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Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：在过去的二十年里，肿瘤来源的RNA表达特征已被开发用于全球最常见的两种肿瘤，即前列腺和乳腺肿瘤，以提高结果预测和治疗决策。在这种情况下，由肿瘤微环境的主要成分获得的分子特征，如癌症相关成纤维细胞（CAF），已被用作预后和治疗工具。然而，关于CAFs相关基因特征在乳腺癌和前列腺癌中的意义的更深入理解仍有待进一步研究.
方法：采用RNA测序技术（RNA-seq）来分析和比较从乳腺和前列腺肿瘤患者中分离的CAFs的转录组。将表征乳腺和前列腺CAF的差异表达基因（DEGs）与来自乳腺和前列腺肿瘤患者的大量RNA-seq谱的公共数据集的数据相交。途径富集分析使我们能够了解DEG的生物学意义。K-均值聚类用于构建对乳腺癌和前列腺癌具有特异性的CAF相关基因特征，并将患者的独立队列分为高基因表达和低基因表达簇。采用Kaplan-Meier存活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，然后使用增强计算来改进决策树算法获得的结果。
结果：在乳腺CAF中获得的数据使我们能够评估包括8个基因（ITGA11，THBS1，FN1，EMP1，ITGA2，FYN，SPP1和EMP2）属于前转移信号途径，如斑粘连途径。生存分析表明,显示上述基因高表达的乳腺癌患者群显示更差的临床结果。接下来,我们确定了前列腺CAFs相关的标签，包括11个基因（IL13RA2，GDF7，IL33，CXCL1，TNFRSF19，CXCL6，LIFR，CXCL5、IL7、TSLP、和TNFSF15）与免疫反应相关。这些基因的低表达预示着前列腺癌患者的低生存率。所获得的结果通过两步法得到了显著验证，基于无监督(聚类)和监督(分类)学习技术，在独立的RNA-seq队列中显示出较高的预测准确性(≥90%)。
结论：我们发现来自乳腺和前列腺肿瘤的CAFs转录谱存在巨大的异质性。值得注意的是，这两个新的CAFs相关基因标记可能被认为是可靠的预后指标和有价值的生物标志物,可以更好地管理乳腺癌和前列腺癌患者.
方法：采用RNA测序技术（RNA-seq）来分析和比较从乳腺和前列腺肿瘤患者中分离的CAFs的转录组。将表征乳腺和前列腺CAF的差异表达基因（DEGs）与来自乳腺和前列腺肿瘤患者的大量RNA-seq谱的公共数据集的数据相交。途径富集分析使我们能够了解DEG的生物学意义。K-均值聚类用于构建对乳腺癌和前列腺癌具有特异性的CAF相关基因特征，并将患者的独立队列分为高基因表达和低基因表达簇。采用Kaplan-Meier存活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，然后使用增强计算来改进决策树算法获得的结果。
结果：在乳腺CAF中获得的数据使我们能够评估包括8个基因（ITGA11，THBS1，FN1，EMP1，ITGA2，FYN，SPP1和EMP2）属于前转移信号途径，如斑粘连途径。生存分析表明,显示上述基因高表达的乳腺癌患者群显示更差的临床结果。接下来,我们确定了前列腺CAFs相关的标签，包括11个基因（IL13RA2，GDF7，IL33，CXCL1，TNFRSF19，CXCL6，LIFR，CXCL5、IL7、TSLP、和TNFSF15）与免疫反应相关。这些基因的低表达预示着前列腺癌患者的低生存率。所获得的结果通过两步法得到了显著验证，基于无监督(聚类)和监督(分类)学习技术，在独立的RNA-seq队列中显示出较高的预测准确性(≥90%)。
结论：我们发现来自乳腺和前列腺肿瘤的CAFs转录谱存在巨大的异质性。值得注意的是，这两个新的CAFs相关基因标记可能被认为是可靠的预后指标和有价值的生物标志物,可以更好地管理乳腺癌和前列腺癌患者.
