Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation.

Osteoarthritis (OA) is a degenerative bone disease characterized by the destruction of joint cartilage and synovial inflammation, involving intricate immune regulation processes. Disulfidptosis, a novel form of programmed cell death, has recently been identified; however, the effects and roles of disulfidptosis-related genes (DR-DEGs) in OA remain unclear. We obtained six OA datasets from the GEO database, using four as training sets and two as validation sets. Differential expression analysis was employed to identify DR-DEGs, and unique molecular subtypes of OA were constructed based on these DR-DEGs. Subsequently, the immune microenvironment of OA patients was comprehensively analyzed using the "CIBERSORT" algorithm for immune infiltration. Various machine learning algorithms were utilized to screen characteristic DR-DEGs, and nomogram models and ROC curves were built based on these genes. The scRNA dataset (GSE169454) was used to classify chondrocytes in OA samples into distinct cell types, further exploring the gene distribution and correlation of characteristic DR-DEGs with specific cell subpopulations. Moreover, the expression levels of four characteristic DR-DEGs were validated through OA cell models and rat models. In our study, we identified 10 DR-DEGs with significant differences in expression within OA samples. Based on these DR-DEGs, two distinct molecular subtypes were recognized (cluster 1 and 2). ZNF484 and NDUFS1 were found to be significantly overexpressed in subtype 1, while the infiltration abundance of activated mast cells was markedly elevated in subtype 2. Moreover, significant differences were observed in the infiltration proportions of 11 immune cell types between OA and control samples, with 9 DR-DEGs demonstrating substantial correlations with immune cell infiltration levels. Further analysis of the scRNA dataset revealed that SLC3A2 and NDUFC1 were predominantly expressed in the preHTC subpopulation. All 10 DR-DEGs exhibited notably higher expression in the EC subpopulation across various cell types. The proportion of EC subgroups with high SLC3A2 expression increased, mainly enriching pathways related to inflammation, such as the IL-17 signaling pathway and TGF-beta signaling pathway. Using machine learning, we identified four characteristic DR-DEGs, which, in combination with the nomogram and ROC models, demonstrated promising performance in the diagnosis of OA. Additionally, in vivo validation confirmed a significant elevation of PPM1F expression in OA models. This study identified DR-DEGs as potential biomarkers for the diagnosis and classification of OA and provided a preliminary understanding of their role in the immune microenvironment. However, further experimental and clinical studies are required to validate their diagnostic value and therapeutic potential.

Wei M ，Shi X ，Tang W ，Lv Q ，Wu Y ，Xu Y ... -  《Scientific Reports》

[Exploration of key ferroptosis-related genes as therapeutic targets for sepsis based on bioinformatics and the depiction of their immune profiles characterization].

Li M ，Mei Y ，Pan A 《-》

Development of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as hyperandrogenemia, oligo or anovulation and polycystic ovarian, significantly impacting quality of life. However, the practical implementation of machine learning (ML) in PCOS diagnosis is hindered by the limitations related to data size and algorithmic models. To address this research gap, we have increased the sample size in our study and aim to utilize two ML algorithms to analyze and validate diagnostic biomarkers, as well as explore immune cell infiltration patterns in PCOS. We performed RNA-seq analysis on granulosa cell, including 13 samples from normal controls and 25 samples from women with PCOS. The data from our study were combined with publicly available databases. Batch effects were corrected using the 'sva' package in R software. Differential expression analysis was performed to identify genes that exhibited significant differences between the two groups. These differentially expressed genes (DEGs) were further analyzed for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Hub genes were selected by intersecting the results of both methods after using LASSO and SVM-RFE for central gene selection for DEGs. Receiver Operating Characteristic (ROC) curves were employed to verify the accuracy of models by SVM and XGBoost. CIBERSORT analysis was performed to determine the relative abundances of immune cell populations. GSEA was analyzed to illustrate the expression patterns of genes within highly enriched functional pathways. RT-qPCR was used to validate the reliability of hub genes. 824 DEGs were found between the normal control and PCOS groups, including 376 upregulated and 448 downregulated genes. These DEGs were associated with endocytosis, salmonella infection and focal adhesion based on the KEGG enrichment analysis. Through overlapping LASSO and SVM-RFE algorithms, we identified four hub genes (CNTN2, CASR, CACNB3, MFAP2) that are significantly associated with the PCOS group. The diagnostic efficacy validation set using SVM and XGBoost yielded AUC values of 0.795 and 0.875, respectively, indicating their potential as diagnostic biomarkers. Consistent with the data analysis, the upregulation of CNTN2, CASR, CACNB3, and MFAP2 in PCOS was confirmed by RT-qPCR analysis on human granulosa cells. Furthermore, according to CIBERSORT analysis, a significant reduction in CD4 memory resting T cells was revealed in the PCOS group compared to the normal control group (P < 0.05). This study identified CNTN2, CASR, CACNB3, and MFAP2 as potential diagnostic biomarkers for PCOS, which provides strong evidence for existing research on hub genes. Furthermore, the analysis of immune cell infiltration revealed the significant involvement of CD4 memory resting T cells in the onset and progression of PCOS. These findings shed light on potential mechanisms underlying PCOS pathogenesis and provide valuable insights for future research and therapeutic interventions.

Chen W ，Miao J ，Chen J ，Chen J ... -  《Journal of Ovarian Research》

Identification of an immune-related gene panel for the diagnosis of pulmonary arterial hypertension using bioinformatics and machine learning.

This study aimed to screen an immune-related gene (IRG) panel and develop a novel approach for diagnosing pulmonary arterial hypertension (PAH) utilizing bioinformatics and machine learning (ML). Gene expression profiles were retrieved from the Gene Expression Omnibus (GEO) database to identify differentially expressed immune-related genes (IRG-DEGs). We employed five machine learning algorithms-LASSO, random forest (RF), boosted regression trees (BRT), XGBoost, and support vector machine recursive feature elimination (SVM-RFE) to identify biomarkers derived from IRG-DEGs associated with the diagnosis of PAH, incorporating them into the IRG-DEGs panel. Validation of these biomarker levels in lung tissue was conducted in a hypoxia-induced mouse model of PAH, investigating the correlation between AIMP1, IL-15, GLRX, SOD1, Fulton's index (RVHI), and the ratio of pulmonary artery medial thickness to external diameter (MT%). Subsequently, we developed a nomogram model based on the IRG-DEGs panel in lung tissue for diagnosing PAH. The expression, distribution, and pseudotime analysis of these biomarkers across various immune cell types were assessed using single-cell sequencing datasets. Finally, we evaluated the diagnostic utility of the nomogram model based on the IRG-DEGs panel in peripheral blood mononuclear cells (PBMCs) for diagnosing PAH. A total of 36 upregulated and 17 downregulated IRG-DEGs were identified in lung tissue from patients with PAH. AIMP1, IL-15, GLRX, and SOD1 were subsequently selected as novel immune-related biomarkers for PAH through the aforementioned machine learning algorithms and incorporated into the IRG-DEGs panel. Experimental results from mice with PAH validated that the expression levels of AIMP1, IL-15, and GLRX in lung tissue were elevated, while SOD1 expression was significantly reduced. Additionally, GLRX and AIMP1 exhibited positive correlations with Fulton's index (RVHI). The expression levels of GLRX, IL-15, and AIMP1 showed positive correlations with MT%, whereas SOD1 exhibited negative correlations with MT%. Analysis of single-cell sequencing data further revealed that the levels of IRG-DEG panel members gradually increased during the pseudotime trajectory from PBMCs to macrophages, correlating with macrophage activation. The area under the curve (AUC) for diagnosing PAH using a nomogram model based on the IRG-DEGs panel derived from lung tissue samples and PBMCs was ≥0.969 and 0.900, respectively. We developed an IRG-DEGs panel containing AIMP1, IL-15, GLRX, and SOD1, which may facilitate the diagnosis of pulmonary arterial hypertension (PAH). These findings provide novel insights that may enhance diagnostic and therapeutic approaches for PAH.

Xiong P ，Huang Q ，Mao Y ，Qian H ，Yang Y ，Mou Z ，Deng X ，Wang G ，He B ，You Z ... -  《-》

The interplay between dysregulated metabolites and signaling pathway alterations involved in osteoarthritis: a systematic review.

Osteoarthritis (OA) is a common degenerative joint disease that poses a significant global healthcare challenge due to its complexity and limited treatment options. Advances in metabolomics have provided insights into OA by identifying dysregulated metabolites and their connection to altered signaling pathways. However, a comprehensive understanding of these biomarkers in OA is still required. This systematic review aims to identify metabolomics biomarkers associated with dysregulated signaling pathways in OA, using data from various biological samples, including in vitro models, animal studies, and human research. A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data were gathered from literature published between August 2017 and May 2024, using databases such as "PubMed," "Scopus," "Web of Science," and "Google Scholar." Studies were selected based on keywords like "metabolomics," "osteoarthritis," "amino acids," "molecular markers," "biomarkers," "diagnostic markers," "inflammatory cytokines," "molecular signaling," and "signal transduction." The review focused on identifying key metabolites and their roles in OA-related pathways. Limitations include the potential exclusion of studies due to keyword selection and strict inclusion criteria. The meta-analysis identified dysregulated metabolites and associated pathways, highlighting a distinct set of related metabolites consistently altered across the studies analyzed. The dysregulated metabolites, including amino acids, lipids, and carbohydrates, were found to play critical roles in inflammation, oxidative stress, and energy metabolism in OA. Metabolites such as alanine, lysine, and proline were frequently linked to pathways involved in inflammation, cartilage degradation, and apoptosis. Key pathways, including nuclear factor kappa B, mitogen-activated protein kinase, Wnt/β-catenin, and mammalian target of rapamycin, were associated with changes in metabolite levels, particularly in proinflammatory lipids and energy-related compounds. This review reveals a complex interplay between dysregulated metabolites and signaling pathways in OA, offering potential biomarkers and therapeutic targets. Further research is needed to explore the molecular mechanisms driving these changes and their implications for OA treatment.

Aziz A ，Ganesan Nathan K ，Kamarul T ，Mobasheri A ，Sharifi A ... -  《-》