背景:肿瘤微环境(TME)与肿瘤之间的相互作用为建立抗肿瘤免疫疗法提供了多种靶标。然而,子宫内膜癌(EC)的预后生物标志物仍然有限.这里,我们旨在分析TME的特征,并确定EC的新型预后生物标志物.
方法:估计,CIBERSORT,蛋白质-蛋白质相互作用(PPI)网络,单变量和多变量Cox回归,和功能富集分析进行鉴定免疫和生存相关的hub基因以及可能的分子机制。采用边缘包和去卷积算法来估计肿瘤浸润性免疫细胞(TIC)的丰度及其与靶基因的关系。在验证部分,组织微阵列(TMAs)的EC和多重免疫组织化学(m-IHC)进行评估,以验证TNFRSF4的表达,及其与免疫标志物的相关性,包括CD4、CD8和FOXP3。此外,绘制受试者工作特征(ROC)曲线,以确定TNFRSF4,CD4,CD8和FOXP3在EC中的诊断性能.
结果:两个基因,从EC中的ImmuneScore和StromalScore共有的386个交叉差异表达基因(DEG)中筛选出TNFRSF4和S1PR4。通过TNFRSF4,我们发现它不仅与TICs(主要是CD4+T细胞,CD8+T细胞,和Tregs),但与EC患者的预后显着相关,两者都通过癌症基因组Altas(TCGA)-EC数据库和临床样本的数据进行了验证。同时,TNFRSF4的表达趋势通过基于基因表达综合数据库(GEO)6个微阵列的整合荟萃分析得到进一步证实.
结论:总的来说,TNFRSF4,EC中以前无法识别的关键参与者,可作为EC预后预测和免疫调节的潜在生物标志物。
BACKGROUND: The interaction between tumor microenvironment (TME) and tumors offers various targets in mounting anti-tumor immunotherapies. However, the prognostic biomarkers in endometrial carcinoma (EC) are still limited. Here, we aimed to analyze the TME features and identify novel prognostic biomarkers for EC.
METHODS: ESTIMATE, CIBERSORT, protein-protein interaction (PPI) network, univariate and multivariate Cox regression, and functional enrichment analysis were performed to identify immune- and survival-related hub genes as well as possible molecular mechanisms. The limma package and deconvolution algorithm were adopted to estimate the abundance of tumor-infiltrating immune cells (TICs) and their relationship with the target gene. In the validation section, tissue microarrays (TMAs) of EC and multiplex immunohistochemistry (m-IHC) were evaluated to validate the expression of TNFRSF4, and its correlation with immune markers, including CD4, CD8, and FOXP3. Besides, the receiver operating characteristic (ROC) curve was plotted to determine the diagnostic performance of TNFRSF4, CD4, CD8, and FOXP3 in EC.
RESULTS: Two genes, TNFRSF4 and S1PR4, were screened out from 386 intersection differential expression genes (DEGs) shared by ImmuneScore and StromalScore in EC. Highlighted by TNFRSF4, we found that it was not only positively correlated with the TICs (mainly CD4+ T cells, CD8+ T cells, and Tregs) but significantly related to the prognosis in patients of EC, both verified by data from The Cancer Genome Altas (TCGA)-EC database and clinical samples. At the same time, the expression trend of TNFRSF4 was further confirmed by an integrated meta-analysis based on six microarrays from the Gene Expression Omnibus database (GEO).
CONCLUSIONS: Collectively, TNFRSF4, a previously unrecognized key player in EC, could serve as a potential biomarker for prognosis prediction and immunomodulation of EC.