基于 KLRB1 模型的机器学习免疫相关基因预测乳腺癌的预后和免疫细胞浸润。

Machine learning immune-related gene based on KLRB1 model for predicting the prognosis and immune cell infiltration of breast cancer.

机构信息

Hengyang Medical School, University of South China, Hengyang, Hunan, China.

The Second Affiliated Hospital, Department of Breast and Thyroid Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

出版信息

Front Endocrinol (Lausanne). 2023 Jun 7;14:1185799. doi: 10.3389/fendo.2023.1185799. eCollection 2023.

DOI:10.3389/fendo.2023.1185799

PMID:37351109

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10282768/

Abstract

OBJECTIVE

Breast cancer is a prevalent malignancy that predominantly affects women. The development and progression of this disease are strongly influenced by the tumor microenvironment and immune infiltration. Therefore, investigating immune-related genes associated with breast cancer prognosis is a crucial approach to enhance the diagnosis and treatment of breast cancer.

METHODS

We analyzed data from the TCGA database to determine the proportion of invasive immune cells, immune components, and matrix components in breast cancer patients. Using this data, we constructed a risk prediction model to predict breast cancer prognosis and evaluated the correlation between KLRB1 expression and clinicopathological features and immune invasion. Additionally, we investigated the role of KLRB1 in breast cancer using various experimental techniques including real-time quantitative PCR, MTT assays, Transwell assays, Wound healing assays, EdU assays, and flow cytometry.

RESULTS

The functional enrichment analysis of immune and stromal components in breast cancer revealed that T cell activation, differentiation, and regulation, as well as lymphocyte differentiation and regulation, play critical roles in determining the status of the tumor microenvironment. These DEGs are therefore considered key factors affecting TME status. Additionally, immune-related gene risk models were constructed and found to be effective predictors of breast cancer prognosis. Further analysis through KM survival analysis and univariate and multivariate Cox regression analysis demonstrated that KLRB1 is an independent prognostic factor for breast cancer. KLRB1 is closely associated with immunoinfiltrating cells. Finally, experiments confirmed that overexpression of KLRB1 inhibits breast cancer cell proliferation, migration, invasion, and DNA replication ability. KLRB1 was also found to inhibit the proliferation of breast cancer cells by blocking cell division in the G1/M phase.

CONCLUSION

KLRB1 may be a potential prognostic marker and therapeutic target associated with the microenzymic environment of breast cancer tumors, providing a new direction for breast cancer treatment.

摘要

目的

乳腺癌是一种常见的恶性肿瘤，主要影响女性。这种疾病的发展和进展受到肿瘤微环境和免疫浸润的强烈影响。因此，研究与乳腺癌预后相关的免疫相关基因是提高乳腺癌诊断和治疗水平的关键方法。

方法

我们分析了 TCGA 数据库中的数据，以确定乳腺癌患者浸润性免疫细胞、免疫成分和基质成分的比例。利用这些数据，我们构建了一个风险预测模型来预测乳腺癌的预后，并评估了 KLRB1 表达与临床病理特征和免疫浸润的相关性。此外，我们还使用各种实验技术，包括实时定量 PCR、MTT 测定、Transwell 测定、划痕愈合测定、EdU 测定和流式细胞术，研究了 KLRB1 在乳腺癌中的作用。

结果

乳腺癌中免疫和基质成分的功能富集分析表明，T 细胞的激活、分化和调节以及淋巴细胞的分化和调节在决定肿瘤微环境状态方面起着关键作用。这些差异表达基因因此被认为是影响 TME 状态的关键因素。此外，构建了免疫相关基因风险模型，并发现它们是预测乳腺癌预后的有效指标。通过 KM 生存分析和单因素及多因素 Cox 回归分析进一步分析表明，KLRB1 是乳腺癌的独立预后因素。KLRB1 与免疫浸润细胞密切相关。最后，实验证实过表达 KLRB1 可抑制乳腺癌细胞的增殖、迁移、侵袭和 DNA 复制能力。KLRB1 还通过阻止细胞在 G1/M 期分裂来抑制乳腺癌细胞的增殖。