Bioinformatics Combined With Biological Experiments to Identify the Pathogenetic Link of Type 2 Diabetes for Breast Cancer

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-04-09 DOI:10.1002/cam4.70759

Xin Bao, Zhirui Zeng, Wenjing Tang, Dahuan Li, Xianrui Fan, Kang Chen, Yongkang Wang, Weijie Ai, Qian Yang, Shu Liu, Tengxiang Chen

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引用次数: 0

Abstract

Background

Type 2 diabetes mellitus (T2DM) constitutes a significant risk factor for breast cancer (BC), with affected women exhibiting a two- to three-fold increased likelihood of developing BC. Furthermore, women diagnosed with both BC and T2DM tend to experience poorer prognoses and exhibit greater resistance to various treatments compared to their non-diabetic counterparts. Consequently, elucidating the comorbidities associated with T2DM and BC is instrumental in enhancing the diagnostic and therapeutic strategies for BC.

Methods

A series of bioinformatics methods including weighted gene co-expression network analysis (WGCNA), differentially expressed gene (DEG) analysis, machine learning, and single-cell sequencing analysis were used to identify the pathogenetic molecules of T2DM for BC. Biological experiments including CCK-8, colony formation, wound healing, transwell assay, immunohistochemistry, and immunofluorescence were performed to determine the molecule effect.

Results

By conducting WGCNA and DEG analysis on the profiles of T2DM (GSE25724 and GSE20966) and the TCGA cohort of BC, we identified a total of 27 common hub genes shared between T2DM and BC. These genes were significantly enriched in pathways related to cell differentiation, cellular developmental processes, focal adhesion, and the MAPK signaling pathway. Notably, among these 27 genes, CCNB2, XRCC2, and CENPI were associated with poor prognosis in BC. Moreover, single-cell RNA sequencing analysis revealed that CCNB2, XRCC2, and CENPI are enriched in cancer cells within BC tissues. Additionally, we observed that CCNB2, XRCC2, and CENPI were elevated in BC tissues provided by patients with a diabetes history and associated with KI67 expression. Hyperglycemia treatment elevated the expression levels of CCNB2, XRCC2, and CENPI in BC cells, which correlated with increased cell proliferation and mobility. Conversely, the knockdown of these genes partially mitigated the pro-proliferative and pro-migratory effects induced by hyperglycemia in BC cells.

Conclusion

Our findings suggested that CCNB2, XRCC2, and CENPI may serve as key pathogenic mediators linking T2DM and BC. Targeting these molecules could potentially attenuate the adverse impacts of T2DM on BC progression.

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背景 2 型糖尿病（T2DM）是乳腺癌（BC）的一个重要危险因素，患病妇女罹患乳腺癌的可能性增加两到三倍。此外，与未患糖尿病的妇女相比，同时被诊断出患有乳腺癌和 T2DM 的妇女往往预后较差，对各种治疗的抵抗力也更强。因此，阐明与 T2DM 和 BC 相关的合并症有助于加强 BC 的诊断和治疗策略。方法采用一系列生物信息学方法，包括加权基因共表达网络分析（WGCNA）、差异表达基因（DEG）分析、机器学习和单细胞测序分析，来确定T2DM对BC的致病分子。此外，还进行了包括 CCK-8、菌落形成、伤口愈合、Transwell 试验、免疫组织化学和免疫荧光在内的生物学实验，以确定分子的作用。结果通过对 T2DM（GSE25724 和 GSE20966）和 BC 的 TCGA 队列进行 WGCNA 和 DEG 分析，我们发现了 T2DM 和 BC 之间共有的 27 个共同枢纽基因。这些基因明显富集于与细胞分化、细胞发育过程、病灶粘附和 MAPK 信号通路相关的通路中。值得注意的是，在这27个基因中，CCNB2、XRCC2和CENPI与BC的不良预后有关。此外，单细胞RNA测序分析表明，CCNB2、XRCC2和CENPI富集于BC组织中的癌细胞。此外，我们还观察到，在有糖尿病史的患者提供的BC组织中，CCNB2、XRCC2和CENPI均升高，且与KI67的表达有关。高血糖治疗会升高 BC 细胞中 CCNB2、XRCC2 和 CENPI 的表达水平，这与细胞增殖和移动性增加有关。相反，敲除这些基因可部分缓解高血糖在 BC 细胞中诱导的促增殖和促迁移效应。结论我们的研究结果表明，CCNB2、XRCC2 和 CENPI 可能是连接 T2DM 和 BC 的关键致病介质。以这些分子为靶点有可能减轻 T2DM 对 BC 进展的不利影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.