整合生物信息学和机器学习筛选免疫相关基因，用于诊断非酒精性脂肪肝合并缺血性脑卒中及 RRS1 泛癌分析。

Integrated bioinformatics and machine-learning screening for immune-related genes in diagnosing non-alcoholic fatty liver disease with ischemic stroke and RRS1 pan-cancer analysis.

机构信息

Department of Medical Imaging Center; Guangxi Key Clinical Specialty (Medical Imaging Department); Dominant Cultivation Discipline of Guangxi Medical University Cancer Hospital (Medical Imaging Department), Guangxi Medical University Cancer Hospital, Nanning, China.

Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China.

出版信息

Front Immunol. 2023 Apr 5;14:1113634. doi: 10.3389/fimmu.2023.1113634. eCollection 2023.

DOI:10.3389/fimmu.2023.1113634

PMID:37090698

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10115222/

Abstract

BACKGROUND

The occurrence of ischemic stroke (IS) is associated with nonalcoholic fatty liver disease (NAFLD). The cancer burden of NAFLD complicated by IS also warrants attention. This study aimed to identify candidate immune biomarkers linked to NAFLD and IS and analyze their association with cancer.

METHODS

Two of each of the NAFLD and IS datasets were downloaded, differentially expressed genes (DEGs) were identified, and module genes were screened weighted gene coexpression network analysis (WGCNA). Subsequently, utilizing machine learning (least absolute shrinkage and selection operator regression, random forest and support vector machine-recursive feature elimination) and immune cell infiltration analysis, immune-related candidate biomarkers for NAFLD with IS were determined. Simultaneously, a nomogram was established, the diagnostic efficacy was assessed, and the role of candidate biomarkers in cancer was ascertained through pan-cancer analyses.

RESULTS

In this study, 117 and 98 DEGs were identified from the combined NAFLD and IS datasets, respectively, and 279 genes were obtained from the most significant modules of NAFLD. NAFLD module genes and IS DEGs were intersected to obtain nine genes, which were enriched in the inflammatory response and immune regulation. After overlapping the results of the three machine learning algorithms, six candidate genes were obtained, based on which a nomogram was constructed. The calibration curve demonstrated good accuracy, and the candidate genes had high diagnostic values. The genes were found to be related to the immune dysregulation of stroke, and was strongly associated with the prognosis, immune cell infiltration, microsatellite instability (MSI), and tumor mutation burden (TMB).

CONCLUSION

Six common candidate immune-related genes (, and ) of NAFLD and IS were identified, and a nomogram for diagnosing NAFLD with IS was established. may serve as a candidate gene for predicting the prognosis of patients with cancer who have NAFLD complicated by IS, which could aid in their diagnosis and treatment.

摘要

背景

缺血性脑卒中（IS）的发生与非酒精性脂肪性肝病（NAFLD）有关。NAFLD 合并 IS 的癌症负担也值得关注。本研究旨在确定与 NAFLD 和 IS 相关的候选免疫生物标志物，并分析它们与癌症的关系。

方法

下载了 2 个 NAFLD 和 2 个 IS 数据集，进行差异表达基因（DEGs）鉴定和模块基因筛选——加权基因共表达网络分析（WGCNA）。然后，利用机器学习（最小绝对收缩和选择算子回归、随机森林和支持向量机递归特征消除）和免疫细胞浸润分析，确定了具有 IS 的 NAFLD 的免疫相关候选生物标志物。同时，建立了一个列线图，评估了诊断效能，并通过泛癌分析确定了候选生物标志物在癌症中的作用。

结果

本研究从联合的 NAFLD 和 IS 数据集中分别鉴定出 117 个和 98 个 DEGs，从 NAFLD 的最显著模块中获得 279 个基因。NAFLD 模块基因和 IS DEGs 相互交叠得到 9 个基因，这些基因富集在炎症反应和免疫调节中。重叠三种机器学习算法的结果后，得到 6 个候选基因，基于这些基因构建了一个列线图。校准曲线表明具有良好的准确性，候选基因具有较高的诊断价值。这些基因与脑卒中的免疫失调有关，与预后、免疫细胞浸润、微卫星不稳定性（MSI）和肿瘤突变负荷（TMB）强烈相关。