过度活跃的肿瘤微环境(TME)驱动不受限制的癌细胞存活,耐药性,卵巢癌(OC)的转移。然而,OC的TME内的治疗目标仍然难以捉摸,和量化TME活性的有效方法仍然有限。在这里,我们采用整合的生物信息学方法来确定哪些免疫相关基因(IRGs)调节TME,并进一步评估其在OC进展中的潜在治疗意义(治疗+诊断).使用稳健的方法,我们从癌症基因组图谱(TCGA)数据库中建立了一个预测风险模型,以回顾性分析OC患者的临床病理参数.来自国际癌症基因组联盟(ICGC)队列的数据证实了预后模型的有效性。我们的方法确定了九个IRG,AKT2,FGF7,FOS,IL27RA,LRP1,OBP2A,PAEP,PDGFRA,和PI3,形成OC进展的预后模型,在低危组中区分临床结局明显更好的患者.我们验证了该模型作为一个独立的预后指标,并证明当与临床列线图一起使用时具有增强的预后意义,以进行准确的预测。LRP1表达升高,这表明膀胱癌(BLCA)预后不良,OC,低级别胶质瘤(LGG),和胶质母细胞瘤(GBM),在其他几种癌症中也与免疫浸润有关。与免疫检查点基因(ICG)的显着相关性突出了LRP1作为生物标志物和治疗靶标的潜在重要性。此外,基因集富集分析强调了LRP1参与代谢相关途径,在BLCA中也支持其预后和治疗相关性,OC,低级别胶质瘤(LGG),GBM,肾癌,OC,BLCA,肾透明细胞癌(KIRC),胃腺癌(STAD),和胃和食道癌(STES)。我们的研究在TME中产生了9个IRG在癌症中的新特征,这可能是潜在的预后预测因子,并为改善OC的预后提供了宝贵的资源。
A hyperactive tumour microenvironment (TME) drives unrestricted cancer cell survival, drug resistance, and metastasis in ovarian carcinoma (OC). However, therapeutic targets within the TME for OC remain elusive, and efficient methods to quantify TME activity are still limited. Herein, we employed an integrated bioinformatics approach to determine which immune-related genes (IRGs) modulate the TME and further assess their potential theragnostic (therapeutic + diagnostic) significance in OC progression. Using a robust approach, we developed a predictive risk model to retrospectively examine the clinicopathological parameters of OC patients from The Cancer Genome Atlas (TCGA) database. The validity of the prognostic model was confirmed with data from the International Cancer Genome Consortium (ICGC) cohort. Our approach identified nine IRGs, AKT2, FGF7, FOS, IL27RA, LRP1, OBP2A, PAEP, PDGFRA, and PI3, that form a prognostic model in OC progression, distinguishing patients with significantly better clinical outcomes in the low-risk group. We validated this model as an independent prognostic indicator and demonstrated enhanced prognostic significance when used alongside clinical nomograms for accurate prediction. Elevated LRP1 expression, which indicates poor prognosis in bladder cancer (BLCA), OC, low-grade gliomas (LGG), and glioblastoma (GBM), was also associated with immune infiltration in several other cancers. Significant correlations with immune checkpoint genes (ICGs) highlight the potential importance of LRP1 as a biomarker and therapeutic target. Furthermore, gene set enrichment analysis highlighted LRP1\'s involvement in metabolism-related pathways, supporting its prognostic and therapeutic relevance also in BLCA, OC, low-grade gliomas (LGG), GBM, kidney cancer, OC, BLCA, kidney renal clear cell carcinoma (KIRC), stomach adenocarcinoma (STAD), and stomach and oesophageal carcinoma (STES). Our study has generated a novel signature of nine IRGs within the TME across cancers, that could serve as potential prognostic predictors and provide a valuable resource to improve the prognosis of OC.