Construction and validation of molecular subtype and signature of immune cell-related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients

IF 3.2 4区医学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Journal of Gene Medicine Pub Date : 2024-01-06 DOI:10.1002/jgm.3606

Lele Ling, Bingrong Li, Huijing Wu, Kaiyong Zhang, Siwen Li, Boliang Ke, Zhengyang Zhu, Te Liu, Peng Liu, Bimeng Zhang

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Abstract

Background

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers.

Methods

ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation.

Results

WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS.

Conclusions

ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

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构建和验证免疫细胞相关端粒基因的分子亚型和特征，预测卵巢癌患者的预后和免疫疗法疗效

背景卵巢癌（OVC）已成为一种致命的妇科恶性肿瘤，原因是缺乏可靠的早期检测方法、生物标志物有限以及治疗方案少。免疫细胞相关端粒基因（ICRTGs）有望成为潜在的生物标志物。方法利用加权基因共表达网络分析（WGCNA）发现了ICRTGs。利用单向 Cox 回归分析筛选出对预后有显著影响的 ICRTGs。随后，构建并验证了与预后相关的 ICRTGs 分子亚型，并对不同亚型的免疫微环境进行了比较。利用预后相关的 ICRTGs 建立并验证了 OVC 预后模型。此外，还利用癌症药物易感性基因组学（GDSC）筛选了 ICRTGs 低风险组和高风险组 OVC 患者的化疗易感药物。最后，利用 GSE78220 的数据检测了低风险组和高风险组的免疫治疗反应。我们对预后显著的 ICRTGs 进行了免疫指数相关性分析。预后相关系数最高的 MAP3K4 基因通过组织芯片验证了预后与免疫指数的相关性。结果 WGCNA分析构建了一个ICRTGs基因集，筛选出22个具有预后意义的基因。无监督聚类分析揭示了两个亚型的最佳分子分型。基因组变异分析算法被用来计算端粒得分和验证分子亚型。利用 17 个 ICRTGs 构建了一个预后模型。在癌症基因组图谱-OVC训练集和基因表达总集验证集（GSE30161）中，风险评分模型预测的风险组别和实际预后显示出显著的相关性。GDSC 筛选了阿西替尼、贝沙罗汀、恩贝林和 GSE78220 数据集，结果表明 ICRTGs 能有效区分免疫疗法应答组和非应答组。此外，组织芯片验证结果显示，MAP3K4 能显著预测患者的预后。此外，MAP3K4 与 PD-L1 呈正相关，而与 M1 巨噬细胞标记物 CD86 和 INOS 呈负相关。结论 ICRTGs 可能是对 OVC 患者进行分子分型的可靠生物标记物，可预测预后和免疫疗法的疗效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.