Construction and validation of a prognostic model based on mitochondria-associated endoplasmic reticulum membranes gene signature in LUAD patients
Abstract
Background Mitochondrial-associated endoplasmic reticulum membranes (MAM) are implicated in various malignancies, but their prognostic value in lung adenocarcinoma (LUAD) remains underexplored. Methods The Cancer Genome Atlas (TCGA) and GeneCards databases provided LUAD patient data and MAM-related genes. Differentially expressed genes (DEGs) were identified using the “Limma” package. Enrichment analyses included Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Variation Analysis (GSVA), and Gene Set Enrichment Analysis (GSEA). A prognostic risk model based on MAM genes was constructed using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses, validated by Receiver Operating Characteristic (ROC) curve analysis. PPI explored intergene relationships, while immune infiltration analysis investigated underlying mechanisms. The Human Protein Atlas (HPA) validated key gene protein expression, and drug sensitivity was analyzed using the Gene Set Cancer Analysis (GSCA) database. Finally, we also performed immunohistochemistry (IHC) staining in the tissue samples. Results A prognostic risk model with 3 MAM genes (ERO1A, SHC1, CCT6A) was established from 194 DEGs-MAM. Kaplan-Meier analysis showed significantly longer OS in the low-risk group. Enrichment analyses indicated MAM genes were primarily involved in immune-related pathways. TIMER analysis linked the 3 MAM genes with immune cell infiltration (CD8 + T cells, CD4 + T cells, B cells, macrophages). Expression and prognostic analyses revealed high expression of these mRNAs and proteins in LUAD tissues. Conclusions This study constructed a prognostic risk model for LUAD based on 3 MAM genes, revealing a potential link between MAM genes and LUAD, offering new insights into clinical treatment and prognosis.
Article Details
Authors (8)
Qichen Zhang
Caihong Fu
Shasha Liu
Yue Leng
Ling Duan
Na Wang
Longxia Zhang
Hui Qiao