Table 1_Identification of anoikis-related genes and immune infiltration characteristics in Sjögren’s syndrome based on machine learning.xlsx

ObjectiveAnoikis, a recently identified type of programmed cell death analogous to apoptosis, has been implicated in the pathogenesis of Sjögren’s syndrome (SS). Although accumulating evidence indicates its involvement in modulating immune responses and contributing to SS progression, the precise role of anoikis in SS remains inadequately understood. This study aimed to explore anoikis-related genes (ARGs) and their molecular mechanisms in SS using public databases.
MethodsSS datasets (GSE23117, GSE84844 and GSE12795) were retrieved from the GEO database. In total, 924 ARGs were extracted from the GeneCards and Harmonizome databases, followed by differential expression gene (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms were utilized to screen candidate biomarkers, and their diagnostic effectiveness was assessed using receiver operating characteristic (ROC) curve analysis. Concurrently, a mouse model of SS was established and validated through in vivo experiments. Immune cell infiltration in SS tissues was evaluated using CIBERSORT, and correlations between characteristic genes and immune cell profiles were analyzed. Potential drug candidates targeting these genes were identified using the DGIdb database. Subsequently, an lncRNA-miRNA-mRNA network associated with these genes was constructed, and preliminary experimental validation was conducted.
ResultsA total of 35 differentially expressed anoikis-related genes (DEARGs) were identified. GO and KEGG enrichment analyses demonstrated that DEARGs were primarily associated with inflammation, viral infections, and the necroptosis signaling pathway. Machine learning analysis pinpointed 14 feature genes, among seven were associated with cancer (NAT1, BIRC3, EZH2, MAD2L1, ATP2A3, HMGA1, and BST2). Given the unclear roles of SKI and PRDX4 in SS, the study focused specifically on five relevant genes, MAPK3, IL15, S100A9, IFI27, and CXCL10, which were validated by in vivo experiments. Immune cell analysis revealed increased proportions of B cells, T cells, macrophages, and other immune cells in SS tissues. Furthermore, ceRNA and drug-gene interaction networks were established, underscoring the regulatory significance of five key miRNAs (miR-30b-5p, miR-148a-3p, miR-130a, miR-483-5p, and miR-486-3p) in SS. In addition, eight candidate drugs were identified with potential for modulating SS pathogenesis.
ConclusionThis study substantiates the significant involvement of anoikis in SS and suggests that MAPK3, IL15, S100A9, IFI27, and CXCL10 may serve as critical biomarkers in the inflammatory progression of SS. These genes likely mediate their effects by influencing immune cell infiltration, participating in immune regulation, and modulating inflammatory responses. Our findings offer new insights into drug selection and immunotherapeutic strategies for SS.

失巢凋亡（anoikis）是近年发现的一类类似细胞凋亡的程序性细胞死亡类型，已被证实与干燥综合征（Sjögren’s syndrome, SS）的发病机制相关。尽管越来越多的证据表明其参与调控免疫应答并促进SS的疾病进展，但失巢凋亡在SS中的具体作用仍未被充分阐明。本研究旨在利用公共数据库探究SS中的失巢凋亡相关基因（anoikis-related genes, ARGs）及其分子机制。
方法：从GEO数据库检索SS数据集（GSE23117、GSE84844及GSE12795）。从GeneCards和Harmonizome数据库中共提取得到924个ARGs，随后进行差异表达基因（differential expression gene, DEGs）分析及加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）。采用机器学习算法筛选候选生物标志物，并通过受试者工作特征（receiver operating characteristic, ROC）曲线分析评估其诊断效能。同时，构建SS小鼠模型并通过体内实验进行验证。使用CIBERSORT评估SS组织中的免疫细胞浸润情况，并分析特征基因与免疫细胞谱之间的相关性。利用DGIdb数据库鉴定靶向这些基因的潜在候选药物。随后构建与这些基因相关的长链非编码RNA-微小RNA-信使RNA（lncRNA-miRNA-mRNA）调控网络，并开展初步实验验证。
结果：共鉴定得到35个差异表达失巢凋亡相关基因（DEARGs）。GO及KEGG富集分析显示，DEARGs主要与炎症、病毒感染及坏死性凋亡信号通路相关。机器学习分析筛选出14个特征基因，其中7个与癌症相关（NAT1、BIRC3、EZH2、MAD2L1、ATP2A3、HMGA1及BST2）。鉴于SKI和PRDX4在SS中的作用尚不明确，本研究重点聚焦于5个相关基因：MAPK3、IL15、S100A9、IFI27及CXCL10，并通过体内实验对其进行了验证。免疫细胞分析显示，SS组织中B细胞、T细胞、巨噬细胞及其他免疫细胞的比例升高。此外，本研究构建了内源竞争RNA（competing endogenous RNA, ceRNA）调控网络及药物-基因相互作用网络，明确了5种关键微小RNA（miR-30b-5p、miR-148a-3p、miR-130a、miR-483-5p及miR-486-3p）在SS中的调控意义。同时，鉴定得到8种可潜在调控SS发病机制的候选药物。
结论：本研究证实失巢凋亡在SS中发挥重要作用，并提示MAPK3、IL15、S100A9、IFI27及CXCL10可作为SS炎症进展过程中的关键生物标志物。这些基因可能通过影响免疫细胞浸润、参与免疫调控及调节炎症反应来发挥其功能。本研究结果为SS的药物筛选及免疫治疗策略提供了新的思路。