A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer

IF 3.2 4区医学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Journal of Gene Medicine Pub Date : 2024-02-11 DOI:10.1002/jgm.3668

Qingqing Li, Yuxin Chu, Yi Yao, Qibin Song

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Abstract

Background

Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC.

Methods

We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored.

Results

Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients’ survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients’ survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples.

Conclusion

We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

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Treg相关风险评分模型可改善结直肠癌的预后评估。

背景：结肠直肠癌（CRC）是一项重大的健康挑战。本研究旨在探讨调节性 T 细胞（Treg）相关基因特征在 CRC 中的预后价值：方法：我们从癌症基因组图谱（TCGA）和基因表达总库（GEO）数据库中提取了有关 CRC 的基因表达和临床数据。通过加权基因共表达网络分析（WGCNA）确定了与Treg相关的基因模块。通过单变量 Cox、最小绝对收缩和选择算子（LASSO）和多变量 Cox 回归分析筛选出重要模块中的基因。根据与 Treg 相关的关键基因建立了风险评分模型。利用外部 GEO 数据集验证了该风险评分模型的可靠性。研究还探讨了风险分数与临床特征、突变模式和信号通路之间的关联：结果：蓝色模块中的基因与Tregs的关联性最强。经过一系列筛选后，七个与Treg相关的关键基因（GDE1、GSR、HSPB1、AOC2、TBX19、TAMM41和TIGD6）被选中用于构建风险评分模型。该模型在评估 TCGA 队列中患者的生存率方面表现良好，并得到了 GSE17536 验证队列的进一步肯定。为了精确评估患者的生存率，我们根据风险评分和临床因素建立了一个提名图。不同风险组的患者具有不同的临床特征、突变模式和信号通路活性。五个关键基因的表达与 CRC 样本中 Treg 的浸润显著相关：结论：我们根据七个 Treg 相关基因建立了一个有用的风险评分模型。结论：我们根据七个 Treg 相关基因建立了一个有用的风险评分模型，该模型可能有助于预后评估，指导有针对性的治疗，并有望改善 CRC 患者的临床预后。

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来源期刊

Journal of Gene Medicine 医学-生物工程与应用微生物

CiteScore

6.40

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： The aims and scope of The Journal of Gene Medicine include cutting-edge science of gene transfer and its applications in gene and cell therapy, genome editing with precision nucleases, epigenetic modifications of host genome by small molecules, siRNA, microRNA and other noncoding RNAs as therapeutic gene-modulating agents or targets, biomarkers for precision medicine, and gene-based prognostic/diagnostic studies. Key areas of interest are the design of novel synthetic and viral vectors, novel therapeutic nucleic acids such as mRNA, modified microRNAs and siRNAs, antagomirs, aptamers, antisense and exon-skipping agents, refined genome editing tools using nucleic acid /protein combinations, physically or biologically targeted delivery and gene modulation, ex vivo or in vivo pharmacological studies including animal models, and human clinical trials. Papers presenting research into the mechanisms underlying transfer and action of gene medicines, the application of the new technologies for stem cell modification or nucleic acid based vaccines, the identification of new genetic or epigenetic variations as biomarkers to direct precision medicine, and the preclinical/clinical development of gene/expression signatures indicative of diagnosis or predictive of prognosis are also encouraged.