Identification and development of a novel 5-gene diagnostic model based on immune infiltration analysis of osteoarthritis

Identification and experimental validation of key genes in osteoarthritis based on machine learning algorithms and single-cell sequencing analysis

Purpose

Osteoarthritis (OA) is a prevalent cause of disability in older adults. Identifying diagnostic markers for OA is essential for elucidating its mechanisms and facilitating early diagnosis.

Methods

We analyzed 53 synovial tissue samples (n = 30 for OA, n = 23 for the control group) from two datasets in the Gene Express Omnibus (GEO) database. We identified differentially expressed genes (DEGs) between the groups and applied dimensionality reduction using six machine learning algorithms to pinpoint characteristic genes (key genes). We classified the OA samples into subtypes based on these key genes and explored the differences in biological functions and immune characteristics among subtypes, as well as the roles of the key genes. Additionally, we constructed a protein-protein interaction network to predict small molecules that target these genes. Further, we accessed synovial tissue sample data from the single-cell RNA dataset GSE152805, categorized the cells into various types, and examined variations in gene expression and their correlation with OA progression. Validation of key gene expression was conducted in cellular experiments using the qPCR method.

Results

Four genes AGMAT, MAP3K8, PER1, and XIST, were identified as characteristic genes of OA. All can independently predict the occurrence of OA. With these genes, the OA samples can be clustered into two subtypes, which showed significant differences in functional pathways and immune infiltration. Eight cell types were obtained by analyzing the single-cell RNA data, with synovial intimal fibroblasts (SIF) accounting for the highest proportion in each sample. The key genes were found over-expressed in SIF and significantly correlated with OA progression and the content of immune cells (ICs). We validated the relative levels of key genes in OA and normal cartilage tissue cells, which showed an expression trend consistency with the bioinformatics result except for XIST.

Conclusion

Four genes, AGMAT, MAP3K8, PER1, and XIST are closely related to the progression of OA, and play as diagnostic and predictive markers in early OA.

Effect of Different Dietary Iron Contents on Liver Transcriptome Characteristics in Wujin Pigs

Iron is an important trace element that affects the growth and development of animals and regulates oxygen transport, hematopoiesis, and hypoxia adaptations. Wujin pig has unique hypoxic adaptability and iron homeostasis; however, the specific regulatory mechanisms have rarely been reported. This study randomly divided 18 healthy Wujin piglets into three groups: the control group, supplemented with 100 mg/kg iron (as iron glycinate); the low-iron group, no iron supplementation; and the high-iron group, supplemented with 200 mg/kg iron (as iron glycinate). The pre-feeding period was 5 days, and the formal period was 30 days. Serum was collected from empty stomachs before slaughter and at slaughter to detect changes in the serum iron metabolism parameters. Gene expression in the liver was analyzed via transcriptome analysis to determine the effects of low- and high-iron diets on transcriptome levels. Correlation analysis was performed for apparent serum parameters, and transcriptome sequencing was performed using weighted gene co-expression network analysis to reveal the key pathways underlying hypoxia regulation and iron metabolism. The main results are as follows. (1) Except for the hypoxia-inducible factor 1 (HIF-1) content (between the low- and high-iron groups), significant differences were not observed among the serum iron metabolic parameters. The serum HIF-1 content of the low-iron group was significantly higher than that of the high-iron group (p < 0.05). (2) Sequencing analysis of the liver transcriptome revealed 155 differentially expressed genes (DEGs) between the low-iron and control groups, 229 DEGs between the high-iron and control groups, and 279 DEGs between the low- and high-iron groups. Bioinformatics analysis showed that the HIF-1 and transforming growth factor-beta (TGF-β) signaling pathways were the key pathways for hypoxia regulation and iron metabolism. Four genes were selected for qPCR validation, and the results were consistent with the transcriptome sequencing data. In summary, the serum iron metabolism parameter results showed that under the influence of low- and high-iron diets, Wujin piglets maintain a steady state of physiological and biochemical indices via complex metabolic regulation of the body, which reflects their stress resistance and adaptability. The transcriptome results revealed the effects of low-iron and high-iron diets on the gene expression level in the liver and showed that the HIF-1 and TGF-β signaling pathways were key for regulating hypoxia adaptability and iron metabolism homeostasis under low-iron and high-iron diets. Moreover, HIF-1α and HEPC were the key genes. The findings provide a theoretical foundation for exploring the regulatory pathways and characteristics of iron metabolism in Wujin pigs.

Analysis of drought and heat stress response genes in rice using co-expression network and differentially expressed gene analyses

Studies on Oryza sativa (rice) are crucial for improving agricultural productivity and ensuring global sustenance security, especially considering the increasing drought and heat stress caused by extreme climate change. Currently, the genes and mechanisms underlying drought and heat resistance in rice are not fully understood, and the scope for enhancing the development of new strains remains considerable. To accurately identify the key genes related to drought and heat stress responses in rice, multiple datasets from the Gene Expression Omnibus (GEO) database were integrated in this study. A co-expression network was constructed using a Weighted Correlation Network Analysis (WGCNA) algorithm. We further distinguished the core network and intersected it with differentially expressed genes and multiple expression datasets for screening. Differences in gene expression levels were verified using quantitative real-time polymerase chain reaction (PCR). OsDjC53, MBF1C, BAG6, HSP23.2, and HSP21.9 were found to be associated with the heat stress response, and it is also possible that UGT83A1 and OsCPn60a1, although not directly related, are affected by drought stress. This study offers significant insights into the molecular mechanisms underlying stress responses in rice, which could promote the development of stress-tolerant rice breeds.

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes

BACKGROUND

Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.

METHODS

In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.

RESULTS

38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.

CONCLUSION

We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.

Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage

Aims

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA.

Methods

Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.

Results

A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms.

Conclusion

The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets.

Delineation and authentication of ferroptosis genes in ventilator-induced lung injury

BACKGROUND

Mechanical ventilation, a critical support strategy for individuals enduring severe respiratory failure and general anesthesia, paradoxically engenders ventilator-induced lung injury (VILI). Ferrostatin-1 mitigates lung injury via ferroptosis inhibition, yet the specific ferroptosis genes contributing significantly to VILI remain obscure.

METHODS

Leveraging the Gene Expression Omnibus database, we acquired VILI-associated datasets and identified differentially expressed genes (DEGs). To identify the hub genes, we constructed a protein-protein interaction network and used three parameters from CytoHubba. Consequently, we identified hub genes and ferroptosis genes as ferroptosis hub genes for VILI (VFHGs). We conducted enrichment analysis and established receiver operating characteristic (ROC) curves for VFHGs. Subsequently, to confirm the correctness of the VFHGs, control group mice and VILI mouse models, as well as external dataset validation, were established. For further research, a gene-miRNA network was established. Finally, the CIBERSORT algorithm was used to fill the gap in the immune infiltration changes in the lung during VILI.

RESULTS

We identified 64 DEGs and 4 VFHGs (Il6,Ptgs2,Hmox1 and Atf3) closely related to ferroptosis. ROC curves demonstrated the excellent diagnostic performance of VFHGs in VILI. PCR and external dataset validation of the VILI model demonstrated the accuracy of VFHGs. Subsequently, the gene-miRNA network was successfully established. Ultimately, an Immune cell infiltration analysis associated with VILI was generated.

CONCLUSIONS

The results emphasize the importance of 4 VFHGs and their involvement in ferroptosis in VILI, confirming their potential as diagnostic biomarkers for VILI.

Screening of Biomarkers Associated with Osteoarthritis Aging Genes and Immune Correlation Studies

Purpose

Osteoarthritis (OA) is a joint disease with a long and slow course, which is one of the major causes of disability in middle and old-aged people. This study was dedicated to excavating the cellular senescence-associated biomarkers of OA.

Methods

The Gene Expression Omnibus (GEO) database was searched and five datasets pertaining to OA were obtained. After removing the batch effect, the GSE55235, GSE55457, GSE82107, and GSE12021 datasets were integrated together for screening of the candidate genes by differential analysis and weighted gene co-expression network analysis (WGCNA). Next, those genes were further filtered by machine learning algorithms to obtain cellular senescence-associated biomarkers of OA. Subsequently, enrichment analyses based on those biomarkers were conducted, and we profiled the infiltration levels of 22 types immune cells with the ERSORT algorithm. A lncRNA-miRNA-mRNA regulatory and drug-gene network were constructed. Finally, we validated the senescence-associated biomarkers at both in vivo and in vitro levels.

Results

Five genes (BCL6, MCL1, SLC16A7, PIM1, and EPHA3) were authenticated as cellular senescence-associated biomarkers in OA. ROC curves demonstrated the reliable capacity of the five genes as a whole to discriminate OA samples from normal samples. The nomogram diagnostic model based on 5 genes proved to be a reliable predictor of OA. Single-gene GSEA results pointed to the involvement of the five biomarkers in immune-related pathways and oxidative phosphorylation in the development of OA. Immune infiltration analysis manifested that the five genes were significantly correlated with differential immune cells. Subsequently, a lncRNA-miRNA-mRNA network and gene-drug network containing were generated based on five cellular senescence-associated biomarkers in OA.

Conclusion

A foundation for understanding the pathophysiology of OA and new insights into OA diagnosis and treatment were provided by the identification of five genes, namely BCL6, MCL1, SLC16A7, PIM1, and EPHA3, as biomarkers associated with cellular senescence in OA.

VAMP8 suppresses the metastasis via DDX5/β-catenin signal pathway in osteosarcoma

Osteosarcoma is a highly metastatic malignant bone tumor, necessitating the development of new treatments to target its metastasis. Recent studies have revealed the significance of VAMP8 in regulating various signaling pathways in various types of cancer. However, the specific functional role of VAMP8 in osteosarcoma progression remains unclear. In this study, we observed a significant downregulation of VAMP8 in osteosarcoma cells and tissues. Low levels of VAMP8 in osteosarcoma tissues were associated with patients' poor prognosis. VAMP8 inhibited the migration and invasion capability of osteosarcoma cells. Mechanically, we identified DDX5 as a novel interacting partner of VAMP8, and the conjunction of VAMP8 and DDX5 promoted the degradation of DDX5 via the ubiquitin-proteasome system. Moreover, reduced levels of DDX5 led to the downregulation of β-catenin, thereby suppressing the epithelial-mesenchymal transition (EMT). Additionally, VAMP8 promoted autophagy flux, which may contribute to the suppression of osteosarcoma metastasis. In conclusion, our study anticipated that VAMP8 inhibits osteosarcoma metastasis by promoting the proteasomal degradation of DDX5, consequently inhibiting WNT/β-catenin signaling and EMT. Dysregulation of autophagy by VAMP8 is also implicated as a potential mechanism. These findings provide new insights into the biological nature driving osteosarcoma metastasis and highlight the modulation of VAMP8 as a potential therapeutic strategy for targeting osteosarcoma metastasis.

Identification of Novel Diagnostic Biomarkers and Classification Patterns for Osteoarthritis by Analyzing a Specific Set of Genes Related to Inflammation

Osteoarthritis (OA) is a prevalent joint disease globally. TNFA is recognized as a crucial inflammatory cytokine that plays a significant role in the pathophysiological mechanisms that occur during the progression of OA. However, the TNFA_SIGNALING_VIA_NFKB (TSVN)-related genes (TRGs) during the progression of OA remain unclear. By conducting a combinatory analysis of OA transcriptome data from three datasets, various differentially expressed TRGs were identified. The logistic regression model was used to mine hub TRGs for OA, and a nomogram prediction model was subsequently constructed using these TRGs. To identify new molecular subgroups, we performed consensus clustering. We then conducted functional analyses, including GO, KEGG, GSVA, and GSEA, to elucidate the underlying mechanisms. To determine the immune microenvironment, we applied xCell. The logistic regression analysis identified three hub TRGs (BHLHE40, BTG2, and CCNL1) as potential biomarkers for OA. Based on these TRGs, we constructed an OA predictive model. This model has demonstrated promising results in enhancing the accuracy of OA diagnosis, as evident from the ROC analysis (AUC merged dataset = 0.937, AUC validating dataset = 0.924). We identified two molecular subtypes, C1 and C2, and found that the C1 subtype showed activation of immune- and inflammation-related pathways. The involvement of TSVN in the development and progression of OA has been established. We identified several hub genes, such as BHLHE40, BTG2, and CCNL1, that may have a significant association with the progression of OA. Furthermore, our logistic regression model based on these genes has shown promising results in accurately diagnosing OA patients.

Identification of autophagy-related genes in osteoarthritis articular cartilage and their roles in immune infiltration

Background

Autophagy plays a critical role in the progression of osteoarthritis (OA), mainly by regulating inflammatory and immune responses. However, the underlying mechanisms remain unclear. This study aimed to investigate the potential relevance of autophagy-related genes (ARGs) associated with infiltrating immune cells in OA.

Methods

GSE114007, GSE169077, and ARGs were obtained from the Gene Expression Omnibus (GEO) database and the Human Autophagy database. R software was used to identify the differentially expressed autophagy-related genes (DEARGs) in OA. Functional enrichment and protein-protein interaction (PPI) analyses were performed to explore the role of DEARGs in OA cartilage, and then Cytoscape was utilized to screen hub ARGs. Single-sample gene set enrichment analysis (ssGSEA) was used to conduct immune infiltration analysis and evaluate the potential correlation of key ARGs and immune cell infiltration. Then, the expression levels of hub ARGs in OA were further verified by the GSE169077 and qRT-PCR. Finally, Western blotting and immunohistochemistry were used to validate the final hub ARGs.

Results

A total of 24 downregulated genes and five upregulated genes were identified, and these genes were enriched in autophagy, mitophagy, and inflammation-related pathways. The intersection results identified nine hub genes, namely, CDKN1A, DDIT3, FOS, VEGFA, RELA, MAP1LC3B, MYC, HSPA5, and HSPA8. GSE169077 and qRT-PCR validation results showed that only four genes, CDKN1A, DDT3, MAP1LC3B, and MYC, were consistent with the bioinformatics analysis results. Western blotting and immunohistochemical (IHC) showed that the expression of these four genes was significantly downregulated in the OA group, which is consistent with the qPCR results. Immune infiltration correlation analysis indicated that DDIT3 was negatively correlated with immature dendritic cells in OA, and FOS was positively correlated with eosinophils.

Conclusion

CDKN1A, DDIT3, MAP1LC3B, and MYC were identified as ARGs that were closely associated with immune infiltration in OA cartilage. Among them, DDIT3 showed a strong negative correlation with immature dendritic cells. This study found that the interaction between ARGs and immune cell infiltration may play a crucial role in the pathogenesis of OA; however, the specific interaction mechanism needs further research to be clarified. This study provides new insights to further understand the molecular mechanisms of immunity involved in the process of OA by autophagy.

Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA

Background

Recurrent pregnancy loss defined as the occurrence of two or more pregnancy losses before 20-24 weeks of gestation, is a prevalent and significant pathological condition that impacts human reproductive health. However, the underlying mechanism of RPL remains unclear. This study aimed to investigate the biomarkers and molecular mechanisms associated with RPL and explore novel treatment strategies for clinical applications.

Methods

The GEO database was utilized to retrieve the RPL gene expression profile GSE165004. This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. ANN model were constructed to assess the performance of hub genes in the dataset. The expression of hub genes in both the RPL and control group samples was validated using RT-qPCR. The immune cell infiltration level of RPL was assessed using CIBERSORT. Additionally, pan-cancer analysis was conducted using Sangerbox, and small-molecule drug screening was performed using CMap.

Results

A total of 352 DEGs were identified, including 198 up-regulated genes and 154 down-regulated genes. Enrichment analysis indicated that the DEGs were primarily associated with Fc gamma R-mediated phagocytosis, the Fc epsilon RI signaling pathway, and various metabolism-related pathways. The turquoise module, which showed the highest relevance to clinical symptoms based on WGCNA results, contained 104 DEGs. Three hub genes, WBP11, ACTR2, and NCSTN, were identified using machine learning algorithms. ROC curves demonstrated a strong diagnostic value when the three hub genes were combined. RT-qPCR confirmed the low expression of WBP11 and ACTR2 in RPL, whereas NCSTN exhibited high expression. The immune cell infiltration analysis results indicated an imbalance of macrophages in RPL. Meanwhile, these three hub genes exhibited aberrant expression in multiple malignancies and were associated with a poor prognosis. Furthermore, we identified several small-molecule drugs.

Conclusion

This study identifies and validates hub genes in RPL, which may lead to significant advancements in understanding the molecular mechanisms and treatment strategies for this condition.

Cuproptosis-related gene CDKN2A as a molecular target for IPF diagnosis and therapeutics

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive chronic interstitial lung disease with limited therapeutic options. Cuproptosis is a recently proposed novel form of programmed cell death, which has been strongly implicated in the development of various human diseases. However, the prognostic and therapeutic value of cuproptosis-related genes (CRGs) in IPF remains to be elucidated.

METHODS

In the present study, weighted gene co-expression network analysis (WGCNA) was employed to identify the key CRGs associated with the development of IPF. The subsequent GSEA, immune cell correlation analysis, and single-cell RNA-Seq analysis were conducted to explore the potential role of the identified CRGs in IPF. In addition, ROC curves and survival analysis were used to assess the prognostic value of the key CRGs in IPF. Moreover, we explored the molecular mechanisms of participation of identified key CRGs in the development of pulmonary fibrogenesis through in vivo and in vitro experiments.

RESULTS

The expression level of cyclin-dependent kinase inhibitor 2A (CDKN2A) is upregulated in the lung tissues of IPF patients and associated with disease severity. Notably, CDKN2A was constitutively expressed by fibrosis-promoting M2 macrophages. Decreased CDKN2A expression sensitizes M2 macrophages to elesclomol-induced cuproptosis in vitro. Inhibition of CDKN2A decreases the number of viable macrophages and attenuates bleomycin-induced pulmonary fibrosis in mice.

CONCLUSION

These findings indicate that CDKN2A mediates the resistance of fibrosis-promoting M2 macrophages to cuproptosis and promotes pulmonary fibrosis in mice. Our work provides fresh insights into CRGs in IPF with potential value for research in the pathogenesis, diagnosis, and a new therapy strategy for IPF.

A comprehensive analysis of the hub genes for oxidative stress in ischemic stroke

Ischemic stroke (IS), resulting from the occlusion of the cerebral artery and subsequent interruption of blood flow, represents a major and critical threat to public health. Oxidative stress (OS) has been confirmed to play a role in the IS pathological process and neural death. Understanding the essential role of OS-related genes in ischemic stroke is critical to understanding the current perception of the pathophysiological process in IS. Herein, by integrating three IS datasets (GSE16561, GSE22255, and GSE58294), we divided IS samples into the low- and high-OS groups by calculating the OS score identified by the oxidative stress gene set. The functional enrichment analysis of differentially expressed genes (DEGs) between the low- and high-OS groups indicated that DEGs were associated with hypoxia, the inflammatory response, and oxidative phosphorylation pathways. Furthermore, nine hub genes (namely TLR1, CXCL1, MMP9, TLR4, IL1R2, EGR1, FOS, CXCL10, and DUSP1) were identified through the Girvan-Newman algorithm and cytoHubba algorithms. Nine hub genes were highly expressed in IS samples and positively related to neutrophils and macrophages. Drug-sensitive analysis targeting hub genes defined allopurinol and nickel sulfate as potential candidates for impairing the neural death caused by oxidative stress in IS. Finally, we employed five machine learning methods to check the efficacy of the predictive model identified by nine hub genes. The results showed that our model had superior power for predicting the OS activity of IS patients. TLR4 was found to have excellent diagnostic value and a wide-spectrum interaction with other hub genes. Our research emphasized the impact of oxidative stress on ischemic stroke, which supports the idea that antioxidants hold great promise in ischemic stroke therapy.

Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles

BACKGROUND

Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking.

METHODS

Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein-protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB.

RESULTS

We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB.

CONCLUSIONS

In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB.

Deletion of DYRK1A Accelerates Osteoarthritis Progression Through Suppression of EGFR-ERK Signaling

Dual-specificity tyrosine phosphorylation regulated kinase 1A (DYRK1A) signaling is involved in the dynamic balance of catabolism and anabolism in articular chondrocytes. This study aimed to investigate the roles and mechanism of DYRK1A in the pathogenesis of osteoarthritis (OA). The expressions of DYRK1A and its downstream signal epidermal growth factor receptor (EGFR) were detected in the cartilage of adult wild-type mice with destabilized medial meniscus (DMM) and articular cartilage of patients with OA. We measured the progression of osteoarthritis in chondrocyte-specific knockout DYRK1A(DYRK1A-cKO) mice after DMM surgery. Knee cartilage was histologically scored and assessed the effects of DYRK1A deletion on chondrocyte catabolism and anabolism. The effect of inhibiting EGFR signaling in chondrocytes from DYRK1A-cKO mice was analyzed. Trauma-induced OA mice and OA patients showed downregulation of DYRK1A and EGFR signaling pathways. Conditional DYRK1A deletion aggravates DMM-induced cartilage degeneration, reduces the thickness of the superficial cartilage, and increases the number of hypertrophic chondrocytes. The expression of collagen type II, p-ERK, and aggrecan was also downregulated, and the expression of collagen type X was upregulated in the articular cartilage of these mice. Our findings suggest that DYRK1A delays the progression of knee osteoarthritis in mice, at least in part, by maintaining EGFR-ERK signaling in articular chondrocytes.

Osteoarthritis year in review: genetics, genomics and epigenetics

This "year in review" provides a summary of the research findings on the topic of genetics, genomics and epigenetics for osteoarthritis (OA) between Mar 2021-Apr 2022. A search routine of the literature in PubMed for the keyword, osteoarthritis, together with topics on genetics, genomics, epigenetics, polymorphism, DNA methylation, noncoding RNA, lncRNA, proteomics, and single cell RNA sequencing, returned key research articles and relevant reviews. Following filtering of duplicates across search routines, 695 unique research articles and 112 reviews were identified. We manually curated these articles and selected 90 as references for this review. However, we were unable to refer to all these articles, and only used selected articles to highlight key outcomes and trends. The trend in genetics is on the meta-analysis of existing cohorts with comparable genetic and phenotype characterisation of OA; in particular, clear definition of endophenotypes to enhance the genetic power. Further, many researchers are realizing the power of big data and multi-omics approaches to gain molecular insights for OA, and this has opened innovative approaches to include transcriptomics and epigenetics data as quantitative trait loci (QTLs). Given that most of the genetic loci for OA are not located within coding regions of genes, implying the impact is likely to be on gene regulation, epigenetics is a hot topic, and there is a surge in studies relating to the role of miRNA and long non-coding RNA on cartilage biology and pathology. The findings are exciting and new insights are provided in this review to summarize a year of research and the road map to capture all new innovations to achieve the desired goal in OA prevention and treatment.

Identification of Immune-Related Risk Genes in Osteoarthritis Based on Bioinformatics Analysis and Machine Learning

In this research, we aimed to perform a comprehensive bioinformatic analysis of immune cell infiltration in osteoarthritic cartilage and synovium and identify potential risk genes. Datasets were downloaded from the Gene Expression Omnibus database. We integrated the datasets, removed the batch effects and analyzed immune cell infiltration along with differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to identify the positively correlated gene modules. LASSO (least absolute shrinkage and selection operator)-cox regression analysis was performed to screen the characteristic genes. The intersection of the DEGs, characteristic genes and module genes was identified as the risk genes. The WGCNA analysis demonstrates that the blue module was highly correlated and statistically significant as well as enriched in immune-related signaling pathways and biological functions in the KEGG and GO enrichment. LASSO-cox regression analysis screened 11 characteristic genes from the hub genes of the blue module. After the DEG, characteristic gene and immune-related gene datasets were intersected, three genes, PTGS1, HLA-DMB and GPR137B, were identified as the risk genes in this research. In this research, we identified three risk genes related to the immune system in osteoarthritis and provide a feasible approach to drug development in the future.

Title: Bioinformatic Identification of Genes Involved in Diabetic Nephropathy Fibrosis and their Clinical Relevance

Tubulointerstitial fibrosis is an important pathological feature of diabetic nephropathy that is associated with impaired renal function. However, the mechanism by which fibrosis occurs in diabetic nephropathy is unclear. Differentially expressed genes were identified from transcriptome profiles of renal tissue from diabetic patients and unilateral ureteral obstruction mice and intersected to obtain genes that may be involved in diabetic fibrosis. Biological function analysis and protein-protein interaction network analysis were performed. ROC curve and Pearson correlation analysis between hub genes were performed and glomerular filtration rate estimated. Finally, the RNA levels of hub genes were measured using real-time PCR. A total of 283 genes were identified as potentially involved in diabetic nephropathy fibrosis. TYROBP, CTSS, LCP2, LUM and TLR7 were identified as aberrantly expressed hub genes. Immune cell infiltration analysis demonstrated higher numbers of cytotoxic lymphocytes, B lineage cells, monocyte lineage cells, myeloid dendritic cells, neutrophils, and fibroblasts in the diabetic nephropathy group. The areas under ROC curves for TYROBP, CTSS, LCP2, LUM and TLR7 were 0.9167, 0.9583, 0.9917, 0.93333, and 0.9583, respectively (P < 0.001), and their correlation coefficients with estimated glomerular filtration rate were - 0.8332, - 0.752, - 0.7875, - 0.7567, and - 0.7136, respectively (P < 0.001). The RNA levels of TYROBP, CTSS, LUM and TLR7 were upregulated in high-glucose-treated human renal tubular epithelial cells (P < 0.005). Our study identified TYROBP, CTSS, LCP2, LUM and TLR7 as potentially involved in diabetic nephropathy fibrosis. Furthermore, TYROBP, CTSS, LUM and TLR7 may be associated with epithelial-mesenchymal transition of tubular epithelial cells.

Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages

Background

Osteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.

Objective

The study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.

Methods

To identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein-protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.

Results

A total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.

Conclusion

Overall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis.

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue

Osteoarthritis (OA) is one of the most common diseases in the orthopedic clinic, characterized by progressive cartilage degradation. RNA-binding proteins (RBPs) are capable of binding to RNAs at transcription and translation levels, playing an important role in the pathogenesis of OA. This study aims to investigate the diagnosis values of RBP-related genes in OA. The RBPs were collected from previous studies, and the GSE114007 dataset (control = 18, OA = 20) was downloaded from the Gene Expression Omnibus (GEO) as the training cohort. Through various bioinformatical and machine learning methods, including genomic difference detection, protein-protein interaction network analyses, Lasso regression, univariate logistic regression, Boruta algorithm, and SVM-RFE, RNMT and RBM24 were identified and then included into the random forest (RF) diagnosis model. GSE117999 dataset (control = 10, OA = 10) and clinical samples collected from local hospital (control = 10, OA = 11) were used for external validation. The RF model was a promising tool to diagnose OA in the training dataset (area under curve [AUC] = 1.000, 95% confidence interval [CI] = 1.000-1.000), the GSE117999 cohort (AUC = 0.900, 95% CI = 0.769-1.000), and local samples (AUC = 0.759, 95% CI = 0.568-0.951). Besides, qPCR and Western Blotting experiments showed that RNMT (P < 0.05) and RBM24 (P < 0.01) were both down-regulated in CHON-001 cells with IL-1β treatment. In all, an RF model to diagnose OA based on RNMT and RBM24 in cartilage tissue was constructed, providing a promising clinical tool and possible cut-in points in molecular mechanism clarification.

Development and validation of cuproptosis-related genes in synovitis during osteoarthritis progress

Osteoarthritis (OA) is one of the most common refractory degenerative joint diseases worldwide. Synovitis is believed to drive joint cartilage destruction during OA pathogenesis. Cuproptosis is a novel form of copper-induced cell death. However, few studies have examined the correlations between cuproptosis-related genes (CRGs), immune infiltration, and synovitis. Therefore, we analyzed CRGs in synovitis during OA. Microarray datasets (GSE55235, GSE55457, GSE12021, GSE82107 and GSE176308) were downloaded from the Gene Expression Omnibus database. Next, we conducted differential and subtype analyses of CRGs across synovitis. Immune infiltration and correlation analyses were performed to explore the association between CRGs and immune cell abundance in synovitis. Finally, single-cell RNA-seq profiling was performed using the GSE176308 dataset to investigate the expression of CRGs in the various cell clusters. We found that the expression of five CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) was significantly increased in the OA synovium. Moreover, abundant and various types of immune cells infiltrated the synovium during OA, which was correlated with the expression of CRGs. Additionally, single-cell RNA-seq profiling revealed that the cellular composition of the synovium was complex and that their proportions varied greatly as OA progressed. The expression of CRGs differed across various cell types in the OA synovium. The current study predicted that cuproptosis may be involved in the pathogenesis of synovitis. The five screened CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) could be explored as candidate biomarkers or therapeutic targets for OA synovitis.

Identification of susceptibility modules and hub genes of osteoarthritis by WGCNA analysis

Osteoarthritis (OA) is a major cause of pain, disability, and social burden in the elderly throughout the world. Although many studies focused on the molecular mechanism of OA, its etiology remains unclear. Therefore, more biomarkers need to be explored to help early diagnosis, clinical outcome measurement, and new therapeutic target development. Our study aimed to retrieve the potential hub genes of osteoarthritis (OA) by weighted gene co-expression network analysis (WGCNA) and assess their clinical utility for predicting OA. Here, we integrated WGCNA to identify novel OA susceptibility modules and hub genes. In this study, we first selected 477 and 834 DEGs in the GSE1919 and the GSE55235 databases, respectively, from the Gene Expression Omnibus (GEO) website. Genes with p-value&lt;0.05 and | log2FC | &gt; 1 were included in our analysis. Then, WGCNA was conducted to build a gene co-expression network, which filtered out the most relevant modules and screened out 23 overlapping WGCNA-derived hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated that these hub genes were associated with cell adhesion molecules pathway, leukocyte activation, and inflammatory response. In addition, we conducted the protein–protein interaction (PPI) network in 23 hub genes, and the top four upregulated hub genes were sorted out (CD4, SELL, ITGB2, and CD52). Moreover, our nomogram model showed good performance in predicting the risk of OA (C-index = 0.76), and this model proved to be efficient in diagnosis by ROC curves (AUC = 0.789). After that, a single-sample gene set enrichment (ssGSEA) analysis was performed to discover immune cell infiltration in OA. Finally, human primary synoviocytes and immunohistochemistry study of synovial tissues confirmed that those candidate genes were significantly upregulated in the OA groups compared with normal groups. We successfully constructed a co-expression network based on WGCNA and found out that OA-associated susceptibility modules and hub genes, which may provide further insight into the development of pre-symptomatic diagnosis, may contribute to understanding the molecular mechanism study of OA risk genes.

Integrated Analysis and Identification of CSF-Derived Risk miRNAs and Pivotal Genes in Multiple Sclerosis

Multiple sclerosis (MS) is a common chronic autoimmune disorder of the central nervous system that predominantly affects young adults. Mounting evidence indicates that deregulation of microRNAs (miRNAs) in cerebrospinal fluid (CSF) has been implicated in MS as a potential biomarker. However, comprehensive assessments of CSF miRNAs and their target genes are lacking. Here, aberrantly expressed CSF miRNAs of MS patients were obtained from numerous studies by manual search. With detailed information on these miRNAs, we utilized online databases to screen out immune-related target genes and further performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. To identify MS high-risk pathways and pivotal genes, pathway crosstalk and pathway-gene networks were constructed, followed by the establishment of a protein-protein interaction (PPI) network. The datasets collected from ArrayExpress were used to assess pivotal genes. Overall, 21 MS-related CSF miRNAs were included in this study. Subsequently, we identified 469 MS-related genes and 14 high-risk pathways. In the pathway-gene network, 27 critical MS-related genes participated in at least half of the high-risk pathways, and these genes were used to identify pivotal genes. Finally, miR-150, miR-328, and miR-34c-5p were determined to be risk miRNAs via the regulation of the pivotal risk genes MAPK1, AKT1, and VEGFA. Among them, VEGFA was validated to be significantly decreased in the CSF cells of MS patients by transcriptomic datasets. These findings may provide potential biomarkers or therapeutic targets and help elucidate the molecular mechanisms underlying the pathogenesis of MS.