生物技术进展 2024 年 第 14 卷 第 2 期 312 ~ 322Current Biotechnology ISSN 2095‑2341研究论文Articles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。
关键词：乳腺癌；肿瘤相关成纤维细胞；肿瘤突变负荷；预后模型；免疫浸润DOI ：10.19586/j.20952341.2023.0161中图分类号：Q75， R737.9 文献标志码：AConstruction of Prognostic Prediction Model of Breast Cancer Based on Tumor -associated Fibroblast Genes and Analysis of Immune InfiltrationSUN Lili ， ANWAIER Yuemaierabola ， LIU Fuzhong ， BUERLAN Yeerkenbieke ， DILINAER Ye ，GUO Wenjia *Affiliated Cancer Hospital of Xinjiang Medical University ， Urumqi 830011， ChinaAbstract ：Metastasis and malignant progression of breast cancer are deeply related to the tumor microenvironment. Tumor -associ‐ated fibroblasts （CAFs ） are comparatively important cells in the tumor microenvironment which have implications on tumor pro‐gression and treatment. We obtained single -cell sequencing data of breast cancer downloaded from gene expression omnibus data‐base ， clustered the cells of tumor microenvironment ， and then used WGCNA to identify the key genes related to CAF ， and con‐structed 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 somaticmutation ， immune infiltration analysis ， stemness index analysis ， and drug sensitivity analysis. A prognostic prediction modelbased 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收稿日期：2023‐12‐13； 接受日期：2024‐02‐27基金项目：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 ）。
