A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

Abstract

Background: Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations. Methods: In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association. Results: Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly. Conclusion: The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.

Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing

“…As well, the TIMER tool is a comprehensive resource designed to analyze immune cell infiltrates across diverse cancer subtypes (139). The TCGA has a publicly available human stomach adenocarcinoma (STAD) dataset containing clinical annotations along with molecular profiles that were utilized as a reference or primary dataset in several studies (25,28,36,37,41,45,133). FirebrowseR, also generated by the Broad Institute, is a database which provides access to the PanCanAtlas, containing conserved cancer gene expression data (134).…”

“…Most groups obtained primary gastric cancer tissues from patients to generate scRNAseq data (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35). Some utilized previously published primary tumor datasets for further analysis (36)(37)(38)(39)(40)(41)(42)(43). Others transcriptionally profiled "liquid biopsies" of metastatic ascites fluid taken from gastric cancer patients (44,45).…”