ArticleBMC medical informatics and decision making2024
Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Platelet-to-lymphocyte ratio for prognostication in immune checkpoint inhibitor-treated cancer patients: a meta-analysis of 13027 patients highlighting nivolumab-responsive renal cell carcinoma.Frontiers in immunology · 2026Pooled it
- Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape.Human vaccines & immunotherapeutics · 2026Article
- Tumor genomics in patients younger than 40 years of age with metastatic breast cancer.NPJ precision oncology · 2026Article
- AI-driven pathology in esophageal cancer: from early screening to precision prognostics.Frontiers in oncology · 2026Review
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5 authors.
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Abstract
backgroundTumor mutation burden (TMB) has been considered a biomarker for utilization of immune checkpoint inhibitors(ICIs), but whole exome sequencing(WES) and cancer gene panel(CGP) based on next generation sequencing for TMB detection are costly. Here, we use transcriptome data of TCGA to construct a model for TMB prediction in gastrointestinal tumors.
methodsTranscriptome data, somatic mutation data and clinical data of four gastrointestinal tumors from TCGA, including esophageal cancer (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ). Using R, we performed visual analysis of somatic mutation data, differentially expressed genes (DEGs) function enrichment analysis, gene set enrichment analysis (GSEA), and estimated TMB value in clinic. Finally, a deep neural network (DNN) model was constructed for TMB prediction.
resultsVisualization of somatic mutation data summarized the classification of mutation, frequency of each mutation type, and top-mutated genes. GSEA showed the enrichment of CD4
conclusionTMB correlates with clinicopathological parameters in gastrointestinal carcinoma, and patients with high TMB have higher levels of immune infiltration. In addition, the DNN model based on 31 genes predicts TMB of gastrointestinal tumors in a high accuracy.
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