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一种结合免疫图谱扩展的新型免疫亚型分类系统及用于预测前列腺癌生存的11基因特征的开发。

Development of a Novel Immune Subtyping System Expanded with Immune Landscape and an 11-Gene Signature for Predicting Prostate Cancer Survival.

作者信息

Li Nan, Yu Kai, Lin Zhong, Zeng Dingyuan

机构信息

Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou 545001, China.

Guangxi Health Commission Key Laboratory of Birth Cohort Study in Pregnant Women of Advanced Age, Liuzhou 545001, China.

出版信息

J Oncol. 2022 Feb 16;2022:1183173. doi: 10.1155/2022/1183173. eCollection 2022.

Abstract

BACKGROUND

Nearly half of patients with prostate cancer will develop metastasis. Immunotherapy is currently a promising strategy for treating metastatic prostate cancer. This study aimed to construct an immune subtyping system and provide a more comprehensive understanding of tumor microenvironment.

METHODS

Data were downloaded from TCGA database and cBioPortal database. Consensus clustering was used to identify immune subtypes. Immune features were scored by ESTIMATE and CIBERSORT. Efficacy of different subtypes in immunotherapy was predicted by TIDE tool. Immune landscape was delineated through "monocle." Coexpressed gene modules were identified by weighted correlation network analysis. Univariate Cox regression analysis and LASSO analysis were applied to construct a prognostic model.

RESULTS

Four immune subtypes (IS1 to IS4) were identified. Prognosis, mutation patterns, expression of immune genes, immune biomarkers, immunohistochemical biomarkers, and prediction efficacy of immunotherapy were significantly different among four immune subtypes. Five coexpressed gene modules were identified and an 11-gene prognostic model was constructed based on the modules.

CONCLUSIONS

The study developed a novel immune subtyping system and an 11-gene prognostic model of prostate cancer, which could guide personalized treatment and immunotherapy for patients with prostate cancer.

摘要

背景

近一半的前列腺癌患者会发生转移。免疫疗法是目前治疗转移性前列腺癌的一种有前景的策略。本研究旨在构建一种免疫亚型分类系统,并更全面地了解肿瘤微环境。

方法

数据从TCGA数据库和cBioPortal数据库下载。采用一致性聚类来识别免疫亚型。通过ESTIMATE和CIBERSORT对免疫特征进行评分。利用TIDE工具预测不同亚型在免疫疗法中的疗效。通过“monocle”描绘免疫图谱。通过加权相关网络分析识别共表达基因模块。应用单变量Cox回归分析和LASSO分析构建预后模型。

结果

识别出四种免疫亚型(IS1至IS4)。四种免疫亚型在预后、突变模式、免疫基因表达、免疫生物标志物、免疫组化生物标志物以及免疫疗法的预测疗效方面存在显著差异。识别出五个共表达基因模块,并基于这些模块构建了一个11基因的预后模型。

结论

本研究开发了一种新的前列腺癌免疫亚型分类系统和一个11基因的预后模型,可为前列腺癌患者的个性化治疗和免疫疗法提供指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2f3/8866019/7f8665c08cfe/JO2022-1183173.001.jpg

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