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In Aging ; h5-index 49.0

Colon adenocarcinoma (COAD) is one of the most common gastrointestinal malignant tumors and is characterized by a high mortality rate. Here, we integrated whole-exome and RNA sequencing data from The Cancer Genome Atlas and investigated the mutational spectra of COAD-overexpressed genes to define clinically relevant diagnostic/prognostic signatures and to unmask functional relationships with both tumor-infiltrating immune cells and regulatory miRNAs. We identified 24 recurrently mutated genes (frequency > 5%) encoding putative COAD-specific neoantigens. Five of them (NEB, DNAH2, ABCA12, CENPF and CELSR1) had not been previously reported as COAD biomarkers. Through machine learning-based feature selection, four early-stage-related (COL11A1, TG, SOX9, and DNAH2) and four late-stage-related (COL11A1, SOX9, TG and BRCA2) candidate neoantigen-encoding genes were selected as diagnostic signatures. They respectively showed 100% and 97% accuracy in predicting early- and late-stage patients, and an 8-gene signature had excellent prognostic performance predicting disease-free survival (DFS) in COAD patients. We also found significant correlations between the 24 candidate neoantigen genes and the abundance and/or activation status of 22 tumor-infiltrating immune cell types and 56 regulatory miRNAs. Our novel neoantigen-based signatures may improve diagnostic and prognostic accuracy and help design targeted immunotherapies for COAD treatment.

Wang Chong, Xue Wenhua, Zhang Haohao, Fu Yang

2021-Jan-10

colon adenocarcinoma, machine learning, neoantigens, recurrent mutations, sequencing