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用于预测结直肠癌预后的端粒相关基因风险模型。

Telomere-related gene risk model for prognosis prediction in colorectal cancer.

作者信息

Chen Hao, Pan Yuhao, Lv Chenhui, He Wei, Wu Dingting, Xuan Qijia

机构信息

Department of Medical Oncology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China.

Department of Clinical Nutrition, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China.

出版信息

Transl Cancer Res. 2024 Jul 31;13(7):3495-3521. doi: 10.21037/tcr-24-43. Epub 2024 Jul 19.

DOI:10.21037/tcr-24-43
PMID:39145075
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11319979/
Abstract

BACKGROUND

Colorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.

METHODS

We retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.

RESULTS

The model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4 T cells, effector memory CD4 T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature's capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (, , , , , , , , , and ) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of , , , , and in CRC cells were elevated, whereas those of , , , , and were reduced.

CONCLUSIONS

Overall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.

摘要

背景

结直肠癌（CRC）是全球第三大常见癌症。越来越多的数据突显了端粒在CRC致癌作用和进展中的生物学意义。然而，关于端粒相关基因（TRGs）如何影响CRC预后，我们所知甚少。因此，本研究的目的是探究TRGs在CRC预后中的作用。

方法

我们从公共数据库中回顾性获取了CRC患者的表达谱和临床数据。利用最小绝对收缩和选择算子（LASSO）回归分析，我们创建了一个端粒相关风险模型来预测生存结果，识别出10个端粒相关差异表达基因（TRDEGs）。基于TRDEGs，我们将来自癌症基因组图谱（TCGA）的患者分层为低风险和高风险亚组。随后，我们进行了全面分析，包括使用Kaplan-Meier曲线、ESTIMATE、CIBERSORT、OncoPredict等方法进行生存评估、免疫细胞浸润分析、药物敏感性分析以及分子相互作用预测。

结果

该模型对生存具有出色的预测准确性。根据该模型分组的两组参与者之间观察到显著的生存差异（P<0.001），并且这种差异在外部验证集（GSE39582）中得到进一步证实（P=0.004）。此外，与低风险组相比，高风险组表现出显著更高的肿瘤淋巴结转移（TNM）分期、更低比例的活化CD4 T细胞、效应记忆CD4 T细胞和记忆B细胞，但M2巨噬细胞和调节性T细胞（Tregs）的比例增加、肿瘤免疫功能障碍和排除（TIDE）评分升高，以及对达拉非尼、拉帕替尼、喜树碱、多西他赛和端粒酶抑制剂IX的敏感性降低，这反映了该特征区分临床病理特征、免疫环境和药物疗效的能力。最后，我们通过定量实时聚合酶链反应（qRT-PCR）验证了10个TRDEGs（、、、、、、、、和）的表达，发现与正常细胞相比，CRC细胞中、、、和的表达水平升高，而、、、和的表达水平降低。

结论

总体而言，我们构建了一个能够预测CRC个体预后和治疗反应的端粒相关生物标志物，为药物治疗选择和预后预测提供了潜在指导。

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