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
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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70590 |
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| author | Jirong Zhang Lin Lin |
| author_facet | Jirong Zhang Lin Lin |
| author_sort | Jirong Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-257ecfa53eaf4fb7b3ac8caf73d7d7f6 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-257ecfa53eaf4fb7b3ac8caf73d7d7f62025-01-24T08:46:07ZengWileyCancer Medicine2045-76342025-01-01142n/an/a10.1002/cam4.70590Immunoinfiltration Analysis of Mitochondrial Damage‐Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine Learning AlgorithmsJirong Zhang0Lin Lin1Department of Geriatrics The Second Affiliated Hospital of Harbin Medical University Harbin Heilongjiang People's Republic of ChinaDepartment of Respiratory Medicine The Second Affiliated Hospital of Harbin Medical University Harbin Heilongjiang People's Republic of ChinaABSTRACT 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.https://doi.org/10.1002/cam4.70590diagnostic modellung adenocarcinomamachine learningprognostic model |
| spellingShingle | Jirong Zhang Lin Lin 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 Cancer Medicine diagnostic model lung adenocarcinoma machine learning prognostic model |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | diagnostic model lung adenocarcinoma machine learning prognostic model |
| url | https://doi.org/10.1002/cam4.70590 |
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