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与POLE相关的基因特征可预测人子宫内膜样癌的预后、免疫特征及药物治疗效果。

POLE -related gene signature predicts prognosis, immune feature, and drug therapy in human endometrioid carcinoma.

作者信息

Qiu Wei, Zhang Runjie, Qian Yingchen

机构信息

Department of Pathology, The Affiliated Jiangning Hospital of Nanjing Medical University, No.169, HuShan Road, Nanjing, 211100, China.

Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No.1111, XianXia Road, Shanghai, 200336, China.

出版信息

Heliyon. 2024 Apr 16;10(8):e29548. doi: 10.1016/j.heliyon.2024.e29548. eCollection 2024 Apr 30.

Abstract

The subtype of Endometrial carcinoma (EC) is linked to a favourable prognosis in the molecular classification. We proposed to ascertain the potential connection between the POLE subtype and improved prognosis. In order to forecast the prognosis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis and weighted gene co-expression network analysis (WGCNA) were employed, and a -related risk signature (PRS) model was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) with the "GSVA" package was employed to analyse immunity characteristics. Drug susceptibility studies were conducted to compare the half-maximal inhibitory concentration (IC) of medicines between high- and low-risk groups. The PRS model was generated employing the LASSO Cox regression coefficients of the , , genes. Our study demonstrated that the risk score was linked to tumour stage, grade, and survival. Furthermore, the low-risk group possessed elevated levels of gene expression connected with immunological checkpoints and HLA. Our outcomes emerged that the PRS model might have value in identifying patients with a good prognosis and in facilitating personalised treatment in the clinic.

摘要

子宫内膜癌(EC)的亚型在分子分类中与良好的预后相关。我们旨在确定POLE亚型与预后改善之间的潜在联系。为了预测预后,采用了最小绝对收缩和选择算子(LASSO)Cox回归分析和加权基因共表达网络分析(WGCNA),并开发和验证了一个相关风险特征(PRS)模型。使用“GSVA”软件包进行单样本基因集富集分析(ssGSEA)以分析免疫特征。进行药物敏感性研究以比较高风险组和低风险组之间药物的半数抑制浓度(IC)。PRS模型是利用、、基因的LASSO Cox回归系数生成的。我们的研究表明,风险评分与肿瘤分期、分级和生存相关。此外,低风险组具有与免疫检查点和HLA相关的基因表达水平升高。我们的结果表明,PRS模型可能在识别预后良好的患者以及促进临床个性化治疗方面具有价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b4/11040042/ca316c3e92e1/gr1.jpg

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