Evidence map›Paper›PMID 41266721›Full record

ArticleDiscover oncology2025

Construction of an eight-basement membrane related gene signature for predicting prognosis and immune response in hepatocellular carcinoma.

Xiaohan Zhang, Guanlin Wu, Jiayao Li, Lianhong Yin, Meng Gao, Xu Han, Xuerong Zhao, Lina Xu

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Article in Discover oncology, 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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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xiaohan Zhang *Department of Pharmacy, Dalian Women and Children's Medical Group, Dalian, 116024, China.
Guanlin Wu *Department of Pharmacy, Central Hospital of Dalian University of Technology, Dalian, 116000, China.
Jiayao LiCollege of Pharmacy, Dalian Medical University, Western 9 Lvshunnan Road, Dalian, 116044, China.
Lianhong YinCollege of Pharmacy, Dalian Medical University, Western 9 Lvshunnan Road, Dalian, 116044, China.
Meng GaoCollege of Pharmacy, Dalian Medical University, Western 9 Lvshunnan Road, Dalian, 116044, China.
Xu HanCollege of Pharmacy, Dalian Medical University, Western 9 Lvshunnan Road, Dalian, 116044, China.
Xuerong ZhaoCollege of Pharmacy, Dalian Medical University, Western 9 Lvshunnan Road, Dalian, 116044, China.
Lina XuCollege of Pharmacy, Dalian Medical University, Western 9 Lvshunnan Road, Dalian, 116044, China. dyyxy2000@dmu.edu.cn.

Funding

Dalian Science and Technology Innovation Foundation 2022JJ13SN066
6 · The paper itself

Abstract

backgroundThe basement membrane plays a very vital role in impeding cancer progression and metastatic colonization. However, the relationship between basement membrane-related genes (BMRGs) and hepatocellular carcinoma (HCC) remains poorly understood.

methodsThe transcriptome and clinical data of HCC patients were gathered from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database, and segmented into training and testing sets, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses enrichment subsequently analyzed the differentially expressed BMRGs. A novel prognostic BMRGs signature model was constructed by LASSO (least absolute shrinkage and selection operator) in training set and stratified patients into high-risk and low-risk groups, and which was verified in the testing set. Moreover, the accuracy of the new BMRGs signature was detected by receiver operating characteristic curve (ROC) analysis, followed by clinical correlation analysis, immune function analysis and multi-drug resistance analysis to verify the accuracy and clinical practical application value of the new signature.

resultsWe created and verified a novel prognostic BMRGs signature for the prognosis of patients with HCC. The ROC analysis demonstrated that the 1-, 3-, and 5-year survival rates of HCC patients predicted by the prognostic BMRGs signature model based on the BMRGs signature were consistent with those of real patients. In clinical correlation analysis, univariate and multivariate Cox regression analyses validated that the model supports that the prognostic BMRGs signature can be independent risk factors for overall survival (OS) of HCC patients. In addition, the new signature can indeed differentiate the immunological analysis of HCC patients at different risk groups. In the drug sensitivity analysis, the expression levels of the BMRGs in the signatures were found to be related to the sensitivity of common chemotherapy drugs in HCC patients.

conclusionThe newly prognostic BMRGs signature can be used as a prognostic indicator to predict the potential progression trajectory and therapeutic response in HCC, which may also provide sensible recommendations for immunotherapy and the selection of chemotherapeutic agents. Our findings provided a promising insight into BMRGs in HCC and a personalized prediction tool for prognosis and immune responses in patients.

Indexed as

Chemotherapeutic drug sensitivityGene signatureHepatocellular carcinoma basement membranePrognosisTumor immunology

Identifiers

PMID41266721
PMCPMC12748416

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