基于前列腺癌肿瘤相关成纤维细胞的单细胞转录组的生物信息学分析

Bioinformatics Analysis of Single-Cell Transcriptome Based on Prostate Cancer Cancer-Associated Fibroblasts

本研究运用生物信息学分析方法对前列腺癌(PCa)相关的单细胞数据进行了深入研究。首先,我们从GEO数据库中筛选出了1035个与肿瘤相关成纤维细胞(CAFs)相关的差异表达基因。然后,我们选取了其中最显著的Top100个基因,通过蛋白质相互作用网络分析得到了10个核心差异表达基因。进一步利用GEPIA2工具进行生存预后分析,发现MYLK、MYH11、COL18A1和CALD1可能与前列腺癌的预后显著相关。这些结果为我们深入探究前列腺癌的发病机制提供了重要的信息,有助于制定个体化的治疗策略。

This study utilized bioinformatics analysis methods to conduct in-depth research on single-cell data related to prostate cancer (PCa). Initially, we screened 1035 differentially expressed genes related to cancer-associated fibroblasts (CAFs) from the GEO database. Subsequently, we selected the most significant Top 100 genes and identified 10 core differentially expressed genes through protein-protein interaction network analysis. Further, using GEPIA2 tool, we performed survival prognosis analysis and found that MYLK, MYH11, COL18A1, and CALD1 may be significantly associated with the prognosis of prostate cancer. These results provide important information for further exploration of the pathogenesis of prostate cancer and contribute to the development of personalized treatment strategies.