Department of Oncology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
The First Affiliated Hospital of Naval Military Medical University, Shanghai, China.
Front Immunol. 2024 Oct 18;15:1466762. doi: 10.3389/fimmu.2024.1466762. eCollection 2024.
The incidence of breast cancer remains high and severely affects human health. However, given the heterogeneity of tumor cells, identifying additional characteristics of breast cancer cells is essential for accurate treatment.
This study aimed to analyze the relevant characteristics of matrix genes in breast cancer through the multigroup data of a breast cancer multi-database.
The related characteristics of matrix genes in breast cancer were analyzed using multigroup data from the breast cancer multi database in the Cancer Genome Atlas, and the differential genes of breast cancer matrix genes were identified using the elastic net penalty logic regression method. The risk characteristics of matrix genes in breast cancer were determined, and matrix gene expression in different breast cancer cells was evaluated using real-time fluorescent quantitative polymerase chain reaction (PCR). A consensus clustering algorithm was used to identify the biological characteristics of the population based on the matrix molecular subtypes in breast cancer, followed by gene mutation, immune correlation, pathway, and ligand-receptor analyses.
This study reveals the genetic characteristics of cell matrix related to breast cancer. It is found that 18.1% of stromal genes are related to the prognosis of breast cancer, and these genes are mostly concentrated in the biological processes related to metabolism and cytokines in protein. Five different matrix-related molecular subtypes were identified by using the algorithm, and it was found that the five molecular subtypes were obviously different in prognosis, immune infiltration, gene mutation and drug-making gene analysis.
This study involved analyzing the characteristics of cell-matrix genes in breast cancer, guiding the precise prevention and treatment of the disease.
乳腺癌的发病率仍然很高，严重影响人类健康。然而，鉴于肿瘤细胞的异质性，识别乳腺癌细胞的其他特征对于准确治疗至关重要。
本研究旨在通过癌症基因组图谱中的乳腺癌多数据库的多组数据，分析乳腺癌基质基因的相关特征。
利用癌症基因组图谱中乳腺癌多数据库的多组数据，分析乳腺癌基质基因的相关特征，采用弹性网罚逻辑回归方法识别乳腺癌基质基因的差异基因。确定乳腺癌基质基因的风险特征，并采用实时荧光定量聚合酶链反应（PCR）评估不同乳腺癌细胞中基质基因的表达。基于乳腺癌基质分子亚型，采用共识聚类算法识别人群的生物学特征，然后进行基因突变、免疫相关性、通路和配体-受体分析。
本研究揭示了与乳腺癌相关的细胞基质的遗传特征。研究发现，18.1%的基质基因与乳腺癌的预后相关，这些基因主要集中在蛋白质代谢和细胞因子相关的生物学过程中。通过算法鉴定了 5 种不同的基质相关分子亚型，发现这 5 种分子亚型在预后、免疫浸润、基因突变和药物生成基因分析方面明显不同。
本研究涉及分析乳腺癌细胞基质基因的特征，为疾病的精准预防和治疗提供指导。
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