Abstract

Ovarian metastasis (OM) results in poor survival of gastric cancer (GC) patients. While immunotherapy has emerged as a promising approach for late-stage GC, validated immune-related prognostic signatures still remain in need. In this study, we constructed an ovarian metastasis- and immune-related prognostic signature (OMIRPS), characterized the molecular and immune features of OMIRPS-categorized subgroups and predicted their potential response to immunotherapy. Three individual cohorts were used to construct and evaluate OMIRPS: RNA-seq of matched primary GC and OM from Fudan University Shanghai Cancer Center (FUSCC) (discovery cohort, n=4), The Cancer Genome Atlas (TCGA) (training cohort, n=544) and GSE84437 (validation cohort, n=433). Differentially expressed genes (DEGs) identified between primary GC and OM and immune-related genes (IRGs) from the ImmPort and InnateDB databases were used to identify immune-related prognostic hub genes, which were further used to construct OMIRPS by using LASSO regression analysis. Prognosis, molecular characteristics, immune features, and differential immunotherapy efficacy between different OMIRPS subgroups were analyzed. Functional analyses of DEGs revealed the significance of immune-related signatures and pathways in the OM. Immune-related prognostic hub genes including TNFRSF18, CARD11, BCL11B, NRP1, BNIP3L, and ATF3 were utilized to construct OMIRPS, which was identified as an independent prognostic factor. Comprehensive analyses unveiled the distinctive molecular and immune characteristics of OMIRPS-high and -low subgroup in regard to enriched pathways, mutation rate, tumor mutation burden, microsatellite instability status, infiltrated immune cell, immune exclusion score, andthe prediction ofimmunotherapy efficacy. Additionally, OMIRPS was associated with Immune Subtypes with borderline significance. RNA-seq of paired primary and ovarian metastatic tumors unveiled the significance of immune-related pathways and tumor immune microenvironment in OM. OMIRPS served as a promising biomarker to predict the prognosis of GC patients and distinguish the molecular features, immune characteristics, and efficacy of immunotherapy between different subgroups.

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