Identification of Hub Biomarkers and Immune-Related Pathways Participating in the Progression of Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis

Effect of human heart valve-derived ECM and NP/PCL electrospun nanofibrous sheet on mice bone marrow mononuclear cells and cardiac repair

Background

Biomaterials can improve cardiac repair combined with transplantation of bone marrow mononuclear cells (BMMNCs). In this study, we compared the phenotype and cardiac repair between human heart valve-derived scaffold (hHVS) and natural protein/polycaprolactone (NP/PCL) anchored BMNNCs.

Methods and results

BMMNCs were obtained from mice five days following myocardial infarction. Subsequently, BMMNCs were separately cultured on hHVS and PCL. Proliferation and cardiomyogenic differentiation were detected in vitro. Cardiac function was measured after transplantation of cell-seeded cardiac patch on MI mice. After that, the BMMNCs were collected for mRNA sequencing after culturing on the scaffolds. Upon anchoring onto hHVS or PCL, BMMNCs exhibited an increased capacity for proliferation in vitro, however, the cells on hHVS exhibited superior cardiomyogenic differentiation ability. Moreover, both BMMNCs-seeded biomaterials effectively improved cardiac function after 4 weeks of transplantation, with reduced infarction area and restricted LV remodeling. Cell-seeded hHVS was superior to cell-seeded PCL.

Conclusion

BMMNCs on hHVS showed better capacity in both cell cardiac repairing and improvement for cardiac function than on PCL. Compared with seeded onto PCL, BMMNCs on hHVS had 253 genes up regulated and 189 genes down regulated. The reason for hHVS' better performance than PCL as a scaffold for BMMNCs might be due to the fact that optimized method of decellularization let more cytokines in ECM retained.

Convergent and divergent genes expression profiles associated with brain-wide functional connectome dysfunction in deficit and non-deficit schizophrenia

Deficit schizophrenia (DS) is a subtype of schizophrenia characterized by the primary and persistent negative symptoms. Previous studies have identified differences in brain functions between DS and non-deficit schizophrenia (NDS) patients. However, the genetic regulation features underlying these abnormal changes are still unknown. This study aimed to detect the altered patterns of functional connectivity (FC) in DS and NDS and investigate the gene expression profiles underlying these abnormal FC. The study recruited 82 DS patients, 96 NDS patients, and 124 healthy controls (CN). Voxel-based unbiased brain-wide association study was performed to reveal altered patterns of FC in DS and NDS patients. Machine learning techniques were used to access the utility of altered FC for diseases diagnosis. Weighted gene co-expression network analysis (WGCNA) was employed to explore the associations between altered FC and gene expression of 6 donated brains. Enrichment analysis was conducted to identify the genetic profiles, and the spatio-temporal expression patterns of the key genes were further explored. Comparing to CN, 23 and 20 brain regions with altered FC were identified in DS and NDS patients. The altered FC among these regions showed significant correlations with the SDS scores and exhibited high efficiency in disease classification. WGCNA revealed associations between DS/NDS-related gene expression and altered FC. Additionally, 22 overlapped genes, including 12 positive regulation genes and 10 negative regulation genes, were found between NDS and DS. Enrichment analyses demonstrated relationships between identified genes and significant pathways related to cellular response, neuro regulation, receptor binding, and channel activity. Spatial and temporal gene expression profiles of SCN1B showed the lowest expression at the initiation of embryonic development, while DPYSL3 exhibited rapid increased in the fetal. The present study revealed different altered patterns of FC in DS and NDS patients and highlighted the potential value of FC in disease classification. The associations between gene expression and neuroimaging provided insights into specific and common genetic regulation underlying these brain functional changes in DS and NDS, suggesting a potential genetic-imaging pathogenesis of schizophrenia.

Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

BACKGROUND

Membranous nephropathy (MN) is a chronic glomerular disease that leads to nephrotic syndrome in adults. The aim of this study was to identify novel biomarkers and immune-related mechanisms in the progression of MN through an integrated bioinformatics approach.

METHODS

The microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between MN and normal samples were identified and analyzed by the Gene Ontology analysis, the Kyoto Encyclopedia of Genes and Genomes analysis and the Gene Set Enrichment Analysis (GSEA) enrichment. Hub The hub genes were screened and identified by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm. The receiver operating characteristic (ROC) curves evaluated the diagnostic value of hub genes. The single-sample GSEA analyzed the infiltration degree of several immune cells and their correlation with the hub genes.

RESULTS

We identified a total of 574 DEGs. The enrichment analysis showed that metabolic and immune-related functions and pathways were significantly enriched. Four co-expression modules were obtained using WGCNA. The candidate signature genes were intersected with DEGs and then subjected to the LASSO analysis, obtaining a total of 6 hub genes. The ROC curves indicated that the hub genes were associated with a high diagnostic value. The CD4+ T cells, CD8+ T cells and B cells significantly infiltrated in MN samples and correlated with the hub genes.

CONCLUSIONS

We identified six hub genes (ZYX, CD151, N4BP2L2-IT2, TAPBP, FRAS1 and SCARNA9) as novel biomarkers for MN, providing potential targets for the diagnosis and treatment.

Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches

Crohn's disease (CD) is a recurrent, chronic inflammatory condition of the gastrointestinal tract which is a clinical subtype of inflammatory bowel disease for which timely and non-invasive diagnosis in children remains a challenge. A novel predictive risk signature for pediatric CD diagnosis was constructed from bioinformatics analysis of six mRNAs, adenomatosis polyposis downregulated 1 (APCDD1), complement component 1r, mitogen-activated protein kinase kinase kinase kinase 5 (MAP3K5), lysophosphatidylcholine acyltransferase 1, sphingomyelin synthase 1 and transmembrane protein 184B, and validated using samples. Statistical evaluation was performed by support vector machine learning, weighted gene co-expression network analysis, differentially expressed genes and pathological assessment. Hematoxylin-eosin staining and immunohistochemistry results showed that APCDD1 was highly expressed in pediatric CD tissues. Evaluation by decision curve analysis and area under the curve indicated good predictive efficacy. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and gene set enrichment analysis confirmed the involvement of immune and cytokine signaling pathways. A predictive risk signature for pediatric CD is presented which represents a non-invasive supplementary tool for pediatric CD diagnosis.

Identification of four-gene signature to diagnose osteoarthritis through bioinformatics and machine learning methods

BACKGROUND

Although osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA.

METHODS

Gene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients' samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA).

RESULTS

4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell.

CONCLUSION

A machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA.

Identification and validation of immune-associated NETosis subtypes and biomarkers in anti-neutrophil cytoplasmic antibody associated glomerulonephritis

Background

NETosis is a new form of cell death, marked by DNA chromatin release from dead neutrophils. While it aids in microbe defense, it may worsen inflammation in autoimmune diseases, causing tissue harm. The impact of NETosis on Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN) remains unexplored and requires investigation.

Methods

First, a weighted gene co-expression network analysis (WGCNA) was conducted to uncover differential expression of neutrophil extranuclear trap-associated genes (DE-NETs) in ANCA-GN. The NETosisScore model was established through the single sample gene set enrichment analysis (ssGSEA), which categorized all patients into high-risk and low-risk groups. The accuracy of model was assessed by ROC curve. The biological function of various subgroups was explored through Gene Set Variation Analysis (GSVA), while the abundance of immune cell infiltration was measured with CIBERSORT. Furthermore, the key NETosis-related genes (NRGs) were identified using three machine learning algorithms, and their relationship with renal function was analyzed through the NephroseqV5 database. Through the application of qPCR and immunohistochemical staining techniques, the mRNA and protein expression levels of NRGs were determined in patients with ANCA-GN and control.

Results

A NETosisScore model was developed from 18 DE-NETs using the ssGSEA algorithm. The model's ability to predict ANCA-GN patients with a ROC AUC of 0.921. The high-risk group in ANCA-GN showed enrichment of immune-related pathways and greater infiltration of immune cells, as revealed by KEGG enrichment analysis and CIBERSORT. Using three machine learning algorithms, we identified six NRGs. Significant positive correlations were found between NRGs and CCR, macrophages, T-cell co-inhibition, and TIL. Further KEGG analysis revealed that the functions of NRGs may be closely related to the toll-like receptor signaling pathway. The levels of NRGs increased as kidney function declined and were positively correlated with Scr (serum creatinine) and negatively correlated with GFR (glomerular filtration rate), qPCR analysis showed increased expression of most NRGs in ANCA-GN patients. Furthermore, immunohistochemical staining confirmed higher expression of all NRGs in ANCA-GN patients.

Conclusion

NETosisScore model accurately predicts high-risk patients in ANCA-GN with enriched immune pathways, 6 NRGs identified as potential biomarkers.

Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape

Background

Lung cancer continues to be a problem faced by all of humanity. It is the cancer with the highest morbidity and mortality in the world, and the most common histological type of lung cancer is lung adenocarcinoma (LUAD), accounting for about 40% of lung malignant tumors. This study was conducted to discuss and explore the immune-related biomarkers and pathways during the development and progression of LUAD and their relationship with immunocyte infiltration.

Methods

The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database and the Cancer Genome Atlas Program (TCGA) database. Through the analysis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator(LASSO), selecting the module with the highest correlation with LUAD progression, and then the HUB gene was further determined. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were then used to study the function of these genes. Single-sample GSEA (ssGSEA) analysis was used to investigate the penetration of 28 immunocytes and their relationship with HUB genes. Finally, the receiver operating characteristic curve (ROC) was used to evaluate these HUB genes accurately to diagnose LUAD. In addition, additional cohorts were used for external validation. Based on the TCGA database, the effect of the HUB genes on the prognosis of LUAD patients was assessed using the Kaplan-Meier curve. The mRNA levels of some HUB genes in cancer cells and normal cells were analyzed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

The turquoise module with the highest correlation with LUAD was identified among the seven modules obtained with WGCNA. Three hundred fifty-four differential genes were chosen. After LASSO analysis, 12 HUB genes were chosen as candidate biomarkers for LUAD expression. According to the immune infiltration results, CD4 + T cells, B cells, and NK cells were high in LUAD sample tissue. The ROC curve showed that all 12 HUB genes had a high diagnostic value. Finally, the functional enrichment analysis suggested that the HUB gene is mainly related to inflammatory and immune responses. According to the RT-qPCR study, we found that the expression of DPYSL2, OCIAD2, and FABP4 in A549 was higher than BEAS-2B. The expression content of DPYSL2 was lower in H1299 than in BEAS-2B. However, the expression difference of FABP4 and OCIAD2 genes in H1299 lung cancer cells was insignificant, but both showed a trend of increase.

Conclusions

The mechanism of LUAD pathogenesis and progression is closely linked to T cells, B cells, and monocytes. 12 HUB genes(ADAMTS8, CD36, DPYSL2, FABP4, FGFR4, HBA2, OCIAD2, PARP1, PLEKHH2, STX11, TCF21, TNNC1) may participate in the progression of LUAD via immune-related signaling pathways.

Cross-Platform Transcriptomic Data Integration, Profiling, and Mining in Vibrio cholerae

A large number of transcriptome studies generate important data and information for the study of pathogenic mechanisms of pathogens, including Vibrio cholerae. V. cholerae transcriptome data include RNA-seq and microarray: microarray data mainly include clinical human and environmental samples, and RNA-seq data mainly focus on laboratory processing conditions, including different stresses and experimental animals in vivo. In this study, we integrated the data sets of both platforms using Rank-in and the Limma R package normalized Between Arrays function, achieving the first cross-platform transcriptome data integration of V. cholerae. By integrating the entire transcriptome data, we obtained the profiles of the most active or silent genes. By transferring the integrated expression profiles into the weighted correlation network analysis (WGCNA) pipeline, we identified the important functional modules of V. cholerae in vitro stress treatment, gene manipulation, and in vitro culture as DNA transposon, chemotaxis and signaling, signal transduction, and secondary metabolic pathways, respectively. The analysis of functional module hub genes revealed the uniqueness of clinical human samples; however, under specific expression patterning, the Δhns, ΔoxyR1 strains, and tobramycin treatment group showed high expression profile similarity with human samples. By constructing a protein-protein interaction (PPI) interaction network, we discovered several unreported novel protein interactions within transposon functional modules. IMPORTANCE We used two techniques to integrate RNA-seq data for laboratory studies with clinical microarray data for the first time. The interactions between V. cholerae genes were obtained from a global perspective, as well as comparing the similarity between clinical human samples and the current experimental conditions, and uncovering the functional modules that play a major role under different conditions. We believe that this data integration can provide us with some insight and basis for elucidating the pathogenesis and clinical control of V. cholerae.

Identification and preliminary validation of synovial tissue-specific genes and their-mediated biological mechanisms in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. It is well known that the formation of positive feedback between synovial hyperplasia and inflammatory infiltration is intimately associated with the occurrence and development of RA. However, the exact mechanisms still remain unknown, making the early diagnosis and therapy of RA difficult. This study was designed to identify prospective diagnostic and therapeutic biomarkers, as well as their-mediated biological mechanisms in RA.

METHODS

Three microarray datasets (GSE36700, GSE77298 and GSE153015) and two RNA-sequencing datasets (GSE89408 and GSE112656) of synovial tissues, as well as three other microarray datasets (GSE101193, GSE134087 and GSE94519) of peripheral blood were downloaded for integrated analysis. The differently expressed genes (DEGs) were identified by "limma" package of R software. Then, weight gene co-expression analysis and gene set enrichment analysis were performed to investigate synovial tissue-specific genes and their-mediated biological mechanisms in RA. The expression of candidate genes and their diagnostic value for RA were verified by quantitative real-time PCR and receiver operating characteristic (ROC) curve, respectively. Relevant biological mechanisms were explored through cell proliferation and colony formation assay. The suggestive anti-RA compounds were discovered by CMap analysis.

RESULTS

We identified a total of 266 DEGs, which were mainly enriched in cellular proliferation and migration, infection and inflammatory immune signaling pathways. Bioinformatics analysis and molecular validation revealed 5 synovial tissue-specific genes, which exhibited excellent diagnostic value for RA. The infiltration level of immune cells in RA synovial tissue was significantly higher than that in control individuals. Moreover, preliminary molecular experiments suggested that these characteristic genes may be responsible for the high proliferation potential of RA fibroblast-like synoviocytes (FLSs). Finally, 8 small molecular compounds with anti-RA potential were obtained.

CONCLUSIONS

We have proposed 5 potential diagnostic and therapeutic biomarkers (CDK1, TTK, HMMR, DLGAP5, and SKA3) in synovial tissues that may contribute to the pathogenesis of RA. These findings may shed light on the early diagnosis and therapy of RA.

Longitudinal monitoring of circulating immune cell phenotypes in anti-neutrophil cytoplasmic antibody-associated vasculitis

Anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) is a necrotizing multiorgan autoimmune disease that affects small- to medium-sized blood vessels. Despite the improvements in treatments, half of the patients with AAV still experience disease relapses. In this review, we focus on peripheral leukocyte properties and phenotypes in patients with AAV. In particular, we explore longitudinal changes in circulating immune cell phenotypes during the active phase of the disease and treatment. The numbers and phenotypes of leukocytes in peripheral blood were differs between AAV and healthy controls, AAV in active versus inactive phase, AAV in treatment responders versus non-responders, and AAV with and without severe infection. Therefore, biomarkers detected in peripheral blood immune cells may be useful for longitudinal monitoring of disease activity in AAV.

Identification and validation of BCL6 and VEGFA as biomarkers and ageing patterns correlating with immune infiltrates in OA progression

Osteoarthritis (OA), the most common type of arthritis, is a complex biological response caused by cartilage wear and synovial inflammation that links biomechanics and inflammation. The progression of OA correlates with a rise in the number of senescent cells in multiple joint tissues. However, the mechanisms by which senescent cells and their involvement with immune infiltration promote OA progression are not fully understood. The gene expression profiles and clinical information of OA and healthy control synovial tissue samples were retrieved from the Gene Expression Omnibus database, and then differential analysis of senescence regulators between OA and normal samples was performed. The random forest (RF) was used to screen candidate senescence regulators to predict the occurrence of OA. The reverse transcription quantitative real-time PCR experiments at tissue's level was performed to confirm these biomarkers. Moreover, two distinct senescence patterns were identified and systematic correlation between these senescence patterns and immune cell infiltration was analyzed. The senescence score and senescence gene clusters were constructed to quantify senescence patterns together with immune infiltration of individual OA patient. 73 senescence differentially expressed genes were identified between OA patients and normal controls. The RF method was utilized to build an OA risk model based on two senescence related genes: BCL6 and VEGFA. Next, two distinct aging patterns were determined in OA synovial samples. Most patients from senescence cluster A were further classified into gene cluster B and high senescence score group correlated with a non-inflamed phenotype, whereas senescence cluster B were classified into gene cluster A and low senescence score group correlated with an inflamed phenotype. Our study revealed that senescence played an important role in in OA synovial inflammation. Evaluating the senescence patterns of individuals with OA will contribute to enhancing our cognition of immune infiltration characterization, providing novel diagnostic and prognostic biomarkers, and guiding more effective immunotherapy strategies.

Single-Cell and Transcriptome-Based Immune Cell-Related Prognostic Model in Clear Cell Renal Cell Carcinoma

Traditional studies mostly focus on the role of single gene in regulating clear cell renal cell carcinoma (ccRCC), while it ignores the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database to provide help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. Based on the DEGs of each cluster acquired from single-cell data, immune cell marker genes, and ligand-receptor gene data, we constructed a multilayer network. Then, the genes in the network and the genes in TCGA were used to construct the WGCNA network, which screened out prognosis-associated genes for subsequent analysis. Finally, a prognostic risk scoring model was obtained, and CPTAC data showed that the effectiveness of this model was good. A nomogram based on the predictive model for predicting the overall survival was established, and internal validation was performed well. Our findings suggest that the predictive model built and based on the immune cell scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

Relationship between serum Th1/Th2 imbalance and depression in elderly patients with COPD and its clinical implications

BACKGROUND

Chronic obstructive pulmonary disease (COPD) causes organic damage as well as anxiety, depression, fear, and other psychological disorders, which seriously affect the quality of life and prognosis of patients and cause a huge economic burden to the family and society.

OBJECTIVE

The aim of this study was to investigate the correlation between an imbalance of serum Th1/Th2 indicators and psychiatric depression in elderly patients with COPD and analyze its implications for clinical management.

METHODS

From January 2018 to May 2022, 120 elderly patients with COPD treated at our hospital were categorized into two groups based on the self-rating depression scale (SDS): COPD with depression (SDS score ⩾ 50) and COPD alone (SDS score < 50). Blood gas analysis, pulmonary function, and serum Th1/Th2 index were determined. Receiver operating characteristic (ROC) curves were analyzed to explore the diagnostic value of serum Th1/Th2 ratios for COPD complicated by depression.

RESULTS

Compared with the group without depression, the partial pressure of carbon dioxide and COPD assessment test scores were significantly higher, and the oxygenation index, forced expiratory volume in one second (FEV1), and percent predicted FEV1 were significantly lower in the COPD with depression group (P< 0.05). Interleukin (IL)-1β, IL-2, IL-6, IL-8, IL-10, and tumor necrosis factor-α (TNF-α) were significantly higher in the COPD with depression group than in the group without depression (P< 0.05). Logistic regression analysis indicated that the imbalance of serum IL-1β, IL-2, IL-6, IL-8, IL-10, and TNF-α was a risk factor for mental depression in elderly patients with COPD. When comparing prognostic indices, the interval before the first onset of clinically noticeable deterioration (CID-C) in the COPD with depression group was noticeably shorter than that in the COPD without depression group; the incidence of CID-C within 6 months was noticeably higher in the COPD with depression group than in the group without depression.

CONCLUSION

Elderly patients with COPD and depression had reduced pulmonary function and higher serum Th1/Th2 levels, and an imbalance in serum Th1/Th2 indicators was a potential risk factor for depression. Moreover, elderly patients with COPD and depression were at a higher risk of disease progression and had a worse prognosis. Thus, an imbalance in serum Th1/Th2 indicators is a potential prognostic factor for evaluating depression in patients with COPD.

Machine learning links different gene patterns of viral infection to immunosuppression and immune-related biomarkers in severe burns

Introduction

Viral infection, typically disregarded, has a significant role in burns. However, there is still a lack of biomarkers and immunotherapy targets related to viral infections in burns.

Methods

Virus-related genes (VRGs) that were extracted from Gene Oncology (GO) database were included as hallmarks. Through unsupervised consensus clustering, we divided patients into two VRGs molecular patterns (VRGMPs). Weighted gene co-expression network analysis (WGCNA) was performed to study the relationship between burns and VRGs. Random forest (RF), least absolute shrinkage and selection operator (LASSO) regression, and logistic regression were used to select key genes, which were utilized to construct prognostic signatures by multivariate logistic regression. The risk score of the nomogram defined high- and low-risk groups. We compared immune cells, immune checkpoint-related genes, and prognosis between the two groups. Finally, we used network analysis and molecular docking to predict drugs targeting CD69 and SATB1. Expression of CD69 and SATB1 was validated by qPCR and microarray with the blood sample from the burn patient.

Results

We established two VRGMPs, which differed in monocytes, neutrophils, dendritic cells, and T cells. In WGCNA, genes were divided into 14 modules, and the black module was correlated with VRGMPs. A total of 65 genes were selected by WGCNA, STRING, and differential expression analysis. The results of GO enrichment analysis were enriched in Th1 and Th2 cell differentiation, B cell receptor signaling pathway, alpha-beta T cell activation, and alpha-beta T cell differentiation. Then the 2-gene signature was constructed by RF, LASSO, and LOGISTIC regression. The signature was an independent prognostic factor and performed well in ROC, calibration, and decision curves. Further, the expression of immune cells and checkpoint genes differed between high- and low-risk groups. CD69 and SATB1 were differentially expressed in burns.

Discussion

This is the first VRG-based signature (including 2 key genes validated by qPCR) for predicting survival, and it could provide vital guidance to achieve optimized immunotherapy for immunosuppression in burns.

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

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

TMEM184B promotes proliferation, migration and invasion, and inhibits apoptosis in hypopharyngeal squamous cell carcinoma

Several members of the transmembrane protein family are associated with the biological processes of human malignancies; however, the expression pattern and biological function of one family member, TMEM184B, in hypopharyngeal squamous cell carcinoma (HPSCC) are not fully understood. The expression between HPSCC tumours and adjacent normal tissues was determined by the Immunohistochemistry (IHC). A bioinformatics analysis was performed to verify the expression pattern of TMEM184B in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Furthermore, in vitro assays on cell proliferation, invasion, migration and in vivo experiments on tumour growth and apoptosis of TMEM184B in HPSCC were performed. We found that the HPSCC tissues had a significantly higher expression of TMEM184B than the adjacent normal tissues. Bioinformatics analysis confirmed the different expression of TMEM184B expression in HPSCC. Furthermore, in vitro and in vivo experiments demonstrated that TMEM184B promotes HPSCC cell growth, cell invasion and migration in FaDu cells, whereas flow cytometry assay showed that TMEM184B inhibited cell apoptosis. Our study revealed for the first time that TMEM184B might serve an oncogenic function in HPSCC and could be a potential diagnostic biomarker and therapeutic target for HPSCC.

Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with symptoms characterized by typical circadian rhythmic changes. This study aimed to identify the hub circadian rhythm genes (CRGs) in RA and explore their association with immune cell infiltration and pathogenesis of RA.

Methods

The differentially expressed CRGs (DECRGs) between RA and normal control samples were screened from Datasets GSE12021 and GSE55235. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to explore the potential functional mechanisms of DECRGs in RA. Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator regression analysis were performed to identify hub CRGs of RA. CIBERSORT was conducted to compare the infiltration level of immune cells in RA and control synovial tissue and their relationship with hub genes. In addition, the diagnostic value of hub biomarkers was evaluated by the area under the receiver operator characteristic curve. Further, a nomogram prediction model was constructed and its significance for clinical decision-making was evaluated.

Results

The green module was identified as the hub module associated with RA. Four hub CRGs (EGR1, FOSL2, GADD45B, and NFIL3) were identified and showed that they had the highest specificity and sensitivity for RA diagnosis, respectively. The expression levels and diagnostic values of these genes were externally validated in the dataset GSE55457. A nomogram prediction model based on the four hub CRGs was constructed and proved to have a certain clinical decision value. Additionally, the correlation analysis of immune cells with hub genes showed that all hub genes were significantly positively correlated with activated mast cells, resting memory CD4+ T cells, and monocytes. Whereas, all hub genes were negatively correlated with plasma cells, CD8+ T cells, and activated memory CD4+ T cells. Meanwhile, FOSL2 and GADD45B were negatively correlated with Tfh cells.

Conclusion

Four hub CRGs were identified and showed excellent diagnostic value for RA. These genes may be involved in the pathological process of RA by disrupting the rhythmic oscillations of cytokines through immune-related pathways and could be considered molecular targets for future chronotherapy against RA.

Key biomarkers and latent pathways of dysferlinopathy: Bioinformatics analysis and in vivo validation

Background

Dysferlinopathy refers to a group of muscle diseases with progressive muscle weakness and atrophy caused by pathogenic mutations of the DYSF gene. The pathogenesis remains unknown, and currently no specific treatment is available to alter the disease progression. This research aims to investigate important biomarkers and their latent biological pathways participating in dysferlinopathy and reveal the association with immune cell infiltration.

Methods

GSE3307 and GSE109178 were obtained from the Gene Expression Omnibus (GEO) database. Based on weighted gene co-expression network analysis (WGCNA) and differential expression analysis, coupled with least absolute shrinkage and selection operator (LASSO), the key genes for dysferlinopathy were identified. Functional enrichment analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied to disclose the hidden biological pathways. Following that, the key genes were approved for diagnostic accuracy of dysferlinopathy based on another dataset GSE109178, and quantitative real-time polymerase chain reaction (qRT-PCR) were executed to confirm their expression. Furthermore, the 28 immune cell abundance patterns in dysferlinopathy were determined with single-sample GSEA (ssGSEA).

Results

1,579 differentially expressed genes (DEGs) were screened out. Based on WGCNA, three co-expression modules were obtained, with the MEskyblue module most strongly correlated with dysferlinopathy. 44 intersecting genes were recognized from the DEGs and the MEskyblue module. The six key genes MVP, GRN, ERP29, RNF128, NFYB and KPNA3 were discovered through LASSO analysis and experimentally verified later. In a receiver operating characteristic analysis (ROC) curve, the six hub genes were shown to be highly valuable for diagnostic purposes. Furthermore, functional enrichment analysis highlighted that these genes were enriched mainly along the ubiquitin-proteasome pathway (UPP). Ultimately, ssGSEA showed a significant immune-cell infiltrative microenvironment in dysferlinopathy patients, especially T cell, macrophage, and activated dendritic cell (DC).

Conclusion

Six key genes are identified in dysferlinopathy with a bioinformatic approach used for the first time. The key genes are believed to be involved in protein degradation pathways and the activation of muscular inflammation. And several immune cells, such as T cell, macrophage and DC, are considered to be implicated in the progression of dysferlinopathy.