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

IF 2.9 2区医学 Q2 ONCOLOGY Cancer Medicine Pub Date : 2025-01-16 DOI:10.1002/cam4.70590

Jirong Zhang, Lin Lin

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Abstract

Background

Lung 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.

Aims

The 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 & Methods

Using 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.

Results

The 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.

Conclusion

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

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肺腺癌线粒体损伤相关基因的免疫浸润分析及结合WGCNA和机器学习算法的分类与预后模型构建

背景：肺腺癌（LUAD）表现出分子异质性，线粒体损伤影响进展。线粒体损伤与免疫浸润之间的关系，以及加权基因共表达网络分析（WGCNA）衍生的LUAD分类和预后的生物标志物，仍未得到探索。目的：我们的研究目的是利用WGCNA方法识别与LUAD临床分期密切相关的基因模块。基于这些模块内的基因，我们构建了机器学习（ML）模型进行分类和预后预测，从而促进LUAD的精确诊断和个性化治疗。材料与方法：利用GeneCards和The Cancer Genome Atlas （TCGA）数据库，筛选LUAD中差异表达的线粒体损伤相关基因。采用单样本基因集富集分析（SSGSEA）方法评估免疫细胞浸润模式。通过功能富集分析来探索其生物学功能和信号通路。通过WGCNA鉴定与LUAD临床分期相关的基因模块。构建ML模型用于分类和预后预测，并在独立的Gene Expression Omnibus （GEO）数据集中进行验证。结果：研究显示LUAD患者线粒体损伤与免疫浸润有显著关系。我们发现了一个与LUAD临床分期密切相关的基因模块。所构建的机器学习分类和预后模型显示出良好的有效性和泛化能力。讨论：线粒体损伤相关基因在LUAD进展中至关重要，并与免疫浸润有关。所鉴定的基因模块和模型在LUAD的分类和预后中具有潜在的应用价值，为精准医疗提供了新的标志物。结论：本研究揭示了LUAD中线粒体损伤与免疫浸润的关系，为分子分类、预后预测和个性化治疗策略奠定了基础。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.