基于在线数据库的多组学分析揭示了 BLCA 的预后生物标志物。

A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA.

机构信息

Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

出版信息

Biomed Res Int. 2022 May 25;2022:2449449. doi: 10.1155/2022/2449449. eCollection 2022.

DOI:10.1155/2022/2449449

PMID:35669725

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9165618/

Abstract

BACKGROUND

Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and "CancerSubtypes" package of software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment.

METHOD

BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. "CancerSubtypes" classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm.

RESULTS

In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: "GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN" and "MODULE_253." Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers.

CONCLUSIONS

In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network's core proteins, and several newly discovered biomarkers of BLCA prognosis were identified.

摘要

背景

膀胱癌（BLCA）是全球最常见的泌尿系统恶性肿瘤之一，严重威胁公众健康。结合蛋白质-蛋白质相互作用（PPI）网络分析、蛋白质组学的基因集变异分析（GSVA）和转录组学的“CancerSubtypes”软件包，可以帮助鉴定与 BLCA 预后相关的生物标志物。这将对预防和治疗具有重要意义。

方法

从癌症基因组图谱（TCGA）数据库和基因表达综合数据库（GEO）数据库（GSE13507）下载 BLCA 数据。GSVA 分析将基因表达矩阵转换为基因集表达矩阵。“CancerSubtypes”将患者分为三个亚型，并基于三个亚型之间差异表达基因集（DEGSs）建立预后模型。对于来自预后相关 DEGSs 的基因，进行功能和通路富集分析以及 PPI 网络分析。使用人类蛋白质图谱（HPA）数据库进行验证。最后，使用 CIBERSORT 算法确定肿瘤浸润免疫细胞（TIIC）的比例。

结果

从 TCGA 共获得 414 个肿瘤样本和 19 个相邻肿瘤样本，其中 145 个样本属于亚型 A，126 个样本属于亚型 B，136 个样本属于亚型 C。然后，我们鉴定了 83 个 DEGSs，并构建了一个由两个基因组成的预后特征：“GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN”和“MODULE_253”。最后，建立了两个 PPI 网络的五个子网，并获得了九个核心蛋白：CDH2、COL1A1、EIF2S2、PSMA3、NAA10、DNM1L、TUBA4A、KIF11 和 KIF23。HPA 数据库证实了这九个核心蛋白在 BLCA 组织中的表达。此外，EIF2S2、PSMA3、DNM1L 和 TUBA4A 可能是新的 BLCA 预后生物标志物。