在过去的二十年中，针对世界上两种最常诊断的肿瘤（即前列腺癌和乳腺肿瘤）开发了肿瘤来源的 RNA 表达特征，以改善结果预测和治疗决策。在这种背景下，肿瘤微环境主要成分（例如癌症相关成纤维细胞（CAF））获得的分子特征已被探索作为预后和治疗工具。然而，对 CAF 相关基因特征在乳腺癌和前列腺癌中的重要性的更深入理解仍有待披露。RNA 测序技术 (RNA-seq) 用于分析和比较从乳腺癌和前列腺癌患者中分离出的 CAF 的转录组。前列腺肿瘤。将表征乳腺和前列腺 CAF 的差异表达基因 (DEG) 与来自乳腺和前列腺肿瘤患者的批量 RNA-seq 概况的公共数据集的数据进行交叉。通路富集分析使我们能够理解 DEG 的生物学意义。 K 均值聚类用于构建乳腺癌和前列腺癌特异的 CAF 相关基因特征，并将独立的患者队列分层为高基因表达簇和低基因表达簇。采用卡普兰-迈耶生存曲线和对数秩检验来预测患者群的结果参数的差异。使用决策树分析来验证聚类结果，然后采用提升计算来改进决策树算法获得的结果。在乳房 CAF 中获得的数据使我们能够评估包含 8 个基因（ITGA11、THBS1、FN1）的特征、EMP1、ITGA2、FYN、SPP1 和 EMP2）属于促转移信号通路，例如粘着斑通路。生存分析表明，上述基因高表达的乳腺癌患者群表现出更差的临床结果。接下来，我们鉴定了前列腺 CAF 相关特征，其中包括与免疫反应相关的 11 个基因（IL13RA2、GDF7、IL33、CXCL1、TNFRSF19、CXCL6、LIFR、CXCL5、IL7、TSLP 和 TNFSF15）。这些基因的低表达预示着前列腺癌患者的生存率较低。所获得的结果通过基于无监督（聚类）和监督（分类）学习技术的两步方法得到显着验证，在独立 RNA-seq 队列中显示出较高的预测准确性（≥90%）。我们发现了巨大的异质性来自乳腺和前列腺肿瘤的 CAF 的转录谱。值得注意的是，这两个新的 CAF 相关基因特征可能被认为是可靠的预后指标和有价值的生物标志物，有助于更好地管理乳腺癌和前列腺癌患者。© 2024。作者。

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.© 2024. The Author(s).