Evidence map›Paper›PMID 40264099›Full record

ArticleWorld journal of surgical oncology2025

Identification of cell adhesion-related subtypes and construction of risk model to predict breast cancer prognostic and immunological properties.

De-Ming Lv, Li Yang, Chen Fan, Ling-Hui Fang, Su-Fen Cheng

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

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

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

De-Ming LvDepartment of Breast Thyroid Surgery, Jinhua Maternal And Child Health Care Hospital, Jinhua, 321000, China.
Li YangDepartment of Breast Thyroid Surgery, Jinhua Maternal And Child Health Care Hospital, Jinhua, 321000, China.
Chen FanDepartment of Breast Thyroid Surgery, Jinhua Maternal And Child Health Care Hospital, Jinhua, 321000, China.
Ling-Hui FangDepartment of Breast Thyroid Surgery, Jinhua Maternal And Child Health Care Hospital, Jinhua, 321000, China.
Su-Fen ChengDepartment of Breast Thyroid Surgery, Jinhua Maternal And Child Health Care Hospital, Jinhua, 321000, China. csf15958455391@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast invasive carcinoma is the most common form of breast cancer, often resulting in recurrence or metastasis in patients. Cell adhesion molecules play a crucial role in modulating the interactions between tumor cells and surrounding cells. The study aims to identify breast cancer subtypes related to cell adhesion and develop prognostic models that are essential for evaluating the prognostic risk and immunological profile of breast cancer.

methodsTranscriptome and clinical data were obtained from The Cancer Genome Atlas (TCGA) database, while cell adhesion-related genes (CARGs) from the MSigDB database. Molecular subtyping was performed using NMF clustering. Cox regression and Least absolute shrinkage and selection operator (LASSO) regression analyses were employed to construct a risk model for predicting patient prognosis. This model was validated in independent Gene Expression Omnibus (GEO) datasets, specifically GSE20685 and GSE42568. Immune cell infiltration was explored utilizing the CIBERSORT algorithm. Subsequently, we analyzed tumor mutation burden (TMB). Finally, potential drugs and drug sensitivity was evaluated using pRRobhetic algorithm.

resultsBased on the expression levels of 39 genes related to cell adhesion, we identified 3 distinct subtypes, and LASSO regression analysis identified 8 genes that could be used as prognostic markers. Receiver operating characteristic (ROC) curves demonstrated that these cell adhesion genes were effective in predicting patient prognosis. Compared to the high-risk group, the low-risk group had a more favorable prognosis and a greater response to immunotherapy. These prognostic genes were found to be closely associated with immune cell infiltration and the response to immunotherapy. Furthermore, their significant associations with breast cancer sensitivities to anti-cancer drugs were revealed.

conclusionWe developed a risk model focused on cell adhesion-related genes. This model accurately predicts the prognosis for breast cancer patients. It may also offer new insights for clinical decisions and immunotherapy.

Indexed as

Biomarkers, TumorBreast NeoplasmsCell AdhesionCell Adhesion MoleculesFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMutationPrognosisRisk AssessmentROC CurveSurvival RateTranscriptomeBiomarkers, TumorCell Adhesion MoleculesBreast cancerCell adhesion-related genesPrognostic markers

Identifiers

PMID40264099
PMCPMC12013005

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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.