The top ten related predicted drug compounds.

Background
Primary Sjögren’s syndrome (pSS) is an autoimmune and inflammatory disorder that may affect the lungs, leading to interstitial lung disease (ILD). However, the diagnosis of progression from pSS to ILD is frequently delayed due to unstandardized interdisciplinary diagnostic criteria and a lack of reliable shared biomarkers. This diagnostic challenge, compounded by significant pathophysiological divergence in target organs, has hindered elucidation of their comorbidity mechanisms. This study employs integrated bioinformatics to identify shared biomarkers in pSS and ILD, deciphers their pathogenic mechanisms, and predicts targeted therapeutics via network pharmacology
Methods
From the Gene Expression Omnibus (GEO) database, we retrieved gene expression profiles of pSS and ILD. Differential expression gene (DEG) analysis was performed on the profiles, followed by further screening using four machine learning algorithms. Concurrently, weighted gene co-expression network analysis (WGCNA) was applied to identify gene modules, and enrichment analysis of WGCNA-derived genes was conducted to explore their biological functions. Genes obtained from WGCNA and machine learning approaches were then intersected to identify candidate biomarkers for pSS-ILD. The diagnostic potential of these candidate genes was evaluated in both discovery and validation sets using receiver operating characteristic (ROC) curves. Finally, we performed immune cell infiltration analysis of candidate genes, regulatory network construction for transcription factor (TF)-gene and miRNA-gene interactions, drug-target prediction, and molecular docking coupled with molecular dynamics simulations for predicted drugs.
Results
Differential expression analysis identified 25 shared genes between pSS and ILD gene expression profiles, with machine learning algorithms refining six key genes from these DEGs. WGCNA revealed 39 intersecting genes significantly enriched in biological processes including cell division, oocyte maturation, and metabolic regulation. Intersection of machine learning and WGCNA results yielded two hub genes (CYSLTR1 and SIGLEC10), both demonstrating robust diagnostic value in discovery and validation cohorts. Immune cell infiltration profiling showed: upregulation of activated CD4+ memory T cells and memory B cells; downregulation of resting NK cells. Regulatory network analysis indicated FOXC1, hsa-mir-27a-3p, hsa-mir-195-5p, and hsa-miR-26a-5p as potential coregulators of CYSLTR1 and SIGLEC10 expression. Finally, ten candidate drug compounds targeting the hub genes were prioritized, exemplified by:Rev-5901 (CTD 00002161), Zafirlukast (BOSS database) and Montelukast (CTD 00003205). Molecular docking demonstrated substantial binding affinity of both montelukast and zafirlukast for CYSLTR1, while molecular dynamics simulations further validated the stability of their complexes.
Conclusion
This study revealed that CYSLTR1 and SIGLEC10 demonstrate diagnostic potential for pSS-ILD. Their mechanism of action likely involves synergistically upregulating memory B cells to promote disease progression. Furthermore, we identified montelukast as a potential therapeutic agent. This discovery holds promise for improving clinical outcomes for pSS-ILD patients.

研究背景
原发性干燥综合征（Primary Sjögren’s syndrome, pSS）是一种自身免疫性炎症性疾病，可累及肺部并引发间质性肺疾病（interstitial lung disease, ILD）。但由于跨学科诊断标准不统一、缺乏可靠的共享生物标志物，从pSS进展至ILD的诊断常被延误。这一诊断难题加上靶器官病理生理机制的显著异质性，阻碍了对二者共病机制的阐明。本研究采用整合生物信息学方法筛选pSS与ILD的共享生物标志物，解析其致病机制，并通过网络药理学预测靶向治疗药物。

研究方法
从基因表达综合数据库（Gene Expression Omnibus, GEO）中检索pSS与ILD的基因表达谱数据。先对表达谱进行差异表达基因（differential expression gene, DEG）分析，随后通过四种机器学习算法进一步筛选候选基因。同时，采用加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）识别基因模块，并对WGCNA得到的基因进行富集分析，以探究其生物学功能。将WGCNA与机器学习方法得到的基因取交集，筛选出pSS-ILD的候选生物标志物。通过受试者工作特征（receiver operating characteristic, ROC）曲线在发现集与验证集中评估这些候选基因的诊断效能。最后，对候选基因进行免疫细胞浸润分析，构建转录因子（transcription factor, TF）-基因及miRNA-基因相互作用的调控网络，开展药物-靶点预测，并对预测药物进行分子对接与分子动力学模拟验证。

研究结果
差异表达分析共筛选出pSS与ILD表达谱间的25个共享差异基因，经机器学习算法进一步筛选得到6个关键基因。WGCNA共得到39个交集基因，显著富集于细胞分裂、卵母细胞成熟及代谢调控等生物学过程。将机器学习与WGCNA的结果取交集，最终获得2个枢纽基因（CYSLTR1与SIGLEC10），二者在发现队列与验证队列中均表现出良好的诊断价值。免疫细胞浸润分析显示：活化的CD4+记忆性T细胞与记忆性B细胞表达上调，静息自然杀伤（NK）细胞表达下调。调控网络分析表明，FOXC1、hsa-mir-27a-3p、hsa-mir-195-5p及hsa-miR-26a-5p可能是CYSLTR1与SIGLEC10表达的核心调控因子。最终，我们优先筛选出10种靶向枢纽基因的候选药物，例如Rev-5901（CTD 00002161）、扎鲁司特（Zafirlukast，BOSS数据库）及孟鲁司特（Montelukast，CTD 00003205）。分子对接结果显示，孟鲁司特与扎鲁司特均对CYSLTR1具有较强结合亲和力，分子动力学模拟进一步验证了二者复合物的稳定性。

研究结论
本研究证实CYSLTR1与SIGLEC10具备pSS-ILD的诊断潜力，其致病机制可能通过协同上调记忆性B细胞以促进疾病进展。此外，本研究筛选出孟鲁司特作为潜在治疗药物，该发现有望改善pSS-ILD患者的临床预后。