Key genes and functional coexpression modules involved in the pathogenesis of systemic lupus erythematosus

Understanding exosomes: Part 2-Emerging leaders in regenerative medicine

Exosomes are the smallest subset of extracellular signaling vesicles secreted by most cells with the ability to communicate with other tissues and cell types over long distances. Their use in regenerative medicine has gained tremendous momentum recently due to their ability to be utilized as therapeutic options for a wide array of diseases/conditions. Over 5000 publications are currently being published yearly on this topic, and this number is only expected to dramatically increase as novel therapeutic strategies continue to be developed. Today exosomes have been applied in numerous contexts including neurodegenerative disorders (Alzheimer's disease, central nervous system, depression, multiple sclerosis, Parkinson's disease, post-traumatic stress disorders, traumatic brain injury, peripheral nerve injury), damaged organs (heart, kidney, liver, stroke, myocardial infarctions, myocardial infarctions, ovaries), degenerative processes (atherosclerosis, diabetes, hematology disorders, musculoskeletal degeneration, osteoradionecrosis, respiratory disease), infectious diseases (COVID-19, hepatitis), regenerative procedures (antiaging, bone regeneration, cartilage/joint regeneration, osteoarthritis, cutaneous wounds, dental regeneration, dermatology/skin regeneration, erectile dysfunction, hair regrowth, intervertebral disc repair, spinal cord injury, vascular regeneration), and cancer therapy (breast, colorectal, gastric cancer and osteosarcomas), immune function (allergy, autoimmune disorders, immune regulation, inflammatory diseases, lupus, rheumatoid arthritis). This scoping review is a first of its kind aimed at summarizing the extensive regenerative potential of exosomes over a broad range of diseases and disorders.

Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network

Background: Crohn’s disease (CD) is an inflammatory bowel disease, cases of which have substantially increased in recent years. The classical formula Dajianzhong decoction (DD, Japanese: Daikenchuto) is often used to treat CD, but few studies have evaluated related therapeutic mechanisms. In this study, we investigated the potential targets and mechanisms of DD used for treating CD at the molecular level through the weighted gene co-expression network. Methods: The main chemical components of the three DD herbs (Zanthoxylum bungeanum Maxim., Zingiber officinale (Willd.) Rosc., and Ginseng Radix et Rhizoma) were searched for using the HERB database. The targets for each component were identified using the SwissTargetPrediction and HERB databases, whereas the disease targets for CD were retrieved from the GeneCards and DisGeNET databases. The functional enrichment analysis was performed on the common targets of DD and CD. High-throughput sequencing data for CD patients were retrieved from the Gene Expression Omnibus database, and WGCNA was performed to identify the key targets. The association between the key targets and DD ingredients was verified using molecular docking. Results: By analyzing the interaction targets between DD and CD, 196 overlapping genes were identified. The enrichment results indicated that the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways influenced the mechanism of action of DD in counteracting CD. Combined with WGCNA, four differentially expressed genes (SLC6A4, NOS2, SHBG, and ABCB1) and their corresponding 24 compounds were closely related to the occurrence of CD. Conclusions: By integrating gene co-expression network analysis, this study preliminarily reveals the internal molecular mechanism of DD in treating CD from a systematic perspective, validated by molecular docking. However, these findings require further validation.

Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR

Lung adenocarcinoma (LUAD) is the predominant pathological subtype of lung cancer, which is the most prevalent and lethal malignancy worldwide. Cyclins have been reported to regulate the physiology of various types of tumors by controlling cell cycle progression. However, the key roles and regulatory networks associated with the majority of the cyclin family members in LUAD remain unclear. In total, 556 differentially expressed genes were screened from the GSE33532, GSE40791 and GSE19188 mRNA microarray datasets by R software. Subsequently, protein-protein interaction network containing 499 nodes and 4,311 edges, in addition to a significant module containing 76 nodes and 2,631 edges, were extracted through the MCODE plug-in of Cytoscape. A total of four cyclin family genes [cyclin (CCNA2, CCNB1, CCNB2 and CCNE2] were then found in this module. Further co-expression analysis and associated gene prediction revealed forkhead box M1 (FOXM1), the common transcription factor of CCNB2, CCNB1 and CCNA2. In addition, using GEPIA database, it was found that the high expression of these four genes were simultaneously associated with poorer prognosis in patients with LUAD. Experimentally, it was proved that these four hub genes were highly expressed in LUAD cell lines (Beas-2B and H1299) and LUAD tissues through qPCR, western blot analysis and immunohistochemical studies. The diagnostic value of these 4 hub genes in LUAD was analyzed by logistic regression, CCNA2 was deleted, following which a nomogram diagnostic model was constructed accordingly. The area under the curve values of CCNB1, CCNB2 and FOXM1 diagnostic models were calculated to be 0.92, 0.91 and 0.96 in the training set (Combined dataset of GSE33532, GSE40791 and GSE19188) and two validation sets (GSE10072 and GSE75037), respectively. To conclude, data from the present study suggested that the FOXM1/cyclin (CCNA2, CCNB1 and/or CCNB2) axis may serve a regulatory role in the development and prognosis of LUAD. Specifically, CCNB1, CCNB2 and FOXM1 have potential as diagnostic markers and/or therapeutic targets for LUAD treatment.

Identifying key genes in CD4+ T cells of systemic lupus erythematosus by integrated bioinformatics analysis

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excessive activation of T and B lymphocytes and breakdown of immune tolerance to autoantigens. Despite several mechanisms including the genetic alterations and inflammatory responses have been reported, the overall signature genes in CD4⁺ T cells and how they affect the pathological process of SLE remain to be elucidated. This study aimed to identify the crucial genes, potential biological processes and pathways underlying SLE pathogenesis by integrated bioinformatics. The gene expression profiles of isolated peripheral CD4⁺ T cells from SLE patients with different disease activity and healthy controls (GSE97263) were analyzed, and 14 co-expression modules were identified using weighted gene co-expression network analysis (WGCNA). Some of these modules showed significantly positive or negative correlations with SLE disease activity, and primarily enriched in the regulation of type I interferon and immune responses. Next, combining time course sequencing (TCseq) with differentially expressed gene (DEG) analysis, crucial genes in lupus CD4⁺ T cells were revealed, including some interferon signature genes (ISGs). Among these genes, we identified 4 upregulated genes (PLSCR1, IFI35, BATF2 and CLDN5) and 2 downregulated genes (GDF7 and DERL3) as newfound key genes. The elevated genes showed close relationship with the SLE disease activity. In general, our study identified 6 novel biomarkers in CD4⁺ T cells that might contribute to the diagnosis and treatment of SLE.

Weighted gene co-expression network indicates that the DYNLL2 is an important regulator of chicken breast muscle development and is regulated by miR-148a-3p

Background

The characteristics of muscle fibers determine the growth and meat quality of poultry. In this study, we performed a weighted gene co-expression network analysis (WGCNA) on the muscle fiber characteristics and transcriptome profile of the breast muscle tissue of Gushi chicken at 6, 14, 22, and 30 weeks.

Results

A total of 27 coexpressed biological functional modules were identified, of which the midnight blue module had the strongest correlation with muscle fiber and diameter. In addition, 7 hub genes were found from the midnight blue module, including LC8 dynein light chain 2 (DYNLL2). Combined with miRNA transcriptome data, miR-148a-3p was found to be a potential target miRNA of DYNLL2. Experiments on chicken primary myoblasts (CPMs) demonstrated that miR-148a-3p promotes the expression of myosin heavy chain (MYHC) protein by targeting DYNLL2, proving that it can promote differentiation of myoblasts.

Conclusions

This study proved that the hub gene DYNLL2 and its target miR-148-3p are important regulators in chicken myogenesis. These results provide novel insights for understanding the molecular regulation mechanisms related to the development of chicken breast muscle.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08522-8.

Bioinformatical analysis of the key differentially expressed genes and associations with immune cell infiltration in development of endometriosis

Background

This study explored the key genes related to immune cell infiltration in endometriosis.

Results

The Gene Expression Omnibus (GEO) datasets (GSE7305, GSE7307, and GSE11691), containing a total of 37 endometriosis and 42 normal tissues, were retrieved and analyzed to determine the differentially expressed genes (DEGs). Gene ontology (GO) annotations and Kyoto Encyclopedia of Genes (KEGG) analysis were performed to identify the pathways that were significantly enriched. The xCell software was used to analyze immune cell infiltration and correlation analyses were performed to uncover the relationship between key genes and immune cells. The analysis identified 1031 DEGs (581 upregulated and 450 downregulated DEGs), while GO analysis revealed altered extracellular matrix organization, collagen-containing extracellular matrix, and glycosaminoglycan binding and KEGG enrichment showed genes related to metabolic pathways, pathways in cancer, phosphatidylinositol 3-kinase-protein kinase B (PI3K-Akt) signaling, proteoglycans in cancer, and the mitogen-activated protein kinase (MAPK) signaling pathway. Furthermore, the protein–protein interaction network revealed 10 hub genes, i.e., IL6, FN1, CDH1, CXCL8, IGF1, CDK1, PTPRC, CCNB1, MKI67, and ESR1. The xCell analysis identified immune cells with significant changes in all three datasets, including CD4⁺ and CD8⁺ T cells, CD8⁺ Tem, eosinophils, monocytes, Th1 cells, memory B-cells, activated dendritic cells (aDCs), and plasmacytoid dendritic cells (pDCs). These 10 hub genes were significantly associated with at least three types of immune cells.

Conclusions

Aberrant gene expression was related to abnormal infiltration of different immune cells in endometriosis and was associated with endometriosis development by affecting the tissue microenvironment and growth of ectopic endometrial cells.

LCN2 as a Potential Diagnostic Biomarker for Ulcerative Colitis-Associated Carcinogenesis Related to Disease Duration

BACKGROUND

Patients with long-duration ulcerative colitis (UC) had a higher risk of developing ulcerative colitis-associated carcinogenesis (UCAC) when compared to those with short-duration UC. This study aimed to discover the biomarker for cancer surveillance related to disease duration.

METHODS

The microarrays were divided into short-duration (<10 years) UC, long-duration (≥10 years) UC, UCAC, and normal groups in the Gene Expression Omnibus (GEO) datasets. Differentially expressed genes (DEGs) of GEO and the hub genes of the selected weighted gene co-expression network analysis (WGCNA) were intersected to obtain the overlapping genes. Among these genes, the key gene was identified by using the protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the cytoHubba of Cytoscape, and the expression levels. Also, immunofluorescence of human colonic mucosa and animal experiment were used to validate the expression trend of the key gene in the progress of UC developing into UCAC.

RESULTS

Lipocalin-2 (LCN2) was more relevant with disease duration of UC and significantly negatively correlated with the risk of UCAC. The expression level of LCN2 in short-duration UC was higher than that of long-duration UC (P < 0.01), long-duration UC was higher than that of UCAC (P = 0.001), and UC and UCAC were all higher than that of the normal (P < 0.001). We then discovered that the expression trend of LCN2 in blood and stool samples was consistent with that in colorectal mucosa.

CONCLUSION

The research indicates that LCN2 could be a novel biomarker to evaluate cancer surveillance related to disease duration of developing UC into UCAC. Compared with that of blood samples, stool detection of LCN2 may have more advantages for diagnosis value of early stage of UCAC as a complement to colonoscopy surveillance.

Immunomodulatory Effect of MSCs and MSCs-Derived Extracellular Vesicles in Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a common autoimmune connective tissue disease with unclear etiology and pathogenesis. Mesenchymal stem cell (MSC) and MSC derived extracellular vesicles (EVs) play important roles in regulating innate and adaptive immunity, which are involved in many physiological and pathological processes and contribute to the immune homeostasis in SLE. The effects of MSCs and EVs on SLE have been drawing more and more attention during the past few years. This article reviews the immunomodulatory effects and underlying mechanisms of MSC/MSC-EVs in SLE, which provides novel insight into understanding SLE pathogenesis and guiding the biological therapy.

Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease

Background

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. However, the pathogenesis of DKD remains unclarified, and there is an urgent need for improved treatments. Recently, many crucial genes closely linked to the molecular mechanism underlying various diseases were discovered using weighted gene co-expression network analysis.

Methods

We used a gene expression omnibus series dataset GSE104948 with 12 renal glomerular DKD tissue samples and 18 control samples obtained from the gene expression omnibus database and performed weighted gene co-expression network analysis. After obtaining the trait-related modules, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of the modules were conducted and the key gene associated with DKD was selected from the top two most significant gene ontology terms using the maximal clique centrality method. Finally, we verified the key gene using protein-protein interaction analysis, additional datasets, and explored the relationship between the key gene and DKD renal function using the Nephroseq v5 online database.

Results

Among the 10 gene co-expression modules identified, the darkorange2 and red modules were highly related to DKD and the normal biological process, respectively. Majority of the genes in the darkorange2 module were related to immune and inflammatory responses, and potentially related to the progression of DKD due to their abnormal up-regulation. After performing sub-network analysis of the genes extracted from the top two most significant gene ontology terms and calculating the maximal clique centrality values of each gene, FCER1G, located at the center of the protein-protein interaction network, was identified as a key gene related to DKD. Furthermore, gene expression omnibus validation in additional datasets also showed that FCER1G was overexpressed in DKD compared with normal tissues. Finally, Pearson’s correlation analysis between the expression of FCER1G and DKD renal function revealed that the abnormal upregulation of FCER1G was related to diabetic glomerular lesions.

Conclusions

Our study demonstrated for the first time that FCER1G is a crucial gene associated with the pathogenesis of DKD.

Identification of Biomarkers Related to Systemic Sclerosis With or Without Pulmonary Hypertension Using Co-expression Analysis

Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune disease with multiple system involvement, and pulmonary complications, including pulmonary hypertension (PH), are leading causes of death. This study aimed to develop early biomarkers to distinguish SSc with or without PH from normal population using bioinformatics approaches. The gene expression profile GSE22356, which contains 10 SSc samples with PH, 10 SSc samples without PH, and 10 normal samples, was obtained from the Gene Expression Omnibus database. First, we constructed co-expression networks and identified critical gene modules using the weighted gene co-expression network analysis. Then, functional enrichment analysis of significant modules was performed. Finally, the "real" hub gene was screened out by intramodule analysis and protein-protein interaction networks, and the receiver operating characteristic analysis was conducted. A total of 5046 genes were screened out to construct co-expression networks, and 18 modules were identified. Of these modules, the turquoise module had a strong correlation with SSc only, whereas the midnightblue module showed an obvious positive correlation with SSc with PH. Functional enrichment analysis indicated that the turquoise module was mainly enriched in transcription of DNA template and its regulation and protein ubiquitination and involved in apoptosis and pyrimidine metabolism pathway. The midnightblue module was significantly associated with inflammatory and immune response and pathways in Staphylococcus aureus infection and Chagas disease. The "real" hub genes in the turquoise module were WDR36, POLR1B, and SRSF1, and those in midnightblue were TLR2 and TNFAIP6.

Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis

Background

Ulcerative colitis is a type of inflammatory bowel disease posing a great threat to the public health worldwide. Previously, gene expression studies of mucosal colonic biopsies have provided some insight into the pathophysiological mechanisms in ulcerative colitis; however, the exact pathogenesis is unclear. The purpose of this study is to identify the most related genes and pathways of UC by bioinformatics, so as to reveal the core of the pathogenesis.

Methods

Genome-wide gene expression datasets involving ulcerative colitis patients were collected from gene expression omnibus database. To identify most close genes, an integrated analysis of gene expression signature was performed by employing robust rank aggregation method. We used weighted gene co-expression network analysis to explore the functional modules involved in ulcerative colitis pathogenesis. Besides, biological process and pathways analysis of co-expression modules were figured out by gene ontology enrichment analysis using Metascape.

Results

A total of 328 ulcerative colitis patients and 138 healthy controls were from 14 datasets. The 150 most significant differentially expressed genes are likely to include causative genes of disease, and further studies are needed to demonstrate this. Seven main functional modules were identified, which pathway enrichment analysis indicated were associated with many biological processes. Pathways such as ‘extracellular matrix, immune inflammatory response, cell cycle, material metabolism’ are consistent with the core mechanism of ulcerative colitis. However, ‘defense response to virus’ and ‘herpes simplex infection’ suggest that viral infection is one of the aetiological agents. Besides, ‘Signaling by Receptor Tyrosine Kinases’ and ‘pathway in cancer’ provide new clues for the study of the risk and process of ulcerative colitis cancerization.

Identification of key genes and pathways associated with Crohn's disease by bioinformatics analysis

Aims: Crohn's disease (CD) is a type of inflammatory bowel disease. The present study aimed to identify key genes and significant signaling pathways associated with CD by bioinformatics analysis. A total of 179 CD patients and 94 healthy controls from nine genome-wide gene expression datasets were included.Results: MMP1 and CLDN8 were two key genes screened from the differentially expressed genes. Connectivity Map predicted several small molecules as possible adjuvant drugs to treat CD. Besides, we used weighted gene coexpression network analysis to explore the functional modules involved in CD pathogenesis. Seven main functional modules were identified, of which black module showed the highest correlation with CD. The genes in black module mainly enriched in interferon signaling and defense response to virus. Blue module was another important module and enriched in several signaling pathways, including extracellular matrix organization, inflammatory response and blood vessel development.Conclusions: This study identified a number of key genes and pathways involved in CD and potential drugs to combat it, which might offer insights into CD pathogenesis and provide a clue to potential treatments.

Network-based analysis reveals novel gene signatures in the peripheral blood of patients with sporadic nonsyndromic thoracic aortic aneurysm

Thoracic aortic aneurysm (TAA), a serious cardiovascular disease that causes morbidity and mortality worldwide. At present, few biomarkers can accurately diagnose the appearance of TAA before dissection or rupture. Our research has the intention to investigate the developing applicable biomarkers for TAA promising clinically diagnostic biomarkers or probable regulatory targets for TAA. In our research, we built correlation networks utilizing the expression profile of peripheral blood mononuclear cell obtained from a public microarray data set (GSE9106). Furthermore, we chose the turquoise module, which has the strongest significance with TAA and was further analyzed. Fourteen genes that overlapped with differentially expressed proteins in the medial aortic layer were obtained. Subsequently, we verified the results applying quantitative polymerase chain reaction (Q-PCR) to our clinical specimen. In general, the Q-PCR results coincide with the majority of the expression profile. Fascinatingly, a notable change occurred in CLU, DES, MYH10, and FBLN5. In summary, using weighted gene coexpression analysis, our study indicates that CLU, DES, MYH10, and FBLN5 were identified and validated to be related to TAA and might be candidate biomarkers or therapeutic targets for TAA.

An Allele-Specific Functional SNP Associated with Two Systemic Autoimmune Diseases Modulates IRF5 Expression by Long-Range Chromatin Loop Formation

Both systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are autoimmune diseases sharing similar genetic backgrounds. Genome-wide association studies have constantly disclosed numerous genetic variants conferring to both disease risks at 7q32.1, but the functional mechanisms underlying them are still largely unknown. Through a series of bioinformatics and functional analyses, we prioritized a potential independent functional single-nucleotide polymorphism (rs13239597) within TNPO3 promoter region, residing in a putative enhancer element and validated that IRF5 is the distal target gene (∼118 kb) of rs13239597, which is a key regulator involved in pathogenic autoantibody dysregulation, increasing risk of both SLE and SSc. We experimentally validated the long-range chromatin interactions between rs13239597 and IRF5 using chromosome conformation capture assay. We further demonstrated that rs13239597-A acted as an allele-specific enhancer regulating IRF5 expression, independently of TNPO3 by using dual-luciferase reporter assays and CRISPR-Cas9. Particularly, the transcription factor EVI1 could preferentially bind to rs13239597-A allele and increase the enhancer activity to regulate IRF5 expression. Taken together, our results uncovered a mechanistic insight of a noncoding functional variant acting as an allele-specific distal enhancer to directly modulate IRF5 expression, which might obligate in understanding of complex genetic architectures of SLE and SSc pathogenesis.

Association of Key Genes and Pathways with Atopic Dermatitis by Bioinformatics Analysis

Background

Atopic dermatitis is a chronic inflammatory disease of the skin. It has a high prevalence worldwide and affected persons are prone to recurrent attacks, seriously affecting the physical and mental of patients. The exact etiology of the disease is still unclear.

Material/Methods

There are 7 datasets on atopic dermatitis in the Gene Expression Omnibus database, including 142 lesional and 134 non-lesional skin biopsy samples. Differential analysis was performed after datasets were integrated by robust multi-array average method. Functional modules of GSE99802 were explored by weighted gene co-expression network analysis. The 4 most important modules were enriched into the pathways by Metascape.

Results

Significantly differentially expressed genes included 41 upregulated and 10 downregulated genes. The following 5 of the most important upregulated genes had the strongest association with atopic dermatitis. SERPINB3&4 promote inflammation and impaired skin barrier function in the early stage of atopic dermatitis. S100A9 aggravates the inflammatory response by inducing the activation of toll-like receptor 4, neutrophil chemotaxis, neutrophilic inflammation, and the amplification of interleukin-8. MMP1 is the key protease of skin collagen degradation, keeping the extracellular matrix in dynamic balance. MMP12 induces the aggregation of various inflammatory cells into inflammatory tissue. The enriched pathways of each module mainly include Cellular responses to external stimuli, Metabolism of RNA and Translation, and Infectious disease.

Conclusions

The associated pathways and genes not only help us understand the molecular mechanism of the disease, but also provide research directions or targets for accurate diagnosis and treatment.

Weighted Gene Coexpression Network Analysis Identifies Key Genes and Pathways Associated with Idiopathic Pulmonary Fibrosis

BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease with an unknown etiology. Gene expression microarray data have provided some insights into the molecular mechanisms of IPF. This study aimed to identify key genes and significant signaling pathways involved in IPF using bioinformatics analysis. MATERIAL AND METHODS Differentially expressed genes (DEGs) were identified using integrated analysis of gene expression data with a robust rank aggregation (RRA) method. The Connectivity Map (CMAP) was used to identify gene-expression signatures associated with IPF. Weighted gene coexpression network analysis (WGCNA) was used to explore the functional modules involved in the pathogenesis of IPF. RESULTS A total of 191 patients with IPF and 101 normal controls from six genome-wide expression datasets were included. CMAP predicted several small molecular agents as potential gene targets in IPF. Several functional modules were detected that showed the highest correlation with IPF, including an extracellular matrix (ECM) component, and a myeloid leukocyte migration and activation component involved in the immune response. Hub genes were identified in the key functional modules that might have a role in the progression of IPF. CONCLUSIONS WGCNA was used to identify functional modules and hub genes involved in the pathogenesis of IPF.

Aberrant Expressions of Co-stimulatory and Co-inhibitory Molecules in Autoimmune Diseases

Co-signaling molecules include co-stimulatory and co-inhibitory molecules and play important roles in modulating immune responses. The roles of co-signaling molecules in autoimmune diseases have not been clearly defined. We assessed the expressions of co-stimulatory and co-inhibitory molecules in autoimmune diseases through a bioinformatics-based study. By using datasets of whole-genome transcriptome, the expressions of 54 co-stimulatory or co-inhibitory genes in common autoimmune diseases were analyzed using Robust rank aggregation (RRA) method. Nineteen array datasets and 6 RNA-seq datasets were included in the RRA discovery study and RRA validation study, respectively. Significant genes were further validated in several autoimmune diseases including Graves' disease (GD). RRA discovery study suggested that CD160 was the most significant gene aberrantly expressed in autoimmune diseases (Adjusted P = 5.9E-12), followed by CD58 (Adjusted P = 5.7E-06) and CD244 (Adjusted P = 9.5E-05). RRA validation study also identified CD160 as the most significant gene aberrantly expressed in autoimmune diseases (Adjusted P = 5.9E-09). We further found that the aberrant expression of CD160 was statistically significant in multiple autoimmune diseases including GD (P < 0.05), and CD160 had a moderate role in diagnosing those autoimmune diseases. Flow cytometry confirmed that CD160 was differentially expressed on the surface of CD8⁺ T cells between GD patients and healthy controls (P = 0.002), which proved the aberrant expression of CD160 in GD at the protein level. This study suggests that CD160 is the most significant co-signaling gene aberrantly expressed in autoimmune diseases. Treatment strategy targeting CD160-related pathway may be promising for the therapy of autoimmune diseases.

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Non-alcoholic Fatty Liver Disease

Background: Non-alcoholic fatty liver disease (NAFLD) is a well-known cause of liver dysfunction and has become a common chronic liver disease in many countries. However, the intrinsic molecular mechanisms underlying the pathogenesis of NAFLD have not yet been fully elucidated. Methods: We obtained the gene expression datasets of NAFLD through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between NAFLD patients and controls. Gene functional annotation and PPI network analysis were performed to explore the potential function of the DEGs. Finally, we used a sequencing dataset GSE126848 to validate our results. Results: In this study, GSE48452, GSE66676, GSE72756, GSE63067, GSE89632, and GSE107231 were included, including 125 NAFLD patients and 116 control patients. The RRA integrated analysis determined 96 significant DEGs (50 up-regulated and 46 down-regulated) and the most significant gene aberrantly expressed in NAFLD was ENO3 (P-value = 7.17E-05), followed by CYP7A1 (P-value = 9.04E-05), and P4HA1 (P-value = 1.67E-04). Carboxylic acid metabolic process (GO:0019752; P-value = 1.39E-03) was the most significantly enriched for biological process in GO (gene ontology) analysis. KEGG pathway enrichment analysis showed that steroid hormone biosynthesis (hsa00140; P-value = 6.68E-03) and PPAR signaling pathway (hsa03320; P-value = 9.95E-03) were significantly enriched. Based on the results of the PPI and the results of the RRA, we finally defined the four most critical genes as the hub genes, including ENO3, CYP7A1, P4HA1, and CYP1A1. Conclusions: Our integrated analysis identified novel gene signatures and will contribute to the understanding of comprehensive molecular changes in NAFLD.

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.