Headings aims: To establish a microsatellite instability (MSI) predictive model in pan-cancer and compare the multi-omics characterization of MSI-related molecular features.
Materials and methods: We established a 15-gene signature for predicting MSI status and performed a systematic assessment of MSI-related molecular features including gene and miRNA expression, DNA methylation, and somatic mutation, in approximately 10,000 patients across 30 cancer types from The Cancer Genome Atlas, Gene Expression Omnibus database, and our institution. Then we identified common MSI-associated dysregulated molecular features across six cancers and explored their mutual interfering relationships and the drug sensitivity.
Key findings: we demonstrated the model's high prediction performance and found the samples with high-MSI were mainly distributed in six cancers: BRCA, COAD, LUAD, LIHC, STAD, and UCEC. We found RPL22L1 was up-regulated in the high-MSI group of 5/6 cancer types. CYP27A1 and RAI2 were down-regulated in 4/6 cancer types. More than 20 miRNAs and 39 DMGs were found up-regulated in MSI-H at least three cancers. We discovered some drugs, including OSI-027 and AZD8055 had a higher sensitivity in the high MSI-score group. Functional enrichment analysis revealed the correlation between MSI score and APM score, HLA score, or glycolysis score. The complicated regulatory mechanism of tumor MSI status in multiple dimensions was explored by an integrated analysis of the correlations among MSI-related genes, miRNAs, methylation, and drug response data.
Significance: Our pan-cancer study provides a valuable predictive model and a comprehensive atlas of tumor MSI, which may guide more precise and personalized therapeutic strategies for tumor patients.
Keywords: Immunity; Microsatellite instability; Multi-omics; Pan-cancer; TCGA.
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