Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning.

Mao R ，Li J 《BMC Medical Genomics》

Identification of diagnostic genes and drug prediction in metabolic syndrome-associated rheumatoid arthritis by integrated bioinformatics analysis, machine learning, and molecular docking.

Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS. Three RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein-protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA. MetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes (TYK2 and TRAF2) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, TYK2, 0.92; TRAF2, 0.90). qRT-PCR results showed that the expression of TYK2 and TRAF2 in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. In vitro validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA. Two hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum-based diagnosis and therapeutic intervention of MetS-RA.

Huang Y ，Yue S ，Qiao J ，Dong Y ，Liu Y ，Zhang M ，Zhang C ，Chen C ，Tang Y ，Zheng J ... -  《Frontiers in Immunology》

Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms.

The cause and mechanism of non-obstructive azoospermia (NOA) is complicated; therefore, an effective therapy strategy is yet to be developed. This study aimed to analyse the pathogenesis of NOA at the molecular biological level and to identify the core regulatory genes, which could be utilised as potential biomarkers. Three NOA microarray datasets (GSE45885, GSE108886, and GSE145467) were collected from the GEO database and merged into training sets; a further dataset (GSE45887) was then defined as the validation set. Differential gene analysis, consensus cluster analysis, and WGCNA were used to identify preliminary signature genes; then, enrichment analysis was applied to these previously screened signature genes. Next, 4 machine learning algorithms (RF, SVM, GLM, and XGB) were used to detect potential biomarkers that are most closely associated with NOA. Finally, a diagnostic model was constructed from these potential biomarkers and visualised as a nomogram. The differential expression and predictive reliability of the biomarkers were confirmed using the validation set. Furthermore, the competing endogenous RNA network was constructed to identify the regulatory mechanisms of potential biomarkers; further, the CIBERSORT algorithm was used to calculate immune infiltration status among the samples. A total of 215 differentially expressed genes (DEGs) were identified between NOA and control groups (27 upregulated and 188 downregulated genes). The WGCNA results identified 1123 genes in the MEblue module as target genes that are highly correlated with NOA positivity. The NOA samples were divided into 2 clusters using consensus clustering; further, 1027 genes in the MEblue module, which were screened by WGCNA, were considered to be target genes that are highly correlated with NOA classification. The 129 overlapping genes were then established as signature genes. The XGB algorithm that had the maximum AUC value (AUC=0.946) and the minimum residual value was used to further screen the signature genes. IL20RB, C9orf117, HILS1, PAOX, and DZIP1 were identified as potential NOA biomarkers. This 5 biomarker model had the highest AUC value, of up to 0.982, compared to other single biomarker models; additionally, the results of this biomarker model were verified in the validation set. As IL20RB, C9orf117, HILS1, PAOX, and DZIP1 have been determined to possess the strongest association with NOA, these five genes could be used as potential therapeutic targets for NOA patients. Furthermore, the model constructed using these five genes, which possessed the highest diagnostic accuracy, may be an effective biomarker model that warrants further experimental validation.

Tang Q ，Su Q ，Wei L ，Wang K ，Jiang T ... -  《Frontiers in Endocrinology》

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

Wang J ，Li L ，Chen P ，He C ，Niu X ... -  《Scientific Reports》

Neurogenic rosacea in Korea.

Rosacea with severe neurological symptoms such as burning and stinging is often not treated effectively by conventional therapies. The aim of this study was to investigate the clinical characteristics of Korean rosacea patients with prominent neurological symptoms. The demographic features, medical history, clinical manifestations and treatment modalities of 17 neurogenic rosacea patients who had prominent neurological symptoms and 106 erythematotelangiectatic rosacea (ETR) patients as a control group were investigated. All 17 neurogenic rosacea patients had severe persistent erythema with burning/stinging sensation limited to both cheeks. Among these patients, 94.1% were female (16/17). Heat stimuli (58.8%, 10/17) and stress (52.9%, 9/17) were major aggravating factors. Fourteen of 17 patients (82.3%) improved after receiving anticonvulsants and antidepressants. In conclusion, rosacea patients with severe neurological symptoms show distinct clinical manifestations and should be classified separately, and a different therapeutic approach is necessary for them.

Kim HO ，Kang SY ，Kim KE ，Cho SY ，Kim KH ，Kim IH ... -  《-》