A Novel Mitochondrial Quality Regulation Gene Signature for Anticipating Prognosis, TME, and Therapeutic Response in LUAD by Multi-Omics Analysis And Experimental Verification

Abstract

Background Lung adenocarcinoma (LUAD) is the predominant form of non-small cell lung cancer (NSCLC). Mitochondrial quality-related genes (MQRGs) contribute to the genesis and advancement of tumors. Despite advances in LUAD treatment and detection, early diagnostic biomarkers are still lacking, and the roles of MQRGs in LUAD are not well understood. Methods We extensively examined transcriptome and clinical data from TCGA and GEO databases to discover differentially expressed MQRGs. Utilizing the LASSO algorithm and multivariate COX regression, a predictive risk model was created. Kaplan-Meier study and ROC curves were implemented to predict patient prognosis, resulting in a new Mitochondrial Quality Regulation Gene Signature for accurate prognosis forecasting. R software and packages facilitated statistical, consensus cluster, survival, Cox regression, Lasso regression, and tumor microenvironment analyses. Model-related gene expression was measured using RT-qPCR, single-cell sequencing, HPA data, and UNCAN data. Results We created a concise risk model using four MQRGs (STRAP, SHCBP1, PKP2, and CRTAC1) to forecast overall survival in LUAD patients. High-risk patients experienced significantly lower survival rates. Functional analysis linked these MQRGs to alpha-linolenic acid metabolism pathways. Moreover, the tumor immune microenvironment supports previous findings that higher CD8 + T cell infiltration improves LUAD outcomes. Analysis of different risk scores showed increased activated memory T-cell CD4, suggesting its activation is crucial for LUAD prognosis. Nomograms were generated with clinical data and the MQRGscore model. mRNA and IHC analysis manifested significantly upregulated STRAP, SHCBP1, and PKP2 expression and mitigated CRTAC1 expression in the LUAD contrasted with normal lung tissue. qRT-PCR confirmed these findings, aligning with TCGA data. Conclusions We created a succinct MQRGs risk model to ascertain the LUAD patient's prognosis, potentially offering a novel method for diagnosing and treating this condition.