Association of FLG mutation with tumor mutation load and clinical outcomes in patients with gastric cancer.

Background: Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancers in the world with a poor prognosis due to genetic heterogeneity. The present study aimed to explore potential prognostic predictors and therapeutic targets that can be used for STAD treatment. Methods: We collected relevant data of STAD patients from the Cancer Genome Atlas (TCGA), including somatic mutation, transcriptome, and survival data. We performed a series of analyses such as tumor mutational burden (TMB), immune infiltration, and copy number variation (CNV) analysis to evaluate the potential mechanism of filaggrin (FLG) mutation in gastric cancer. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were performed for annotation of differentially expressed genes (DEGs). The STRING online database was used to construct the protein-protein interaction (PPI) and ceRNA network and hub genes were identified. Univariate and multivariate Cox regression analyses were used to determine the effect of selected DEGs on tumor prognosis. Results: The FLG-mutant group (FLG-MT) showed a higher mutation load and immunogenicity in gastric cancer. GO and KEGG analyses identified and ranked unique biologic processes and immune-related pathway maps that correlated with the FLG-mutant target. GSEA analysis showed that several tumorigenesis and metastasis-related pathways were indeed enriched in FLG-mutant tumor tissue. Both cell cycle-related pathways and the DNA damage and repair associated pathways were also enriched in the FLG-MT group. The FLG mutations resulted in increased gastric cancer sensitivity to 24 chemotherapeutic drugs. The ceRNA network was established using Cytoscape and the PPI network was established in the STRING database. The results of the prognostic information further demonstrated that the OS and DFS were significantly higher in FLG mutation carriers, and the FLG gene mutation might be a protective factor. Conclusion: The multiple molecular mechanisms of the FLG gene in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.