Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes.

Gastric cancer (GC) is a common digestive system cancer, characterized by a significant mortality rate. Mitochondria is an indispensable organelle in eukaryotic cells. It was previously revealed that a series of nucleus-encoded mitochondrial genes (NMG) mutations and dysfunctions potentially contribute to the initiation and progression of GC. However, the correlation between NMG mutations and survival outcomes for GC patients is still unclear. In this study, NMG expression profile and clinical information in GC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through consistent clustering and functional enrichment analysis, we have identified three NMG clusters and three gene clusters that are associated with patterns of immune cell infiltration. Prognostic genes were identified through Univariate Cox regression analysis. The principal component analysis was conducted to set up a scoring system. Subsequently, the Single‑cell RNA sequencing (scRNA-seq) data of GC patients and cancer cell drug sensitivity data were retrieved from the GEO database. Patients with high NMG scores exhibited increased microsatellite instability status and a heightened tumor mutation rate compared to those with low NMG scores. Survival analysis revealed that GC samples with high NMG scores could achieve a better prognosis. Additionally, These patients were observed to be more responsive to immunotherapy. Moreover, we delved into prognostic genes at the level of single cells, revealing that MRPL4 and MRPL37 exhibit high expression in epithelial cells, while TPM1 demonstrates high expression in tissue stem cells. Utilizing cancer cell drug sensitivity data from the Drug Sensitivity in Cancer (GDSC) database, we noted a heightened sensitivity to chemotherapy in the high NMG group. Furthermore, we discovered a significant enrichment of cuproptosis-related genes in clusters with high NMG scores. Consequently, employing the scoring system could facilitate the prediction of GC patients' sensitivity to cuproptosis-induced therapy. Our study confirmed the potency of this scoring system as a therapeutic response biomarker for gastric cancer, potentially informing clinical treatment strategies.