Yu Yiping, Yin Wen, Feng Jing, Qian Sumin
Gynecology Department 2, Cangzhou Central Hospital, No. 16, Xinhua West Road, Yunhe District, Cangzhou, 061000, Hebei Province, China.
Sci Rep. 2025 May 13;15(1):16556. doi: 10.1038/s41598-025-01212-x.
The tumor microenvironment (TME) plays a critical role in ovarian cancer (OC) progression, yet the relationship between immune and stromal scores within the TME and prognostic outcomes remains poorly understood. Immune and stromal cell scores were computed using the "estimate" R package, which enabled the assessment of immune and stromal components in OC samples. We then performed univariate and multivariate Cox regression analyses to identify prognostic factors associated with these scores using data from The Cancer Genome Atlas (TCGA). Additionally, LASSO Cox regression were employed to identify key prognostic genes linked to immune infiltration. Our analysis of OC expression data identified 1,667 differentially expressed genes (DEGs) associated with immune and stromal scores. From these, we developed a 6-gene risk model, consisting of ALOX5AP, FCGR1C, GBP2, IL21R, KLRB1, and PIK3CG, which effectively stratified OC patients into high-risk and low-risk groups. Survival analysis and area under the curve (AUC) assessment confirmed the model's strong predictive accuracy. Furthermore, drug sensitivity predictions indicated that sorafenib was particularly effective in high-risk patients, with this finding validated through in vitro experiments. The 6-gene TME-related risk model offers robust prognostic capabilities for OC and could serve as a valuable tool for clinical stratification and personalized treatment approaches.
肿瘤微环境（TME）在卵巢癌（OC）进展中起着关键作用，但TME内免疫和基质评分与预后结果之间的关系仍知之甚少。使用“estimate”R包计算免疫和基质细胞评分，该包能够评估OC样本中的免疫和基质成分。然后，我们使用来自癌症基因组图谱（TCGA）的数据进行单变量和多变量Cox回归分析，以确定与这些评分相关的预后因素。此外，采用LASSO Cox回归来识别与免疫浸润相关的关键预后基因。我们对OC表达数据的分析确定了1667个与免疫和基质评分相关的差异表达基因（DEG）。从中，我们开发了一个由ALOX5AP、FCGR1C、GBP2、IL21R、KLRB1和PIK3CG组成的6基因风险模型，该模型有效地将OC患者分为高风险和低风险组。生存分析和曲线下面积（AUC）评估证实了该模型具有很强的预测准确性。此外，药物敏感性预测表明，索拉非尼在高风险患者中特别有效，这一发现通过体外实验得到了验证。6基因TME相关风险模型为OC提供了强大的预后能力，可作为临床分层和个性化治疗方法的有价值工具。
