{"title":"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。","authors":"Hao Lv, Peiqiang Liu, Yunfei Wang, Jingyu Huang, Yulie Xie, Mengting Guan, Jianchao Cong, Yang Jiang, Yu Xu","doi":"10.1007/s10528-025-11085-4","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":482,"journal":{"name":"Biochemical Genetics","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of DIO2 as a Molecular Therapeutic Target for Depression in Chronic Rhinosinusitis: A Comprehensive Bioinformatics and Experimental Study.\",\"authors\":\"Hao Lv, Peiqiang Liu, Yunfei Wang, Jingyu Huang, Yulie Xie, Mengting Guan, Jianchao Cong, Yang Jiang, Yu Xu\",\"doi\":\"10.1007/s10528-025-11085-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":482,\"journal\":{\"name\":\"Biochemical Genetics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biochemical Genetics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1007/s10528-025-11085-4\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochemical Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s10528-025-11085-4","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Identification of DIO2 as a Molecular Therapeutic Target for Depression in Chronic Rhinosinusitis: A Comprehensive Bioinformatics and Experimental Study.
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.
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.