Evidence map›Paper›PMID 39695419›Full record

ArticleBMC cancer2024

Genetic overlap between breast cancer and sarcopenia: exploring the prognostic implications of SLC38A1 gene expression.

Ye Wang, Pei Zhong, Congjun Wang, Weijia Huang, Hong Yang

Abstract read
In one paragraph

Article in BMC cancer, 2024. 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

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ye WangInternet Hospital Operation Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Pei ZhongFirst clinical college of medicine, Guangxi Medical University, Nanning, China.
Congjun WangGuangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Weijia HuangGuangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Hong YangDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China. yanghong@gxmu.edu.cn.

Funding

National Natural Science Foundation of China 82160336the Natural Science Foundation of Guangxi 2023GXNSFDA026013Youth Science Foundation of Guangxi Medical University GXMUYSF202327
6 · The paper itself

Abstract

backgroundSarcopenia, an age-related syndrome characterized by a decline in muscle mass, not only affects patients' quality of life but may also increase the risk of breast cancer recurrence and reduce survival rates. Therefore, investigating the genetic mechanisms shared between breast cancer and sarcopenia is significant for the prevention, diagnosis, and treatment of breast cancer.

methodsThis study downloaded gene expression datasets and clinical data related to breast cancer and skeletal muscle aging from the GEO database. Data preprocessing, integration, differential gene identification, functional enrichment analysis, and construction of protein-protein interaction networks were performed using R language. Subsequently, COX proportional hazards model analysis and survival analysis were conducted, and survival curves and nomograms were generated. The expression levels of genes in tissues were detected using qRT-PCR, and the Radiant DICOM viewer software was used to delineate the pectoralis major muscle area in CT images.

resultsWe identified 152 differentially expressed genes (P < .05) and 226 sarcopenia-related genes (r > .4) associated with skeletal muscle aging. The TCGA-BRCA dataset revealed 106 genes associated with breast cancer (P < .05, logFC = 1). Functional enrichment analysis indicated significant enrichment in cell proliferation and growth pathways. The PPI network identified critical molecules involved in muscle aging and tumor progression. After dimensionality reduction, a strong correlation was observed between the expression of the muscle aging-related gene set and the prognosis of breast cancer patients (P < .01). The expression of SLC38A1 identified through multivariate COX analysis was significantly associated with poor prognosis in breast cancer patients (P = .03). Incorporating SLC38A1 expression, the prognostic model precisely forecasted breast cancer survival (P < .01). External validation confirmed the higher expression of the SLC38A1 gene in breast cancer tissues compared to adjacent non-cancerous tissues (P < .01). The SLC38A1 index, calculated in combination with the patient's age and BMI, can optimize the prognostic prediction model, providing a powerful tool for personalized treatment of breast cancer.

conclusionHigh SLC38A1 gene expression was significantly associated with poor prognosis in breast cancer patients. The combination of SLC38A1 expression and the pectoralis major muscle area provided an optimized prognostic prediction model, offering a potential tool for personalized breast cancer treatment.

Indexed as

Breast NeoplasmsSarcopeniaBiomarkers, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiddle AgedNomogramsPrognosisProtein Interaction MapsBiomarkers, TumorBreast cancerMultivariate analysisPrognostic prediction modelSarcopeniaSLC38A1

Identifiers

PMID39695419
PMCPMC11656554

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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.