Evidence map›Paper›PMID 39823156›Full record

ArticleCancer medicine2025

Immunoinfiltration Analysis of Mitochondrial Damage-Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine Learning Algorithms.

Jirong Zhang, Lin Lin

Abstract read
In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Jirong ZhangDepartment of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, People's Republic of China.
Lin LinDepartment of Respiratory Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung adenocarcinoma (LUAD) exhibits molecular heterogeneity, with mitochondrial damage affecting progression. The relationship between mitochondrial damage and immune infiltration, and Weighted Gene Co-expression Network Analysis (WGCNA)-derived biomarkers for LUAD classification and prognosis, remains unexplored.

aimsThe objective of our research is to identify gene modules closely related to the clinical stages of LUAD using the WGCNA method. Based on the genes within these modules, we constructed machine learning (ML) models for classification and prognosis prediction, thereby facilitating precise diagnosis and personalized treatment of LUAD. MATERIALS &

methodsUsing GeneCards and The Cancer Genome Atlas (TCGA) databases, we screened differentially expressed mitochondrial damage-related genes in LUAD. Immune cell infiltration patterns were assessed using Single-Sample Gene Set Enrichment Analysis (SSGSEA) method. Functional enrichment analyses were conducted to explore biological functions and signaling pathways. Gene modules related to clinical stages of LUAD were identified by WGCNA. ML models were constructed for classification and prognosis prediction, and validated in an independent Gene Expression Omnibus (GEO) dataset.

resultsThe study revealed a significant relationship between mitochondrial damage and immune infiltration in LUAD. We identified a gene module closely associated with the clinical stages of LUAD. The ML models for classification and prognosis that were constructed demonstrated good effectiveness and generalization capabilities. DISCUSSION: Mitochondrial damage-related genes are crucial in LUAD progression and linked to immune infiltration. The gene module and models identified have potential applications in LUAD classification and prognosis, offering novel markers for precision medicine.

conclusionThis study uncovers the relationship between mitochondrial damage and immune infiltration in LUAD, paving the way for molecular classification, prognosis prediction, and personalized treatment strategies.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorGene Regulatory NetworksLung NeoplasmsMachine LearningMitochondriaComputational BiologyDatabases, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisBiomarkers, Tumordiagnostic modellung adenocarcinomamachine learningprognostic model

Identifiers

PMID39823156
PMCPMC11737511

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