基于m5C相关基因构建三阴性乳腺癌预后预测模型及药物敏感性分析

doi:10.19586/j.2095-2341.2023.0134

生物技术进展 ›› 2024, Vol. 14 ›› Issue (1): 149-159.DOI: 10.19586/j.2095-2341.2023.0134

• 研究论文 • 上一篇

基于m5C相关基因构建三阴性乳腺癌预后预测模型及药物敏感性分析

安外尔·约麦尔阿卜拉(), 布尔兰·叶尔肯别克, 孙莉莉, 刘富中, 迪丽娜尔·叶尔夏提, 郭文佳()

新疆医科大学附属肿瘤医院，乌鲁木齐 830011

收稿日期:2023-10-23 接受日期:2023-11-28 出版日期:2024-01-25 发布日期:2024-02-05
通讯作者: 郭文佳
作者简介:安外尔·约麦尔阿卜拉 E-mail: anwar1118@163.com；
基金资助:
新疆维吾尔自治区自然科学基金杰出青年科学基金项目(2022D01E27);新疆维吾尔自治区天池英才项目(2022TCYCGWJ)

Prognosis Prediction Model and Drug Sensitivity Analysis of Triple-negative Breast Cancer Based on m5C Related Genes

Yuemaierabola ANWAIER(), Yeerkenbieke BUERLAN, Lili SUN, Fuzhong LIU, Yeerxiati DILINAER, Wenjia GUO()

Affiliated Cancer Hospital of Xinjiang Medical University，Urumqi 830011，China

Received:2023-10-23 Accepted:2023-11-28 Online:2024-01-25 Published:2024-02-05
Contact: Wenjia GUO

摘要/Abstract

摘要：

为了探讨5-甲基胞嘧啶（5-methylcytosine，m5C）相关基因在三阴性乳腺癌（triple negative breast cancer，TNBC）患者治疗及预后中的潜在价值，构建了基于m5C相关基因的预后预测模型，用于评估TNBC患者的预后和生存状况。从基因表达总库（gene expression omnibus，GEO）数据库和癌症基因组图谱（the cancer genome atlas，TCGA）数据库中下载TNBC基因表达谱和相应的临床数据。通过Pearson分析确定了99个m5C相关基因，进一步采用单因素Cox分析鉴定出5个与预后有关的m5C相关基因（SLC6A14、BCL11A、UGT8、LMO4、PSAT1）并构建了风险评分(risk score)预测模型，根据风险评分中位值将患者划分为高风险组和低风险组。使用Kaplan-Meier（K-M）生存分析、受试者工作特征（receiver operating characteristic，ROC）曲线、多变量Cox回归分析、构建列线图和校准曲线评估了模型的预测效能。训练集和验证集的K-M生存曲线、受试者工作特征曲线下面积（area under the curve，AUC）分析均验证了模型具有良好的预测能力。多变量Cox回归分析显示，风险评分可作为独立的预后生物标志物。使用ssGSEA、免疫评分分析和化疗药物对高低风险组患者的半最大抑制浓度（half maximal inhibitory concentration，IC₅₀）值差异分析显示，免疫细胞和免疫检查点基因以及大多数化疗药物的IC₅₀值在不同风险组之间的表达存在显著差异。研究结果构建了基于5个m5C相关基因的风险评分预后预测模型，这将有助于阐明TNBC中m5C相关基因的作用机制，进而提供更有价值的预后及诊断的生物标志物和潜在的治疗靶点，为TNBC患者临床个性化治疗提供理论指导。

Abstract:

In order to explore the potential value of 5-methylcytosine （m5C）-related genes in the prognosis of patients with triple negative breast cancer （TNBC） and in the treatment of TNBC， a prognostic prediction model based on m5C-related genes was constructed for assessing the prognosis and survival of TNBC patients. TNBC gene expression profiles and corresponding clinical data were downloaded from the gene expression omnibus （GEO） database and the cancer genome atlas （TCGA） database.99 m5C related genes were identified through pearson correlation analysis and one-way cox analysis was used to identify m5C-related genes， from which five prognosis-related m5C-related genes （SLC6A14， BCL11A， UGT8， LMO4， PSAT1） were identified and a risk score prediction model was constructed， and the patients were classified into high-risk and low-risk groups according to the median value of the risk score. The predictive efficacy of the model was assessed using Kaplan-Meier （K-M） survival analyses， receiver operating characteristic （ROC） curves， multivariate Cox regression analyses， construction of column line plots and calibration curves. The K-M survival curve， area under the curve （AUC） analysis of the training and validation sets verified the good predictive ability of the model. Multivariate Cox regression showed risk score could be used as an independent prognostic biomarker. Analysis of differences in half maximal inhibitory concentration （IC₅₀） values of chemotherapeutic agents using ssGSEA， immune score analysis and chemotherapeutic agents in patients in the high and low risk groups showed significant differences in the expression of immune cells and immune checkpoint genes， as well as the IC₅₀ values of most chemotherapeutic agents， between the different risk groups. In this study， we constructed a risk score prognostic prediction model based on five m5C-related genes， which would help to elucidate the mechanism of action of m5C-related genes in TNBC， and then provided more valuable biomarkers for prognosis and diagnosis and potential therapeutic targets， and provided theoretical guidance for the clinical individualisation of treatment for TNBC patients.

Key words: m5C related genes, triple negative breast cancer, prognostic model, IC₅₀ of chemotherapeutic agents, immunoinfiltration, immune checkpoint

中图分类号:

安外尔·约麦尔阿卜拉, 布尔兰·叶尔肯别克, 孙莉莉, 刘富中, 迪丽娜尔·叶尔夏提, 郭文佳. 基于m5C相关基因构建三阴性乳腺癌预后预测模型及药物敏感性分析[J]. 生物技术进展, 2024, 14(1): 149-159.

Yuemaierabola ANWAIER, Yeerkenbieke BUERLAN, Lili SUN, Fuzhong LIU, Yeerxiati DILINAER, Wenjia GUO. Prognosis Prediction Model and Drug Sensitivity Analysis of Triple-negative Breast Cancer Based on m5C Related Genes[J]. Current Biotechnology, 2024, 14(1): 149-159.

图/表 11

图1 研究流程图

Fig. 1 Flowchart of the study

图2 筛选差异表达基因A：GSE76275数据集的主成分分析图；B：GSE38959数据集的主成分分析图；C：GSE76275数据集样本差异基因火山图；D：GSE38959数据集样本差异基因火山图；E：GSE76275数据集样本差异基因热图；F：GSE38959数据集样本差异基因热图

Fig. 2 Screening differentially expressed genes

图3 GSE76275和GSE38959差异表达基因韦恩图

Fig. 3 Venn diagram of differentially expressed genes of GSE76275 and GSE38959

图4 差异表达基因功能富集分析A：气泡图；B：弦图；C：柱状图；D：圈图显示了所富集的条目以及基因

Fig. 4 Functional enrichment analysis of differentially expressed genes

表1 差异表达基因功能富集特征

Table 1 Characters of functional enrichment of differentially expressed genes

本体	ID	描述	基因比	P值	数量	Z值
BP	GO:0001655	泌尿生殖系统发育	20/183	8.216×10^-7	20	-1.789
BP	GO:0048285	细胞器裂变	22/183	4.704×10^-6	22	3.411
BP	GO:0048732	腺发育	20/183	8.971×10^-6	20	-1.789
CC	GO:0031528	微绒毛膜	4/189	0.022	4	0
CC	GO:0000779	浓缩的染色体，着丝粒区域	8/189	0.022	8	2.828
CC	GO:0000775	染色体，着丝粒区	9/189	0.038	9	3
MF	GO:0001228	DNA结合转录激活活性、RNA 聚合酶Ⅱ特异性	17/188	0.001 44	17	-1.213
MF	GO:0001216	DNA结合转录激活活性	17/188	0.001 44	17	-1.213
MF	GO:0001223	转录辅激活子结合	5/188	0.008 07	5	-2.236
KEGG	hsa04110	细胞周期	9/99	0.004 39	9	3
KEGG	hsa04114	卵母细胞减数分裂	8/99	0.019 55	8	-0.707

图5 m5C相关基因的鉴定

Fig. 5 Identification of m5C-related genes

图6 预后模型的构建A:训练集；B：验证集K-M曲线；C：训练集；D：验证集风险因子图；E：训练集；F：验证集显示在高低风险组中5个m5C相关基因表达；*、**、***表示在P<0.05、0.01、0.001水平上有统计学意义。

Fig. 6 Construction of prognostic model

图7 模型预测效能的验证A：基于临床特征构建的列线图；B：列线图校正曲线；C：多因素Cox分析；D：训练集ROC曲线；E：验证集ROC曲线；*、**、***表示在P<0.05、0.01、0.001水平上有统计学意义。

Fig. 7 Validation of model prediction efficacy

图8 高低风险组间免疫浸润分析A~B：箱线图显示高低风险组间PD-1与LMTK3的表达差异；C~D：高低险组间的基质评分和免疫评分分析；E：高低风险组间28种免疫细胞丰度分析；*、**、***表示在P<0.05、0.01、0.001水平上有统计学意义，ns表示无统计学意义。

Fig. 8 Analysis of immune infiltration between high and low risk groups

图9 高低风险组间化疗药物IC50值差异分析注：*表示在P<0.05水平上有统计学意义；ns表示无统计学意义。

Fig. 9 Analysis of differences in IC50 values for chemotherapy drugs between high and low risk groups

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基于m5C相关基因构建三阴性乳腺癌预后预测模型及药物敏感性分析

Prognosis Prediction Model and Drug Sensitivity Analysis of Triple-negative Breast Cancer Based on m5C Related Genes

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