Evidence map›Paper›PMID 39292390›Full record

ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2025

Construction and clinical significance of prognostic risk markers based on cancer driver genes in lung adenocarcinoma.

Yazhou Su, Tingting Huo, Yanan Wang, Jingyan Li

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Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2025. 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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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yazhou SuDepartment of Thoracic Surgery, The First Affiliated Hospital of Xinxiang Medical University, 88 Jiankang Road, Weihui, 453100, Henan province, China. suyazhousu@163.com.ORCID http://orcid.org/0009-0003-8722-9886
Tingting HuoDepartment of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, Henan province, China.
Yanan WangDepartment of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, Henan province, China.
Jingyan LiDepartment of Oncology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, Henan province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer driver genes (CDGs) have been reported as key factors influencing the progression of lung adenocarcinoma (LUAD). However, the role of CDGs in LUAD prognosis has not been fully elucidated.

methodsLUAD transcriptome data and CDG-related data were obtained from public databases and literature. Differentially expressed CDGs (DE-CDGs) greatly associated with LUAD survival (P < 0.05) were identified to establish a prognostic model. In addition, immune analysis of high-risk (HR) and low-risk (LR) groups was conducted by utilizing the CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms to assess immune differences. Subsequently, mutation analysis was conducted using maftools. Finally, candidate drugs were identified using the CellMiner database.

results40 DE-CDGs significantly associated with LUAD survival and 11 DE-CDGs associated with prognosis were identified through screening. Regression analysis revealed that risk score can independently predict LUAD prognosis (P < 0.05). Immune landscape analysis revealed that compared to the HR group, the LR group had higher immune scores and high infiltration of various immune cells such as follicular helper B cells and T cells. Mutation landscape analysis demonstrated that missense mutation was the most common mutation type in both risk groups. Drug prediction analysis revealed strong correlations of fulvestrant, S-63845, sapacitabine, lomustine, BLU-667, SR16157, motesanib, AZD-9496, XK-469, dimethylfasudil, P-529, and imatinib with the model genes, suggesting their potential as candidate drugs targeting the model genes.

conclusionThis study identified 11 effective biomarkers, DE-CDGs, which can predict LUAD prognosis and explored the biological significance of CDGs in LUAD prognosis, immunotherapy, and treatment.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorLung NeoplasmsClinical RelevanceHumansMutationPrognosisTranscriptomeBiomarkers, TumorBiomarkersCancer driver genesDrug sensitivityLung adenocarcinomaMutation landscapeTumor microenvironment

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.