Bioinformatics-Led Discovery of Osteoarthritis Biomarkers and Inflammatory Infiltrates

Comprehensive Analysis Reveals the Potential Diagnostic Value of Biomarkers Associated With Aging and Circadian Rhythm in Knee Osteoarthritis

OBJECTIVE

Knee osteoarthritis (KOA) is characterized by structural changes. Aging is a major risk factor for KOA. Therefore, the objective of this study was to examine the role of genes related to aging and circadian rhythms in KOA.

METHODS

This study identified differentially expressed genes (DEGs) by comparing gene expression levels between normal and KOA samples from the GEO database. Subsequently, we intersected the DEGs with aging-related circadian rhythm genes to obtain a set of aging-associated circadian rhythm genes differentially expressed in KOA. Next, we conducted Mendelian randomization (MR) analysis, using the differentially expressed aging-related circadian rhythm genes in KOA as the exposure factors, their SNPs as instrumental variables, and KOA as the outcome event, to explore the causal relationship between these genes and KOA. We then performed Gene Set Enrichment Analysis (GSEA) to investigate the pathways associated with the selected biomarkers, conducted immune infiltration analysis, built a competing endogenous RNA (ceRNA) network, and performed molecular docking studies. Additionally, the findings and functional roles of the biomarkers were further validated through experiments on human cartilage tissue and cell models.

RESULTS

A total of 75 differentially expressed aging-circadian rhythm related genes between the normal group and the KOA group were identified by difference analysis, primarily enriched in the circadian rhythm pathway. Two biomarkers (PFKFB4 and DDIT4) were screened by MR analysis. Then, immune infiltration analysis showed significant differences in three types of immune cells (resting dendritic cells, resting mast cells, and M2 macrophages), between the normal and KOA groups. Drug prediction and molecular docking results indicated stable binding of PFKFB4 to estradiol and bisphenol_A, while DDIT4 binds stably to nortriptyline and trimipramine. Finally, cell lines with stable expression of the biomarkers were established by lentiviral infection and resistance screening, Gene expression was significantly elevated in overexpressing cells of PFKFB4 and DDIT4 and reversed the proliferation and migration ability of cells after IL-1β treatment.

CONCLUSIONS

Two diagnostic and therapeutic biomarkers associated with aging-circadian rhythm in KOA were identified. Functional analysis, molecular mechanism exploration, and experimental validation further elucidated their roles in KOA, offering novel perspectives for the prevention and treatment of the disease.

Construction of mitochondrial quality regulation genes-related prognostic model based on bulk-RNA-seq analysis in multiple myeloma

Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)-related prognostic model for MM patients. The Genomic Data Commons-MM of bulk RNA-seq, mutation, and single-cell RNA-seq (scRNA-seq) dataset were downloaded, and the MQRGs gene set was collected previous study. "maftools" and CIBERSORT were used for mutation and immune-infiltration analysis. Subsequently, the "ConsensusClusterPlus" was used to perform the unsupervised clustering analysis, "survminer" and "ssGSEA" R package was used for the Kaplan-Meier survival and enrichment analysis, "limma" R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The "timeROC" R package was used for Receiver Operating Characteristic Curve analysis. Finally, the "Seurat" R package was used for scRNA-seq analysis. These MQRGs are mainly located on chromosome-1,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs-related prognostic model in MM.

Mitochondria-associated programmed cell death: elucidating prognostic biomarkers, immune checkpoints, and therapeutic avenues in multiple myeloma

Background

Multiple myeloma (MM) is a hematological malignancy characterized by the abnormal proliferation of plasma cells. Mitochondrial dysfunction and dysregulated programmed cell death (PCD) pathways have been implicated in MM pathogenesis. However, the precise roles of mitochondria-related genes (MRGs) and PCD-related genes (PCDRGs) in MM prognosis remain unclear.

Methods

Transcriptomic data from MM patients and healthy controls were analyzed to identify differentially expressed genes (DEGs). Candidate genes were selected by intersecting DEGs with curated lists of MRGs and PCDRGs. Univariate Cox, least absolute shrinkage and selection operator (LASSO), multivariate Cox, and stepwise regression analyses identified prognostic genes among the candidates. A risk model was constructed from these genes, and patients were stratified into high- and low-risk groups for survival analysis. Independent prognostic factors were incorporated into a nomogram to predict MM patient outcomes. Model performance was evaluated using calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Finally, associations between prognostic genes and immune cell infiltration/drug responses were explored.

Results

2,192 DEGs were detected between MM and control samples. 30 candidate genes were identified at the intersection of DEGs, 1,136 MRGs, and 1,548 PCDRGs. TRIAP1, TOMM7, PINK1, CHCHD10, PPIF, BCL2L1, and NDUFA13 were selected as prognostic genes. The risk model stratified patients into high- and low-risk groups with significantly different survival probabilities. Age, gender, ISS stage, and risk score were independent prognostic factors. The nomogram displayed good calibration and discriminative ability (AUC) in predicting survival, with clinical utility demonstrated by DCA. 9 immune cell types showed differential infiltration between MM and controls, with significant associations to risk scores and specific prognostic genes. 57 drugs, including nelarabine and vorinostat, were predicted to interact with the prognostic genes. Ultimately, qPCR in clinical samples from MM patients and healthy donors validated the expression levels of the seven key prognostic genes, corroborating the bioinformatic findings.

Conclusion

Seven genes (TRIAP1, TOMM7, PINK1, CHCHD10, PPIF, BCL2L1, NDUFA13) involved in mitochondrial function and PCD pathways were identified as prognostic markers in MM. These findings provide insights into MM biology and prognosis, highlighting potential therapeutic targets.

Integrative bioinformatics analysis reveals novel insights into osteoarthritis pathogenesis and diagnostic biomarkers

BACKGROUND

Osteoarthritis (OA) is a prevalent joint disorder characterized by degeneration and inflammation. Understanding its molecular mechanisms is crucial for diagnosis and treatment.

METHODS

We employed bioinformatics analyses to study OA using gene expression data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted. Enrichment analyses were performed to elucidate the biological significance of identified genes. Additionally, signature genes were identified using LASSO regression analysis, and a diagnostic nomogram was developed. qRT-PCR was conducted to confirm the expression levels of signature genes.

RESULTS

We identified 200 differentially expressed genes (DEGs) and a lightgreen module strongly correlated with OA. Within this module, 97 core genes were identified. Fifteen core lipopolysaccharide-related genes (LRGs) were found, enriched in immune and inflammatory pathways. Three hub genes (CCL3, ZFP36, and CCN1) emerged as potential biomarkers for OA diagnosis, with a nomogram showing high predictive accuracy, and validated by using clinical samples. Gene set enrichment analysis (GSEA) revealed distinct signaling pathways associated with the signature genes. Immunological analysis indicated altered immune profiles in OA, with the signature genes influencing immune cell infiltration and immune response pathways.

CONCLUSION

Our study provides insights into OA pathogenesis and identifies potential diagnostic biomarkers. The developed nomogram shows promise for accurate OA diagnosis. Furthermore, the signature genes play crucial roles in modulating the immune microenvironment in OA, suggesting their therapeutic potential.

CLINICAL TRIAL NUMBER

Not applicable.

CDK5 Upregulated by ELF3 Transcription Promotes IL-1β-induced Inflammation and Extracellular Matrix Degradation in Human Chondrocytes

Osteoarthritis (OA) is a common chronic disease with age-associated increase in both incidence and prevalence. The cyclin-dependent kinase 5 (CDK5), which is a member of the CDK family, is involved in many chronic diseases. This study was performed to explore the functional role of CDK5 in OA and to discuss the detailed molecular mechanisms. The expressions of CDK5 and ELF3 before or after transfection were detected with reverse transcription-quantitative PCR (RT-qPCR) and western blot. 5-ethynyl-2'-deoxyuridine (Edu) and terminal deoxynucleoitidyl transferase-mediated nick-end labeling (TUNEL) assays were used to detect the proliferation and apoptosis of C28/I2 cells. The levels of inflammatory cytokines were estimated using enzyme-linked immunosorbent assay (ELISA) while the expressions of proteins implicated in extracellular matrix (ECM) degradation- and apoptosis were detected using western blot. Additionally, the activity of CDK5 promoters and its binding with ELF3 were detected using luciferase activity assay and chromatin immunoprecipitation (CHIP) assay. In the present study, it was discovered that the mRNA and protein expressions of CDK5 were significantly increased in IL-1β-induced C28/I2 cells. After depleting CDK5 expression, the apoptosis, inflammation and ECM in C28/I2 cells with IL-1β induction were suppressed. It was also found that ELF3 expression was increased in IL-1β-induced C28/I2 cells and acted as a transcription factor binding to the CDK5 promoter to regulate its transcriptional expression. The further experiments evidenced that ELF3 overexpression partially reversed the inhibitory effects of CDK5 deficiency on IL-1β-induced apoptosis, inflammation and ECM in C28/I2 cells. Collectively, CDK5 that upregulated by ELF3 transcription could promote the development of OA.

Ononin delays the development of osteoarthritis by down-regulating MAPK and NF-κB pathways in rat models

BACKGROUND

Osteoarthritis (OA) is featured as cartilage loss, joint pain and loss of labor, which the inflammatory reaction may play critical roles. Ononin is an isoflavone isolating from medicinal plants and has anti-inflammatory effects. Our study investigated the anti-inflammation response of ononin on OA.

METHODS

Anterior cruciate ligament transection (ACLT)-induced OA operation was used to establish research model, then treated with ononin for 8 weeks. The condition of joint injury was assessed using pathological staining. The concentration of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) in serum were measured by Elisa kit. The expression of collagen II and matrix metalloproteinase 13 (MMP-13) proteins to assess cartilage metabolism level by immunohistochemistry and Western blot. We detected the expression of proteins involved in the MAPK and NF-κB signaling pathways. Finally, we used molecular docking to assess the affinity of ononin for the target proteins ERK1/2, JNK1/2, p38 and p65.

RESULTS

Our results confirmed that ononin ameliorated cartilage impairment through histopathological analysis by improving the morphological structures and cartilage tidal lines and decreasing Osteoarthritis Research Society International (OARSI) scores in OA rats. Moreover, ononin inhibited the secretion of above factors in OA rats. Furthermore, ononin has been shown to improve cartilage content levels in OA rats. In addition, ononin inhibited the reactivity of MAPK and NF-κB pathways in OA rats. And molecular docking indicated the ligand molecules could stably bind to the proteins of above receptors.

CONCLUSION

Our results demonstrated that ononin may ameliorate cartilage damage and inflammatory response in OA rats by downgrading MAPK and NF-κB pathways, thus identifying ononin as a potential novel drug to treat OA.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer

BACKGROUND

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.

METHODS

Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.

RESULTS

This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.

CONCLUSION

We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

Identification of key ubiquitination-related genes in gestational diabetes mellitus: A bioinformatics-driven study

Background and Aims

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance that occurs during pregnancy. This study aimed to identify key ubiquitination-related genes associated with GDM pathogenesis.

Methods

Microarray data from GSE154377 was analyzed to identify differentially expressed genes (DEGs) in GDM vs normal pregnancy samples. Weighted gene co-expression network analysis was performed on ubiquitination-related genes. Functional enrichment, protein-protein interaction network, and TF-mRNA-miRNA interaction network analyses were conducted on differentially expressed ubiquitination-related genes (DE-URGs).

Results

We identified 2337 DEGs and 65 DE-URGs in GDM. Functional enrichment analysis of the 65 DE-URGs revealed involvement in protein ubiquitination and ubiquitin-dependent catabolic processes. Protein-protein interaction network analysis identified 8 hub genes, including MAP1LC3C, USP26, USP6, UBE2U, USP2, USP43, UCHL1, and USP44. ROC curve analysis showed these hub genes have high diagnostic accuracy for GDM (AUC > 0.6). The TF-mRNA-miRNA interaction network suggested USP2 and UCHL1 may be key ubiquitination genes in GDM.

Conclusion

In conclusion, this study contributes to our understanding of the molecular landscape of GDM by uncovering key ubiquitination-related genes. These findings may serve as a foundation for further investigations, offering potential biomarkers and therapeutic targets for clinical applications in GDM management.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes-EP300, MTOR, NFE2L1, TXN-exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

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.

Investigation of the Mechanism of SEMA5A and Its Associated Autophagy-Related Genes in Gastric Cancer

Purpose

Semaphorin 5A (SEMA5A) and autophagy-related genes (ARGs) are pivotal in the pathogenesis of gastric cancer (GC). However, the potential regulatory role of SEMA5A in autophagy via its associated ARGs and the underlying molecular mechanisms remain unresolved.

Patients and Methods

GC-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes (DEGs) between GC and control samples. The intersection of DEGs with ARGs produced candidate genes, which were further analyzed using Spearman correlation with SEMA5A to identify signature genes. Stratification of GC samples based on signature gene expression, followed by Kaplan-Meier survival analysis, identified key genes. Subsequent analyses, including gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint evaluation, were conducted on the key genes and SEMA5A. The mRNA expression level was quantified using real-time quantitative polymerase chain reaction (RT-qPCR).

Results

Ninety candidate genes were identified for Spearman correlation with SEMA5A, revealing TNFSF11, BMP6, ITPR1, and DLC1 with correlation coefficients exceeding 0.3. Survival analysis underscored DLC1 and BMP6 as key genes due to significant prognostic differences. GSEA implicated SEMA5A, BMP6, and DLC1 in the ECM receptor interaction pathway. Immune infiltration analysis indicated a negative correlation of SEMA5A and BMP6 with M1 macrophages, while DLC1 exhibited the strongest association with the immune checkpoint PDCD1LG2 (p < 0.05, cor = 0.43). The mRNA expression level of SEMA5A was significantly upregulated in AGS parental cells compared to GES-1 cells (p < 0.01), whereas DLC1 and BMP6 mRNA levels were markedly downregulated in AGS parental cells relative to GES-1 (p < 0.0001).

Conclusion

ARGs BMP6 and DLC1, associated with SEMA5A, were identified, and their prognostic significance in GC was demonstrated. Additionally, their regulatory mechanisms were further elucidated through immune infiltration analysis and molecular network construction, providing a theoretical foundation for future research on the molecular mechanisms in patients with GC.

Unraveling the causal links and novel molecular classification of Crohn's disease in breast Cancer: a two-sample mendelian randomization and transcriptome analysis with prognostic modeling

BACKGROUND

Crohn's disease (CD), a prominent manifestation of chronic gastrointestinal inflammation, and breast cancer (BC), seemingly disparate in the medical domain, exhibit a shared characteristic. This convergence arises from their involvement in chronic inflammation and immune responses, an aspect that has progressively captivated the attention of investigators but remain controversial.

METHODS

We used two-sample Mendelian Randomization (MR) and transcriptomics to explore the relationship between CD and BC. MR assessed causality of CD on different BC subtypes and reverse causality of BC on CD. We identified CD-related differentially expressed genes and their prognostic impact on BC, and developed a new molecular BC classification based on these key genes.

RESULTS

MR revealed a causal link between CD and increased BC risk, especially in estrogen receptor-positive (ER+) patients, but not in ER-negative (ER-) cases. BC showed no causal effect on CD. Transcriptomics pinpointed genes like B4GALNT2 and FGF19 that affected BC prognosis in CD patients. A nomogram based on these genes predicted BC outcomes with high accuracy. Using these genes, a new molecular classification of BC patients was proposed.

CONCLUSIONS

CD is a risk factor for ER + BC but not for ER- BC. BC does not causally affect CD. Our prognostic model and new BC molecular classifications offer insights for personalized treatment strategies.

Machine learning algorithm-based biomarker exploration and validation of mitochondria-related diagnostic genes in osteoarthritis

The role of mitochondria in the pathogenesis of osteoarthritis (OA) is significant. In this study, we aimed to identify diagnostic signature genes associated with OA from a set of mitochondria-related genes (MRGs). First, the gene expression profiles of OA cartilage GSE114007 and GSE57218 were obtained from the Gene Expression Omnibus. And the limma method was used to detect differentially expressed genes (DEGs). Second, the biological functions of the DEGs in OA were investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Wayne plots were employed to visualize the differentially expressed mitochondrial genes (MDEGs) in OA. Subsequently, the LASSO and SVM-RFE algorithms were employed to elucidate potential OA signature genes within the set of MDEGs. As a result, GRPEL and MTFP1 were identified as signature genes. Notably, GRPEL1 exhibited low expression levels in OA samples from both experimental and test group datasets, demonstrating high diagnostic efficacy. Furthermore, RT-qPCR analysis confirmed the reduced expression of Grpel1 in an in vitro OA model. Lastly, ssGSEA analysis revealed alterations in the infiltration abundance of several immune cells in OA cartilage tissue, which exhibited correlation with GRPEL1 expression. Altogether, this study has revealed that GRPEL1 functions as a novel and significant diagnostic indicator for OA by employing two machine learning methodologies. Furthermore, these findings provide fresh perspectives on potential targeted therapeutic interventions in the future.

Causal relationship between varicose veins and mean corpuscular hemoglobin concentration based on Mendelian randomization study

BACKGROUND

Increased hemoglobin concentrations may increase the risk of varicose veins. However, the underlying relationship between them was not yet understood.

METHODS

Mendelian randomization (MR) analysis was performed to investigate causal effect between mean corpuscular hemoglobin concentration (MCHC, exposure factor) and varicose veins (outcome). Afterward, sensitivity analysis was used to ensure the reliability of MR analysis results. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of SNPs were performed. A search tool for recurring instances of neighbouring genes (STRING) database was used to construct a protein-protein interaction (PPI) network.

RESULTS

Therefore, the inverse-variance weighted (IVW) results showed there existed a causal relationship between MCHC and varicose veins (p = 0.0026), with MCHC serving as a significant risk factor. (odd ratio [OR] = 1.2321). In addition, the validity of the results of the forward MR analysis was verified by sensitivity analysis. Further, a PPI network of 92 single-nucleotide polymorphisms (SNPs) which used for forward MR analysis related genes was constructed. And they were found to be closely associated with the peroxisome proliferator-activated receptor (PPAR) signalling pathway and cellular response to external stimulus by enrichment analysis. In addition, we clarified that the effect of varicose veins on MCHC was minimal by reverse MR analysis, suggesting that the results of forward MR analysis were not disturbed by reverse results.

CONCLUSION

This study found a causal relationship between varicose veins and MCHC, which provided strong evidence for the effect of hemoglobin on varicose veins, and a new thought for the diagnosis and prevention of varicose veins in the future.

Identification of biomarkers related to tryptophan metabolism in osteoarthritis

Background

OA (osteoarthritis) is a common joint disease characterized by damage to the articular cartilage and affects the entire joint tissue, with its main manifestations being joint pain, stiffness, and limited movement.Currently,we know that OA is a complex process composed of inflammatory and metabolic factors.It is reported that the occurrence and development of OA is related to the change of tryptophan metabolism.Therefore, the study of tryptophan metabolism and OA related genes is hopeful to find a new therapeutic target for OA.

Methods

Differentially expressed genes (DEGs) in GSE55235 were gained via differential expression analysis (OA samples vs normal samples). The tryptophan metabolic related DEGs (TMR-DEGs) were obtained by overlapping tryptophan metabolism related genes (TMRGs) and DEGs. Further, biomarkers were screening via Least absolute shrinkage and selection operator (LASSO), naive bayes (NB) and supportvector machine-recursive feature elimination (SVM-RFE) algorithm to establish a diagnostic model. Afterward, Gene Set Enrichment Analysis (GSEA) and drug prediction were performed based on diagnostic biomarkers by multiple software and databases. Eventually, expression level of biomarker public databases was verified using real-time quantitative polymerase chain reaction (RT-qPCR).

Results

Three tryptophan metabolism related biomarkers (TDO2, AOX1 and SLC3A2) were identified in OA. GSEA analysis demonstrated that biomarkers were associated with the function of 'FoxO signaling pathway', 'spliceosome' and 'ribosome'. There were seven drugs with therapeutic potential on TDO2 and AOX1. Ultimately, compared with normal group, expression of AOX1 and SLC3A2 in OA group remarkable lower.

Conclusion

Overall, three tryptophan metabolic related diagnostic biomarkers that associated with OA were obtained, which provided a original direction for the diagnosis and treatment of OA.

Identification of Pyroptosis-Related Molecular Subtypes and Diagnostic Model development in Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent psychological disorder associated with inflammation, with complex pathological mechanisms. Pyroptosis has been suggested to contribute to inflammation in central nervous system diseases. Little research, however, has examined what role pyroptosis played in MDD. In the present study, the differential expression pyroptosis-related genes (DE-PRGs) in MDD were identified from the GEO database (GSE98793 and GSE19738). Then, consensus clustering analysis was used to evaluate differences in MDD molecular subtypes characteristics based on PRGs. The characteristic diagnostic biomarkers for MDD were identified by Weighted Correlation Network Analysis (WGCNA) and multiple machine learning algorithms. Three intersection genes (GZMA, AKR1C3, and CD52) were obtained, which are expected to become potential biomarkers for MDD with excellent reliability and accuracy. Subsequently, the immune infiltration characteristics result indicated that the development of MDD is mediated by immune-related function, where three DE-PRGs were strongly related to the immune infiltration landscape of MDD. The biological experiments in vitro further proved that three unique PRGs are emerging as important players in MDD diagnosis. Our research aimed to provide novel ideas and biomarkers targeting MDD.

EnvZ/OmpR Controls Protein Expression and Modifications in Cronobacter sakazakii for Virulence and Environmental Resilience

Cronobacter sakazakii is a notorious foodborne opportunistic pathogen, particularly affecting vulnerable populations such as premature infants, and poses significant public health challenges. This study aimed to elucidate the role of the envZ/ompR genes in environmental tolerance, pathogenicity, and protein regulation of C. sakazakii. An envZ/ompR knockout mutant was constructed and assessed for its impact on bacterial growth, virulence, environmental tolerance, and protein regulation. Results demonstrate that deletion of envZ/ompR genes leads to reduced growth rate and attenuated virulence in animal models. Additionally, the knockout strain exhibited compromised environmental tolerance, particularly in desiccation and oxidative stress conditions, along with impaired adhesion and invasion abilities in epithelial cells. Proteomic analysis revealed significant alterations in protein expression and phosphorylation patterns, highlighting potential compensatory mechanisms triggered by gene deletion. Furthermore, investigation into protein deamidation and glucose metabolism uncovered a link between envZ/ompR deletion and energy metabolism dysregulation. Interestingly, the downregulation of MalK and GrxC proteins was identified as contributing factors to altered desiccation tolerance and disrupted redox homeostasis, respectively, providing mechanistic insights into the phenotypic changes observed. Overall, this study enhances understanding of the multifaceted roles of envZ/ompR in C. sakazakii physiology and pathogenesis, shedding light on potential targets for therapeutic intervention and food safety strategies.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration

BACKGROUND

Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.

METHODS

We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.

RESULTS

WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.

CONCLUSIONS

HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Fe3O4@R837 Nanoplatform Enhances Chemical Dynamic Therapy and Immunotherapy: Integrated Transcriptomic Analysis Reveals Key Genes in Breast Cancer Prognosis

Breast cancer represents a substantial contributor to mortality rates among women with cancer. Chemical dynamic therapy is a promising anticancer strategy that utilizes the Fenton reaction to transform naturally occurring hydrogen peroxide (H2O2) into hydroxyl radicals (•OH). Additionally, cancer immunotherapy using immune drugs, such as imiquimod (R837), has shown promise in activating T cells to kill tumor cells. In this study, we proposed a Fe3O4@R837 smart nanoplatform that can trigger the Fenton reaction and induce immune responses in breast cancer treatment. Furthermore, we performed transcriptome sequencing on breast cancer samples and used the R package (limma) to analyze differential expression profiles and select differentially expressed genes (DEGs). We obtained clinical information and RNA expression matrix data from The Cancer Genome Atlas database to perform survival analysis and identify prognostic-related genes (PRGs) and molecular subtypes with distinct prognoses. We used the TIMER 2.0 web and other methods to determine the tumor immune microenvironment and immune status of different prognostic subtypes. We identified DPGs by taking the intersection of DEGs and PRGs and performed functional analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, to elucidate potential mechanisms. Subsequently, we constructed a protein-protein interaction network using the STRING database to visualize the interactions between the DPGs. We screened hub genes from the DPGs using the Cytoscape plugin and identified six hub genes: CD3E, GZMK, CD27, SH2D1A, ZAP70, and TIGIT. Our results indicate that these six key genes regulate immune cell recruitment to increase T-cell cytotoxicity and kill tumors. Targeting these key genes can enhance immunotherapy and improve the breast cancer prognosis.

Bile acid metabolism modulates intestinal immunity involved in ulcerative colitis progression

The bile acids (BA) in the intestine promote inflammation by interacting with immune cells, playing a crucial role in the progression of UC, but the specific mechanism between the two remains elusive. This study aims to explore the relationship between BAMand UC inflammation and determine its potential mechanisms.Firstly, we employed a hybrid approach using Lasso regression and support vector machine (SVM) feature selection in bioinformatics to identify genes linked to UC and BAM. The relationship between these genes and immune infiltration was explored, along with their correlation with immune factors in the Tumor-Immune System Interaction Database (TISIDB) database. Gene Set Enrichment Analysis (GSEA) pathway enrichment analysis was then used to predict signaling pathways associated with key genes in UC. Single-cell data from the GSE13464 dataset was also analyzed. Finally, Five differentially expressed genes (DEGs) related to BAM (APOA1, AMACR, PEX19, CH25H, and AQP9) were significantly upregulated/downregulated in UC immune cells. The expression of important genes in UC tissue was confirmed in the experimental validation section and AQP9, which showed significant differential expression, was chosen for further validation. The results showed that the AQP9 gene may regulate the IFN - γ/JAK signaling axis, thereby promoting CD8+T cell activation. This research has greatly advanced our comprehension of the pathogenesis and underlying mechanism of BAM in immune cells linked to UC.

NOX4-mediated astrocyte ferroptosis in Alzheimer's disease

This study investigates NADPH oxidase 4 (NOX4) involvement in iron-mediated astrocyte cell death in Alzheimer's Disease (AD) using single-cell sequencing data and transcriptomes. We analyzed AD single-cell RNA sequencing data, identified astrocyte marker genes, and explored biological processes in astrocytes. We integrated AD-related chip data with ferroptosis-related genes, highlighting NOX4. We validated NOX4's role in ferroptosis and AD in vitro and in vivo. Astrocyte marker genes were enriched in AD, emphasizing their role. NOX4 emerged as a crucial player in astrocytic ferroptosis in AD. Silencing NOX4 mitigated ferroptosis, improved cognition, reduced Aβ and p-Tau levels, and alleviated mitochondrial abnormalities. NOX4 promotes astrocytic ferroptosis, underscoring its significance in AD progression.

Role of gut microbiota in the pathogenesis of castration-resistant prostate cancer: a comprehensive study using sequencing and animal models

CRPC remains a significant challenge in prostate cancer research. We aimed to elucidate the role of gut microbiota and its specific mechanisms in CRPC using a multidisciplinary approach. We analyzed 16S rRNA sequencing data from mouse fecal samples, revealing substantial differences in gut microbiota composition between CRPC and castration-sensitive prostate cancer mice, particularly in Firmicutes and Bacteroidetes. Functional analysis suggested different bacteria may influence CRPC via the α-linolenic acid metabolism pathway. In vivo, experiments utilizing mouse models and fecal microbiota transplantation (FMT) demonstrated that FMT from healthy control mice could decelerate tumor growth in CRPC mice, reduce TNF-α levels, and inhibit the activation of the TLR4/MyD88/NF-κB signaling pathway. Transcriptome sequencing identified crucial genes and pathways, with rescue experiments confirming the gut microbiota's role in modulating CRPC progression through the TLR4/MyD88/NF-κB pathway. The activation of this pathway by TNF-α has been corroborated by in vitro cell experiments, indicating its role in promoting prostate cancer cell proliferation, migration, and invasion while inhibiting apoptosis. Gut microbiota dysbiosis may promote CRPC development through TNF-α activation of the TLR4/MyD88/NF-κB signaling pathway, potentially linked to α-linolenic acid metabolism.

Comprehensive Analysis of Sphingolipid Metabolism-Related Genes in Osteoarthritic Diagnosis and Synovial Immune Dysregulation

BACKGROUND Osteoarthritis (OA) is a chronic degenerative disease characterized by synovitis and has been implicated in sphingolipid metabolism disorder. However, the role of sphingolipid metabolism pathway (SMP)-related genes in the occurrence of OA and synovial immune dysregulation remains unclear. MATERIAL AND METHODS In this study, we obtained synovium-related databases from GEO (n=40 for both healthy controls and OA) and analyzed the expression levels of SMP-related genes. Using 2 algorithms, we identified hub genes and developed a diagnostic model incorporating these hub genes to predict the occurrence of OA. Subsequently, the hub genes were further validated in peripheral blood samples from OA patients. Additionally, CIBERSORT and MCP-counter analyses were employed to explore the correlation between hub genes and immune dysregulation in OA synovium. WGCNA was used to determine enriched modules in different clusters. RESULTS Overall, the expression levels of SMP genes were upregulated in OA synovium. We identified 6 hub genes of SMP and constructed an excellent diagnostic model (AUC=0.976). The expression of re-confirmed hub genes showed associations with immune-related cell infiltration and levels of inflammatory cytokines. Furthermore, we observed heterogeneity in the expression patterns of hub genes across different clusters of OA. Notably, older patients displayed increased susceptibility to elevated levels of pain-related inflammatory cytokines and infiltration of immune cells. CONCLUSIONS The SMP-related hub genes have the potential to serve as diagnostic markers for OA patients. Moreover, the 4 hub genes of SMP demonstrate wide participation in immune dysregulation in OA synovium. The activation of different pathways is observed among different populations of patients with OA.

Mitochondria-Related Gene MAOB is a Key Biomarker of Osteoarthritis and Inhibition of Its Expression Reduces LPS-induced Chondrocyte Damage

The mitochondria are an important organelle in cells responsible for producing energy, and its abnormal function is closely related to the occurrence and development of osteoarthritis. Finding key genes associated with mitochondrial dysfunction in osteoarthritis can provide new ideas for the study of its pathogenesis. Firstly, 371 differential expressed genes (DEGs) were obtained through bioinformatics analysis of the GSE12021 and GSE55235 datasets in the GEO database, and 24 mitochondria-related DEGs (Mito-DEGs) were obtained by crossing differential genes with mitochondrial related genes. Next, KEGG and GO analysis of Mito-DEGs showed that upregulated Mito-DEGs were mainly enriched in small molecule catabolic process and tryptophan metabolism, while downregulated Mito-DEGs were mainly enriched in acetyl-CoA metabolic process and fatty acid biosynthesis. Furthermore, the key genes ME2 and MAOB were obtained through protein-protein interaction network analysis and lasso cox analysis of the 24 Mito-DEGs. In addition, the comparison results of immune cell scores showed differences between T cells CD4 memory resting, T cells regulatory (Tregs), Mast cells resting, and Mast cells activated in the OA group and the control group. More importantly, the potential regulatory mechanisms of key genes were studied through GSEA analysis and their correlation with immune infiltrating cells, immune checkpoints, m6A, and ferroptosis. Finally, in LPS-induced C28/I2 cells, silencing MAOB reduced inflammation injury and inhibited mitochondrial damage. Our research findings suggest that MAOB may hold potential as a target for the diagnosis and treatment of osteoarthritis.

Harnessing GMNP-loaded BMSC-derived EVs to target miR-3064-5p via MEG3 overexpression: Implications for diabetic osteoporosis therapy in rats

Diabetic osteoporosis (DO) is a significant complication of diabetes, characterized by a decrease in bone mineral density and an increase in fracture risk. Magnetic nanoparticles (GMNPs) have emerged as potential drug carriers for various therapeutic applications. This study investigated the molecular mechanism of GMNPs loaded with bone marrow mesenchymal stem cell (BMSC) derived extracellular vesicles (EVs) overexpressing MEG3 target miR-3064-5p to induce NR4A3 for treating DO in rats. Initial analysis was carried out on GEO datasets GSE7158 and GSE62589, revealing a notable downregulation of NR4A3 in osteoporotic samples. Subsequent in vitro studies demonstrated the effective uptake of BMSC-EVs-MEG3 by osteoblasts and its potential to inhibit miR-3064-5p, activating the PINK1/Parkin signaling pathway and thus promoting mitochondrial autophagy, osteoblast proliferation, and differentiation. In vivo, experiments using DO rat models further substantiated the therapeutic efficacy of GMNPE-EVs-MEG3 in alleviating osteoporosis symptoms. In conclusion, GMNPs loaded with BMSC-EVs, through the delivery of MEG3 targeting miR-3064-5p, can effectively promote NR4A3 expression, activate the PINK1/Parkin pathway, and thereby enhance osteoblast proliferation and differentiation, offering a promising treatment for DO.

Animal Models of Osteoarthritis: Updated Models and Outcome Measures 2016-2023

Purpose

Osteoarthritis (OA) is a global musculoskeletal disorder that affects primarily the knee and hip joints without any FDA-approved disease-modifying therapies. Animal models are essential research tools in developing therapies for OA; many animal studies have provided data for the initiation of human clinical trials. Despite this, there is still a need for strategies to recapitulate the human experience using animal models to better develop treatments and understand pathogenesis. Since our last review on animal models of osteoarthritis in 2016, there have been exciting updates in OA research and models. The main purpose of this review is to update the latest animal models and key features of studies in OA research.

Method

We used our existing classification method and screened articles in PubMed and bibliographic search for animal OA models between 2016 and 2023. Relevant and high-cited articles were chosen for inclusion in this narrative review.

Results

Recent studies were analyzed and classified. We also identified ex vivo models as an area of ongoing research. Each animal model offers its own benefit in the study of OA and there are a full range of outcome measures that can be assessed. Despite the vast number of models, each has its drawbacks that have limited translating approved therapies for human use.

Conclusion

Depending on the outcome measures and objective of the study, researchers should pick the best model for their work. There have been several exciting studies since 2016 that have taken advantage of regenerative engineering techniques to develop therapies and better understand OA.

Lay Summary

Osteoarthritis (OA) is a chronic debilitating disease without any cure that affects mostly the knee and hip joints and often results in surgical joint replacement. Cartilage protects the joint from mechanical forces and degrades with age or in response to injury. The many contributing causes of OA are still being investigated, and animals are used for preclinical research and to test potential new treatments. A single consensus OA animal model for preclinical studies is non-existent. In this article, we review the many animal models for OA and provide a much-needed update on studies and model development since 2016.

Machine learning-based identification and immune characterization of ferroptosis-related molecular clusters in osteoarthritis and validation

Osteoarthritis (OA), a degenerative joint disease, involves synovial inflammation, subchondral bone erosion, and cartilage degeneration. Ferroptosis, a regulated non-apoptotic programmed cell death, is associated with various diseases. This study investigates ferroptosis-related molecular subtypes in OA to comprehend underlying mechanisms. The Gene Expression Omnibus datasets GSE206848, GSE55457, GSE55235, GSE77298 and GSE82107 were used utilized. Unsupervised clustering identified the ferroptosis-related gene (FRG) subtypes, and their immune characteristics were assessed. FRG signatures were derived using LASSO and SVM-RFE algorithms, forming models to evaluate OA's ferroptosis-related immune features. Three FRG clusters were found to be immunologically heterogeneous, with cluster 1 displaying robust immune response. Models identified nine key signature genes via algorithms, demonstrating strong diagnostic and prognostic performance. Finally, qRT-PCR and Western blot validated these genes, offering consistent results. In addition, some of these genes may have implications as new therapeutic targets and can be used to guide clinical applications.

Prognositic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma

OBJECTIVES

Osteosarcoma is a highly aggressive primary malignant bone tumor commonly seen in children and adolescents, with a poor prognosis. Anchorage-dependent cell death (anoikis) has been proven to be indispensable in tumor metastasis, regulating the migration and adhesion of tumor cells at the primary site. However, as a type of programmed cell death, anoikis is rarely studied in osteosarcoma, especially in the tumor immune microenvironment. This study aims to clarify prognostic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma.

METHODS

Anoikis-related genes (ANRGs) were obtained from GeneCards. Clinical information and ANRGs expression profiles of osteosarcoma patients were sourced from the therapeutically applicable research to generate effective therapies and Gene Expression Omnibus (GEO) databases. ANRGs highly associated with tumor immune microenvironment were identified by the estimate package and the weighted gene coexpression network analysis (WGCNA) algorithm. Machine learning algorithms were performed to construct long-term survival predictive strategy, each sample was divided into high-risk and low-risk subgroups, which was further verified in the GEO cohort. Finally, based on single-cell RNA-seq from the GEO database, analysis was done on the function of signature genes in the osteosarcoma tumor microenvironment.

RESULTS

A total of 51 hub ANRGs closely associated with the tumor microenvironment were identified, from which 3 genes (MERTK, BNIP3, S100A8) were selected to construct the prognostic model. Significant differences in immune cell activation and immune-related signaling pathways were observed between the high-risk and low-risk groups based on tumor microenvironment analysis (all P<0.05). Additionally, characteristic genes within the osteosarcoma microenvironment were identified in regulation of intercellular crosstalk through the GAS6-MERTK signaling pathway.

CONCLUSIONS

The prognostic model based on ANRGs and tumor microenvironment demonstrate good predictive power and provide more personalized treatment options for patients with osteosarcoma.

Potential mechanism of Chai Gui Zexie Decoction for NSCLC treatment assessed using network pharmacology, bioinformatics, and molecular docking: An observational study

To explore the potential mechanism of Chai Gui Zexie Decoction for non-small cell lung cancer (NSCLC) treatment using network pharmacology, bioinformatics, and molecular docking. The active ingredients of Chai Gui Zexie Decoction and the associated predicted targets were screened using the TCMSP database. NSCLC-related targets were obtained from GeneCards and OMIM. Potential action targets, which are intersecting drug-predicted targets and disease targets, were obtained from Venny 2.1. The protein-protein interaction network was constructed by importing potential action targets into the STRING database, and the core action targets and core ingredients were obtained via topological analysis. The core action targets were entered into the Metascape database, and Gene Ontology annotation analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. Differentially expressed genes were screened using the Gene Expression Omnibus, and the key targets were obtained by validating the core action targets. The key targets were input into The Tumor IMmune Estimation Resource for immune cell infiltration analysis. Finally, the molecular docking of key targets and core ingredients was performed. We obtained 60 active ingredients, 251 drug prediction targets, and 2133 NSCLC-related targets. Meanwhile, 147 potential action targets were obtained, and 47 core action targets and 40 core ingredients were obtained via topological analysis. We detected 175 pathways related to NSCLC pharmaceutical therapy. In total, 1249 Gene Ontology items were evaluated. Additionally, 3102 differential genes were screened, and tumor protein P53, Jun proto-oncogene, interleukin-6, and mitogen-activated protein kinase 3 were identified as the key targets. The expression of these key targets in NSCLC was correlated with macrophage, CD4+ T, CD8+ T, dendritic cell, and neutrophil infiltration. The molecular docking results revealed that the core ingredients have a potent affinity for the key targets. Chai Gui Zexie Decoction might exert its therapeutic effect on NSCLC through multiple ingredients, targets, and signaling pathways.

Exploring m6A-linked aging genes in osteoarthritis and broad cancer spectrum: Prospects for diagnostic and therapeutic advancements

Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts individuals and healthcare systems worldwide. However, the exploration of N6-methyladenosine (m6A)-related aging genes in OA pathogenesis remains largely underexplored. This study aimed to elucidate the role of m6A-related aging genes in OA and to develop a robust diagnostic model based on their expression profiles. Leveraging publicly available gene expression datasets, we conducted consensus clustering to categorize OA into distinct subtypes, guided by the expression patterns of m6A-related aging genes. Utilizing XGBoost, a cutting-edge machine learning approach, we identified key diagnostic genes and constructed a predictive model. Our investigation extended to the immune functions of these genes, shedding light on potential therapeutic targets and underlying regulatory mechanisms. Our analysis unveiled specific OA subtypes, each marked by unique expression profiles of m6A-related aging genes. We pinpointed a set of pivotal diagnostic genes, offering potential therapeutic avenues. The developed diagnostic model exhibited exceptional capability in distinguishing OA patients from healthy controls. To corroborate our computational findings, we performed quantitative real-time polymerase chain reaction analyses on two cell lines: HC-OA (representing adult osteoarthritis cells) and C-28/I2 (representative of normal human chondrocytes). The gene expression patterns observed were consistent with our bioinformatics predictions, further validating our initial results. In conclusion, this study underscores the significance of m6A-related aging genes as promising biomarkers for diagnosis and prognosis, as well as potential therapeutic targets in OA. Although these findings are encouraging, further validation and functional analyses are crucial for their clinical application.

Meta-analysis and transcriptomic analysis reveal that NKRF and ZBTB17 regulate the NF-κB signaling pathway, contributing to the shared molecular mechanisms of Alzheimer's disease and atherosclerosis

INTRODUCTION

Alzheimer's disease (AD) and atherosclerosis (AS) are widespread diseases predominantly observed in the elderly population. Despite their prevalence, the underlying molecular interconnections between these two conditions are not well understood.

METHODS

Utilizing meta-analysis, bioinformatics methodologies, and the GEO database, we systematically analyzed transcriptome data to pinpoint key genes concurrently differentially expressed in AD and AS. Our experimental validations in mouse models highlighted the prominence of two genes, NKRF (NF-κB-repressing factor) and ZBTB17 (MYC-interacting zinc-finger protein 1).

RESULTS

These genes appear to influence the progression of both AD and AS by modulating the NF-κB signaling pathway, as confirmed through subsequent in vitro and in vivo studies.

CONCLUSIONS

This research uncovers a novel shared molecular pathway between AD and AS, underscoring the significant roles of NKRF and ZBTB17 in the pathogenesis of these disorders.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study

Background

Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA.

Methods

We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed.

Results

Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes.

Conclusion

CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and machine learning

Background

The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.

Methods

Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.

Results

Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.

Conclusion

In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

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.

MIR143HG promotes methylation of transcription factor HOXB7 promoter by recruiting methyltransferase DNMT1 to prevent the progression of colon cancer

In recent years, accumulating evidence has demonstrated the role of long noncoding RNAs (lncRNAs) in colon cancer. We aim to investigate the role of MIR143HG, also known as CARMN (Cardiac mesoderm enhancer-associated noncoding RNA) in colon cancer and explore the related mechanisms. An RNAseq data analysis was performed to screen differentially expressed lncRNAs associated with colon cancer. Next, MIR143HG expression was quantified in colon cancer cells. Moreover, the contributory roles of MIR143HG in the progression of colon cancer with the involvement of DNMT1 and HOXB7 (Homeobox B7) were evaluated after restored MIR143HG or depleted HOXB7. Finally, the effects of MIR143HG were investigated in vivo by measuring tumor formation in nude mice. High-throughput transcriptome sequencing was employed to validate the specific mechanisms by which MIR143HG and HOXB7 affect tumor growth in vivo. MIR143HG was found to be poorly expressed, while HOXB7 was highly expressed in colon cancer. MIR143HG could promote HOXB7 methylation by recruiting DNMT1 to reduce HOXB7 expression. Upregulation of MIR143HG or downregulation of HOXB7 inhibited cell proliferation, invasion and migration and facilitated apoptosis in colon cancer cells so as to delay the progression of colon cancer. The same trend was identified in vivo. Our study provides evidence that restoration of MIR143HG suppressed the progression of colon cancer via downregulation of HOXB7 through DNMT1-mediated HOXB7 promoter methylation. Thus, MIR143HG may be a potential candidate for the treatment of colon cancer.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.

METHODS

We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).

RESULTS

The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.

CONCLUSIONS

In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Developing a RiskScore Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction

AIM

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment.

BACKGROUND

Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD.

OBJECTIVE

To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment.

METHODS

Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package.

RESULTS

Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment.

CONCLUSION

We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence

BACKGROUND

People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic features and clinical value of peripheral blood biomarkers for osteoarthritis.

OBJECTIVE

The goal of this project was to investigate the diagnostic features of peripheral blood and immune cell infiltration in osteoarthritis (OA).

METHODS

Two eligible datasets (GSE63359 and GSE48556) were obtained from the GEO database to discern differentially expressed genes (DEGs). The machine learning strategy was employed to filtrate diagnostic biomarkers for OA. Additional verification was implemented by collecting clinical samples of OA. The CIBERSORT website estimated relative subsets of RNA transcripts to evaluate the immune-inflammatory states of OA. The link between specific DEGs and clinical immune-inflammatory markers was found by correlation analysis.

RESULTS

Overall, 67 robust DEGs were identified. The nuclear receptor subfamily 2 group C member 2 (NR2C2), transcription factor 4 (TCF4), stromal antigen 1 (STAG1), and interleukin 18 receptor accessory protein (IL18RAP) were identified as effective diagnostic markers of OA in peripheral blood. All four diagnostic markers showed significant increases in expression in OA. Analysis of immune cell infiltration revealed that macrophages are involved in the occurrence of OA. Candidate diagnostic markers were correlated with clinical immune-inflammatory indicators of OA patients.

CONCLUSION

We highlight that DEGs associated with immune inflammation (NR2C2, TCF4, STAG1, and IL18RAP) may be potential biomarkers for peripheral blood in OA, which are also associated with clinical immune-inflammatory indicators.

Bioinformatics analysis of hub genes as osteoarthritis prognostic biomarkers

Osteoarthritis (OA) is a progressive cartilage degradation disease, concomitant with synovitis, osteophyte formation, and subchondral bone sclerosis. Over 37% of the elderly population is affected by OA, and the number of cases is increasing as the global population ages. Therefore, the objective of this study was to identify and analyze the hub genes of OA combining with comprehensive bioinformatics analysis tools to provide theoretical basis in further OA effective therapies. Two sample sets of GSE46750 contained 12 pairs OA synovial membrane and normal samples harvested from patients as well as GSE98918 including 12 OA and non-OA patients were downloaded from the Gene Expression Omnibus database (GEO) database. Differentially expressed genes (DEGs) were identified using Gene Expression Omnibus 2R (GEO2R), followed by functional enrichment analysis, protein-protein interaction networks construction. The hub genes were identified and evaluated. An OA rat model was constructed, hematoxylin and eosin staining, safranin O/fast green staining, cytokines concentrations of serum were used to verify the model. The hub genes expression level in the knee OA samples were verified using RT-qPCR. The top 20 significantly up-regulated and down-regulated DEGs were screened out from the two datasets, respectively. The top 18 GO terms and 10 KEGG pathways were enriched. Eight hub genes were identified, namely MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2. Among them, the hub genes were all up-regulated in in vivo OA rat model, compared with healthy controls. The eight hub genes identified (MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2) were shown to be associated with OA. These genes can serve as disease markers to discriminate OA patients from healthy controls.

Cucurbitacin E reduces IL-1β-induced inflammation and cartilage degeneration by inhibiting the PI3K/Akt pathway in osteoarthritic chondrocytes

BACKGROUND

Osteoarthritis is a degenerative joint disease. Cartilage degeneration is the earliest and most important pathological change in osteoarthritis, and persistent inflammation is one of the driving factors of cartilage degeneration. Cucurbitacin E, an isolated compound in the Cucurbitacin family, has been shown to have anti-inflammatory effects, but its role and mechanism in osteoarthritic chondrocytes are unclear.

METHODS

For in vitro experiments, human chondrocytes were stimulated with IL-1β, and the expression of inflammatory genes was measured by Western blotting and qPCR. The expression of extracellular matrix proteins was evaluated by immunofluorescence staining, Western blotting and saffron staining. Differences in gene expression between cartilage from osteoarthritis patients and normal cartilage were analysed by bioinformatics methods, and the relationship between Cucurbitacin E and its target was analysed by a cellular thermal shift assay, molecular docking analysis and molecular dynamics simulation. For in vivo experiments, knee osteoarthritis was induced by DMM in C57BL/6 mouse knee joints, and the effect of Cucurbitacin E on knee joint degeneration was evaluated.

RESULTS

The in vitro experiments confirmed that Cucurbitacin E effectively inhibited the production of the inflammatory cytokine interleukin-1β(IL-1β) and cyclooxygenase-2 (COX-2) by IL-1β-stimulated chondrocytes and alleviates extracellular matrix degradation. The in vivo experiments demonstrated that Cucurbitacin E had a protective effect on the knee cartilage of C57BL/6 mice with medial meniscal instability in the osteoarthritis model. Mechanistically, bioinformatic analysis of the GSE114007 and GSE117999 datasets showed that the PI3K/AKT pathway was highly activated in osteoarthritis. Immunohistochemical analysis of PI3K/Akt signalling pathway proteins in pathological slices of human cartilage showed that the level of p-PI3K in patients with osteoarthritis was higher than that in the normal group. PI3K/Akt were upregulated in IL-1β-stimulated chondrocytes, and Cucurbitacin E intervention reversed this phenomenon. The cellular thermal shift assay, molecular docking analysis and molecular dynamics experiment showed that Cucurbitacin E had a strong binding affinity for the inhibitory target PI3K. SC79 activated Akt phosphorylation and reversed the effect of Cucurbitacin E on IL-1β-induced chondrocyte degeneration, demonstrating that Cucurbitacin E inhibits IL-1β-induced chondrocyte inflammation and degeneration by inhibiting the PI3K/AKT pathway.

CONCLUSION

Cucurbitacin E inhibits the activation of the PI3K/AKT pathway, thereby alleviating the progression of OA. In summary, we believe that Cucurbitacin E is a potential drug for the treatment of OA.

Identification of age-specific biomarkers of spinal cord injury: A bioinformatics analysis of young and aged mice models

BACKGROUND

Spinal cord injury (SCI) is a debilitating event that often results in long-term physical damage, disability, and a significant impact on a patient's quality of life. Past research has noted an age-dependent decline in regeneration post-SCI. This study seeks to identify potential biomarkers and enriched pathways in young and aged SCI mouse models.

METHODS

We retrieved the microarray data of spinal cord samples from SCI mice and control mice from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R software and the Linear Models for Microarray Data (limma) package. The Gene Set Enrichment Analysis (GSEA) determined enrichment differences among gene sets. The Weighted Gene Co-expression Network Analysis (WGCNA) pinpointed clinically significant modules and hub genes in SCI. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was employed to uncover significantly related pathways of crucial genes in SCI.

RESULTS

We found 2560 DEGs in the young SCI group, comprised of 1733 upregulated RNAs and 827 downregulated RNAs. In the aged SCI group, 3048 DEGs were revealed including 1856 upregulated and 1192 downregulated genes. The GSEA revealed 12 enriched signaling pathways in the young SCI group, such as IL6/JAK/STAT3 signaling, interferon alpha response, and interferon gamma response, and 21 signaling pathways in the aged SCI group such as IL6/JAK/STAT3 signaling, E2F targets, and angiogenesis-related pathways. The WGCNA identified the turquoise module as significantly associated with the clinical traits of both young and aged SCI, and revealed 3181 hub genes. Ultimately, 1858 significant genes in SCI were found, with associated signaling pathways including epithelial-mesenchymal transition (EMT), interferon gamma response, and KARS signaling.

CONCLUSION

Our study explored the RNA expression patterns and enriched signaling pathways in young and aged SCI mice. These findings may provide potential biomarkers for targeted SCI therapy.

An in vitro model for osteoarthritis using long-cultured inflammatory human macrophages repeatedly stimulated with TLR agonists

Osteoarthritis (OA) is characterized by an abundance of inflammatory M1-like macrophages damaging local tissues. The search for new potential drugs for OA suffers from the lack of appropriate methods of long-lasting inflammation. Here we developed and characterized an in vitro protocol of long-lasting culture of primary human monocyte-derived macrophages differentiated with a combination of M-CSF+GM-CSF that optimally supported long-cultured macrophages (LC-Mϕs) for up to 15 days, unlike their single use. Macrophages repeatedly stimulated for 15 days with the TLR2 ligand Pam3CSK4 (LCS-Mϕs), showed sustained levels over time of IL-6, CCL2, and CXCL8, inflammatory mediators that were also detected in the synovial fluids of OA patients. Furthermore, macrophages isolated from the synovia of two OA patients showed an expression profile of inflammation-related genes similar to that of LCS-Mϕs, validating our protocol as a model of chronically activated inflammatory macrophages. Next, to confirm that these LCS-Mϕs could be modulated by anti-inflammatory compounds, we employed dexamethasone and/or celecoxib, two drugs widely used in OA treatment, that significantly inhibited the production of inflammatory mediators. This easy-to-use in vitro protocol of long-lasting inflammation with primary human macrophages could be useful for the screening of new compounds to improve the therapy of inflammatory disorders.

Identification of a new risk score model based on hypoxia and EMT-related genes for predicting lung squamous cell carcinoma prognosis

A complicated analysis of the prognostic characteristics of lung squamous cell carcinoma (LUSC) is needed. The aim of this study was to develop a risk score model to predict immunotherapeutic response and prognosis for patients with LUSC. A hypoxia and epithelial-mesenchymal transition-related risk score model was developed for prediction of LUSC. The correlation between risk score and clinical characteristics was determined. The single sample gene set enrichment analysis algorithm was utilized to determine the abundance of cell infiltration in tumor immune microenvironment in LUSC. The predictive value of risk score model in response to immunotherapy was evaluated. A hypoxia and epithelial-mesenchymal transition-related risk score model was constructed. This risk score model was correlated with the overall survival of LUSC. Patients with low-risk presented a high survival possibility. The high-risk group was involved in ECM receptor interaction, complement and coagulation cascades, intestinal immune network for IgA production. Finally, patients with low-risk score had significant clinical benefit. The risk score model was constructed to predict immunotherapeutic response and prognosis for patients with LUSC. In addition to identifying LUSC patients with poor survival, the results provide more information for the immune immunotherapy and microenvironment for LUSC.

Deciphering the implications of mitophagy-related signatures in clinical outcomes and microenvironment heterogeneity of clear cell renal cell carcinoma

BACKGROUND

The role of mitophagy in various cancer-associated biological processes is well recognized. Nonetheless, the comprehensive implications of mitophagy in clear cell renal cell carcinoma (ccRCC) necessitate further exploration.

METHODS

Based on the transcriptomic data encompassing 25 mitophagy-related genes (MRGs), we identified the distinct mitophage patterns in 763 ccRCC samples. Subsequently, a mitophage-related predictive signature with machine learning algorithms was constructed, designated as RiskScore, to quantify the individual mitophagy status in ccRCC patients. Employing multispectral immunofluorescence (mIF) and immunohistochemistry (IHC) staining, we detected the effect of PTEN-induced putative kinase 1 (PINK1) in the prognosis and immune microenvironment of ccRCC.

RESULTS

Our analysis initially encompassed a comprehensive assessment of the expression profiling, genomic variations, and interactions among the 25 MRGs in ccRCC. Subsequently, the consensus clustering algorithm was applied to stratify ccRCC patients into three clusters with distinct prognostic outcomes, tumor microenvironment (TME) characteristics, and underlying biological pathways. We screened eight pivotal genes (CLIC4, PTPRB, SLC16A12, ENPP5, FLRT3, HRH2, PDK4, and SCD5) to construct a mitophagy-related predictive signature, which showed excellent prognostic value for ccRCC patients. Moreover, patient subgroups divided by the RiskScore showed contrasting expression levels of immune checkpoints (ICPs), abundance of immune cells, and immunotherapy response. Additionally, a nomogram was established with robust predictive power integrating the RiskScore and clinical features. Notably, we observed that PINK1 expression markedly correlated with favorable treatment response and advanced maturation stages of tertiary lymphoid structures, which potentially shed light on enhancing anti-tumor immunity of ccRCC.

CONCLUSION

Collectively, this study initially developed a signature associated with mitophagy, which demonstrated an excellent ability to predict the clinical prognosis, TME characterization, and responsiveness to targeted therapy and immunotherapy for ccRCC patients. Of particular note is the pivotal role of PINK1 in mediating the treatment response and immune microenvironment for ccRCC patients.

Role of neutrophil extracellular trap and immune infiltration in atherosclerotic plaque instability: Novel insight from bioinformatics analysis and machine learning

The instability of atherosclerotic plaques increases the risk of acute coronary syndrome. Neutrophil extracellular traps (NETs), mesh-like complexes consisting of extracellular DNA adorned with various protein substances, have been recently discovered to play an essential role in atherosclerotic plaque formation and development. This study aimed to investigate novel diagnostic biomarkers that can identify unstable plaques for early distinction and prevention of plaque erosion or disruption. Differential expression analysis was used to identify the differentially expressed NET-related genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. We filtered the characteristic genes using machine learning and estimated diagnostic efficacy using receiver operating characteristic curves. Immune infiltration was detected using single-sample gene set enrichment analysis and the biological signaling pathways involved in characteristic genes utilizing gene set enrichment analysis were explored. Finally, miRNAs- and transcription factors-target genes networks were established. We identified 8 differentially expressed NET-related genes primarily involved in immune-related pathways. Four were identified as capable of distinguishing unstable plaques. More immune cells infiltrated unstable plaques than stable plaques, and these cells were predominantly positively related to characteristic genes. These 4 diagnostic genes are involved in immune responses and the modulation of smooth muscle contractility. Several miRNAs and transcription factors were predicted as upstream regulatory factors, providing further information on the identification and prevention of atherosclerotic plaques rupture. We identified several promising NET-related genes (AQP9, C5AR1, FPR3, and SIGLEC9) and immune cell subsets that may identify unstable atherosclerotic plaques at an early stage and prevent various complications of plaque disruption.

Identification of basement membrane-related biomarkers associated with the diagnosis of osteoarthritis based on machine learning

BACKGROUND

Osteoarthritis is a very common clinical disease in middle-aged and elderly individuals, and with the advent of ageing, the incidence of this disease is gradually increasing. There are few studies on the role of basement membrane (BM)-related genes in OA.

METHOD

We used bioinformatics and machine learning methods to identify important genes related to BMs in OA patients and performed immune infiltration analysis, lncRNA‒miRNA-mRNA network prediction, ROC analysis, and qRT‒PCR.

RESULT

Based on the results of machine learning, we determined that LAMA2 and NID2 were the key diagnostic genes of OA, which were confirmed by ROC and qRT‒PCR analyses. Immune analysis showed that LAMA2 and NID2 were closely related to resting memory CD4 T cells, mast cells and plasma cells. Two lncRNAs, XIST and TTTY15, were simultaneously identified, and lncRNA‒miRNA‒mRNA network prediction was performed.

CONCLUSION

LAMA2 and NID2 are important potential targets for the diagnosis and treatment of OA.

A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis

Introduction

Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis.

Methods

A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx.

Results

A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein-protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results.

Discussion

In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

BACKGROUND

Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases.

METHODS AND RESULTS

First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944.

CONCLUSION

We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.

Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms

Sepsis, a serious inflammatory response that can be fatal, has a poorly understood pathophysiology. The Metabolic syndrome (MetS), however, is associated with many cardiometabolic risk factors, many of which are highly prevalent in adults. It has been suggested that Sepsis may be associated with MetS in several studies. Therefore, this study investigated diagnostic genes and metabolic pathways associated with both diseases. In addition to microarray data for Sepsis, PBMC single cell RNA sequencing data for Sepsis and microarray data for MetS were downloaded from the GEO database. Limma differential analysis identified 122 upregulated genes and 90 downregulated genes in Sepsis and MetS. WGCNA identified brown co-expression modules as Sepsis and MetS core modules. Two machine learning algorithms, RF and LASSO, were used to screen seven candidate genes, namely, STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR and UROD, all with an AUC greater than 0.9. XGBoost assessed the co-diagnostic efficacy of Hub genes in Sepsis and MetS. The immune infiltration results show that Hub genes were expressed at high levels in all immune cells. After performing Seurat analysis on PBMC from normal and Sepsis patients, six immune subpopulations were identified. The metabolic pathways of each cell were scored and visualized using ssGSEA, and the results show that CFLAR plays an important role in the glycolytic pathway. Our study identified seven Hub genes that serve as co-diagnostic markers for Sepsis and MetS and revealed that diagnostic genes play an important role in immune cell metabolic pathway.

Mechanism of immune infiltration in synovial tissue of osteoarthritis: a gene expression-based study

BACKGROUND

Osteoarthritis is a chronic degenerative joint disease, and increasing evidences suggest that the pathogenic mechanism involves immune system and inflammation.

AIMS

The aim of current study was to uncover hub genes linked to immune infiltration in osteoarthritis synovial tissue using comprehensive bioinformatics analysis and experimental confirmation.

METHODS

Multiple microarray datasets (GSE55457, GSE55235, GSE12021 and GSE1919) for osteoarthritis in Gene Expression Omnibus database were downloaded for analysis. Differentially expressed genes (DEGs) were identified using Limma package in R software, and immune infiltration was evaluated by CIBERSORT algorithm. Then weighted gene co-expression network analysis (WGCNA) was performed to uncover immune infiltration-associated gene modules. Protein-protein interaction (PPI) network was constructed to select the hub genes, and the tissue distribution of these genes was analyzed using BioGPS database. Finally, the expression pattern of these genes was confirmed by RT-qPCR using clinical samples.

RESULTS

Totally 181 DEGs between osteoarthritis and normal control were screened. Macrophages, mast cells, memory CD4 T cells and B cells accounted for the majority of immune cell composition in synovial tissue. Osteoarthritis synovial showed high abundance of infiltrating resting mast cells, B cells memory and plasma cells. WGCNA screened 93 DEGs related to osteoarthritis immune infiltration. These genes were involved in TNF signaling pathway, IL-17 signaling pathway, response to steroid hormone, glucocorticoid and corticosteroid. Ten hub genes including MYC, JUN, DUSP1, NFKBIA, VEGFA, ATF3, IL-6, PTGS2, IL1B and SOCS3 were selected by using PPI network. Among them, four genes (MYC, JUN, DUSP1 and NFKBIA) specifically expressed in immune system were identified and clinical samples revealed consistent change of these four genes in synovial tissue retrieved from patients with osteoarthritis.

CONCLUSION

A 4-gene-based diagnostic model was developed, which had well predictive performance in osteoarthritis. MYC, JUN, DUSP1 and NFKBIA might be biomarkers and potential therapeutic targets in osteoarthritis.