GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration

S100A9 Induces Macrophage M2 Polarization and Immunomodulatory Role in the Lesion Site After Spinal Cord Injury in Rats

Immune response is pivotal in the secondary injury of spinal cord injury (SCI). Polarization of macrophages (MΦ) influences the immune response in the secondary injury, which is regulated by several immune-related proteins. M2Φ plays the immunomodulatory role in the central nervous system. This study used bioinformatic analysis and machine algorithms to screen hub immune-related proteins after SCI and experimentally investigate the role of the target protein in the M2Φ polarization and immunomodulation in rats and in vitro after SCI. We downloaded GSE151371 and GSE45006, hub immune-related genes were screened using machine learning algorithms, and the expression of S100A9 was verified by datasets. Allen's weight-drop injury SCI model in Sprague-Dawley rat and bone marrow-derived rat MΦ with myelin debris model were used to study the effects of S100A9 on M2Φ polarization and immunomodulation at the lesion site and in vitro. Bioinformatic analysis showed that S100A9 acts as a hub immune-related gene in the SCI patients and rats. S100A9 increased at the lesion site in SCI rats, and its inhibition reduced CD206 and ARG-1 expression. Exogenous S100A9 promoted CD206 and ARG-1 expression in MΦ. S100A9 also increased the expression of PD-L1 and decreased MHC II at the lesion site in SCI rats and MΦ with myelin debris, and enhanced mitochondrial activity in rat MΦ with myelin debris. In conclusion, S100A9 is an indispensable factor in the immune process in secondary injury following SCI.

Targeting CSF1R in myeloid-derived suppressor cells: insights into its immunomodulatory functions in colorectal cancer and therapeutic implications

OBJECTIVE

This study aimed to investigate the critical role of MDSCs in CRC immune suppression, focusing on the CSF1R and JAK/STAT3 signaling axis. Additionally, it assessed the therapeutic efficacy of LNCs@CSF1R siRNA and anti-PD-1 in combination.

METHODS

Single-cell transcriptome sequencing data from CRC and adjacent normal tissues identified MDSC-related differentially expressed genes. RNA-seq analysis comprehensively profiled MDSC gene expression in murine CRC tumors. LNCs@CSF1R siRNA nanocarriers effectively targeted and inhibited CSF1R. Flow cytometry quantified changes in MDSC surface markers post-CSF1R inhibition. RNA-seq and pathway enrichment analyses revealed the impact of CSF1R on MDSC metabolism and signaling. The effect of CSF1R inhibition on the JAK/STAT3 signaling axis was validated using Colivelin and metabolic assessments. Glucose and fatty acid uptake were measured via fluorescence-based flow cytometry. The efficacy of LNCs@CSF1R siRNA and anti-PD-1, alone and in combination, was evaluated in a murine CRC model with extensive tumor section analyses.

RESULTS

CSF1R played a significant role in MDSC-mediated immune suppression. LNCs@CSF1R siRNA nanocarriers effectively targeted MDSCs and inhibited CSF1R. CSF1R regulated MDSC fatty acid metabolism and immune suppression through the JAK/STAT3 signaling axis. Inhibition of CSF1R reduced STAT3 activation and target gene expression, which was rescued by Colivelin. Combined treatment with LNCs@CSF1R siRNA and anti-PD-1 significantly slowed tumor growth and reduced MDSC abundance within CRC tumors.

CONCLUSION

CSF1R via the JAK/STAT3 axis critically regulates MDSCs, particularly in fatty acid metabolism and immune suppression. Combined therapy with LNCs@CSF1R siRNA and anti-PD-1 enhances therapeutic efficacy in a murine CRC model, providing a strong foundation for future clinical applications.

Delivery of miR-15b-5p via magnetic nanoparticle-enhanced bone marrow mesenchymal stem cell-derived extracellular vesicles mitigates diabetic osteoporosis by targeting GFAP

Diabetic osteoporosis (DO) presents significant clinical challenges. This study aimed to investigate the potential of magnetic nanoparticle-enhanced extracellular vesicles (GMNPE-EVs) derived from bone marrow mesenchymal stem cells (BMSCs) to deliver miR-15b-5p, thereby targeting and downregulating glial fibrillary acidic protein (GFAP) expression in rat DO models. Data was sourced from DO-related RNA-seq datasets combined with GEO and GeneCards databases. Rat primary BMSCs, bone marrow-derived macrophages (BMMs), and osteoclasts were isolated and cultured. EVs were separated, and GMNPE targeting EVs were synthesized. Bioinformatic analysis revealed a high GFAP expression in DO-related RNA-seq and GSE26168 datasets for disease models. Experimental results confirmed elevated GFAP in rat DO bone tissues, promoting osteoclast differentiation. miR-15b-5p was identified as a GFAP inhibitor, but was significantly downregulated in DO and enriched in BMSC-derived EVs. In vitro experiments showed that GMNPE-EVs could transfer miR-15b-5p to osteoclasts, downregulating GFAP and inhibiting osteoclast differentiation. In vivo tests confirmed the therapeutic potential of this approach in alleviating rat DO. Collectively, GMNPE-EVs can effectively deliver miR-15b-5p to osteoclasts, downregulating GFAP expression, and hence, offering a therapeutic strategy for rat DO.

Novel insights into STIM1's role in store-operated calcium entry and its implications for T-cell mediated inflammation in trigeminal neuralgia

This investigation aims to elucidate the novel role of Stromal Interaction Molecule 1 (STIM1) in modulating store-operated calcium entry (SOCE) and its subsequent impact on inflammatory cytokine release in T lymphocytes, thereby advancing our understanding of trigeminal neuralgia (TN) pathogenesis. Employing the Gene Expression Omnibus (GEO) database, we extracted microarray data pertinent to TN to identify differentially expressed genes (DEGs). A subsequent comparison with SOCE-related genes from the Genecards database helped pinpoint potential target genes. The STRING database facilitated protein-protein interaction (PPI) analysis to spotlight STIM1 as a gene of interest in TN. Through histological staining, transmission electron microscopy (TEM), and behavioral assessments, we probed STIM1's pathological effects on TN in rat models. Additionally, we examined STIM1's influence on the SOCE pathway in trigeminal ganglion cells using techniques like calcium content measurement, patch clamp electrophysiology, and STIM1- ORAI1 co-localization studies. Changes in the expression of inflammatory markers (TNF-α, IL-1β, IL-6) in T cells were quantified using Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) in vitro, while immunohistochemistry and flow cytometry were applied in vivo to assess these cytokines and T cell count alterations. Our bioinformatic approach highlighted STIM1's significant overexpression in TN patients, underscoring its pivotal role in TN's etiology and progression. Experimental findings from both in vitro and in vivo studies corroborated STIM1's regulatory influence on the SOCE pathway. Furthermore, STIM1 was shown to mediate SOCE-induced inflammatory cytokine release in T lymphocytes, a critical factor in TN development. Supportive evidence from histological, ultrastructural, and behavioral analyses reinforced the link between STIM1-mediated SOCE and T lymphocyte-driven inflammation in TN pathogenesis. This study presents novel evidence that STIM1 is a key regulator of SOCE and inflammatory cytokine release in T lymphocytes, contributing significantly to the pathogenesis of trigeminal neuralgia. Our findings not only deepen the understanding of TN's molecular underpinnings but also potentially open new avenues for targeted therapeutic strategies.

Molecular mechanisms of Codonopsis pilosula in inhibiting hepatocellular carcinoma growth and metastasis

BACKGROUND

Liver cancer, one of the most common types of cancer worldwide, accounts for millions of cases annually. With its multi-target and wide-ranging therapeutic effects, traditional Chinese medicine has emerged as a potential approach for treating various tumors. Codonopsis pilosula, a traditional herb, is known for its anti-inflammatory and antioxidant properties. In this study, we investigated the potential molecular mechanisms of Codonopsis pilosula in regulating the inhibition of CDK1 and the modulation of PDK1/β-catenin, which are involved in hepatocellular carcinoma growth and metastasis.

STUDY DESIGN/METHODS

Firstly, we screened the active chemical constituents of Codonopsis pilosula and identified their respective target proteins using the Herb database. Then, we applied the GeneCards database and transcriptome sequencing analysis to screen for critical genes associated with the occurrence and development of liver cancer. The intersection of the target proteins and disease-related genes was used to determine the potential targets of Codonopsis pilosula in hepatocellular carcinoma. Protein-protein interaction analysis and GO/KEGG analysis were subsequently performed to uncover the pathways through which Codonopsis pilosula acts on liver cancer. The Huh-7 cell line, exhibiting the highest sensitivity to Codonopsis pilosula polysaccharide solution (CPP) intervention, was chosen for subsequent studies. Cell viability was evaluated using the CCK-8 assay, colony formation assay was conducted to determine cell proliferation capacity, flow cytometry was used to analyze cell cycle, TUNEL staining was performed to assess cell apoptosis, scratch assay was carried out to evaluate cell migration ability, the expression of EMT-related proteins was detected and analyzed, and cell sphere formation assay was conducted to investigate cell stemness. Finally, a liver cancer animal model was established, and different doses of CPP were administered via gavage the next day. The expression levels of CDK1, PDK1, and β-catenin in mouse liver tissues were detected and analyzed, immunohistochemistry staining was performed to assess the expression of tumor cell proliferation-related proteins Ki67 and PCNA in mouse xenografts, and TUNEL staining was carried out to evaluate cell apoptosis in mouse liver tissues. After intervention with CDK1 expression, the expression levels of CDK1, PDK1, and β-catenin proteins and mRNA in each group of cells were detected using Western blot and RT-qPCR.

RESULTS

Through network pharmacology analysis, transcriptome sequencing, and bioinformatics analysis, 35 target genes through which Codonopsis pilosula acts on liver cancer were identified. Among them, CDK1, with the highest degree in the PPI network, was considered an essential target protein for Codonopsis pilosula in treating liver cancer. In vitro cell experiments revealed that CPP could inhibit the expression of CDK1/PDK1/β-catenin signaling axis factors, suppress cell proliferation, decrease cell migration ability, influence the EMT process, and reduce cell stemness by inhibiting CDK1 and affecting the PDK1/β-catenin signaling axis. Similarly, in vivo experiments demonstrated that CPP could regulate the CDK1/PDK1/β-catenin signaling axis, inhibit tumor growth, and induce cell apoptosis.

CONCLUSION

Codonopsis pilosula may inhibit hepatocellular carcinoma growth by suppressing CDK1 and affecting the PDK1/β-catenin signaling axis, limiting cell EMT and reducing cell stemness. These findings provide insights into the potential therapeutic role of Codonopsis pilosula in liver cancer.

HIF-1α Pathway Orchestration by LCN2: A Key Player in Hypoxia-Mediated Colitis Exacerbation

In this study, we investigated the role of hypoxia in the development of chronic inflammatory bowel disease (IBD), focusing on its impact on the HIF-1α signaling pathway through the upregulation of lipocalin 2 (LCN2). Using a murine model of colitis induced by sodium dextran sulfate (DSS) under hypoxic conditions, transcriptome sequencing revealed LCN2 as a key gene involved in hypoxia-mediated exacerbation of colitis. Bioinformatics analysis highlighted the involvement of crucial pathways, including HIF-1α and glycolysis, in the inflammatory process. Immune infiltration analysis demonstrated the polarization of M1 macrophages in response to hypoxic stimulation. In vitro studies using RAW264.7 cells further elucidated the exacerbation of inflammation and its impact on M1 macrophage polarization under hypoxic conditions. LCN2 knockout cells reversed hypoxia-induced inflammatory responses, and the HIF-1α pathway activator dimethyloxaloylglycine (DMOG) confirmed LCN2's role in mediating inflammation via the HIF-1α-induced glycolysis pathway. In a DSS-induced colitis mouse model, oral administration of LCN2-silencing lentivirus and DMOG under hypoxic conditions validated the exacerbation of colitis. Evaluation of colonic tissues revealed altered macrophage polarization, increased levels of inflammatory factors, and activation of the HIF-1α and glycolysis pathways. In conclusion, our findings suggest that hypoxia exacerbates colitis by modulating the HIF-1α pathway through LCN2, influencing M1 macrophage polarization in glycolysis. This study contributes to a better understanding of the mechanisms underlying IBD, providing potential therapeutic targets for intervention.

Identification of MACC1 as a potential biomarker for pulmonary arterial hypertension based on bioinformatics and machine learning

BACKGROUND

Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by abnormal early activation of pulmonary arterial smooth muscle cells (PASMCs), yet the underlying mechanisms remain to be elucidated.

METHODS

Normal and PAH gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and analyzed using gene set enrichment analysis (GSEA) to uncover the underlying mechanisms. Weighted gene co-expression network analysis (WGCNA) and machine learning methods were deployed to further filter hub genes. A number of immune infiltration analysis methods were applied to explore the immune landscape of PAH. Enzyme-linked immunosorbent assay (ELISA) was employed to compare MACC1 levels between PAH and normal subjects. The important role of MACC1 in the progression of PAH was verified through Western blot and real-time qPCR, among others.

RESULTS

39 up-regulated and 7 down-regulated genes were identified by 'limma' and 'RRA' packages. WGCNA and machine learning further narrowed down the list to 4 hub genes, with MACC1 showing strong diagnostic capacity. In vivo and in vitro experiments revealed that MACC1 was highsly associated with malignant features of PASMCs in PAH.

CONCLUSIONS

These findings suggest that targeting MACC1 may offer a promising therapeutic strategy for treating PAH, and further clinical studies are warranted to evaluate its efficacy.

Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The hub genes were identified through the intersection of common DEGs and WGCNA-derived genes. The Random Forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms were subsequently employed to identify key shared target genes associated with two diseases. Additionally, RA immune infiltration analysis and COVID-19 single-cell transcriptome analysis revealed the correlation between these key genes and immune cells. A total of 59 shared genes were identified from the intersection of DEGs and gene clusters obtained through WGCNA, which analyzed the integrated gene matrix of ALI/ARDS and RA. The RF and LASSO algorithms were employed to screen for target genes specific to ALI/ARDS and RA, respectively. The final set of overlapping genes (FCMR, ADAM28, HK3, GRB10, UBE2J1, HPSE, DDX24, BATF, and CST7) all exhibited a strong predictive effect with an area under the curve (AUC) value greater than 0.8. Then, the immune infiltration analysis revealed a strong correlation between UBE2J1 and plasma cells in RA. Furthermore, scRNA-seq analysis demonstrated differential expression of these nine target genes primarily in T cells and NK cells, with CST7 showing a significant positive correlation specifically with NK cells. Beyond that, transcriptome sequencing was conducted on lung tissue collected from ALI mice, confirming the substantial differential expression of FCMR, HK3, UBE2J1, and BATF. This study provides unprecedented evidence linking the pathophysiological mechanisms of ALI/ARDS and RA to immune regulation, which offers novel understanding for future clinical treatment and experimental research.

circRNA-PTPN4 mediated regulation of FOXO3 and ZO-1 expression: implications for blood-brain barrier integrity and cognitive function in uremic encephalopathy

Uremic encephalopathy (UE) poses a significant challenge in neurology, leading to the need to investigate the involvement of non-coding RNA (ncRNA) in its development. This study employed ncRNA-seq and RNA-seq approaches to identify fundamental ncRNAs, specifically circRNA and miRNA, in the pathogenesis of UE using a mouse model. In vitro and in vivo experiments were conducted to explore the circRNA-PTPN4/miR-301a-3p/FOXO3 axis and its effects on blood-brain barrier (BBB) function and cognitive abilities. The research revealed that circRNA-PTPN4 binds to and inhibits miR-301a-3p, leading to an increase in FOXO3 expression. This upregulation results in alterations in the transcriptional regulation of ZO-1, affecting the permeability of human brain microvascular endothelial cells (HBMECs). The axis also influences the growth, proliferation, and migration of HBMECs. Mice with UE exhibited cognitive deficits, which were reversed by overexpression of circRNA-PTPN4, whereas silencing FOXO3 exacerbated these deficits. Furthermore, the uremic mice showed neuronal loss, inflammation, and dysfunction in the BBB, with the expression of circRNA-PTPN4 demonstrating therapeutic effects. In conclusion, circRNA-PTPN4 plays a role in promoting FOXO3 expression by sequestering miR-301a-3p, ultimately leading to the upregulation of ZO-1 expression and restoration of BBB function in mice with UE. This process contributes to the restoration of cognitive abilities.

Identification of biomarkers and immune infiltration characterization of lipid metabolism-associated genes in osteoarthritis based on machine learning algorithms

Osteoarthritis (OA) is a prevalent degenerative condition commonly observed in the elderly, leading to consequential disability. Despite notable advancements made in clinical strategies for OA, its pathogenesis remains uncertain. The intricate association between OA and metabolic processes has yet to receive comprehensive exploration. In our investigation, we leveraged public databases and applied machine learning algorithms, including WGCNA, LASSO, RF, immune infiltration analysis, and pathway enrichment analysis, to scrutinize the role of lipid metabolism-associated genes (LAGs) in the OA. Our findings identified three distinct biomarkers, and evaluated their expression to assess their diagnostic value in the OA patients. The exploration of immune infiltration in these patients revealed an intricate relationship between immune cells and the identified biomarkers. In addition, in vitro experiments, including qRT-PCR, Western blot, chondrocyte lipid droplets detection and mitochondrial fatty acid oxidation measurement, further verified abnormal expressions of selected LAGs in OA cartilage and confirmed the correlation between lipid metabolism and OA.

Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

Osteopontin interacts with dendritic cells and macrophages in pulp inflammation: Comprehensive transcriptomic analysis and laboratory investigations

AIM

To investigate novel diagnostic markers for pulpitis and validate by clinical samples from normal and inflamed pulp. To explore the relationship between diagnostic markers and immune cells or their phenotypes during pulp inflammation.

METHODOLOGY

Two microarray datasets, GSE77459 and GSE92681, and identified differential expression genes were integrated. To understand immune features, gene functions, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) and ImmuneSigDB Gene Set Enrichment Analysis (GSEA) were analysed. For predictive purposes, machine learning techniques were applied to detect diagnostic markers. Immune infiltration in inflamed pulp was studied using CIBERSORT. The relationship between diagnostic markers and immune cells was investigated and validated their gene expression in clinical samples from the normal or inflamed pulp by qRT-PCR. Finally, the correlation between one marker, secreted phosphoprotein 1 (SPP1), encoding osteopontin (OPN), and dendritic cells (DCs)/macrophages was identified via HE staining and multiplex immunohistochemistry. An in vitro inflammatory dental pulp microenvironment model of THP-1 macrophages cocultured with dental pulp cells derived conditioned media (DPCs-CM) to investigate OPN production and macrophage phenotypes was established.

RESULTS

Analysis revealed unique immunologic features in inflamed pulp. Three diagnostic markers for pulpitis: endothelin-1 (EDN1), SPP1, and purine nucleoside phosphorylase (PNP), and validated them using qRT-PCR were predicted. Multiplex immunohistochemistry demonstrated OPN co-localized with activated DCs and M2 macrophages during pulp inflammation. In vitro experiments showed that THP-1 macrophages produced the highest levels of OPN when stimulated with DPCs-CM derived from the 20 μg/mL LPS pre-conditioned group, suggesting an M2b-like phenotype by increasing surface marker CD86 and expression of IL6, TNFα, IL10, and CCL1 but not CCL17 and MerTK. Levels of CCL1 and IL10 elevated significantly in the macrophages' supernatant from the 20 μg/mL LPS pre-conditioned CM group. OPN was proven co-localizing with CD86 in the inflamed pulp by immunofluorescence.

CONCLUSIONS

The current findings suggest that OPN can serve as a promising biomarker for pulpitis, correlated with DCs and macrophages. OPN+ macrophages in the inflamed pulp are associated with M2b-like phenotypes. These insights offer the potential for improved diagnosis and targeted therapy.

Pyroptosis-related signatures predict immune characteristics and prognosis in IPF

The purpose of this work was to use integrated bioinformatics analysis to screen for pyroptosis-related genes (PRGs) and possible immunological phenotypes linked to the development and course of IPF. Transcriptome sequencing datasets GSE70866, GSE47460 and GSE150910 were obtained from GEO database. From the GSE70866 database, 34 PRGs with differential expression were found in IPF as compared to healthy controls. In addition, a diagnostic model containing 4 genes PRGs (CAMP, MKI67, TCEA3 and USP24) was constructed based on LASSO logistic regression. The diagnostic model showed good predictive ability to differentiate between IPF and healthy, with ROC-AUC ranging from 0.910 to 0.997 in GSE70866 and GSE150910 datasets. Moreover, based on a combined cohort of the Freiburg and the Siena cohorts from GSE70866 dataset, we identified ten PRGs that might predict prognosis for IPF. We constructed a prognostic model that included eight PRGs (CLEC5A, TREM2, MMP1, IRF2, SEZ6L2, ADORA3, NOS2, USP24) by LASSO Cox regression and validated it in the Leuven cohort. The risk model divided IPF patients from the combined cohort into high-risk and low-risk subgroups. There were significant differences between the two subgroups in terms of IPF survival and GAP stage. There is a close correlation between leukocyte migration, plasma membrane junction, and poor prognosis in a high-risk subgroup. Furthermore, a high-risk score was associated with more plasma cells, activated NK cells, monocytes, and activated mast cells. Additionally, we identified HDAC inhibitors in the cMAP database that might be therapeutic for IPF. To summarize, pyroptosis and its underlying immunological features are to blame for the onset and progression of IPF. PRG-based predictive models and drugs may offer new treatment options for IPF.

Identification and validation of iron metabolism genes in osteoporosis

BACKGROUND

Osteoporosis is the most common metabolic bone disease in humans. Exploring the expression difference of iron metabolism-related genes in osteoporosis can provide a new target for diagnosis and treatment.

METHODS

First, we used online databases to identify differentially expressed genes (DEGs) related to iron metabolism in patients with osteoporosis. The differential genes were comprehensively analyzed by bioinformatics method (GO, KEGG, GSEA, immune infiltration analysis, PPI). The expression levels of hub genes and important signaling pathways were verified by qRT-PCR and Western blotting.

RESULTS

A total of 23 iron metabolism-related genes with significant differences were identified, which were enriched in "regulation of protein dephosphorylation" and "negative regulation of protein dephosphorylation". The GSEA results, heme metabolism and Myc targets v1 were among the top two pathways, both upregulated. The immune infiltration analysis revealed that the expressions of genes such as ABCA5, D2HGDH, GNAI2, and CTSW were correlated with the infiltration degree of significantly different cells. The PPI network contained 12 differentially expressed iron metabolism-related genes. Additionally, YWHAE, TGFB1, PPP1R15A, TOP2A, and CALR were mined as hub genes using the Cytoscape software. qRT PCR showed that the expression of TGF-β1, YWHAE, TOP2A and CALR increased. We also verified the expression of related proteins and genes in the oxidative stress signaling pathway by qRT PCR and Western blotting. The results showed that Mob1, YAP and TAZ molecules were highly expressed at the gene and protein levels.

CONCLUSIONS

These differentially expressed iron metabolism-related genes could provide new potential targets for the diagnosis and treatment of osteoporosis.

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

Background

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

Methods

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

Results

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

Conclusion

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

Identifying Ferroptosis-Related Genes Associated with Weight Loss Outcomes and Regulation of Adipocyte Microenvironment

SCOPE

The study is about the influence of ferroptosis-related genes combined with the immune microenvironment exerted on weight control outcomes and systematic analysis.

METHODS AND RESULTS

Subcutaneous adipose tissue (sWAT) samples from 11 subjects with good outcome and 10 subjects with poor outcome in weight management are obtained from the Gene Expression Omnibus database. The results are validated in vivo in animal models with different weight loss outcomes. The CIBERSORT algorithm is used to evaluate the differences in immune cell infiltration in each sample. Patients with poor outcome have higher levels of ferroptosis in the adipose tissue. Remarkable differences in cytokine production, nuclear factor kappa-B(NF-κB) transcription factor activity, leukocyte migration involved in the inflammatory response, and other biological processes are also observed compared to that in the well-controlled group. Aldo-keto reductase family 1-member C1(AKR1C1), nuclear receptor coactivator 4(NCOA4), and glutamate-cysteine ligase catalytic subunit(GCLC) are identified as core predictive markers and their expression patterns are confirmed in animal models.

CONCLUSIONS

Ferroptosis and its mediated inflammation play an important role in long-term weight control, and analyses of the role of ferroptosis-related genes(FRGs) in weight control may provide new potential therapeutic targets for long-term weight control. Anti-inflammatory diets that mitigate inflammatory responses and affect ferroptosis may be considered in the future to improve weight control.

Association of the LEP gene with immune infiltration as a diagnostic biomarker in preeclampsia

Objective: Preeclampsia (PE) is a serious condition in pregnant women and hence an important topic in obstetrics. The current research aimed to recognize the potential and significant immune-related diagnostic biomarkers for PE. Methods: From the Gene Expression Omnibus (GEO) data sets, three public gene expression profiles (GSE24129, GSE54618, and GSE60438) from the placental samples of PE and normotensive pregnancy were downloaded. Differentially expressed genes (DEGs) were selected and determined among 73 PE and 85 normotensive control pregnancy samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the expression levels and diagnostic value of biomarkers in PE were verified in the GSE75010 data set (80 PE and 77 controls) and validated by qRT-RCR, Western blot, and immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in PE. Results: In total, 15 DEGs were recognized. The GO and KEGG analyses revealed that the DEGs were enriched in the steroid metabolic process, receptor ligand activity, GnRH secretion, and neuroactive ligand-receptor interaction. The recognized DEGs were primarily implicated in cell-type benign neoplasm, kidney failure, infertility, and PE. Gene sets related to hormone activity, glycosylation, multicellular organism process, and response to BMP were activated in PE. The LEP gene was distinguished as a diagnostic biomarker of PE (AUC = 0.712) and further certified in the GSE75010 data set (AUC = 0.850). The high expression of LEP was associated with PE in clinical samples. In addition, the analysis of the immune microenvironment showed that gamma delta T cells, memory B cells, M0 macrophages, and regulatory T cells were positively correlated with LEP expression (P < 0.05). Conclusion: LEP expression can be considered to be a diagnostic biomarker of PE and can offer a novel perspective for future studies regarding the occurrence and molecular mechanisms of PE.

Biomarkers of ulcerative colitis disease activity CXCL1, CYP2R1, LPCAT1, and NEU4 and their relationship to immune infiltrates

The diagnosis and assessment of ulcerative colitis (UC) poses significant challenges, which may result in inadequate treatment and a poor prognosis for patients. This study aims to identify potential activity biomarkers for UC and investigate the role of infiltrating immune cells in the disease. To perform gene set enrichment analysis, we utilized the cluster profiler and ggplot2 packages. Kyoto encyclopedia of genes and genomes was used to analyze degenerate enrichment genes. Significant gene set enrichment was determined using the cluster profiler and ggplot2 packages. Additionally, quantitative PCR (qRT-PCR) was employed to validate the expression of each marker in the ulcerative colitis model. We identified 651 differentially expressed genes (DEGs) and further investigated potential UC activity biomarkers. Our analysis revealed that CXCL1 (AUC = 0.710), CYP2R1 (AUC = 0.863), LPCAT1 (AUC = 0.783), and NEU4 (AUC = 0.833) were promising activity markers for the diagnosis of UC. Using rat DSS model, we validated these markers through qRT-PCR, which showed statistically significant differences between UC and normal colon mucosa. Infiltrating immune cell analysis indicated that M1 macrophages, M2 macrophages, activated dendritic cells (DCs), and neutrophils played crucial roles in the occurrence and progression of UC. Moreover, the activity markers exhibited varying degrees of correlation with activated memory CD4 T cells, M0 macrophages, T follicular helper cells, memory B cells, and activated DCs. The potential diagnostic genes for UC activity, such as CXCL1, CYP2R1, LPCAT1, and NEU4, as well as the infiltration of immune cells, may contribute to the pathogenesis and progression of UC.

Bioinformatics analysis reveals the landscape of immune cell infiltration and novel immune-related biomarkers in moyamoya disease

Objective: This study aimed to identify immune infiltration characteristics and new immunological diagnostic biomarkers in the cerebrovascular tissue of moyamoya disease (MMD) using bioinformatics analysis. Methods: GSE189993 and GSE141022 were downloaded from the GEO database. Differentially expressed gene and PPI analysis were performed. After performing WGCNA, the most significant module associated with MMD was obtained. Next, functional pathways according to GSEA, GO, and KEGG were enriched for the aforementioned core genes obtained from PPI and WGCNA. Additionally, immune infiltration, using the CIBERSORT deconvolution algorithm, immune-related biomarkers, and the relationship between these genes, was further explored. Finally, diagnostic accuracy was verified with ROC curves in the validation dataset GSE157628. Results: A total of 348 DEGs were screened, including 89 downregulated and 259 upregulated genes. The thistle^l module was detected as the most significant module associated with MMD. Functional analysis of the core genes was chiefly involved in the immune response, immune system process, protein tyrosine kinase activity, secretory granule, and so on. Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity. Meanwhile, a higher proportion of eosinophils, not T cells or B cells, was demonstrated in the specific immune infiltration landscape of MMD. Conclusion: Immune activities and immune cells were actively involved in the progression of MMD. BTK, FGR, PTPN11, and SYK were identified as potential immune diagnostic biomarkers. These immune-related genes and cells may provide novel insights for immunotherapy in the future.

Expression patterns of eight RNA-modified regulators correlating with immune infiltrates during the progression of osteoarthritis

Background

RNA modifications in eukaryotic cells have emerged as an exciting but under-explored area in recent years and are considered to be associated with many human diseases. While several studies have been published relating to m6A in osteoarthritis (OA), we only have limited knowledge of other kinds of RNA modifications. Our study investigated eight RNA modifiers’ specific roles in OA including A-to-I, APA, m5C, m6A, m7G, mcm5s2U, Nm and Ψ together with their relationship with immune infiltration.

Methods

RNA modification patterns in OA samples were identified based on eight-type RNA modifiers and their correlation with the degree of immune infiltration was also methodically investigated. Receiver operating characteristic curves (ROC) and qRT-PCR was performed to confirm the abnormal expression of hub genes. The RNA modification score (Rmscore) was generated by the applications of principal component analysis (PCA) algorithm in order to quantify RNA modification modes in individual OA patients.

Results

We identified 21 differentially-expressed RNA modification related genes between OA and healthy samples. For example, CFI, CBLL1 and ALKBH8 were expressed at high levels in OA (P&lt;0.001), while RPUSD4, PUS1, NUDT21, FBL and WDR4 were expressed at low levels (P&lt;0.001). Two candidate RNA modification regulators (WDR4 and CFI) were screened out utilizing a random forest machine learning model. We then identified two distinctive RNA modification modes in OA which were found to display distinctive biological features. High Rmscore, characterized by increased immune cell infiltration, indicated an inflamed phenotype.

Conclusions

Our study was the first to systematically reveal the crosstalk and dysregulations eight-type of RNA modifications in OA. Assessing individuals’ RNA modification patterns will be conductive to enhance our understanding of the properties of immune infiltration, provide novel diagnostic and prognostic biomarkers, and guide more effective immunotherapy strategies in the future.

EXOC3L1: A Novel Prognostic Biomarker Correlated with Immune Infiltration in Esophageal Squamous Cell Carcinoma

BACKGROUND Exocyst complex component 3-like 1 (EXOC3L1) is ubiquitously present in multiple organs. However, its role in esophageal squamous cell carcinoma (ESCC) remains unknown. The aim of this study was to explore the relationship between EXOC3L1 and ESCC. MATERIAL AND METHODS A total of 652 normal samples and 82 ESCC samples obtained from the University of California Santa Cruz (UCSC) Xena were applied to detect the expression difference of EXOC3L1. GSE53625 with 179 paired samples and GSE161533 with 28 paired samples were used for validation. The correlation between clinicopathological features and EXOC3L1 expression was calculated. Kaplan-Meier method was employed to assess the prognostic value of EXOC3L1 in ESCC. Univariate and multivariate Cox regression analyses were carried out to screen the factors contributing to the prognosis of ESCC. In addition, functional enrichment analysis, protein-protein interaction (PPI) network analysis, and immune infiltration analysis were conducted to identify the significantly involved functions of EXOC3L1. RESULTS EXOC3L1 was significantly overexpressed in ESCC compared to normal samples. High expression of EXOC3L1 was associated with worse prognosis, and univariate and multivariate Cox regression analysis demonstrated that EXOC3L1 was an independent prognostic predictor of ESCC. Functional enrichment analysis and immune infiltration analysis disclosed that the expression of EXOC3L1 was correlated with the abundance of several types of immune cells. CONCLUSIONS EXOC3L1 plays a crucial role in the prognosis of ESCC, and it may serve as a reliable biomarker for predicting the survival and a potential therapeutic target for ESCC.

IRF8 and its related molecules as potential diagnostic biomarkers or therapeutic candidates and immune cell infiltration characteristics in steroid-induced osteonecrosis of the femoral head

PURPOSE

Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people, but the pathogenesis of SONFH remained unclear. We aimed to identify the potential genes and screen potential therapeutic compounds for SONFH.

METHODS

The microarray was obtained for blood tissue from the GEO database, and then it identifies differentially expressed genes (DEGs). The DEGs were analyzed to obtain the differences in immune cell infiltration. The gene functional enrichment analysis of SONFH was analyzed. The PPI of DEGs was identified through the STRING database, and the cluster modules and hub genes were ascertained using MCODE and CytoHubba, and the ROC curve of hub genes was analyzed, and the tissues distribution of hub genes was understood by the HPA, Bgee and BioGPS databases. The hub genes and target miRNAs and corresponding upstream lncRNAs were predicted by TargetScan, miRDB and ENCORI database. Subsequently, we used CMap, DGIdb and L1000FWD databases to identify several potential therapeutic molecular compounds for SONFH. Finally, the AutoDockTools Vina, PyMOL and Discovery Studio were employed for molecular docking analyses between compounds and hub genes.

RESULTS

The microarray dataset GSE123568 was obtained related to SONFH. There were 372 DEGs including 197 upregulated genes and 175 downregulated genes by adjusted P value < 0.01 and |log₂FC|> 1. Several significant GSEA enrichment analysis and biological processes and KEGG pathway associated with SONFH were identified, which were significantly related to cytoskeleton organization, nucleobase-containing compound catabolic process, NOD-like receptor signaling pathway, MAPK signaling pathway, FoxO signaling pathway, neutrophil-mediated immunity, neutrophil degranulation and neutrophil activation involved in immune response. Activated T cells CD4 memory, B cells naïve, B cells memory, T cells CD8 and T cells gamma delta might be involved in the occurrence and development of SONFH. Three cluster modules were identified in the PPI network, and eleven hub genes including FPR2, LILRB2, MNDA, CCR1, IRF8, TYROBP, TLR1, HCK, TLR8, TLR2 and CCR2 were identified by Cytohubba, which were differed in bone marrow, adipose tissue and blood, and which had good diagnostic performance in SONFH. We identified IRF8 and 10 target miRNAs that was utilized including Targetsan, miRDB and ENCORI databases and 8 corresponding upstream lncRNAs that was revealed by ENCORI database. IRF8 was detected with consistent expression by qRT-PCR. Based on the CMap, DGIdb and L1000FWD databases, the 11 small molecular compounds that were most strongly therapeutic correlated with SONFH were estradiol, genistein, domperidone, lovastatin, myricetin, fenbufen, rosiglitazone, sirolimus, phenformin, vorinostat and vinblastine. All of 11 small molecules had good binding affinity with the IRF8 in molecular docking.

CONCLUSION

The occurrence of SONFH was associated with a "multi-target" and "multi-pathway" pattern, especially related to immunity, and IRF8 and its noncoding RNA were closely related to the development of SONFH. The CMap, DGIdb and L1000FWD databases could be effectively used in a systematic manner to predict potential drugs for the prevention and treatment of SONFH. However, additional clinical and experimental research is warranted.

Immune infiltration patterns and identification of new diagnostic biomarkers GDF10, NCKAP5, and RTKN2 in non-small cell lung cancer

This study aimed to identify potential biomarkers for non-small cell lung cancer (NSCLC) and analyze the role of immune cell infiltration in NSCLC. R software was used to screen differentially expressed genes (DEGs) from NSCLC datasets obtained from the Gene Expression Omnibus (GEO) database, and functional correlation analysis was performed. The machine learning algorithms were used to screen the potential biomarkers of NSCLC. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. The protein and mRNA expression levels of potential biomarkers were verified based on the Human Protein Atlas (HPA) database and qRT-PCR. CIBERSORT was used to evaluate the infiltration of immune cells in NSCLC tissues, and the correlation between potential biomarkers and infiltrated immune cell was analyzed. Finally, specific siRNAs were utilized to reduce the GDF10, NCKAP5, and RTKN2 expression in A549 and H1975 cells. The proliferation ability of A549 and H1975 cells was detected by MTT assay. A total of 848 upregulated DEGs and 1308 downregulated DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the DEGs were mainly related to cell division. Disease ontology (DO) enrichment analysis showed that the diseases with these DEGs were mainly lung diseases, including NSCLC. In addition,three potential biomarkers were identified: GDF10, NCKAP5, and RTKN2. Immune cell infiltration analysis showed that resting NK cells, activated dendritic cells, and Tregs may be involved in the pathogenesis of NSCLC. Meanwhile, GDF10, NCKAP5, and RTKN2 were negatively correlated with Tregs and naïve B cells but were positively correlated with activated dendritic cells and resting NK cells. Immunohistochemical staining showed that the expression of GDF10, NCKAP5, and RTKN2 in the lung tissue of patients with NSCLC was lower than that of normal lung tissue. qRT-PCR also confirmed that the mRNA expression of three biomarkers in NSCLC cell lines A549 and H1975 were significantly lower than those in human normal lung epithelial cells BEAS-2B. An MTT assay showed that GDF10, NCKAP5, and RTKN2 knockdown significantly promoted the proliferation of A549 and H1975 cells. The in vitro experiments showed that GDF10, NCKAP5, and RTKN2 played the inhibitory effects on NSCLC cell lines proliferation. Hence, GDF10, NCKAP5, and RTKN2 can be used as diagnostic biomarkers for NSCLC.

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

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

FCGR2C: An emerging immune gene for predicting sepsis outcome

Background

Sepsis is a life-threatening disease associated with immunosuppression. Immunosuppression could ultimately increase sepsis mortality. This study aimed to identify the prognostic biomarkers related to immunity in sepsis.

Methods

Public datasets of sepsis downloaded from the Gene Expression Omnibus (GEO) database were divided into the discovery cohort and the first validation cohort. We used R software to screen differentially expressed genes (DEGs) and analyzed DEGs' functional enrichment in the discovery dataset. Immune-related genes (IRGs) were filtered from the GeneCards website. A Lasso regression model was used to screen candidate prognostic genes from the intersection of DEGs and IRGs. Then, the candidate prognostic genes with significant differences were identified as prognostic genes in the first validation cohort. We further validated the expression of the prognostic genes in the second validation cohort of 81 septic patients recruited from our hospital. In addition, we used four immune infiltration methods (MCP-counter, ssGSEA, ImmuCellAI, and CIBERSORT) to analyze immune cell composition in sepsis. We also explored the correlation between the prognostic biomarker and immune cells.

Results

First, 140 genes were identified as prognostic-related immune genes from the intersection of DEGs and IRGs. We screened 18 candidate prognostic genes in the discovery cohort with the lasso regression model. Second, in the first validation cohort, we identified 4 genes (CFHR2, FCGR2C, GFI1, and TICAM1) as prognostic immune genes. Subsequently, we found that FCGR2C was the only gene differentially expressed between survivors and non-survivors in 81 septic patients. In the discovery and first validation cohorts, the AUC values of FCGR2C were 0.73 and 0.67, respectively. FCGR2C (AUC=0.84) had more value than SOFA (AUC=0.80) and APACHE II (AUC=0.69) in evaluating the prognosis of septic patients in our recruitment cohort. Moreover, FCGR2C may be closely related to many immune cells and functions, such as B cells, NK cells, neutrophils, cytolytic activity, and inflammatory promotion. Finally, enrichment analysis showed that FCGR2C was enriched in the phagosome signaling pathway.

Conclusion

FCGR2C could be an immune biomarker associated with prognosis, which may be a new direction of immunotherapy to reduce sepsis mortality.

Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease

BACKGROUND

Protein glycosylation has been confirmed to be involved in the pathological mechanisms of Alzheimer's disease (AD); however, there is still a lack of systematic analysis of the immune processes mediated by protein glycosylation-related genes (PGRGs) in AD.

MATERIALS AND METHODS

Transcriptomic data of AD patients were obtained from the Gene Expression Omnibus database and divided into training and verification datasets. The core PGRGs of the training set were identified by weighted gene co-expression network analysis, and protein glycosylation-related subtypes in AD were identified based on k-means unsupervised clustering. Protein glycosylation scores and neuroinflammatory levels of different subtypes were compared, and functional enrichment analysis and drug prediction were performed based on the differentially expressed genes (DEGs) between the subtypes. A random forest model was used to select important DEGs as diagnostic markers between subtypes, and a line chart model was constructed and verified in other datasets. We evaluated the differences in immune cell infiltration between the subtypes through the single-sample gene set enrichment analysis, analyzed the correlation between core diagnostic markers and immune cells, and explored the expression regulation network of the core diagnostic markers.

RESULTS

Eight core PGRGs were differentially expressed between the training set and control samples. AD was divided into two subtypes with significantly different biological processes, such as vesicle-mediated transport in synapses and neuroactive ligand-receptor interactions. The high protein glycosylation subtype had a higher level of neuroinflammation. Riluzole and sulfasalazine were found to have potential clinical value in this subtype. A reliable construction line chart model was constructed based on nine diagnostic markers, and SERPINA3 was identified as the core diagnostic marker. There were significant differences in immune cell infiltration between the two subtypes. SERPINA3 was found to be closely related to immune cells, and the expression of SERPINA3 in AD was found to be regulated by a competing endogenous RNA network that involves eight long non-coding RNAs and seven microRNAs.

CONCLUSION

Protein glycosylation and its corresponding immune process play an important role in the occurrence and development of AD. Understanding the role of PGRGs in AD may provide a new potential therapeutic target for AD.

AKAP12 and RNF11 as Diagnostic Markers of Fibromyalgia and Their Correlation with Immune Infiltration

Fibromyalgia (FM) is a chronic nonarticular rheumatic disease mainly characterized by diffuse disseminated skeletal muscle pain, with varied symptoms including anxiety, sleep disturbance, and fatigue. Due to its unknown etiology and pathogenesis, FM is easily ignored in clinical practice, resulting in unclear diagnosis and difficult treatment. This study is aimed at investigating whether AKAP12 and RNF11 can be used as biomarkers for the diagnosis of FM and at determining their correlation with immune infiltration. The FM dataset in Gene Expression Omnibus (GEO) database was downloaded and was randomly divided into the training and test sets. Differentially expressed genes (DEGs) were screened, and functional correlation analysis was performed. Diagnostic markers of FM were screened and validated by random forest (RF). The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was then used to evaluate immune cell infiltration in the FM patients' peripheral blood. Finally, Spearman's rank correlation analysis was used to identify correlation between the diagnostic indexes and immune cell infiltration. A total of 69 DEGs were selected. Results indicated that AKAP12 and RNF11 can be used as diagnostic markers of FM, and CD8 + T cells might contribute in the pathogenesis of FM. In addition, AKAP12 was positively correlated with CD8 + T cells, while RNF11 was negatively correlated with CD8 + T cells. In conclusion, AKAP12 and RNF11 can be used as diagnostic indicators of FM, and CD8 + T cells may be involved in the occurrence and development of FM.

Identification of Biomarkers in Intracranial Aneurysm and Their Immune Infiltration Characteristics

BACKGROUND

Intracranial aneurysm (IA), known as the intracranial "unscheduled bomb," is one of the most dangerous cerebrovascular diseases, with unclear pathogenesis. This study aimed to show the mechanisms and identify the new biological targets by applying bioinformatics analysis.

METHODS

Expression profiling for control superficial temporal artery and IA walls in GSE26969 and GSE75436 datasets were downloaded. By executing the LIMMA package in R software, the differentially expressed genes (DEGs) were filtered, and the functional enrichments were consequently performed. Further cross-linking with the 2483 immune-related genes (IRGs) from the ImmPort database, the differentially expressed IRGs were identified. Based on them, the least absolute shrinkage and selection operator logistic regression and support vector machine-recursive feature elimination algorithms were used to screen the biomarkers, which were validated in the GSE54083 datasets. The CIBERSORT algorithm was applied to evaluate the infiltration of immune cells in tissues.

RESULTS

A total of 668 DEGs were obtained, and the functional enrichment suggested that they were closely related to the immune process. After intersecting them with the IRGs, 90 differentially expressed IRGs emerged, and ADIPOQ and ESM1 were identified as the biomarkers. Besides, we found that the infiltrated immune cells, such as the mast cells resting, might be associated with them.

CONCLUSIONS

We explored the contributing factors involving IA, which may generate a better understanding of the complex interactions among them and inspire a promising strategy for clinical works.

Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis

Objective

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.

Materials and methods

Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.

Results

A total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.

Conclusion

DUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.

Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning

Objectives: DFU is a serious chronic disease with high disability and fatality rates, yet there is no completely effective therapy. While ferroptosis is integrated to inflammation and infection, its involvement in DFU is still unclear. The study aimed to identify ferroptosis-related genes in DFU, providing potential therapeutic targets.

Methods: In the GEO database, two DFU microarray datasets (GSE147890 and GSE80178) were collected. WGCNA was conducted to identify the modular genes most involved in DFU. Subsequently, enrichment analysis and PPI analysis were performed. To yield the DFU-associated ferroposis genes, the ferroposis genes were retrieved from the FerrDb database and overlapped with the modular genes. Eventually, an optimal DFU prediction model was created by combining multiple machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to detect ferroposis genes most closely associated with DFU. The accuracy of the model was verified by utilizing external datasets (GSE7014) based on ROC curves.

Results: WGCNA yielded seven modules in all, and 1223 DFU-related modular genes were identified. GO analysis revealed that inflammatory response, decidualization, and protein binding were the most highly enriched terms. These module genes were also enriched in the ErbB signaling, IL-17 signaling, MAPK signaling, growth hormone synthesis, secretion and action, and tight junction KEGG pathways. Twenty-five DFU-associated ferroposis genes were obtained by cross-linking with modular genes, which could distinguish DFU patients from controls. Ultimately, the prediction model based on machine learning algorithms was well established, with high AUC values (0.79 of LASSO, 0.80 of SVM, 0.75 of Boruta, 0.70 of XGBoost). MAFG and MAPK3 were identified by the prediction model as the most highly associated ferroposis-genes in DFU. Furthermore, the external dataset (GSE29221) validation revealed that MAPK3 (AUC = 0.81) had superior AUC values than MAFG (AUC = 0.62).

Conclusion: As the most related ferroptosis-genes with DFU, MAFG and MAPK3 may be employed as potential therapeutic targets for DFU patients. Moreover, MAPK3, with higher accuracy, could be the more potential ferroptosis-related biomarker for further experimental validation.

Construction and validation model of necroptosis-related gene signature associates with immunity for osteosarcoma patients

Osteosarcoma is the most common malignant tumor in children and adolescents and its diagnosis and treatment still need to be improved. Necroptosis has been associated with many malignancies, but its significance in diagnosing and treating osteosarcoma remains unclear. The objective is to establish a predictive model of necroptosis-related genes (NRGs) in osteosarcoma for evaluating the tumor microenvironment and new targets for immunotherapy. In this study, we download the osteosarcoma data from the TARGET and GEO websites and the average muscle tissue data from GTEx. NRGs were screened by Cox regression analysis. We constructed a prediction model through nonnegative matrix factorization (NMF) clustering and the least absolute shrinkage and selection operator (LASSO) algorithm and verified it with a validation cohort. Kaplan–Meier survival time, ROC curve, tumor invasion microenvironment and CIBERSORT were assessed. In addition, we establish nomograms for clinical indicators and verify them by calibration evaluation. The underlying mechanism was explored through the functional enrichment analysis. Eight NRGs were screened for predictive model modeling. NRGs prediction model through NMF clustering and LASSO algorithm was established. The survival, ROC and tumor microenvironment scores showed significant statistical differences among subgroups (P < 0.05). The validation model further verifies it. By nomogram and calibration, we found that metastasis and risk score were independent risk factors for the poor prognosis of osteosarcoma. GO and KEGG analyses demonstrate that the genes of osteosarcoma cluster in inflammatory, apoptotic and necroptosis signaling pathways. The significant role of the correlation between necroptosis and immunity in promoting osteosarcoma may provide a novel insight into detecting molecular mechanisms and targeted therapy.

Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis

Background: As the most prevalent valvular heart disease, calcific aortic valve disease (CAVD) has become a primary cause of aortic valve stenosis and insufficiency. We aim to illustrate the roles of immune related genes (IRGs) and immune cells infiltration in the occurrence of CAVD.

Methods: Integrative meta-analysis of expression data (INMEX) was adopted to incorporate multiple gene expression datasets of CAVD from Gene Expression Omnibus (GEO) database. By matching the differentially expressed genes (DEGs) to IRGs from “ImmPort” database, differentially expressed immune related genes (DEIRGs) were screened out. We performed enrichment analysis and found that DEIRGs in CAVD were closely related to inflammatory response and immune cells infiltration. We also constructed protein–protein interaction (PPI) network of DEIRGs and identified 5 key DEIRGs in CAVD according to the mixed character calculation results. Moreover, CIBERSORT algorithm was used to explore the profile of infiltrating immune cells in CAVD. Based on Spearman’s rank correlation method, correlation analysis between key DEIRGs and infiltrating immune cells was performed.

Results: A total of 220 DEIRGs were identified and the enrichment analysis of DEIRGs showed that they were significantly enriched in inflammatory responses. PPI network was constructed and PTPN11, GRB2, SYK, PTPN6 and SHC1 were identified as key DEIRGs. Compared with normal aortic valve tissue samples, the proportion of neutrophils, T cells CD4 memory activated and macrophages M0 was elevated in calcified aortic valves tissue samples, as well as reduced infiltration of macrophages M2 and NK cells activated. Furthermore, key DEIRGs identified in the present study, including PTPN11, GRB2, PTPN6, SYK, and SHC1, were all significantly correlated with infiltration of various immune cells.

Conclusion: This meta-analysis suggested that PTPN11, GRB2, PTPN6, SYK, and SHC1 might be key DEIRGs associated with immune cells infiltration, which play a pivotal role in pathogenesis of CAVD.

Identification of Potential Therapeutic Target Genes in Osteoarthritis

Objective

Osteoarthritis (OA), also known as joint failure, is characterized by joint pain and, in severe cases, can lead to loss of joint function in patients. Immune-related genes and immune cell infiltration play a crucial role in OA development. We used bioinformatics approaches to detect potential diagnostic markers and available drugs for OA while initially exploring the immune mechanisms of OA.

Methods

The training set GSE55235 and validation set GSE51588 and GSE55457 were obtained from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified by the limma package. Gene set enrichment analysis (GSEA) was performed on the GSE55235 dataset using the cluster profiler package. At the same time, DEGs were analyzed by gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). In addition, protein-protein interaction (PPI) analysis was performed on the common DEGs of the three datasets using the STRING database. Proteins with direct linkage were identified as hub genes, and the relation of hub genes was subsequently analyzed using the GOSemSim package. Hub genes' expression profiles and diagnostic capabilities (ROC curves) were analyzed and validated using three datasets. In addition, we performed RT-qPCR to validate the levels of hub genes. The immune microenvironment was analyzed using the CIBERSORT package, and the relationship between hub genes and immune cells was evaluated. In addition, we used a linkage map (CMAP) database to identify available drug candidates. Finally, the GSEA of hub genes was used to decipher the potential pathways corresponding to hub genes.

Results

Three hub genes (CX3CR1, MYC, and TLR7) were identified. CX3CR1 and TLR7 were highly expressed in patients with OA, whereas the expression of MYC was low. The results of RT-qPCR validation were consistent with those obtained using datasets. Among these genes, CX3CR1 and TLR7 can be used as diagnostic markers. It was found that CX3CR1, MYC, and TLR7 affect the immune microenvironment of OA via different immune cells. In addition, we identified a potential drug for the treatment of OA. Altogether, CX3CR1, MYC, and TLR7 affect the immune response of OA through multiple pathways.

Conclusion

CX3CR1, MYC, and TLR7 are associated with various immune cells and are the potential diagnostic markers and therapeutic targets for OA.

Inhibition of IL1R1 or CASP4 attenuates spinal cord injury through ameliorating NLRP3 inflammasome-induced pyroptosis

Spinal cord injury (SCI) is a devastating trauma characterized by serious neuroinflammation and permanent neurological dysfunction. However, the molecular mechanism of SCI remains unclear, and few effective medical therapies are available at present. In this study, multiple bioinformatics methods were used to screen out novel targets for SCI, and the mechanism of these candidates during the progression of neuroinflammation as well as the therapeutic effects were both verified in a rat model of traumatic SCI. As a result, CASP4, IGSF6 and IL1R1 were identified as the potential diagnostic and therapeutic targets for SCI by computational analysis, which were enriched in NF-κB and IL6-JAK-STATA3 signaling pathways. In the injured spinal cord, these three signatures were up-regulated and closely correlated with NLRP3 inflammasome formation and gasdermin D (GSDMD) -induced pyroptosis. Intrathecal injection of inhibitors of IL1R1 or CASP4 improved the functional recovery of SCI rats and decreased the expression of these targets and inflammasome component proteins, such as NLRP3 and GSDMD. This treatment also inhibited the pp65 activation into the nucleus and apoptosis progression. In conclusion, our findings of the three targets shed new light on the pathogenesis of SCI, and the use of immunosuppressive agents targeting these proteins exerted anti-inflammatory effects against spinal cord inflammation by inhibiting NF-kB and NLRP3 inflammasome activation, thus blocking GSDMD -induced pyroptosis and immune activation.

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

Background

Although studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.

Methods

Transcriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.

Results

Nineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.

Conclusion

Pyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma.

Prediction of Immune Infiltration Diagnostic Gene Biomarkers in Kawasaki Disease

Kawasaki disease (KD) is characterized by disorder of immune response with unknown etiology. Immune cells may be closely related to the onset of KD. The focus of this research was to evaluate the significance of the infiltration of immune cells for this disease and find possible diagnostic biomarkers for KD. The Gene Expression Omnibus database was utilized to retrieve two freely accessible gene expression patterns (GSE68004 and GSE18606 datasets) from human KD and control specimens. 114 KD, as well as 46 control specimens, were searched for obtaining differentially expressed genes (DEGs). Candidate biological markers were determined utilizing the support vector machine recursive feature elimination and the least absolute shrinkage and selection operator regression model analysis. To assess discriminating capacity, the area under the receiver operating characteristic curve (AUC) was computed. The GSE73461 dataset was utilized to observe the biomarkers' expression levels and diagnostic significance in KD (78 KD patients and 55 controls). CIBERSORT was employed to assess the composition profiles of the 22 subtypes of immune cell fraction in KD on the basis of combined cohorts. 37 genes were discovered. The DEGs identified were predominantly involved in arteriosclerotic cardiovascular disease, atherosclerosis, autoimmune disease of the urogenital tract, and bacterial infectious disease. Gene sets related to complement and coagulation cascades, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis, NOD-like receptor signaling pathway, and regulation of actin cytoskeleton underwent differential activation in KD as opposed to the controls. KD diagnostic biomarkers, including the alkaline phosphatase (ALPL), endoplasmic reticulum degradation-enhancing alpha-mannosidase-like protein 2 (EDEM2), and histone cluster 2 (HIST2H2BE), were discovered (AUC = 1.000) and verified utilizing the GSE73461 dataset (AUC = 1.000). Analyses of immune cell infiltration demonstrated that ALPL, EDEM2, and HIST2H2BE were linked to CD4 memory resting T cells, monocytes, M0 macrophages, CD8 T cells, neutrophils, and memory CD4 T cells. ALPL, EDEM2, and HIST2H2BE could be utilized as KD diagnostic indicators, and they can also deliver useful information for future research on the disease's incidence and molecular processes.

Immune Cell Infiltration Characteristics of Pigmented Villous Nodular Synovitis and Prediction of Potential Diagnostic Markers Based on Bioinformatics

Background

Pigmented villous nodular synovitis (PVNS) is a tumor-like proliferative disease characterized by impairment of daily activities, decreased quality of life, and a high recurrence rate. However, the specific pathological mechanisms are still ill-defined and controversial. The purpose of this study was to define potential diagnostic markers and evaluate immune cell infiltration in the pathogenesis of PVNS.

Method

The expression profile of GSE3698 was reanalyzed in the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) were identified using the R package “limma” and analyzed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Next, the DEGs were imported into the STRING database and Cytoscape to construct a protein–protein interaction (PPI) network. Then, cytoHubba and ROC curve analyses were used to determine potential diagnostic biomarkers of PVNS. Finally, we used CIBERSORT to estimate the proportions of 22 immune cell subtypes in PVNS and analyzed the correlation between diagnostic markers and infiltrating immune cells.

Result

We found 139 DEGs (including 93 upregulated genes and 46 downregulated genes). TYROBP, FCER1G, LAPTM5, and HLA-DPB1 were identified as potential diagnostic biomarkers of PVNS. Immune cell infiltration analysis indicated that neutrophils and M2 macrophages might be associated with the genesis and progression of PVNS. Furthermore, our correlation analysis of diagnostic markers and infiltrating immune cells found that TYROBP, FCER1G, LAPTM5, and HLA-DPB1 were positively correlated with M2 macrophage infiltration and that neutrophils, TYROBP, FCER1G, and LAPTM5 were negatively correlated with plasma cell infiltration.

Conclusions

We identified TYROBP, FCER1G, LAPTM5, and HLA-DPB1 as potential diagnostic markers for PVNS and concluded that immune cell infiltration plays an important role in the genesis and progression of PVNS.

Autophagy-Related Genes Are Involved in the Progression and Prognosis of Asthma and Regulate the Immune Microenvironment

Background

Autophagy has been proven to play an important role in the pathogenesis of asthma and the regulation of the airway epithelial immune microenvironment. However, a systematic analysis of the clinical importance of autophagy-related genes (ARGs) regulating the immune microenvironment in patients with asthma remains lacking.

Methods

Clustering based on the k-means unsupervised clustering method was performed to identify autophagy-related subtypes in asthma. ARG-related diagnostic markers in low-autophagy subtypes were screened, the infiltration of immune cells in the airway epithelium was evaluated by the CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. On the basis of the expression of ARGs and combined with asthma control, a risk prediction model was established and verified by experiments.

Results

A total of 66 differentially expressed ARGs and 2 subtypes were identified between mild to moderate and severe asthma. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes, and the low-autophagy subtype was closely associated with severe asthma, energy metabolism, and hormone metabolism. The autophagy gene SERPINB10 was identified as a diagnostic marker and was related to the infiltration of immune cells, such as activated mast cells and neutrophils. Combined with asthma control, a risk prediction model was constructed, the expression of five risk genes was supported by animal experiments, was established for ARGs related to the prediction model.

Conclusion

Autophagy plays a crucial role in the diversity and complexity of the asthma immune microenvironment and has clinical value in treatment response and prognosis.

Identification of SMIM1 and SEZ6L2 as Potential Biomarkers for Genes Associated with Intervertebral Disc Degeneration in Pyroptosis

Background. Inflammatory reactions and pyroptosis play an important role in the pathology of intervertebral disc degeneration (IDD). The aim of the present study was to investigate pyroptosis in the nucleus pulposus cells (NPCs) of inflammatory induced IDD by bioinformatic methods and to search for possible diagnostic biomarkers. Methods. Gene expression profiles related to IDD were downloaded from the GEO database to identify differentially expressed genes (DEGs) between inflammation-induced IDD and non-inflammatory intervention samples. Pyroptosis genes were then searched for, and their expression in IDD was analyzed. Weighted gene co-expression network analysis (WGCNA) was then used to search for modules of IDD genes associated with pyroptosis and intersected with DEGs to discover candidate genes that would be diagnostically valuable. A LASSO model was developed to screen for genes that met the requirements, and ROC curves were created to clarify the diagnostic value of the genetic markers. Ultimately, the screened genes were further validated, and their diagnostic value assessed by selecting gene sets from the GEO database. RT-PCR was used to assess the mRNA expression of diagnostic markers in the nucleus pulposus (NP). Pan-cancer analysis was applied to demonstrate the expression and prognostic value of the screened genes in various tumors. Results. A total of 733 DEGs were identified in GSE41883 and GSE27494, which were mainly enriched in transmembrane receptor protein serine/threonine, kinase signaling pathway, response to lipopolysaccharide, and other biological processes, and they were mainly related to TGF beta signaling pathway, toll-like receptor signaling pathway, and TNF signaling pathway. A total of 81 genes related to pyroptosis were identified in the literature, and eight genes related to IDD were identified in the Veen diagram, namely, IL1A, IL1B, NOD2, GBP1, IL6, AK1, EEF2K, and PYCARD. Eleven candidate genes were obtained after locating the intersection of pyroptosis-related module genes and DEGs according to WGCNA analysis. A total of six valid genes were obtained after constructing a machine learning model, and five key genes were finally identified after correlation analysis. GSE23132 and GSE56081 validated the candidate genes, and the final IDD-related diagnostic markers were obtained as SMIM1 and SEZ6L2. RT-PCR results indicated that the mRNA expression of both was significantly elevated in IDD. The pan-cancer analysis demonstrated that SMIM1 and SEZ6L2 have important roles in the expression and prognosis of various tumors. Conclusion. In conclusion, this research identifies SMIM1 and SEZ6L2 as important biomarkers of IDD associated with pyroptosis, which will help to unravel the development and pathogenesis of IDD and determine potential therapeutic targets.

circHtra1/miR-3960/GRB10 Axis Promotes Neuronal Loss and Immune Deficiency in Traumatic Brain Injury

Circular RNAs (circRNAs) are abundant in the brain and contribute to central nervous system diseases; however, the exact roles of circRNAs in human traumatic brain injury (TBI) have not been established. In this study, we used a competing endogenous RNA (ceRNA) chipset as well as in vitro and in vivo assays to characterize differentially expressed circRNAs in TBI. We detected 3035 differentially expressed circRNAs in the severe TBI group, 2362 in the moderate group, and 433 in the mild group. A ceRNA network was constructed. The circRNA has_circ_0020269 (circHtra1) was significantly upregulated after brain insults and was correlated with the severity of injury. circHtra1 inhibited cell proliferation and promoted apoptosis, and its knockdown reversed these effects. Further analyses revealed that circHtra1 functions as a miR-3960 sponge and increases the expression of GRB10, which is involved in NK cell infiltration after TBI. circHtra1 was identified as a target of the IGF-1/ADAR1 axis. Reduced expression of ADAR1 (involved in A-to-I editing) after brain insults upregulated circHtra1. Our results show that circHtra1 promotes neuronal loss by sponging miR-3960 and regulating GRB10 and apoptosis during brain insults. In addition, A-to-I editing could regulate circRNA expression profiles after TBI, and circHtra1 is a potential therapeutic target.

LANCL1 as the Key Immune Marker in Neuropathic Pain

Objective. This study is to explore key immune markers and changes of immune microenvironment in neuropathic pain (NeuP). Method. The data sets of GSE145199 and GSE145226 in Gene Expression Omnibus (GEO) database was used to analyze, and the key immune markers were verified by GSE70006 and GSE91396, and the infiltration degree of immune cells in different samples were analyzed by CIBERSORT analysis package. Results. In this study, we found a key immune marker, namely, LANCL1. Regulatory axis closely related to LANCL1 has also been found, namely, miR-6325/LANCL1 axis. In the immune infiltration analysis, we also found that the LANCL1 is positively correlated with T cells CD4 naïve ( $r=0.880$ , $p<0.05$ ). Conclusion. In this study, we found that LANCL1 may be a protective factor for NeuP, and the miR-6325/LANCL1 axis may be involved in the occurrence and development of NeuP. Cascade reactions including mast cells, macrophages, and T cells may be an important reason for the aggravation of nerve damage.

Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease

Objective

This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis.

Methods

The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results.

Results

80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells.

Conclusion

STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Objective

We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail.

Methods

Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using “limma” package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells.

Results

129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells.

Conclusions

In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.

Identification of potential blood biomarkers for early diagnosis of schizophrenia through RNA sequencing analysis

Schizophrenia (SCZ) is a highly heritable, polygenic complex mental disorder with imprecise diagnostic boundaries. Finding sensitive and specific novel biomarkers to improve the biological homogeneity of SCZ diagnosis is still one of the research hotspots. To identify the blood specific diagnostic biomarkers of SCZ, we performed RNA sequencing (RNA-seq) on 30 peripheral blood samples from 15 first-episode drug-naïve SCZ patients and 15 healthy controls (CTL). By performing multiple bioinformatics analysis algorithms based on RNA-seq data and microarray datasets, including differential expression genes (DEGs) analysis, WGCNA and CIBERSORT, we first identified 6 specific key genes (TOMM7, SNRPG, KRT1, AQP10, TMEM14B and CLEC12A) in SCZ. Moreover, we found that the proportions of lymphocyte, monocyte and neutrophils were significantly distinct in SCZ patients with CTL samples. Therefore, combining various features including age, sex and the novel blood biomarkers, we constructed the risk prediction model with three classifiers (RF: Random Forest; SVM: support vector machine; DT: decision tree) through repeated k-fold cross validation ensuring better generalizability. Finest result of Area under Receiver Operating Characteristic (AUROC) score of 0.91 was achieved by RF classifier and with a comparable good performance of AUROC 0.77 in external validation dataset. A lower AUROC of 0.63 was demonstrated when it was further applied to a Bipolar disorder (BPD) cohort. In conclusion, the study identified three peripheral core immunocytes and six key genes associated with the occurrence of SCZ, and further studies are required to test and validate these novel biomarkers for early diagnosis and treatment of SCZ.

Using a Machine Learning Approach to Identify Key Biomarkers for Renal Clear Cell Carcinoma

Background

The most common and deadly subtype of renal carcinoma is kidney renal clear cell carcinoma (KIRC), which accounts for approximately 75% of renal carcinoma. However, the main cause of death in KIRC patients is tumor metastasis. There are no obvious clinical features in the early stage of kidney cancer, and 25–30% of patients have already metastasized when they are first diagnosed. Moreover, KIRC patients whose local tumors have been removed by nephrectomy are still at high risk of metastasis and recurrence and are not sensitive to chemotherapy and radiotherapy, leading to poor prognosis. Therefore, early diagnosis and treatment of this disease are very important.

Methods

KIRC-related patient datasets were downloaded from the GEO database and TCGA database. DEG screening and GO, KEGG and GSEA enrichment analysis was firstly conducted and then the LASSO and support vector machine (SVM) RFE algorithms were adopted to identify KIRC-associated key genes in training sets and validate them in the test set. The clinical prognostic analysis including the association between the expression of key genes and the overall survival, stage, grade across KIRC, the immune infiltration difference between normal samples and cancer samples, the correlation between the key genes and immune cells, immunomodulator, immune subtypes of KIRC were investigated in this research.

Results

We finally screened out 4 key genes, including ACPP, ANGPTL4, SCNN1G, SLC22A7. The expression of key genes show difference among normal samples and tumor samples, SCNN1G and SLC22A7 could be predictor of prognosis of patients. The expression of key genes was related with the abundance of tumor infiltration immune cells and the gene expression of immune checkpoint.

Conclusion

This study screened the 4 key genes, which contributed to early diagnosis, prognosis assessment and immune target treatment of patients with KIRC.

Identification of key biomarkers in steroid-induced osteonecrosis of the femoral head and their correlation with immune infiltration by bioinformatics analysis

Objective

This study aimed to identify key diagnostic markers and immune infiltration of (SONFH) by bioinformatics analysis.

Methods

Related SONFH datasets were downloaded from the Gene Expression Omnibus (GEO) database. First, we identified the differentially expressed genes (DEGs) and performed the functional enrichment analysis. Then weighted correlation network analysis (WGCNA) and the MCODE plug-in in Cytoscape were used to identify the diagnostic markers of SONFH. Finally, CIBERSORT was used to analyze the immune infiltration between SONFH and healthy controls, and the correlation between infiltrating immune cells and diagnostic markers was analyzed.

Results

TYROBP, TLR2, P2RY13, TLR8, HCK, MNDA, and NCF2 may be key diagnostic markers of SONFH. Immune cell infiltration analysis revealed that Memory B cells and activated dendritic cells may be related to the SONFH process. Moreover, HCK was negatively correlated with CD8 T cells, and neutrophils were positively correlated with those key diagnostic markers.

Conclusions

TYROBP, TLR2, P2RY13, TLR8, HCK, MNDA, and NCF2 may be used as diagnostic markers of SONFH, and immune-related mechanism of SONFH and the potential immunotherapy are worthy of further study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-04994-7.

Identification of Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA)

Intervertebral disc degeneration (IDD) is a major cause of lower back pain. However, to date, the molecular mechanism of the IDD remains unclear. Gene expression profiles and clinical traits were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, weighted gene coexpression network analysis (WGCNA) was used to screen IDD-related genes. Moreover, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine (SVM) algorithms were used to identify characteristic genes. Furthermore, we further investigated the immune landscape by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and the correlations between key characteristic genes and infiltrating immune cells. Finally, a competing endogenous RNA (ceRNA) network was established to show the regulatory mechanisms of characteristic genes. A total of 2458 genes were identified by WGCNA, and 48 of them were disordered. After overlapping the genes obtained by LASSO and SVM-RFE algorithms, genes including LINC01347, ASAP1-IT1, lnc-SEPT7L-1, B3GNT8, CHRNB3, CLEC4F, LOC102724000, SERINC2, and LOC102723649 were identified as characteristic genes of IDD. Moreover, differential analysis further identified ASAP1-IT1 and SERINC2 as key characteristic genes. Furthermore, we found that the expression of both ASAP1-IT1 and SERINC2 was related to the proportions of T cells gamma delta and Neutrophils. Finally, a ceRNA network was established to show the regulatory mechanisms of ASAP1-IT1 and SERINC2. In conclusion, the present study identified ASAP1-IT1 and SERINC2 as the key characteristic genes of IDD through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of IDD.

Detection of Neanderthal Adaptively Introgressed Genetic Variants That Modulate Reporter Gene Expression in Human Immune Cells

Although some variation introgressed from Neanderthals has undergone selective sweeps, little is known about its functional significance. We used a Massively Parallel Reporter Assay (MPRA) to assay 5,353 high-frequency introgressed variants for their ability to modulate the gene expression within 170 bp of endogenous sequence. We identified 2,548 variants in active putative cis-regulatory elements (CREs) and 292 expression-modulating variants (emVars). These emVars are predicted to alter the binding motifs of important immune transcription factors, are enriched for associations with neutrophil and white blood cell count, and are associated with the expression of genes that function in innate immune pathways including inflammatory response and antiviral defense. We combined the MPRA data with other data sets to identify strong candidates to be driver variants of positive selection including an emVar that may contribute to protection against severe COVID-19 response. We endogenously deleted two CREs containing expression-modulation variants linked to immune function, rs11624425 and rs80317430, identifying their primary genic targets as ELMSAN1, and PAN2 and STAT2, respectively, three genes differentially expressed during influenza infection. Overall, we present the first database of experimentally identified expression-modulating Neanderthal-introgressed alleles contributing to potential immune response in modern humans.

Fatty acid metabolism-related genes in bronchoalveolar lavage fluid unveil prognostic and immune infiltration in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition with an unfavorable prognosis. A recent study has demonstrated that IPF patients exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with IPF pathogenesis. Therefore, in this study, we have explored whether the gene signature associated with fatty acid metabolism could be used as a reliable biological marker for predicting the survival of IPF patients.

METHODS

Data on the fatty acid metabolism-related genes (FAMRGs) were extracted from databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome pathway. The GSE70866 dataset with information on IPF patients was retrieved from the Gene Expression Omnibus (GEO). Next, the consensus clustering method was used to identify novel molecular subgroups. Gene Set Enrichment Analysis (GSEA) was performed to understand the mechanisms involved. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the level of immune cell infiltration in the identified subgroups based on gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression analysis were performed to develop a prognostic risk model.

RESULTS

The gene expression signature associated with fatty acid metabolism was used to create two subgroups with significantly different prognoses. GSEA reveals that immune-related pathways were significantly altered between the two subgroups, and the two subgroups had different metabolic characteristics. High infiltration of immune cells, mainly activated NK cells, monocytes, and activated mast cells, was observed in the subgroup with a poor prognosis. A risk model based on FAMRGs had an excellent ability to predict the prognosis of IPF. The nomogram constructed using the clinical features and the risk model could accurately predict the prognosis of IPF patients.

CONCLUSION

The fatty acid metabolism-related gene expression signature could be used as a potential biological marker for predicting clinical outcomes and the level of infiltration of immune cells. This could eventually enhance the accuracy of the treatment of IPF patients.

Identification of potential biomarkers and immune infiltration characteristics in severe asthma

OBJECTIVES

We hope to identify key molecules that can be used as markers of asthma severity and investigate their correlation with immune cell infiltration in severe asthma.

METHODS

An asthma dataset was downloaded from the Gene Expression Omnibus database and then processed by R software to obtain differentially expressed genes (DEGs). First, multiple enrichment platforms were applied to analyze crucial biological processes and pathways and protein-protein interaction networks related to the DEGs. We next combined least absolute shrinkage and selection operator logistic regression and the support vector machine-recursive feature elimination algorithms to screen diagnostic markers of severe asthma. Then, a local cohort consisting of 40 asthmatic subjects (24 with moderate asthma and 16 with severe asthma) was used for biomarker validation. Finally, infiltration of immune cells in asthma bronchoalveolar lavage fluid and their correlation with the screened markers was evaluated by CIBERSORT.

RESULTS

A total of 97 DEGs were identified in this study. Most of these genes are enriched in T cell activation and immune response in the asthma biological process. CC-chemokine receptor 7 (CCR7) and natural killer cell protein 7(NKG7) were identified as markers of severe asthma. The highest area under the ROC curve (AUC) was from a new indicator combining CCR7 and NKG7 (AUC = 0.851, adj. p < 0.05). Resting and activated memory CD4 T cells, activated NK cells, and CD8 T cells were found to be significantly higher in the severe asthma group (adj. p < 0.01). CCR7 and NKG7 were significantly correlated with these infiltrated cells that showed differences between the two groups. In addition, CCR7 was found to be significantly positively correlated with eosinophils (r = 0.38, adj. p < 0.05) infiltrated in bronchoalveolar lavage fluid.

CONCLUSION

CCR7 and NKG7 might be used as potential markers for asthma severity, and their expression may be associated with differences in immune cell infiltration in the moderate and severe asthma groups.