DataSheet_2_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.

癌症相关成纤维细胞（Cancer-associated fibroblasts, CAFs）是肿瘤微环境（tumor microenvironment, TME）中肿瘤基质互作的核心贡献者，可通过促进血管生成与淋巴管生成加速肿瘤进展。本研究旨在识别与卵巢癌（ovarian cancer, OC）患者高发病率、高死亡率相关的CAFs预后基因。我们针对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CAFs相关核心基因。随后采用机器学习方法筛选出COL16A1、COL5A2、GREM1、LUM、SRPX及TIMP3，并构建预后特征模型。后续通过一系列生物信息学算法开展基于该特征的风险分层分析，结果显示高风险患者预后更差、免疫应答更弱且肿瘤突变负荷（tumor mutational burden, TMB）水平更低，但对常规化疗药物的敏感性更高。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特征分析与验证。综上，本研究结果表明，CAFs相关特征模型或可作为抗CAFs治疗的新型预处理指导方案，而CAFs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。