Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, 87036, Italy.
Department of Cultures, Education and Society, University of Calabria, Rende, 87036, Italy.
J Transl Med. 2024 Jun 27;22(1):597. doi: 10.1186/s12967-024-05413-2.
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.
在过去的二十年中，已经开发出了用于全球最常见的两种诊断肿瘤（即前列腺癌和乳腺癌）的肿瘤衍生 RNA 表达特征，以便改善预后预测和治疗决策。在这种情况下，已经探索了肿瘤微环境的主要成分（例如癌相关成纤维细胞（CAF））获得的分子特征，作为预后和治疗工具。然而，仍然需要更深入地了解 CAF 相关基因特征在乳腺癌和前列腺癌中的意义。
使用 RNA 测序技术（RNA-seq）对来自患有乳腺癌和前列腺癌患者的 CAF 进行了分析和比较。对表征乳腺癌和前列腺 CAF 的差异表达基因（DEG）进行了交叉分析，与从乳腺癌和前列腺肿瘤患者的批量 RNA-seq 图谱中获得的公共数据集的数据进行了交叉分析。通路富集分析使我们能够了解 DEG 的生物学意义。应用 K-均值聚类方法构建了针对乳腺癌和前列腺癌的 CAF 相关基因特征，并将独立的患者队列分为高和低基因表达聚类。Kaplan-Meier 生存曲线和对数秩检验用于预测患者聚类之间的预后参数差异。决策树分析用于验证聚类结果，然后使用提升计算来改善决策树算法的结果。
从乳腺 CAF 中获得的数据使我们能够评估一个包含 8 个基因（ITGA11、THBS1、FN1、EMP1、ITGA2、FYN、SPP1 和 EMP2）的基因特征，这些基因属于转移途径，例如焦点粘附途径。生存分析表明，高表达上述基因的乳腺癌患者聚类显示出更差的临床结局。接下来，我们鉴定了一个包含 11 个基因（IL13RA2、GDF7、IL33、CXCL1、TNFRSF19、CXCL6、LIFR、CXCL5、IL7、TSLP 和 TNFSF15）的前列腺 CAF 相关基因特征，这些基因与免疫反应相关。这些基因的低表达可预测前列腺癌患者的生存率较差。通过基于无监督（聚类）和监督（分类）学习技术的两步方法对结果进行了显著验证，在独立的 RNA-seq 队列中显示了较高的预测准确性（≥90％）。
我们发现源自乳腺癌和前列腺肿瘤的 CAF 的转录谱存在巨大的异质性。值得注意的是，这两个新的 CAF 相关基因特征可以被认为是可靠的预后指标，并且是改善乳腺癌和前列腺癌患者管理的有价值的生物标志物。
