Identification of an m7G-Related lncRNA Signature for Prognostic Prediction and Immune Landscape Characterization in Early Gastric Cancer.

IF 2.8 4区 医学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
OncoTargets and therapy Pub Date : 2026-04-07 eCollection Date: 2026-01-01 DOI:10.2147/OTT.S582487
Yuan Zhang, Hongmei Cai, Kaige Yin, Li Liu, Mingshuang Yang, Chenguang Ji
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引用次数: 0

Abstract

Background: While early gastric cancer (EGC) is generally associated with a favorable prognosis, its clinical outcomes are notably heterogeneous. Unlike advanced gastric cancer requiring uniform intensive treatment, EGC lacks robust biomarkers to identify high-risk patients for personalized therapy. Although both N7-methylguanosine (m7G) modification and long non-coding RNAs (lncRNAs) are involved in tumorigenesis, the prognostic value of m7G-related lncRNAs in EGC remains poorly defined.

Methods: Based on TCGA-EGC data, we identified m7G-related lncRNAs with prognostic significance and constructed a prognostic model for m7G-related lncRNAs using univariate Cox and LASSO. The expression of the model genes was further validated by qRT-PCR. A series of statistical and bioinformatic analyses, including survival analysis, ROC curves, multivariate Cox regression, GSEA, immune infiltration, tumor mutation burden (TMB), and drug sensitivity assays, were performed to evaluate the prognostic performance and underlying biological characteristics of the signature.

Results: A six-m7G-related-lncRNA risk model effectively stratified EGC patients into high- and low-risk groups with significantly distinct overall survival. The risk score was confirmed as an independent prognostic factor by univariate and multivariate Cox analysis. Pathway differences between risk groups were primarily enriched in tumor microenvironment regulation terms. Notably, the high-risk group exhibited an immunosuppressive tumor microenvironment correlated with immune escape. TMB analysis showed a negative correlation between the risk score and TMB, with the high-risk and low-TMB subgroup exhibiting the poorest prognosis. In silico drug sensitivity analysis further suggested that high-risk patients may develop poorer sensitivity to five clinical drugs, including Cisplatin, Oxaliplatin, and Vinorelbine.

Conclusion: The six-m7G-related-lncRNA signature represents a promising prognostic tool for EGC that may facilitate personalized risk stratification and guide clinical decision-making regarding adjuvant or immunotherapeutic strategies.

早期胃癌预后预测和免疫景观特征中m7g相关lncRNA特征的鉴定
背景:虽然早期胃癌(EGC)通常与良好的预后相关,但其临床结果却存在明显的异质性。与需要统一强化治疗的晚期胃癌不同,胃癌缺乏强大的生物标志物来识别高危患者进行个性化治疗。尽管n7 -甲基鸟苷(m7G)修饰和长链非编码rna (lncRNAs)都参与肿瘤发生,但m7G相关lncRNAs在EGC中的预后价值仍不明确。方法:基于TCGA-EGC数据,我们鉴定出具有预后意义的m7g相关lncrna,并利用单变量Cox和LASSO构建m7g相关lncrna的预后模型。通过qRT-PCR进一步验证模型基因的表达。通过一系列统计学和生物信息学分析,包括生存分析、ROC曲线、多变量Cox回归、GSEA、免疫浸润、肿瘤突变负荷(TMB)和药物敏感性分析,评估该特征的预后表现和潜在生物学特征。结果:6 - m7g相关lncrna风险模型有效地将EGC患者分为高风险组和低风险组,总生存率差异显著。单因素和多因素Cox分析证实风险评分为独立预后因素。风险组之间的途径差异主要集中在肿瘤微环境调节方面。值得注意的是,高危组表现出与免疫逃逸相关的免疫抑制肿瘤微环境。TMB分析显示风险评分与TMB呈负相关,高危和低TMB亚组预后最差。计算机药物敏感性分析进一步提示高危患者对顺铂、奥沙利铂、长春瑞滨等5种临床药物的敏感性较差。结论:6 - m7g相关lncrna特征是一种很有前景的EGC预后工具,可以促进个性化的风险分层,指导临床决策有关辅助或免疫治疗策略。
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来源期刊
OncoTargets and therapy
OncoTargets and therapy BIOTECHNOLOGY & APPLIED MICROBIOLOGY-ONCOLOGY
CiteScore
9.70
自引率
0.00%
发文量
221
审稿时长
1 months
期刊介绍: OncoTargets and Therapy is an international, peer-reviewed journal focusing on molecular aspects of cancer research, that is, the molecular diagnosis of and targeted molecular or precision therapy for all types of cancer. The journal is characterized by the rapid reporting of high-quality original research, basic science, reviews and evaluations, expert opinion and commentary that shed novel insight on a cancer or cancer subtype. Specific topics covered by the journal include: -Novel therapeutic targets and innovative agents -Novel therapeutic regimens for improved benefit and/or decreased side effects -Early stage clinical trials Further considerations when submitting to OncoTargets and Therapy: -Studies containing in vivo animal model data will be considered favorably. -Tissue microarray analyses will not be considered except in cases where they are supported by comprehensive biological studies involving multiple cell lines. -Biomarker association studies will be considered only when validated by comprehensive in vitro data and analysis of human tissue samples. -Studies utilizing publicly available data (e.g. GWAS/TCGA/GEO etc.) should add to the body of knowledge about a specific disease or relevant phenotype and must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Bioinformatics studies must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Single nucleotide polymorphism (SNP) studies will not be considered.
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