DataSheet_1_Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer.pdf

Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

癌相关成纤维细胞（Cancer-associated fibroblasts, CAFs）是肿瘤微环境（tumor microenvironment, TME）中肿瘤基质串扰的主要贡献者，可通过促进血管生成与淋巴管生成加速肿瘤进展。本研究旨在筛选与癌相关成纤维细胞相关的预后基因，此类基因可导致卵巢癌（ovarian cancer, OC）患者出现较高的发病率与死亡率。我们对16项多中心研究（共计2742例患者）开展生物信息学分析，并通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）筛选出癌相关成纤维细胞相关的核心基因。本研究采用机器学习方法筛选出COL16A1、COL5A2、GREM1、LUM、SRPX及TIMP3，并构建了预后特征模型。随后，一系列生物信息学算法基于上述预后特征开展风险分层分析，结果显示高风险组患者预后更差、免疫应答更弱、肿瘤突变负荷（tumor mutational burden, TMB）水平更低，但对常规化疗药物敏感性更高。最后，我们通过细胞系、免疫组化及单细胞测序对预后标志物进行了鉴定与表征。综上，本研究结果表明，癌相关成纤维细胞相关的预后特征可作为抗癌相关成纤维细胞治疗的新型治疗前指导方案，而癌相关成纤维细胞中的预后标志物或可成为抑制卵巢癌进展的潜在治疗靶点。