Zhang Haojie, Zhang Xiangsheng, Wang Xiaohong, Sun Hongguang, Hou Changran, Yu Yue, Wang Song, Yin Fangxu, Yang Zhenlin
The Second Medical College, Binzhou Medical University, Yantai, China.
Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China.
Front Oncol. 2022 Jul 7;12:941283. doi: 10.3389/fonc.2022.941283. eCollection 2022.
Triple-negative breast cancer (TNBC) is a special subtype of breast cancer. Transient Receptor Potential (TRP) channel superfamily has emerged as a novel and interesting target in a variety of tumors. However, the association of TRP channel-related genes with TNBC is still unclear.
The The Cancer Genome Atlas (TCGA)-TNBC and GSE58812 datasets were downloaded from the public database. The differentially expressed TRP channel-related genes (DETGs) were screened by limma package, and mutations of the above genes were analyzed. Subsequently, new molecular subtypes in TNBC-based DETGs were explored by consensus clustering analysis. In addition, Lasso-Cox regression analysis was used to divide it into two robust risk subtypes: high-risk group and low-risk group. The accuracy and distinguishing ability of above models were verified by a variety of methods, including Kaplan-Meier survival analysis, ROC analysis, calibration curve, and PCA analysis. Meanwhile, CIBERSORT algorithm was used to excavate status of immune-infiltrating cells in TNBC tissues. Last, we explored the therapeutic effect of drugs and underlying mechanisms of risk subgroups by pRRophetic package and GSEA algorithm, respectively.
A total of 19 DETGs were identified in 115 TNBC and 113 normal samples from TCGA database. In addition, missense mutation and SNP were the most common variant classification. According to Lasso-Cox regression analysis, the risky formula performed best when nine genes were used: TRPM5, TRPV2, HTR2B, HRH1, P2RY2, MAP2K6, NTRK1, ADCY6, and PRKACB. Subsequently, Kaplan-Meier survival analysis, ROC analysis, calibration curve, and Principal Components Analysis (PCA) analysis showed an excellent accuracy for predicting OS using risky formula in each cohort (P < 0.05). Specifically, high-risk group had a shorter OS compared with low-risk group. In addition, T-cell CD4 memory activated and macrophages M1 were enriched in normal tissues, whereas Tregs were increased in tumor tissues. Note that the low-risk group was better therapeutic effect to docetaxel, doxorubicin, cisplatin, paclitaxel, and gemcitabine than the high-risk group (P < 0.05). Last, assays, Quantitative Real-time PCR (qRT-PCR) indicated that TRPM5 was significantly highly expressed in MDA-MB-231 and MDA-MB-468 cells compared with that in MCF-10A cells (P < 0.01).
We identified a risky formula based on expression of TRP channel-related genes that can predict prognosis, therapeutic effect, and status of tumor microenvironment for patients with TNBC.
三阴性乳腺癌（TNBC）是乳腺癌的一种特殊亚型。瞬时受体电位（TRP）通道超家族已成为多种肿瘤中一个新颖且有趣的靶点。然而，TRP通道相关基因与TNBC的关联仍不清楚。
从公共数据库下载癌症基因组图谱（TCGA）-TNBC和GSE58812数据集。使用limma软件包筛选差异表达的TRP通道相关基因（DETGs），并分析上述基因的突变情况。随后，通过一致性聚类分析探索基于DETGs的TNBC新分子亚型。此外，使用Lasso-Cox回归分析将其分为两个稳健的风险亚型：高风险组和低风险组。通过多种方法验证上述模型的准确性和区分能力，包括Kaplan-Meier生存分析、ROC分析、校准曲线和主成分分析（PCA）。同时，使用CIBERSORT算法挖掘TNBC组织中免疫浸润细胞的状态。最后，分别通过pRRophetic软件包和GSEA算法探索药物的治疗效果和风险亚组的潜在机制。
在来自TCGA数据库的115例TNBC和113例正常样本中，共鉴定出19个DETGs。此外，错义突变和单核苷酸多态性（SNP）是最常见的变异分类。根据Lasso-Cox回归分析，当使用9个基因时，风险公式表现最佳：TRPM5、TRPV2、HTR2B、HRH1、P2RY2、MAP2K6、NTRK1、ADCY6和PRKACB。随后，Kaplan-Meier生存分析、ROC分析、校准曲线和主成分分析（PCA）表明，在每个队列中使用风险公式预测总生存期具有优异的准确性（P < 0.05）。具体而言，高风险组的总生存期比低风险组短。此外，T细胞CD4记忆激活细胞和M1巨噬细胞在正常组织中富集，而调节性T细胞（Tregs）在肿瘤组织中增加。值得注意的是，低风险组对多西他赛、阿霉素、顺铂、紫杉醇和吉西他滨的治疗效果优于高风险组（P < 0.05）。最后，定量实时聚合酶链反应（qRT-PCR）检测表明，与MCF-10A细胞相比，TRPM5在MDA-MB-231和MDA-MB-468细胞中显著高表达（P < 0.01）。
我们基于TRP通道相关基因的表达确定了一个风险公式，该公式可预测TNBC患者的预后、治疗效果和肿瘤微环境状态。
