Identification of DIO2 as a Molecular Therapeutic Target for Depression in Chronic Rhinosinusitis: A Comprehensive Bioinformatics and Experimental Study.

IF 1.6 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Biochemical Genetics Pub Date : 2026-02-01 Epub Date: 2025-03-16 DOI:10.1007/s10528-025-11085-4
Hao Lv, Peiqiang Liu, Yunfei Wang, Jingyu Huang, Yulie Xie, Mengting Guan, Jianchao Cong, Yang Jiang, Yu Xu
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Abstract

Chronic rhinosinusitis (CRS) and depression are both common conditions with significant socioeconomic impact. The high co-occurrence of depression in CRS patients suggests a common pathophysiology, but the mechanisms are unclear. This study aimed to identify potential molecular links between the two conditions. We retrieved gene expression datasets for CRS and depression from the GEO database. Using differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA), we identified co-expression genes associated with CRS and depression. Enrichment analyses including GO, KEGG, and GSEA were performed to explore biological pathways. Machine learning algorithms including random forest and LASSO regression were engaged to screen for shared hub genes predictive of CRS and depression. Single-cell RNA sequencing (scRNA-seq) data were analyzed to delineate the expression profiles of the shared hub genes across different cell types. Animal experiments were employed to validate the role of core genes in CRS-related depression. We identified five shared hub genes: CHRDL1, DIO2, HSD17B6, PDE3A, and PLA2G5, with the TGF-β signaling, cytokine-cytokine interaction receptors, and cell adhesion as key biological pathways. DIO2, as identified by machine learning, is a promising diagnostic biomarker for CRS and depression. The scRNA-seq analysis showed that DIO2 is primarily expressed in neurons and astrocytes. Animal experiments showed that overexpression of DIO2 improved the depressive-like behaviors in CRS mice. This study sheds new light on the molecular basis of the comorbidity between CRS and depression. DIO2 is a potential diagnostic and therapeutic target for CRS patients with comorbid depression.

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鉴定 DIO2 作为慢性鼻炎抑郁症的分子治疗靶点:一项全面的生物信息学和实验研究。
慢性鼻窦炎(CRS)和抑郁症都是具有重大社会经济影响的常见疾病。CRS患者抑郁症的高发生率提示有共同的病理生理机制,但其机制尚不清楚。这项研究旨在确定这两种疾病之间潜在的分子联系。我们从GEO数据库中检索了CRS和抑郁症的基因表达数据集。通过差异表达基因(DEGs)分析和加权基因共表达网络分析(WGCNA),我们确定了与CRS和抑郁症相关的共表达基因。富集分析包括GO、KEGG和GSEA,以探索生物学途径。包括随机森林和LASSO回归在内的机器学习算法被用于筛选预测CRS和抑郁症的共享枢纽基因。对单细胞RNA测序(scRNA-seq)数据进行分析,以描绘共享枢纽基因在不同细胞类型中的表达谱。通过动物实验验证核心基因在crs相关抑郁中的作用。我们确定了五个共享的中心基因:CHRDL1、DIO2、HSD17B6、PDE3A和PLA2G5,其中TGF-β信号传导、细胞因子-细胞因子相互作用受体和细胞粘附是关键的生物学途径。通过机器学习识别的二氧化碳是一种很有前途的CRS和抑郁症诊断生物标志物。scRNA-seq分析显示,DIO2主要在神经元和星形胶质细胞中表达。动物实验表明,过表达DIO2可改善CRS小鼠的抑郁样行为。这项研究揭示了CRS与抑郁症共病的分子基础。DIO2是CRS合并抑郁症患者潜在的诊断和治疗靶点。
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来源期刊
Biochemical Genetics
Biochemical Genetics 生物-生化与分子生物学
CiteScore
3.90
自引率
0.00%
发文量
133
审稿时长
4.8 months
期刊介绍: Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses. Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication. Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses. Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods. Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.
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