyang Hu, Qihai Sui, Mengnan Zhao, Jiaqi Liang, Zhenyu Liao, Yuansheng Zheng, Hao Wang, Yu Shi

“…The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. …”
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Upregulated GSDMB in Clear Cell Renal Cell Carcinoma Is Associated with Immune Infiltrates and Poor Prognosis by Yuanshan Cui, Zhongbao Zhou, Yumeng Chai, Yong Zhang

“…The transcriptional expression profiles of GSDMB in ccRCC tissues in addition to normal tissues were retrieved from The Cancer Genome Atlas (TCGA) and additionally verified in a different independent cohort, which was obtained from the Gene Expression Omnibus (GEO) database. …”
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YAP1 as a Novel Negative Biomarker of Immune Checkpoint Inhibitors for EGFR-Mutant Non-Small-Cell Lung Cancer by Ling-Chen Li, Xie-Wan Chen, Ling Fang, Chun-Li Jian, Yong-Xin Yu, Xing-Yun Liao, Jian-Guo Sun

“…All the clinical data of NSCLC were downloaded from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) for GSE11969 and GSE72094. …”
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Integrated bioinformatics and experimental analysis of CHAF1B as a novel biomarker and immunotherapy target in LUAD by Wei Du, Xiao-Wei Wu, Qing-Feng Li, Bing-Yu Zhang, Jing Wu, Ya-Ping Xu, Xue Yi

“…Here, CHAF1B expression in LUAD was examined using the GEPIA2 and UALCAN databases. Using The Cancer Genome Atlas (TCGA) LUAD database, we analyzed the diagnostic and prognostic significance of CHAF1B and its association with immune infiltration and immunological checkpoints. …”
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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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CHST1 as a prognostic-related biomarker in gastric cancer correlating with immune infiltration by Long Gui, Jin-hao Yu, Jia-nan Tan, Guang-yu Zhong, Yong-xue Wang, Lin Zhong, Jiao-jiao Su, Dong Hou, Bin Yang, Fang-hai Han, Sheng-ning Zhou

“…In this study, we obtained the transcriptional profile and corresponding clinical information of CHST1 in gastric cancer (GC) tissues and normal tissues from The Cancer Genome Atlas database. Our functional experiments have demonstrated that CHST1 enhances the proliferation and metastasis of GC cells, both in vitro and in vivo. …”
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Pan-cancer analysis of CLDN1 and its functional study in gastric cancer by Zhu Weijing, Zhang Jiaxing, Wei Yucai, Yang Shengrui, Wang Wenxuan, Li Yumin

“…Objective This study employs bioinformatics methods to analyze the expression of Claudin 1 (CLDN1) across various types of cancer and its functional role in gastric cancer. Methods The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to evaluate the expression patterns of CLDN1 in various cancer types and their association with clinical pathology, patient prognosis, and immune characteristics. …”
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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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