WGCNA 揭示了一种用于预测宫颈癌预后的癌症相关成纤维细胞的生物标志物。

WGCNA reveals a biomarker for cancer-associated fibroblasts to predict prognosis in cervical cancer.

机构信息

Department of Epidemiology and Health Statistics, School of Public Health, Xinjiang Medical University, Urumqi, China.

Division of Gynecology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

出版信息

J Chin Med Assoc. 2024 Sep 1;87(9):885-897. doi: 10.1097/JCMA.0000000000001129. Epub 2024 Jul 1.

DOI:10.1097/JCMA.0000000000001129

PMID:38946034

Abstract

BACKGROUND

Cancer-associated fibroblasts (CAFs) are crucial components of the cervical cancer tumor microenvironment, playing a significant role in cervical cancer progression, treatment resistance, and immune evasion, but whether the expression of CAF-related genes can predict clinical outcomes in cervical cancer is still unknown. In this study, we sought to analyze genes associated with CAFs through weighted gene co-expression network analysis (WGCNA) and to create a predictive model for CAFs in cervical cancer.

METHODS

We acquired transcriptome sequencing data and clinical information on cervical cancer patients from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. WGCNA was conducted to identify genes related to CAFs. We developed a prognostic model based on CAF genes in cervical cancer using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Single-cell sequencing data analysis and in vivo experiments for validation of hub genes in CAFs.

RESULTS

A prognostic model for cervical cancer was developed based on CAF genes including COL4A1 , LAMC1 , RAMP3 , POSTN , and SERPINF1 . Cervical cancer patients were divided into low- and high-risk groups based on the optimal cutoff value. Patients in the high-risk group had a significantly worse prognosis. Single-cell RNA sequencing data revealed that hub genes in the CAFs risk model were expressed mainly in fibroblasts. The real-time fluorescence quantitative polymerase chain reaction (PCR) results revealed a significant difference in the expression levels of COL4A1 , LAMC1 , POSTN , and SERPINF1 between the cancer group and the normal group ( p < 0.05). Consistently, the results of the immunohistochemical tests exhibited notable variations in COL4A1, LAMC1, RAMP3, POSTN, and SERPINF1 expression between the cancer and normal groups ( p < 0.001).

CONCLUSION

The CAF risk model for cervical cancer constructed in this study can be used to predict prognosis, while the CAF hub genes can be utilized as crucial markers for cervical cancer prognosis.

摘要

背景

癌症相关成纤维细胞（CAFs）是宫颈癌肿瘤微环境的重要组成部分，在宫颈癌的进展、治疗耐药和免疫逃逸中起着重要作用，但 CAF 相关基因的表达是否可以预测宫颈癌的临床结局尚不清楚。在本研究中，我们试图通过加权基因共表达网络分析（WGCNA）分析与 CAF 相关的基因，并建立宫颈癌 CAF 的预测模型。

方法

我们从癌症基因组图谱（TCGA）和基因表达综合（GEO）数据库中获取宫颈癌患者的转录组测序数据和临床信息。进行 WGCNA 以鉴定与 CAF 相关的基因。我们使用最小绝对收缩和选择算子（LASSO）Cox 回归分析，基于宫颈癌中的 CAF 基因建立了一个预后模型。对 CAF 中的关键基因进行单细胞测序数据分析和体内实验验证。

结果

我们基于 COL4A1、LAMC1、RAMP3、POSTN 和 SERPINF1 等 CAF 基因构建了宫颈癌的预后模型。根据最佳截断值，将宫颈癌患者分为低风险组和高风险组。高风险组患者的预后明显较差。单细胞 RNA 测序数据显示，CAFs 风险模型中的关键基因主要在成纤维细胞中表达。实时荧光定量聚合酶链反应（PCR）结果显示，COL4A1、LAMC1、POSTN 和 SERPINF1 在癌症组和正常组之间的表达水平存在显著差异（p<0.05）。免疫组织化学检测结果也显示，COL4A1、LAMC1、RAMP3、POSTN 和 SERPINF1 在癌症组和正常组之间的表达存在显著差异（p<0.001）。