口腔鳞状细胞癌 N7-甲基鸟苷甲基转移酶相关微RNA预后模型的建立与评估

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-09-30 eCollection Date: 2024-01-01 DOI:10.7150/jca.98350
Jianrong Li, Chu Li, Xiaolian Li, Yuling Chen, Zhangfu Li, Yuntao Lin, Huan Jing, Yufan Wang, Hongyu Yang
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引用次数: 0

摘要

背景:N7-甲基鸟苷(m7G)甲基转移酶和微RNA(miRNA)与肿瘤进展密切相关。然而,m7G甲基转移酶相关的miRNA在口腔鳞状细胞癌(OSCC)中作为预后标志物的作用尚未得到研究。本研究旨在探讨OSCC中的m7G甲基转移酶相关miRNA,建立基于m7G甲基转移酶相关miRNA的预后模型,研究其与免疫细胞浸润的相关性,并评估其潜在的预后价值。研究方法从癌症基因组图谱(TCGA)数据库中获取OSCC患者的转录和临床数据。利用TargetScan和miRWalk预测与m7G甲基转移酶相关的miRNA。随后,筛选出在 TCGA-OSCC 中差异表达的 m7G 甲基转移酶相关 miRNA。利用Cox和最小绝对收缩与选择算子(LASSO)回归分析建立了TCGA-OSCC的m7G甲基转移酶相关miRNA风险预后模型。患者被分为高风险组和低风险组。通过卡普兰-梅耶生存分析、接受者操作特征曲线分析、独立预后分析和提名图,进一步验证了风险预后模型的预测和诊断准确性。最后,利用实时定量聚合酶链反应(qPCR)验证了 60 例 OSCC 患者术后癌组织和邻近正常组织中 m7G 甲基转移酶相关 miRNA 的表达水平。结果显示通过Cox和LASSO回归分析,确定了与OSCC患者预后最相关的6个候选miRNA(hsa-miR-338-3p、hsa-miR-1251-3p、hsa-miR-3129-5p、hsa-miR-4633-3p、hsa-miR-216a-3p和hsa-miR-6503-3p),从而构建了m7G甲基转移酶相关miRNA风险预后模型。在该模型中,高风险组的总生存期(OS)明显短于低风险组(P < 0.001)。该模型能有效预测预后,是OSCC患者的独立预后指标。与低危组相比,高危组的免疫细胞浸润能力明显增强(P < 0.05),而免疫细胞的活化和启动能力下降。最后,在 OSCC 组织样本中验证了六种与 m7G 甲基转移酶相关的 miRNA。结论基于六个m7G甲基转移酶相关miRNA的风险预后模型可以预测OSCC患者的OS率,并有可能指导个体化治疗。该预后模型与 OSCC 患者的免疫细胞浸润密切相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Establishment and assessment of an oral squamous cell carcinoma N7-methylguanosine methyltransferase associated microRNA prognostic model.

Background: N7-methylguanosine (m7G) methyltransferases and microRNAs (miRNAs) are closely associated with tumor progression. However, the role of m7G methyltransferase-related miRNAs as prognostic markers in oral squamous cell carcinoma (OSCC) has not been studied. This study aimed to explore the m7G methyltransferase-related miRNAs in OSCC, establish a prognostic model based on m7G methyltransferase-related miRNAs, investigate their correlation with immune cell infiltration, and assess their potential prognostic value. Methods: Transcriptional and clinical data of patients with OSCC were obtained from The Cancer Genome Atlas (TCGA) database. TargetScan and miRWalk were used to predict m7G methyltransferase-related miRNAs. Subsequently, differentially expressed m7G methyltransferase-related miRNAs in TCGA-OSCC were selected. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to build an m7G methyltransferase-related miRNA risk prognostic model for TCGA-OSCC. Patients were stratified into high- and low-risk groups. The predictive and diagnostic accuracies of the risk prognostic model were further validated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, independent prognosis analysis, and nomogram plots. Finally, quantitative real-time polymerase chain reaction (qPCR) was used to validate the expression levels of m7G methyltransferase-related miRNAs in postoperative cancer and adjacent normal tissues from 60 patients with OSCC. Results: Through Cox and LASSO regression analysis, six candidate miRNAs (hsa-miR-338-3p, hsa-miR-1251-3p, hsa-miR-3129-5p, hsa-miR-4633-3p, hsa-miR-216a-3p, and hsa-miR-6503-3p) most relevant to the prognosis of patients with OSCC were identified to construct an m7G methyltransferase-related miRNA risk prognostic model. In this model, the overall survival (OS) of the high-risk group was significantly shorter than that of the low-risk group (P < 0.001). The model effectively predicted prognosis and served as an independent prognostic indicator for patients with OSCC. Compared with the low-risk group, the high-risk group exhibited a significantly increased capacity for immune cell infiltration (P < 0.05), while the activation and initiation abilities of immune cells were decreased. Finally, six m7G methyltransferase-related miRNAs were validated in OSCC tissue samples. Conclusion: The risk prognostic model based on six m7G methyltransferase-related miRNAs can predict the OS rate of patients with OSCC and has the potential to guide individualized treatment. This prognostic model is closely associated with immune cell infiltration in patients with OSCC.

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