Evidence map›Paper›PMID 41267103›Full record

ArticleEuropean journal of medical research2025

A novel risk model incorporating 4 mitochondrial unfolded protein response-related genes to predict the prognosis, gene mutation landscape, and immunotherapy response in lung adenocarcinoma.

Yi Qian, Jia Peng, Weiguo Jin, Danhong Zeng, Xueqing Zhou, Peiyun Li, Jie Zhou, Baohu Zhang, Yang Zhang, Shucai Yang

Abstract read
In one paragraph

Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Yi Qian *Department of General Practice, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), Shenzhen, 518118, China.
Jia Peng *Department of Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, 999077, China.
Weiguo Jin *Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China.
Danhong ZengDepartment of Clinical Laboratory, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), No. 19 Renmin Street, Pingshan District, Shenzhen, 518118, China.
Xueqing ZhouDepartment of Clinical Laboratory, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), No. 19 Renmin Street, Pingshan District, Shenzhen, 518118, China.
Peiyun LiDepartment of Clinical Laboratory, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), No. 19 Renmin Street, Pingshan District, Shenzhen, 518118, China.
Jie ZhouDepartment of Clinical Laboratory, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), No. 19 Renmin Street, Pingshan District, Shenzhen, 518118, China.
Baohu ZhangDepartment of Clinical Laboratory, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), No. 19 Renmin Street, Pingshan District, Shenzhen, 518118, China.
Yang ZhangDepartment of Clinical Laboratory, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), No. 19 Renmin Street, Pingshan District, Shenzhen, 518118, China. 766312192@qq.com.
Shucai YangDepartment of Clinical Laboratory, Pingshan Hospital, Southern Medical University (Pingshan District People's Hospital of Shenzhen), No. 19 Renmin Street, Pingshan District, Shenzhen, 518118, China. 1155045115@link.cuhk.edu.hk.

Funding

National Natural Science Foundation of China 82272986Natural Science Foundation of Guangdong Province 2023A1515010230Science and Technology Foundation of Shenzhen JCYJ20220531094805012
6 · The paper itself

Abstract

introductionMitochondrial unfolded protein response (UPR MATERIALS AND

methodsThe data were sourced from the cancer genome atlas (TCGA) and GSE31210 dataset and MRGs were retrieved to identify those with prognostic relevance, which were applied to recognize the molecular clusters in LUAD. The cluster-specific differentially expressed genes (DEGs) were identified for the functional enrichment analysis. The independent differentially expressed MRGs were sorted out to develop a risk model. Besides, the tumor immune microenvironment was analyzed using the ESTIMATE, TIMER, MCP-counter, and ssGSEA algorithms. The data were processed with Mutect2 to evaluate the genetic mutation landscape, while the IMvigor210 cohort and pRRophetic package were utilized to predict immunotherapeutic responses and drug sensitivity. Finally, in vitro validation was performed via quantitative real-time PCR (qRT-PCR), cell counting kit-8 (CCK-8), wound healing, and Transwell assays.

resultsMost MRGs were higher expressed in LUAD, and CREB binding protein (CREBBP), lysine demethylase 6B (KDM6B) and leucine rich pentatricopeptide repeat containing (LRPPRC) were the top 3 genes with mutation frequency. 8 MRGs were applied to identify 2 molecular clusters, with the worst prognosis seen in cluster C1. The clusters-specific DEGs were mainly enriched in cell proliferation-related pathways and the established risk model based on 4 hub genes (ANLN, FAM83A, CPS1 and KRT6A) showed satisfying efficacy in predicting the prognosis and was negatively correlated with most immune cells. Besides, the tumor mutation burden tended to be stronger in high risk group with high gene mutation frequency. In IMvigor210 cohort, higher RiskScore was seen in patients with progressive disease and stable disease and related to a worse survival. 3 drug candidates, including Roscovitine, Rapamycin and PHA.665752 were positively correlated with RiskScore. Besides, all 4 MRGs were highly expressed in LUAD cells and the silencing of ANLN repressed the LUAD cell proliferation, migration and invasion. DISCUSSION: The established 4-MRGs signature not only serves as a robust prognostic indicator but also highlights the significant involvement of mitochondrial unfolded protein response in shaping tumor microenvironment and influencing immunotherapy outcomes in LUAD.

conclusionThe 4 MRGs may contribute to the understanding on UPR

Indexed as

Adenocarcinoma of LungImmunotherapyLung NeoplasmsMitochondriaUnfolded Protein ResponseBiomarkers, TumorGene Expression Regulation, NeoplasticHumansMutationPrognosisTumor MicroenvironmentBiomarkers, TumorANLNLung adenocarcinomaMitochondrial unfolded protein responsePrognosisRiskScoreTherapeutic response

Identifiers

PMID41267103
PMCPMC12636162

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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.