Department of Computer Science, City University of Hong Kong, Hong Kong, China.
Biochem Genet. 2024 Jun;62(3):1925-1952. doi: 10.1007/s10528-023-10516-4. Epub 2023 Oct 4.
Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.
结直肠癌是一种发病率、致死率和全球人类健康患病率都很高的恶性肿瘤。分子生物标志物在其预后中起着关键作用。特别是，免疫相关长链非编码 RNA（IRL）在诊断和治疗方面引起了极大的关注，但它们的潜在功能知之甚少。我们旨在研究功能失调的 IRL 并构建风险模型，以改善患者的预后。收集了 19 种免疫细胞类型来鉴定看家长链非编码 RNA（HKLncRNA）。GSE39582 和 TCGA-COAD 分别作为发现和验证数据集。使用四种机器学习算法（LASSO、Random Forest、Boruta 和 Xgboost）和高斯混合模型来挖掘最佳的 lncRNA 组合。使用单变量和多变量 Cox 回归构建风险评分模型。我们根据这个评分系统计算出的免疫角度区分低风险和高风险队列之间的功能差异。最后，我们提供了一个列线图。通过利用微阵列、测序和免疫细胞和结肠癌患者的临床数据，我们确定了 221 个具有低细胞类型特异性指数的 HKLncRNA。与癌细胞相比，免疫细胞中 87 个 lncRNA 上调。12 个 lncRNA 有助于提高性能。提出了一个包含三个 lncRNA（CYB561D2、LINC00638 和 DANCR）的风险评分模型，在独立数据集上具有稳健的 ROC 性能。根据免疫相关分析，风险评分与肿瘤免疫微环境密切相关。我们的研究结果强调，IRL 有可能成为增强结直肠癌预后的一种强大而有效的治疗方法。
