Evidence map›Paper›PMID 39911153›Full record

ArticleOncology letters2025

Construction of a lung adenocarcinoma prognostic model based on KEAP1/NRF2/HO‑1 mutation‑mediated upregulated genes and bioinformatic analysis.

Wei Zhu, Ye Zhang, Lingyun Yang, Lu Chen, Chaobo Chen, Qifeng Shi, Zipeng Xu

Abstract read
In one paragraph

Article in Oncology letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Wei ZhuDepartment of Pathology, Xishan People's Hospital of Wuxi City, Wuxi, Jiangsu 214105, P.R. China.
Ye ZhangDepartment of Pathology, Xishan People's Hospital of Wuxi City, Wuxi, Jiangsu 214105, P.R. China.
Lingyun YangDepartment of Renal and Rheumatology, Affiliated Children's Hospital of Jiangnan University (Wuxi Children's Hospital), Wuxi, Jiangsu 214000, P.R. China.
Lu ChenDepartment of Pathology, Xishan People's Hospital of Wuxi City, Wuxi, Jiangsu 214105, P.R. China.
Chaobo ChenDepartment of General Surgery, Xishan People's Hospital of Wuxi City, Wuxi, Jiangsu 214105, P.R. China.
Qifeng ShiDepartment of Pathology, Xishan People's Hospital of Wuxi City, Wuxi, Jiangsu 214105, P.R. China.
Zipeng XuDepartment of General Surgery, Xishan People's Hospital of Wuxi City, Wuxi, Jiangsu 214105, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) is a prevalent malignant tumor of the respiratory tract. The Kelch like ECH associated protein 1 (KEAP1)/nuclear factor erythroid 2-related factor 2 (NRF2)/heme oxygenase 1 (HO-1) axis serves a pivotal role in the occurrence and progression of LUAD. The present study aimed to identify specific genes regulated by mutations of the KEAP1/NRF2/HO-1 axis and to investigate their prognostic potential in LUAD, as well as their association with the tumor microenvironment. Immunohistochemistry was performed to assess the expression levels of KEAP1, NRF2 and HO-1 in LUAD tissues and to evaluate their association with clinical pathology. Sequencing data and clinical information were obtained from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GSE68465) databases, whilst mutation information was sourced from the cBio Cancer Genomics Portal website. The R package 'limma' and Venn diagram were utilized to identify upregulated differentially expressed genes. Subsequently, a prognostic model was constructed using univariate Cox regression analysis and 101 machine learning methods. A nomogram of the prognostic model was generated to assess its efficacy in evaluating survival among patients with LUAD. The 'ImmuCellAI' and 'oncoPredict' R packages were used to compare and evaluate differences in immune cell infiltration and immunotherapy between high- and low-risk groups, as well as the sensitivity of LUAD to chemotherapy drugs. Compared with the group with negative expression, the results revealed that the group with positive expression of NRF2 and HO-1 exhibited advanced tumor, lymph node and clinical stages and a worse prognosis. A predictive model incorporating four genes (kynureninase, serpin family B member 5, insulin like 4 and γ-aminobutyric acid type A receptor subunit α3) was constructed based on KEAP1/NRF2/HO-1 mutation-mediated upregulated genes (KNHMUGs). Risk score was an independent prognostic factor for patients with LUAD (hazard ratio, 1.038; 95% confidence interval, 1.034-1.043; P<0.001). A nomogram was developed to predict the prognosis of patients with LUAD, which was validated as a reliable prognostic tool. The low-risk group exhibited higher immune cell infiltration, including CD4

Indexed as

drug predictionimmunotherapyKelch like ECH associated protein 1/nuclear factor erythroid 2-related factor 2/heme oxygenase 1lung adenocarcinomamutationprognostic modeltumor immune microenvironment

Identifiers

PMID39911153
PMCPMC11795234

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