Evidence map›Paper›PMID 41969507›Full record

ArticleTranslational cancer research2026

Improvement of prognosis among patients with lung adenocarcinoma through precision therapy: analysis based on The Cancer Genome Atlas.

Ling Gai, Qinfan Wang, Ping Chen, Xiaochun Hou, Liang Ma

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Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Ling Gai *Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Qinfan Wang *Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Ping ChenDepartment of Oncology, Yancheng No. 1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China.
Xiaochun HouDepartment of Oncology, The Second People's Hospital of Nantong, Nantong, China.
Liang MaDepartment of Oncology, Yancheng No. 1 People's Hospital, Affiliated Hospital of Medical School, Nanjing University, Yancheng, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung adenocarcinoma (LUAD) is a malignancy with a high global incidence and cancer-related mortality rate. Over decades of development, the treatment of lung cancer has evolved from empirical approaches such as traditional chemotherapy and radiotherapy to a precision model that integrates targeted therapy, immunotherapy, and combination treatments. Through "molecular profiling and individualized treatment planning", targeted therapies focusing on biomarkers like EGFR and ALK, along with immunotherapy using programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors, have become landmark achievements in precision medicine for lung cancer. Although various clinical trials have improved the prognosis of LUAD patients, their 5-year survival rate remains low, and precision therapy for lung cancer still faces multiple challenges. This study aims to improve the prognosis of LUAD patients through molecular subtype-based precision treatment. Methods: LUAD RNA-sequencing data sourced from an online database were used to screen for differentially expressed genes (DEGs). Weighted gene coexpression network analysis combined with univariate and multifactorial Cox analysis was used to identify hub prognostic genes. Based on these genes, partitioning around medoids clustering was applied to classify LUAD into two subtypes. The estimation of stromal and immune cells in malignant tumor via using expression data, immunophenoscore, and microenvironment cell populations counter algorithm was used to determine the microenvironmental purity and immune response of the two subtypes. Gene set enrichment analysis was performed to analyze the biological function. The correlation between hub gene and Results: This study delineated two distinct subtypes of LUAD, and the survival rate for patients in cluster 2 was found to be significantly superior to that of cluster 1. Additionally, patients in cluster 2 had greater immune cell infiltration, a greater microenvironmental component, and a higher rate of Conclusions: Patients in cluster 1 may benefit from anti-nucleotide repair therapies such as platinum therapy, radiotherapy, targeting of fibroblasts, and targeting of

Indexed as

immune infiltrationLung adenocarcinoma (LUAD)molecular subtypeprognosisweighted gene coexpression network analysis (WGCNA)

Identifiers

PMID41969507
PMCPMC13067195

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