Zhai Xinyu, Chen Xinglin, Wan Zhong, Ge Minyao, Ding Yi, Gu Jianyi, Hua Jinjun, Guo Dongdong, Tan Mingyue, Xu Dongliang
Urology Centre, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Oncol. 2023 Mar 3;13:1136835. doi: 10.3389/fonc.2023.1136835. eCollection 2023.
Globally, prostate cancer remains a leading cause of mortality and morbidity despite advances in treatment. Research on prostate cancer has primarily focused on the malignant epithelium, but the tumor microenvironment has recently been recognized as an important factor in the progression of prostate cancer. Cancer-associated fibroblasts (CAFs) play an important role in prostate cancer progression among multiple cell types in the tumor microenvironment. In order to develop new treatments and identify predictive and prognostic biomarkers for CAFs, further research is needed to understand the mechanism of action of prostate cancer and CAF. In this work, we performed the single-cell RNA sequence analysis to obtain the biomarkers for CAFs, and ten genes were finally regarded as the marker genes for CAFs. Based on the ssGSEA algorithm, the prostate cancer cohort was divided into low- and high-CAFs groups. Further analysis revealed that the CAFs-score is associated with many immune-related cells and immune-related pathways. In addition, between the low- and high-CAFs tissues, a total of 127 hub genes were discovered, which is specific in CAFs. After constructing the prognostic prediction model, SLPI, VSIG2, CENPF, SLC7A1, SMC4, and ITPR2 were finally regarded as the key genes in the prognosis of patients with prostate cancer. Each patient was assigned with the risk score as follows: SLPI* 0.000584811158157081 + VSIG2 * -0.01190627068889 + CENPF * -0.317826812875334 + SLC7A1 * -0.0410213995358753 + SMC4 * 0.202544454923637 + ITPR2 * -0.0824652047622673 + TOP2A * 0.140312081524807 + OR51E2 * -0.00136602095885459. The GSVA revealed the biological features of CAFs, many cancer-related pathways, such as the adipocytokine signaling pathway, ERBB signaling pathway, GnRH signaling pathway, insulin signaling pathway, mTOR signaling pathway and PPAR signaling pathway are closely associated with CAFs. As a result of these observations, similar transcriptomics may be involved in the transition from normal fibroblasts to CAFs in adjacent tissues. As one of the biomarkers for CAFs, CENPF can promote the proliferation ability of prostate cancer cells. The overexpress of CENPF could promote the proliferation ability of prostate cancer cells. In conclusion, we discuss the potential prognostic and therapeutic value of CAF-dependent pathways in prostate cancer.
在全球范围内,尽管治疗方法有所进步,但前列腺癌仍然是导致死亡和发病的主要原因。前列腺癌的研究主要集中在恶性上皮细胞上,但肿瘤微环境最近被认为是前列腺癌进展的一个重要因素。在肿瘤微环境中的多种细胞类型中,癌症相关成纤维细胞(CAFs)在前列腺癌进展中发挥着重要作用。为了开发新的治疗方法并确定CAFs的预测和预后生物标志物,需要进一步研究以了解前列腺癌和CAF的作用机制。在这项工作中,我们进行了单细胞RNA序列分析以获得CAFs的生物标志物,最终将十个基因视为CAFs的标记基因。基于ssGSEA算法,将前列腺癌队列分为低CAFs组和高CAFs组。进一步分析表明,CAFs评分与许多免疫相关细胞和免疫相关途径有关。此外,在低CAFs组织和高CAFs组织之间,共发现了127个枢纽基因,这些基因在CAFs中具有特异性。构建预后预测模型后,最终将SLPI、VSIG2、CENPF、SLC7A1、SMC4和ITPR2视为前列腺癌患者预后的关键基因。为每位患者分配如下风险评分:SLPI * 0.000584811158157081 + VSIG2 * -0.01190627068889 + CENPF * -0.317826812875334 + SLC7A1 * -0.0410213995358753 + SMC4 * 0.202544454923637 + ITPR2 * -0.0824652047622673 + TOP2A * 0.140312081524807 + OR51E2 * -0.00136602095885459。GSVA揭示了CAFs的生物学特征,许多癌症相关途径,如脂肪细胞因子信号通路、ERBB信号通路、GnRH信号通路、胰岛素信号通路、mTOR信号通路和PPAR信号通路都与CAFs密切相关。基于这些观察结果,相似的转录组学可能参与了相邻组织中正常成纤维细胞向CAFs的转变。作为CAFs的生物标志物之一,CENPF可以促进前列腺癌细胞的增殖能力。CENPF的过表达可以促进前列腺癌细胞的增殖能力。总之,我们讨论了CAF依赖性途径在前列腺癌中的潜在预后和治疗价值。