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加权基因共表达网络分析与机器学习的综合分析揭示了乳房植入物疾病合并乳腺癌的诊断生物标志物。

The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Cancer.

作者信息

Huang Zhenfeng, Wang Huibo, Pang Hui, Zeng Mengyao, Zhang Guoqiang, Liu Feng

机构信息

Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province, People's Republic of China.

Department of Emergency Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People's Republic of China.

出版信息

Breast Cancer (Dove Med Press). 2025 Apr 10;17:305-324. doi: 10.2147/BCTT.S507754. eCollection 2025.


DOI:10.2147/BCTT.S507754
PMID:40230814
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11996000/
Abstract

PURPOSE: An increasing number of breast cancer (BC) patients choose prosthesis implantation after mastectomy, and the occurrence of breast implant illness (BII) has received increasing attention and the underlying molecular mechanisms have not been clearly elucidated. This study aimed to identify the crosstalk genes between BII and BC and explored their clinical value and molecular mechanism initially. METHODS: We retrieved the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and identified the differentially expressed genes (DEG) as well as module genes using Limma and weighted gene co-expression network analysis (WGCNA). Enrichment analysis, the protein-protein interaction network (PPI), and machine learning algorithms were performed to explore the hub genes. We employed a nomogram and receiver operating characteristic curve to evaluate the diagnostic accuracy. Single-cell analysis disclosed variations in the expression of key genes across distinct cellular populations. The expression levels of the key genes were further confirmed in BC cell lines. Immunohistochemical analysis was utilized to examine protein levels from 25 patients with breast cancer undergoing prosthetic implant surgery. Ultimately, we deployed single-sample Gene Set Enrichment Analysis (ssGSEA) to scrutinize the immunological profiles between the normal and BC cohorts, as well as between the non-BII and BII groups. RESULTS: WGCNA identified 1137 common genes, whereas DEG analysis found 541 overlapping genes in BII and BC. After constructing the PPI network, 17 key genes were selected, and three potential hub genes include KRT14, KIT, ALB were chosen for nomogram creation and diagnostic assessment through machine learning. The validation of these results was conducted by examining gene expression patterns in the validation dataset, breast cancer cell lines, and BII-BC patients. However, ssGSEA uncovered different immune cell infiltration patterns in BII and BC. CONCLUSION: We pinpointed shared three central genes include KRT14, KIT, ALB and molecular pathways common to BII and BC. Shedding light on the complex mechanisms underlying these conditions and suggesting potential targets for diagnostic and therapeutic strategies.

摘要

目的:越来越多的乳腺癌(BC)患者在乳房切除术后选择假体植入,乳房植入物疾病(BII)的发生受到越来越多的关注,但其潜在的分子机制尚未完全阐明。本研究旨在初步确定BII与BC之间的串扰基因,并探讨其临床价值和分子机制。 方法:我们从基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)中检索数据,使用Limma和加权基因共表达网络分析(WGCNA)确定差异表达基因(DEG)以及模块基因。进行富集分析、蛋白质-蛋白质相互作用网络(PPI)和机器学习算法以探索核心基因。我们采用列线图和受试者工作特征曲线来评估诊断准确性。单细胞分析揭示了关键基因在不同细胞群体中的表达差异。在BC细胞系中进一步证实了关键基因的表达水平。利用免疫组织化学分析检测了25例接受假体植入手术的乳腺癌患者的蛋白质水平。最后,我们采用单样本基因集富集分析(ssGSEA)来审视正常人群与BC人群之间以及非BII组与BII组之间的免疫图谱。 结果:WGCNA鉴定出1137个共同基因,而DEG分析在BII和BC中发现了541个重叠基因。构建PPI网络后,选择了17个关键基因,并通过机器学习选择了三个潜在的核心基因KRT14、KIT、ALB用于创建列线图和诊断评估。通过检查验证数据集中的基因表达模式、乳腺癌细胞系和BII-BC患者来验证这些结果。然而,ssGSEA揭示了BII和BC中不同的免疫细胞浸润模式。 结论:我们确定了三个共同的核心基因KRT14、KIT、ALB以及BII和BC共有的分子途径。揭示了这些疾病背后的复杂机制,并为诊断和治疗策略提供了潜在靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/71a4754a6d54/BCTT-17-305-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/6bc14984b962/BCTT-17-305-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/ebda82f28f60/BCTT-17-305-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/71a4754a6d54/BCTT-17-305-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/6bc14984b962/BCTT-17-305-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/4d7a87019612/BCTT-17-305-g0002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/917d1cc8a9f6/BCTT-17-305-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/43761e9195d3/BCTT-17-305-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/e6264e9e5780/BCTT-17-305-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/e587d3f81ca8/BCTT-17-305-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/79a6ddb94e5e/BCTT-17-305-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/ebda82f28f60/BCTT-17-305-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641d/11996000/71a4754a6d54/BCTT-17-305-g0010.jpg

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本文引用的文献

[1]
National Trends and In-Hospital Outcomes for Immediate Implant-Based Versus Autologous-Based Breast Reconstruction: A Propensity Score-Matched Analysis.

Ann Surg Oncol. 2025-2

[2]
Cordycepin enhances anti-tumor immunity in breast cancer by enhanceing ALB expression.

Heliyon. 2024-4-25

[3]
PI3K/AKT/mTOR signaling pathway: an important driver and therapeutic target in triple-negative breast cancer.

Breast Cancer. 2024-7

[4]
Breast implant illness: we must counter misinformation around this mysterious condition.

BMJ. 2024-2-1

[5]
Biofilm-derived oxylipin 10-HOME-mediated immune response in women with breast implants.

J Clin Invest. 2023-11-30

[6]
Serum Albumin Levels: A Biomarker to Be Repurposed in Different Disease Settings in Clinical Practice.

J Clin Med. 2023-9-17

[7]
Breast implant illness: Is it causally related to breast implants?

Autoimmun Rev. 2024-1

[8]
Advances in Prepectoral Breast Reconstruction.

Ther Clin Risk Manag. 2023-4-18

[9]
Cancer statistics, 2023.

CA Cancer J Clin. 2023-1

[10]
Advances in surface modifications of the silicone breast implant and impact on its biocompatibility and biointegration.

Biomater Res. 2022-12-14

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