基于肿瘤相关成纤维细胞基因构建乳腺癌预后预测模型及免疫浸润分析

doi:10.19586/j.2095-2341.2023.0161

生物技术进展 ›› 2024, Vol. 14 ›› Issue (2): 312-322.DOI: 10.19586/j.2095-2341.2023.0161

• 研究论文 • 上一篇

基于肿瘤相关成纤维细胞基因构建乳腺癌预后预测模型及免疫浸润分析

孙莉莉(), 安外尔·约麦尔阿卜拉, 刘富中, 布尔兰·叶尔肯别克, 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:2023-12-13 接受日期:2024-02-27 出版日期:2024-03-25 发布日期:2024-04-17
通讯作者: 郭文佳
作者简介:孙莉莉 E-mail: lily90109@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)

Construction of Prognostic Prediction Model of Breast Cancer Based on Tumor-associated Fibroblast Genes and Analysis of Immune Infiltration

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

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

Received:2023-12-13 Accepted:2024-02-27 Online:2024-03-25 Published:2024-04-17
Contact: Wenjia GUO

摘要/Abstract

摘要：

乳腺癌的转移和恶性进展与肿瘤微环境密切相关。肿瘤相关成纤维细胞（cancer associated fibroblasts，CAFs）是肿瘤微环境中比较重要的细胞，可影响肿瘤的进展及治疗。从基因表达综合数据库获得乳腺癌单细胞测序数据，对肿瘤微环境细胞进行分簇，再利用WGCNA识别CAF相关的关键基因，用该基因在TCGA-BRCA数据库中构建风险评分模型，进行生存分析、Cox回归分析、ROC曲线、构建列线图预测模型性能；通过GO和KEGG分析模型相关通路；利用体细胞突变、免疫浸润分析、干性指数分析以及药物敏感性分析探讨风险评分与临床特征及肿瘤微环境的关系。研究构建了基于10个CAF基因的乳腺癌预后预测模型，根据风险评分将患者分为高低风险组并进行验证，其中高风险组患者的预后更差，列线图和ROC曲线也显示模型具有良好的预测效能，乳腺癌病人免疫浸润水平更低、干性指数更高，且高风险组病人对紫杉醇及拉帕替尼这2种药物的敏感性更高。结果表明，10个CAF相关基因的风险评分可独立预测乳腺癌的预后及治疗效果，为明确CAF相关基因在乳腺癌中的作用机制提供了思路，也为乳腺癌易感基因患者的临床个体化治疗提供了理论依据。

Abstract:

Metastasis and malignant progression of breast cancer are deeply related to the tumor microenvironment. Tumor-associated fibroblasts （CAFs） are comparatively important cells in the tumor microenvironment which have implications on tumor progression and treatment. We obtained single-cell sequencing data of breast cancer downloaded from gene expression omnibus database， clustered the cells of tumor microenvironment， and then used WGCNA to identify the key genes related to CAF， and constructed a risk score model with the genes in TCGA-BRCA database， and performed survival analysis， Cox regression analysis， ROC curves， and constructed a column line graph to predict the performance of the model. Model-related pathways were analyzed by GO and KEGG. The relationship between risk score and clinical features and tumor microenvironment was explored by somatic mutation， immune infiltration analysis， stemness index analysis， and drug sensitivity analysis. A prognostic prediction model based on 10 CAF genes was constructed and validated in accordance with the risk scores. Patients were classified into high- and low-risk groups according to the risk scores， and the prognosis of patients in the high-risk group was worse， and the column plot and ROC curve also showed that the model had a good predictive efficiency， and the immune infiltration level of patients with breast cancer in the high-risk group was lower and the stemness index was higher， and the patients in the high-risk group were more sensitive to 2 drugs， namely， paclitaxel and lapatinib. Patients in the high-risk group had a lower level of immune infiltration and a higher dryness index， and patients in the high-risk group had a higher sensitivity to paclitaxel and lapatinib. The risk scores of the 10 CAF-related genes can individually predict the progress and therapeutic effects of breast cancer， which provides ideas for clarifying the mechanism of the role of CAF-related genes in breast cancer and provides a theoretical basis for the clinical individualization of treatment for patients with breast cancer susceptibility gene.

Key words: breast cancer, cancer associated fibroblasts, tumor mutcotion bursen, prognostic models, immune infiltration

中图分类号:

孙莉莉, 安外尔·约麦尔阿卜拉, 刘富中, 布尔兰·叶尔肯别克, 迪丽娜尔·叶尔夏提, 郭文佳. 基于肿瘤相关成纤维细胞基因构建乳腺癌预后预测模型及免疫浸润分析[J]. 生物技术进展, 2024, 14(2): 312-322.

Lili SUN, Yuemaierabola ANWAIER, Fuzhong LIU, Yeerkenbieke BUERLAN, Ye DILINAER, Wenjia GUO. Construction of Prognostic Prediction Model of Breast Cancer Based on Tumor-associated Fibroblast Genes and Analysis of Immune Infiltration[J]. Current Biotechnology, 2024, 14(2): 312-322.

图/表 8

图1 BC单细胞数据分析A：降维聚类分析对BRCA的scRNA-seq数据集中的免疫微环境细胞进行注释；B：热图展示各个细胞簇中排名前5的基因表达谱；C：GO富集分析结果；D：KEGG富集分析结果

Fig. 1 BC single-cell databasese analysis

图2 与肿瘤突变负荷相关的关键模块鉴定A：对各种软阈值幂（β）的无标度指数进行分析；B：模块基因层次聚类图；C：基于差异表达基因聚类树状图，指定颜色模块；D：热图显示BC模块特征值与临床特征的相关性。TMB—肿瘤突变负荷；MSI—微卫星不稳定性；T—肿瘤大小；N—淋巴结转移情况；M—远处转移。

Fig. 2 Identification of key modules associated with tumour mutational burden

图3 基于10个特征基因的预后模型的构建A：森林图显示单因素Cox结果；B~C：LASSO回归分析以及LASSO回归自变量的变化轨迹；D：森林图显示多因素Cox分析结果；E：相关性热图显示关键基因之间的相关性；F：分组箱线图显示关键基因在2个风险组中的表达差异。*，**，***，****分别表示差异在P<0.05、P<0.01、P<0.001和P<0.000 1水平上有统计学意义，ns表示差异无统计学意义（P<0.05）。

Fig. 3 Prognostic model construction based on 10 characteristic genes

图4 预后模型的生存分析及模型验证A：TCGA-BRCA队列中的生存分析；B：GSE20685队列中的生存分析;C：TCGA-BRCA队列预后模型验证；D：GSE20685队列预后模型验证

Fig. 4 Survival analysis and model validation of prognostic models

图5 列线图的构建及模型效能验证A：多因素Cox风险森林图分析风险评分和肿瘤分期作为总生存期的独立预测因子；B：模型列线图；C：模型校准曲线分析1、3和5年的列线图准确性；D：模型的1年、3年、和5年的ROC曲线。***表示差异在P<0.001水平上有统计学意义。

Fig. 5 Nomogram construction and validation of model effectiveness

图6 高低风险组的突变分析

Fig. 6 Mutation analysis in high and low risk groups

图7 关键基因的临床特征分析注：*、**、***分别表示在P<0.05、P<0.01、P<0.001水平上有统计学意义；ns表示无统计学意义。

Fig. 7 Clinical signatures of key genes

图8 免疫微环境分析及药物敏感性分析A：高低分险组之间免疫浸润情况；B~F：高低风险组之间的免疫评分、基质评分、评估评分、肿瘤纯度和肿瘤干性评分；G：2个风险组紫杉醇的IC50差异；H：2个风险组拉帕替尼的IC50差异。*、**、***分别表示差异在P<0.05、P<0.01、P<0.001水平上有统计学意义。

Fig. 8 Immunological microenvironmental analysis and drug sensitivity analysis

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基于肿瘤相关成纤维细胞基因构建乳腺癌预后预测模型及免疫浸润分析

Construction of Prognostic Prediction Model of Breast Cancer Based on Tumor-associated Fibroblast Genes and Analysis of Immune Infiltration

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