ArticleCancer cell international2025
A novel mitochondrial quality regulation gene signature for anticipating prognosis, TME, and therapeutic response in LUAD by multi-omics analysis and experimental verification.
Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Multi-omics and network pharmacology identify IGFBP1 as an m6A-Epigenetic target of pueraria in NSCLC therapy.PLoS computational biology · 2026Article
- Multi-omics analyses related to mitochondria and ageing in triple-negative breast cancer implicate PYCR1 potentiates tumor progression.Cancer cell international · 2026Article
- Integrated transcriptomic analysis and experimental validation identify ACADL as a mitochondrial tumor suppressor via the FOXO3a/PUMA axis in lung adenocarcinoma.European journal of medical research · 2026Article
- The Roles of SHCBP1 in Cancer Hallmarks: Molecular Mechanisms and Therapeutic Implications.International journal of molecular sciences · 2025Review
- Identification and validation of selenium metabolism-related genes in lung adenocarcinoma prognosis using bioinformatics analysis.Frontiers in genetics · 2025Article
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7 authors.
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Abstract
backgroundLung 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.
methodsWe 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, immunohistochemistry, single-cell sequencing, HPA data, and UNCAN data.
resultsWe 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 and immunohistochemistry confirmed these findings, aligning with TCGA data.
conclusionsWe created a succinct MQRGs risk model to ascertain the LUAD patient's prognosis, potentially offering a novel method for diagnosing and treating this condition.
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