基于N6-甲基腺苷修饰与免疫细胞浸润的结直肠癌预后模型构建及验证

Construction and validation of a risk model for colorectal cancer prognosis based on N6-methyladenosine modification and immune infiltration

  • 摘要:
    目的 探讨N6-甲基腺苷(m6A)修饰相关基因和免疫细胞浸润在结直肠癌(CRC)中的预后价值并构建相应风险模型预测患者临床结局。
    方法 从癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)下载CRC患者的肿瘤组织转录组数据及匹配临床信息,通过共识聚类及单样本基因组富集分析明确m6A修饰相关基因及免疫细胞浸润在CRC中的预后价值。基于加权基因共表达网络分析(WGCNA)筛选与两者相关的预后基因,运用Lasso回归分析构建多基因风险模型,并在基因表达谱交互分析(GEPIA)数据库中对筛选出的预后基因进行表达差异分析。通过Kaplan-Meier生存分析明确风险模型在不同亚组及外部验证队列中的预测效能。
    结果 基于21个m6A修饰相关基因及24个免疫细胞浸润的特征表型均可以显著区分TCGA队列中CRC患者预后。CRC组织中多数m6A修饰相关基因与免疫细胞浸润显著相关。WGCNA及Lasso回归分析筛选出4个预后基因,分别为内凝集蛋白1、淋巴细胞抗原6复合位点G6D、无调性同族体1和基质金属蛋白酶28。在结直肠癌组织中,淋巴细胞抗原6复合位点G6D和基质金属蛋白酶28的表达与癌旁组织比较,差异有统计学意义(P < 0.05)。基于上述预后基因构建的风险模型能够在TCGA队列的不同临床亚组及GEO验证队列中有效识别预后不良的潜在风险人群。
    结论 基于m6A修饰及免疫细胞浸润构建风险模型能够有效预测CRC患者的临床结局,相关预后基因有望成为抗CRC的潜在分子靶点。

     

    Abstract:
    Objective To investigate the prognostic value of N6-methyladenosine (m6A) modification related genes and immune infiltration in colorectal cancer (CRC) and construct a risk model for predicting outcome of patients.
    Methods The transcriptome data and matched clinical information of CRC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The prognostic value of m6A modification related genes and immune infiltration were investigated using the consensus clustering method and single sample gene set enrichment analysis. The weighted gene co-expression network analysis (WGCNA) was used to identify prognostic genes related with m6A modification and immune infiltration. Lasso regression analysis was used to construct a multi-gene risk model. The expression differences of prognostic genes identified were further validated through expression differential analysis in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, the Kaplan-Meier was used to evaluate the predicting performance of the model in different subgroups and external validation cohorts.
    Results Both the m6A modification and immune infiltration phenotype could effectively stratify the prognosis of CRC patients from the TCGA cohort. Most m6A modification related genes were significantly correlated with immune infiltration in CRC tissues. Four following prognostic genes were selected using the WGCNA method combined with Lasso regression analysis: intelectin-1, lymphocyte antigen 6 complex locus G6D, atonal homolog 1 and matrix metalloproteinase 28. In colorectal cancer tissues, the expression levels of lymphocyte antigen 6 complex locus G6D and matrix metalloproteinase 28 exhibited significant differences compared to adjacent non-cancerous tissues (P < 0.05). The risk model constructed based on the above prognostic genes can effectively identify the potential risk population with poor prognosis in different clinical subgroups of the TCGA cohort and the GEO validated cohort.
    Conclusion A risk model based on m6A modification and immune infiltration could effectively predict the clinical outcome of CRC patients, and related prognostic genes have potential to be developed as molecular targets for CRC therapy.

     

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