Integrating Machine Learning to Identify Prognostic Subtypes and the Cisplatin Resistance Gene RRM2 in Lung Adenocarcinoma.
4 affiliations
- 1Department of Pulmonary and Critical Care Medicine, the Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, Gansu Province, China.
- 2Department of Thoracic Surgery, the Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, Gansu Province, China.
- 3Department of Nephrology, The Affiliated Dazu's Hospital of Chongqing Medical University, Chongging, China.
- 4Department of Genetics, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu Province, China.
Abstract
Lung adenocarcinoma (LUAD) is a highly invasive malignancy associated with poor prognosis. However, reliable molecular subtypes and prognostic signatures for LUAD, particularly their associations with tumor immunity and treatment response, remain poorly characterized. This study aimed to identify distinct molecular subtypes and establish a robust prognostic signature for LUAD, while exploring potential therapeutic targets associated with cisplatin resistance. In this study, we integrated ten clustering algorithms with 101 machine learning methods to identify two distinct molecular subtypes of LUAD and established a robust consensus machine learning-derived signature (CMLS). Patients belonging to the Cluster A subtype exhibited a more favorable prognosis and an enhanced immune response. Furthermore, a CMLS model constructed from 19 genes exhibited strong predictive power. Individuals with high CMLS scores displayed reduced CD4 + T-cell infiltration, poorer survival outcomes, and diminished responses to immunotherapy, whereas those with the low CMLS scores showed improved clinical outcomes, better immunotherapy responsiveness, and features of an immunologically "hot" tumor phenotype. Among the identified genes, ribonucleotide reductase subunit M2 (RRM2) emerged as a core regulator associated with LUAD prognosis. RRM2 was markedly upregulated in lung cancer tissues and the LUAD cell lines and was associated with the cisplatin (DDP) resistance. Suppression of RRM2 expression significantly enhanced the cisplatin sensitivity of A549/DDP cells. In summary, our findings establish a molecular classifier that stratifies LUAD patients into distinct risk categories and highlight RRM2 as a promising biomarker and therapeutic target associated with cisplatin resistance.
Related
- Unveiling a dual-pathway prognostic model: Integrating T cell-related and ubiquitination-related genes in lung adenocarcinoma
- A novel three-CpG signature for lung adenocarcinoma diagnosis: integrating multi-omics, machine learning, and clinical validation
- Prognostic Modeling of Lysosomal Cell Death Signatures in Lung Adenocarcinoma: A Machine Learning Framework for Survival Outcome Prediction
- Quantifying Localized Tumor-Infiltrating Lymphocytes by Deep Learning on H&E Images: Analytical Validation and Prognostic Value in Lung Adenocarcinoma
- Machine learning models integrating 2D and 3D intratumoral heterogeneity features from multi-sequence MRI: accurate prediction of perineural invasion in pancreatic ductal adenocarcinoma
BibTeX
@article{huang2026integrating,
title = {{Integrating Machine Learning to Identify Prognostic Subtypes and the Cisplatin Resistance Gene RRM2 in Lung Adenocarcinoma.}},
author = {Huirong Huang and Ting Tao and Zhenqing Li and Kerong Zhai and Baiqiang Cui and Haiming Feng and Peiqiang Li and Bin Li},
journal = {Thoracic cancer},
year = {2026},
month = oct,
doi = {10.1111/1759-7714.70407},
url = {https://europepmc.org/article/MED/42958928},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Integrating Machine Learning to Identify Prognostic Subtypes and the Cisplatin Resistance Gene RRM2 in Lung Adenocarcinoma.
AU - Huirong Huang
AU - Ting Tao
AU - Zhenqing Li
AU - Kerong Zhai
AU - Baiqiang Cui
AU - Haiming Feng
AU - Peiqiang Li
AU - Bin Li
PY - 2026
DA - 2026/10/30
JO - Thoracic cancer
DO - 10.1111/1759-7714.70407
UR - https://europepmc.org/article/MED/42958928
AB - Lung adenocarcinoma (LUAD) is a highly invasive malignancy associated with poor prognosis. However, reliable molecular subtypes and prognostic signatures for LUAD, particularly their associations with tumor immunity and treatment response, remain poorly characterized. This study aimed to identify distinct molecular subtypes and establish a robust prognostic signature for LUAD, while exploring potential therapeutic targets associated with cisplatin resistance. In this study, we integrated ten clustering algorithms with 101 machine learning methods to identify two distinct molecular subtypes of LUAD and established a robust consensus machine learning-derived signature (CMLS). Patients belonging to the Cluster A subtype exhibited a more favorable prognosis and an enhanced immune response. Furthermore, a CMLS model constructed from 19 genes exhibited strong predictive power. Individuals with high CMLS scores displayed reduced CD4 + T-cell infiltration, poorer survival outcomes, and diminished responses to immunotherapy, whereas those with the low CMLS scores showed improved clinical outcomes, better immunotherapy responsiveness, and features of an immunologically "hot" tumor phenotype. Among the identified genes, ribonucleotide reductase subunit M2 (RRM2) emerged as a core regulator associated with LUAD prognosis. RRM2 was markedly upregulated in lung cancer tissues and the LUAD cell lines and was associated with the cisplatin (DDP) resistance. Suppression of RRM2 expression significantly enhanced the cisplatin sensitivity of A549/DDP cells. In summary, our findings establish a molecular classifier that stratifies LUAD patients into distinct risk categories and highlight RRM2 as a promising biomarker and therapeutic target associated with cisplatin resistance.
SN - 1759-7706
SN - 1759-7714
ER -