Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients. Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients

Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients. Overall design: Identification of CAFs-related gene signatures for breast and prostate cancer patients by integrating RNA-seq data from both primary CAFs and cancer patients datasets.

在过去二十年间，针对全球最常被确诊的两类肿瘤——前列腺肿瘤与乳腺肿瘤，科研人员已开发出肿瘤来源RNA表达特征（RNA expression signatures），用于优化预后预测与治疗决策制定。在此背景下，以肿瘤微环境核心组分为基础构建的分子特征（例如癌症相关成纤维细胞（cancer-associated fibroblasts, CAFs）），已被探索作为预后评估与治疗辅助工具。然而，对于乳腺与前列腺癌症中CAFs相关基因特征的生物学意义，仍有待进一步深入阐明。

本研究采用RNA测序技术（RNA sequencing technology, RNA-seq），对源自乳腺与前列腺肿瘤患者的分离CAFs转录组进行谱分析与比较。将表征乳腺与前列腺CAFs的差异表达基因（differentially expressed genes, DEGs），与来自公共数据集的乳腺及前列腺肿瘤患者批量RNA测序（bulk RNA-seq）谱数据进行交集分析。通路富集分析帮助我们明确了上述差异表达基因的生物学意义。本研究应用K-means聚类算法，构建针对乳腺与前列腺癌症的特异性CAFs相关基因特征，并将独立患者队列划分为高基因表达簇与低基因表达簇。采用Kaplan-Meier生存曲线与对数秩检验（log-rank tests），预测不同患者簇的预后参数差异。通过决策树分析验证聚类结果，并利用提升算法（boosting calculations）优化决策树算法所得的分析结果。

基于乳腺CAFs的分析结果，我们鉴定出包含8个基因的特征集（ITGA11、THBS1、FN1、EMP1、ITGA2、FYN、SPP1及EMP2），这些基因隶属于促转移信号通路，例如黏着斑通路（focal adhesion pathway）。生存分析结果显示，高表达上述基因的乳腺癌患者簇临床预后更差。随后，我们鉴定出包含11个基因的前列腺CAFs相关特征集（IL13RA2、GDF7、IL33、CXCL1、TNFRSF19、CXCL6、LIFR、CXCL5、IL7、TSLP及TNFSF15），这些基因与免疫应答过程相关。低表达该特征集基因的前列腺癌患者，其生存率显著降低。

本研究结果通过两步法得到有效验证——基于无监督（聚类）与有监督（分类）学习技术，在独立RNA-seq队列中展现出≥90%的高预测准确率。我们发现，源自乳腺与前列腺肿瘤的CAFs转录组存在显著异质性。值得注意的是，这两种新型CAFs相关基因特征可作为可靠的预后指标与具有临床应用价值的生物标志物，有助于优化乳腺与前列腺癌症患者的管理方案。

总体实验设计：通过整合原代CAFs的RNA-seq数据与癌症患者公共数据集，鉴定针对乳腺与前列腺癌症患者的CAFs相关基因特征。