Bioinformatics analysis of differentially expressed genes related to ischemia and hypoxia in spinal cord injury and construction of miRNA-mRNA or mRNA-transcription factor interaction network

BACKGROUND

Previous studies show that spinal cord ischemia and hypoxia is an important cause of spinal cord necrosis and neurological loss. Therefore, the study aimed to identify genes related to ischemia and hypoxia after spinal cord injury (SCI) and analyze their functions, regulatory mechanism, and potential in regulating immune infiltration.

METHODS

The expression profiles of GSE5296, GSE47681, and GSE217797 were downloaded from the Gene Expression Omnibus database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to determine the function and pathway enrichment of ischemia- and hypoxia-related differentially expressed genes (IAHRDEGs) in SCI. LASSO model was constructed, and support vector machine analysis was used to identify key genes. The diagnostic values of key genes were evaluated using decision curve analysis and receiver operating characteristic curve analysis. The interaction networks of miRNAs-IAHRDEGs and IAHRDEGs-transcription factors were predicted and constructed with the ENCORI database and Cytoscape software. CIBERSORT algorithm was utilized to analyze the correlation between key gene expression and immune cell infiltration.

RESULTS

There were 27 IAHRDEGs identified to be significantly expressed in SCI at first. These genes were mostly significantly enriched in wound healing function and the pathway associated with lipid and atherosclerosis. Next, five key IAHRDEGs (Abca1, Casp1, Lpl, Procr, Tnfrsf1a) were identified and predicted to have diagnostic value. Moreover, the five key genes are closely related to immune cell infiltration.

CONCLUSION

Abca1, Casp1, Lpl, Procr, and Tnfrsf1a may promote the pathogenesis of ischemic or hypoxic SCI by regulating vascular damage, inflammation, and immune infiltration.

Corrigendum: A microarray data analysis investigating the pathogenesis and potential biomarkers of autophagy and ferroptosis in intervertebral disc degeneration

[This corrects the article DOI: 10.3389/fgene.2022.1090467.].

Identification of Potential Sex-Specific Biomarkers in Pigs with Low and High Intramuscular Fat Content Using Integrated Bioinformatics and Machine Learning

Intramuscular fat (IMF) content is a key determinant of pork quality. Controlling the genetic and physiological factors of IMF and the expression patterns of various genes is important for regulating the IMF content and improving meat quality in pig breeding. Growing evidence has suggested the role of genetic factors and breeds in IMF deposition; however, research on the sex factors of IMF deposition is still lacking. The present study aimed to identify potential sex-specific biomarkers strongly associated with IMF deposition in low- and high-IMF pig populations. The GSE144780 expression dataset of IMF deposition-related genes were obtained from the Gene Expression Omnibus. Initially, differentially expressed genes (DEGs) were detected in male and female low-IMF (162 DEGs, including 64 up- and 98 down-regulated genes) and high-IMF pigs (202 DEGs, including 147 up- and 55 down-regulated genes). Moreover, hub genes were screened via PPI network construction. Furthermore, hub genes were screened for potential sex-specific biomarkers using the least absolute shrinkage and selection operator machine learning algorithm, and sex-specific biomarkers in low-IMF (troponin I (TNNI1), myosin light chain 9(MYL9), and serpin family C member 1(SERPINC1)) and high-IMF pigs (CD4 molecule (CD4), CD2 molecule (CD2), and amine oxidase copper-containing 2(AOC2)) were identified, and then verified by quantitative real-time PCR (qRT-PCR) in semimembranosus muscles. Additionally, the gene set enrichment analysis and single-sample gene set enrichment analysis of hallmark gene sets were collectively performed on the identified biomarkers. Finally, the transcription factor-biomarker and lncRNA-miRNA-mRNA (biomarker) networks were predicted. The identified potential sex-specific biomarkers may provide new insights into the molecular mechanisms of IMF deposition and the beneficial foundation for improving meat quality in pig breeding.

Causality between inflammatory bowel disease and the cerebral cortex: insights from Mendelian randomization and integrated bioinformatics analysis

Background

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), is a chronic, progressive, and recurrent intestinal condition that poses a significant global health burden. The high prevalence of neuropsychiatric comorbidities in IBD necessitates the development of targeted management strategies.

Methods

Leveraging genetic data from genome-wide association studies and Immunochip genotype analyses of nearly 150,000 individuals, we conducted a two-sample Mendelian randomization study to elucidate the driving force of IBD, UC, and CD on cortical reshaping. Genetic variants mediating the causality were collected to disclose the biological pathways linking intestinal inflammation to brain dysfunction.

Results

Here, 115, 69, and 98 instrumental variables genetically predicted IBD, UC, and CD. We found that CD significantly decreased the surface area of the temporal pole gyrus (β = -0.946 mm2, P = 0.005, false discovery rate-P = 0.085). Additionally, we identified suggestive variations in cortical surface area and thickness induced by exposure across eight functional gyri. The top 10 variant-matched genes were STAT3, FOS, NFKB1, JAK2, STAT4, TYK2, SMAD3, IL12B, MYC, and CCL2, which are interconnected in the interaction network and play a role in inflammatory and immune processes.

Conclusion

We explore the causality between intestinal inflammation and altered cortical morphology. It is likely that neuroinflammation-induced damage, impaired neurological function, and persistent nociceptive input lead to morphological changes in the cerebral cortex, which may trigger neuropsychiatric disorders.

Integrated bioinformatics identifies key mediators in cytokine storm and tissue remodeling during Vibrio mimicus infection in yellow catfish (Pelteobagrus fulvidraco)

Introduction

The pathogenesis of Vibrio mimicus infection in yellow catfish (Pelteobagrus fulvidraco) remains poorly understood, particularly regarding the impact of infection with the pathogen on primary target organs such as the skin and muscle.

Methods

In this study, we aim to analyze the pathological intricacies of the skin and muscle of yellow catfish after being infected with V. mimicus using a 1/10 LC₅₀ seven-day post-infection model. Furthermore, we have utilized integrated bioinformatics to comprehensively elucidate the regulatory mechanisms and identify the key regulatory genes implicated in this phenomenon.

Results

Our histopathological examination revealed significant pathological changes in the skin and muscle, characterized by necrosis and inflammation. Moreover, tissue remodeling occurred, with perimysium degeneration and lesion invasion into the muscle along the endomysium, accompanied by a transformation of type I collagen into a mixture of type I and type III collagens in the perimysium and muscle bundles. Our eukaryotic transcriptomic and 4D label-free analyses demonstrated a predominantly immune pathway response in both the skin and muscle, with downregulation observed in several cell signaling pathways that focused on focal adhesion-dominated cell signaling pathways. The upregulated genes included interleukins (IL)-1 and -6, chemokines, and matrix metallopeptidases (mmp)-9 and -13, while several genes were significantly downregulated, including col1a and col1a1a. Further analysis revealed that these pathways were differentially regulated, with mmp-9 and mmp-13 acting as the potential core regulators of cytokine and tissue remodeling pathways. Upregulation of NF-κB1 and FOSL-1 induced by IL-17C and Nox 1/2-based NADPH oxidase may have held matrix metallopeptidase and cytokine-related genes. Also, we confirmed these relevant regulatory pathways by qPCR and ELISA in expanded samples.

Discussion

Our findings unequivocally illustrate the occurrence of a cytokine storm and tissue remodeling, mediated by interleukins, chemokines, and MMPs, in the surface of yellow catfish infected with V. mimicus. Additionally, we unveil the potential bidirectional regulatory role of MMP-9 and MMP-13. These results provide novel perspectives on the intricate immune response to V. mimicus infection in yellow catfish and highlight potential targets for developing therapies.

Identification of AKR1B10 as a key gene in primary biliary cholangitis by integrated bioinformatics analysis and experimental validation

Background: Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease that eventually progresses to cirrhosis and hepatocellular carcinoma (HCC) in the absence of proper treatment. However, Gene expression and molecular mechanisms involved in the pathogenesis of PBC have not been completely elucidated. Methods: Microarray expression profiling dataset GSE61260 was downloaded from the Gene Expression Omnibus (GEO) database. Data were normalized to screen differentially expressed genes (DEGs) using the limma package in R. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed. A protein-protein interaction (PPI) network was constructed to identify hub genes and an integrative regulatory network of transcriptional factor-DEG-microRNA was established. Gene Set Enrichment Analysis (GSEA) was used to analyze differences in biological states for groups with different expressions of aldo-keto reductase family 1 member B10 (AKR1B10). Immunohistochemistry (IHC) analysis was performed to validate the expression of hepatic AKR1B10 in patients with PBC. The association of hepatic AKR1B10 levels with clinical parameters was evaluated using one-way analysis of variance (ANOVA) and Pearson's correlation analysis. Results: This study identified 22 upregulated and 12 downregulated DEGs between patients with PBC and healthy controls. GO and KEGG analysis revealed that DEGs were mainly enriched in immune reactions. AKR1B10 was identified as a key gene and was further analyzed by screening out hub genes from the PPI network. GSEA analysis indicated that high expression of AKR1B10 might promote PBC to develop into HCC. Immunohistochemistry results verified the increased expression of hepatic AKR1B10 in patients with PBC and demonstrated its positive correlation with the severity of PBC. Conclusion: AKR1B10 was identified as a hub gene in PBC by integrated bioinformatics analysis and clinical validation. The increase of AKR1B10 expression in patients with PBC was associated with disease severity and might promote the progression of PBC to HCC.

A microarray data analysis investigating the pathogenesis and potential biomarkers of autophagy and ferroptosis in intervertebral disc degeneration

Background: Intervertebral disc degeneration (IDD) entails complex pathological changes and causes lower back pain (LBP). However, there is still a lack of understanding of the mechanisms involved in IDD, particularly regarding the roles of autophagy and ferroptosis. The current study used microarray data to investigate the pathogenesis of IDD and potential biomarkers related to autophagy and ferroptosis in IDD. Methods: Differentially expressed genes (DEGs) were identified by analyzing the mRNA and miRNA expression profiles of IDD patients from the Gene Expression Omnibus (GEO). The protein-protein interaction network, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were utilized. The Human Autophagy Database (HADb) and Ferroptosis Database were used in conjunction with hub genes to identify autophagy- and ferroptosis-related genes. The Transcription Factor -hub gene-miRNA network was constructed. Lastly, the expression of DEGs in normal and degenerated nucleus pulposus cells (NPCs) was investigated via the quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: A total of 362 DEGs associated with IDD were identified. GO and KEGG analyses indicated that oxidative stress, extracellular matrix, PI3K-AKT signaling pathway, and ferroptosis were key factors in IDD occurrence. GSEA indicated that IDD was associated with changes in autophagy, iron ion homeostasis, extracellular matrix, and oxidative stress. Eighty-nine hub genes were obtained, including five that were autophagy-related and three that were ferroptosis-related. Of these, TP53 and SESN2 were the intersections of autophagy- and ferroptosis-related genes. In qRT-PCR analysis, CANX, SLC38A1, and TP53 were downregulated in degenerative NPCs, whereas GNAI3, SESN2, and VAMP3 were upregulated. Conclusion: The current study revealed aspects of autophagy- and ferroptosis-related genes involved in IDD pathogenesis, warranting further investigation.

Regulatory network of metformin on adipogenesis determined by combining high-throughput sequencing and GEO database

Adipose differentiation and excessive lipid accumulation are the important characteristics of obesity. Metformin, as a classic hypoglycaemic drug, has been proved to reduce body weight in type 2 diabetes, the specific mechanism has not been completely clear. A few studies have explored its effect on adipogenesis in vitro, but the existing experimental results are ambiguous. 3T3-L1 preadipocytes were used to explore the effects of metformin on the morphological and physiological changes of lipid droplets during adipogenesis. A high throughput sequencing was used to examine the effects of metformin on the transcriptome of adipogenesis. Considering the inevitable errors among independent experiments, we performed integrated bioinformatics analysis to identify important genes involved in adipogenesis and reveal potential molecular mechanisms. During the process of adipogenesis, metformin visibly relieved the morphological and functional changes. In addition, metformin reverses the expression pattern of genes related to adipogenesis at the transcriptome level. Combining with integrated bioinformatics analyses to further identify the potential targeted genes regulated by metformin during adipogenesis. The present study identified novel changes in the transcriptome of metformin in the process of adipogenesis that might shed light on the underlying mechanism by which metformin impedes the progression of obesity.

Identification of molecular mechanism and key biomarkers in membranous nephropathy by bioinformatics analysis

OBJECTIVES

Membranous nephropathy (MN) is an autoimmune nephropathy. The incidence of MN is increasing gradually in recent years. Previous studies focused on antibody production, complement activation and podocyte injury in MN. However, the etiology and underlying mechanism of MN remain to be further studied.

METHODS

GSE104948 and GSE108109 of glomerular expression profile were downloaded from Gene Expression Omnibus (GEO) database, GSE47184, GSE99325, GSE104954, GSE108112, GSE133288 of renal tubule expression profile, and GSE73953 of peripheral blood mononuclear cells (PBMCs) expression profile. After data integration by Networkanalyst, differentially expressed genes (DEGs) between MN and healthy samples were obtained. DEGs were enriched in gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG), and protein-protein interaction (PPI) networks of these genes were constructed through Metascape, etc. We further understood the function of hub genes through gene set enrichment analysis (GSEA). The diagnostic value of DEGs in MN was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

A total of 3 genes (TP53, HDAC5, and SLC2A3) were screened out. Among them, the up-regulated TP53 expression may be closely related to MN renal pathological changes. However, the expression of MN podocyte target antigen was not significantly different from that of healthy controls. In addition, the changes of Wnt signaling pathway in PBMCs and the effects of SLC2A3 on the differentiation of M2 monocyte need further study.

CONCLUSION

It is difficult to unify a specific mechanism for the changes of glomerulus, renal tubules and PBMCs in MN patients. This may be related to the pathogenesis, pathology and immune characteristics of MN. MN podocyte target antigen may not be the root cause of the disease, but a stage result in the pathogenesis process.

Identification of Core Genes and Screening of Potential Targets in Intervertebral Disc Degeneration Using Integrated Bioinformatics Analysis

Background: Intervertebral disc degeneration (IDD), characterized by diverse pathological changes, causes low back pain (LBP). However, prophylactic and delaying treatments for IDD are limited. The aim of our study was to investigate the gene network and biomarkers of IDD and suggest potential therapeutic targets.

Methods: Differentially expressed genes (DEGs) associated with IDD were identified by analyzing the mRNA, miRNA, and lncRNA expression profiles of IDD cases from the Gene Expression Omnibus (GEO). The protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis as well as miRNA–lncRNA–mRNA networks were conducted. Moreover, we obtained 71 hub genes and performed a comprehensive analysis including GO, KEGG, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), Disease Ontology (DO), methylation analysis, receiver operating characteristic (ROC) curve analysis, immune infiltration analysis, and potential drug identification. We finally used qRT-PCR to verify 13 significant DEGs in normal and degenerative nucleus pulposus cells (NPCs).

Results: We identified 305 DEGs closely related to IDD. The GO and KEGG analyses indicated that changes in IDD are significantly associated with enrichment of the inflammatory and immune response. GSEA analysis suggested that cell activation involved in the inflammatory immune response amide biosynthetic process was the key for the development of IDD. The GSVA suggested that DNA repair, oxidative phosphorylation, peroxisome, IL-6-JAK-STAT3 signaling, and apoptosis were crucial in the development of IDD. Among the 71 hub genes, the methylation levels of 11 genes were increased in IDD. A total of twenty genes showed a high functional similarity and diagnostic value in IDD. The result of the immune cell infiltration analysis indicated that seven genes were closely related to active natural killer cells. The most relevant targeted hub genes for potential drug or molecular compounds were MET and PIK3CD. Also, qRT-PCR results showed that ARHGAP27, C15orf39, DEPDC1, DHRSX, MGAM, SLC11A1, SMC4, and LINC00887 were significantly downregulated in degenerative NPCs; H19, LINC00685, mir-185-5p, and mir-4306 were upregulated in degenerative NPCs; and the expression level of mir-663a did not change significantly in normal and degenerative NPCs.

Conclusion: Our findings may provide new insights into the functional characteristics and mechanism of IDD and aid the development of IDD therapeutics.

Identification of Diagnostic Biomarkers in Systemic Lupus Erythematosus Based on Bioinformatics Analysis and Machine Learning

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Exploring new insights on genetic factors may help reveal SLE etiology and improve the survival of SLE patients. The current study is designed to identify key genes involved in SLE and develop potential diagnostic biomarkers for SLE in clinical practice. Expression data of all genes of SLE and control samples in GSE65391 and GSE72509 datasets were downloaded from the Gene Expression Omnibus (GEO) database. A total of 11 accurate differentially expressed genes (DEGs) were identified by the "limma" and "RobustRankAggreg" R package. All these genes were functionally associated with several immune-related biological processes and a single KEGG (Kyoto Encyclopedia of Genes and Genome) pathway of necroptosis. The PPI analysis showed that IFI44, IFI44L, EIF2AK2, IFIT3, IFITM3, ZBP1, TRIM22, PRIC285, XAF1, and PARP9 could interact with each other. In addition, the expression patterns of these DEGs were found to be consistent in GSE39088. Moreover, Receiver operating characteristic (ROC) curves analysis indicated that all these DEGs could serve as potential diagnostic biomarkers according to the area under the ROC curve (AUC) values. Furthermore, we constructed the transcription factor (TF)-diagnostic biomarker-microRNA (miRNA) network composed of 278 nodes and 405 edges, and a drug-diagnostic biomarker network consisting of 218 nodes and 459 edges. To investigate the relationship between diagnostic biomarkers and the immune system, we evaluated the immune infiltration landscape of SLE and control samples from GSE6539. Finally, using a variety of machine learning methods, IFI44 was determined to be the optimal diagnostic biomarker of SLE and then verified by quantitative real-time PCR (qRT-PCR) in an independent cohort. Our findings may benefit the diagnosis of patients with SLE and guide in developing novel targeted therapy in treating SLE patients.

Integrated analysis of transcriptomics to identify hub genes in primary Sjögren's syndrome

OBJECTIVE

The treatment of patients with primary Sjögren's syndrome is a clinical challenge. Gene expression profile analysis and comprehensive network methods for complex diseases can provide insight into molecular characteristics in the clinical context.

MATERIALS AND METHODS

We downloaded gene expression datasets from the Gene Expression Omnibus (GEO) database. We screened differentially expressed genes (DEG) between the pSS patients and the controls by the robust rank aggregation (RRA) method. We explored DEGs' potential function using gene function annotation and PPI network analysis.

RESULTS

GSE23117 GSE40611 GSE80805 and GSE127952were included, including 38 patients and 30 controls. The RRA integrated analysis determined 294 significant DEGs (241 upregulated and 53 downregulated), and the most significant gene aberrantly expressed in SS was CXCL9 (p = 6.39E-15), followed by CXCL13 (p = 1.53E-13). Immune response (GO:0006955; p = 4.29E-32) was the most significantly enriched biological process in GO (gene ontology) analysis. KEGG pathway enrichment analysis showed that cytokine-cytokine receptor interaction (hsa04060; p = 6.46E-10) and chemokine signaling pathway (hsa04062; p = 9.54E-09) were significantly enriched. We defined PTPRC, CD86, and LCP2 as the hub genes based on the PPI results.

CONCLUSION

Our integrated analysis identified gene signatures and helped understand molecular changes in pSS.

Integrated bioinformatics analysis for the identification of key genes and signaling pathways in thyroid carcinoma

Thyroid carcinoma (TC) is one of the most common types of endocrine neoplasm with poor prognosis due to its aggressive behavior. Biomarkers for early diagnosis and prevention of TC are in urgent demand. By using a bioinformatics analysis, the present study aimed to identify essential genes and pathways associated with TC. First, the GSE27155 and GSE50901 expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were obtained using the two microarray datasets and further subjected to integrated analysis. A gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed 45 common DEGs in the two datasets. GO and KEGG pathway analysis indicated that the biological functions of the DEGs included protein binding, cardiac muscle cell potential involved in contraction, aldehyde dehydrogenase activity, the TGF-β receptor signaling pathway and the canonical Wnt signaling pathway. A protein-protein interaction network was also constructed and visualized to display the nodes of the top 9 up- and 36 downregulated common DEGs. The integrated bioinformatics analysis indicated that potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) was the most significantly upregulated DEG. The transcriptional levels of KCNJ2 were confirmed to be elevated in TC tissues compared with those in normal tissues using reverse transcription-quantitative PCR analysis. Furthermore, the expression level of KCNJ2 was significantly associated with the 5-year survival rate of patients with TC, which was determined using the Kaplan-Meier method. In TC cell lines, KCNJ2 was also upregulated as compared with that in a normal control cell line. Finally, small interfering RNA was used to knock down the expression of KCNJ2, which was demonstrated to inhibit cell proliferation, migration and invasion, while increasing apoptosis in TC cells. In conclusion, in the present study, KCNJ2 was screened as an oncogene with a crucial role in TC development and progression and may represent a promising candidate biomarker and therapeutic target for TC.

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Identifications of Candidate Genes Significantly Associated With Rectal Cancer by Integrated Bioinformatics Analysis

INTRODUCTION

Rectal cancer ranks as the eighth in cancer-related morbidity and the tenth in the cancer-related mortality. A few studies have explored several biomarkers for colorectal cancer. However, there is still a great need for us to excavate novel biomarkers with effective and efficient diagnostic and prognostic values to discover the etiology and pathogenesis of rectal cancer separately. Therefore, we aimed to identify more novel candidate genes that were significantly associated with rectal cancer through integrated bioinformatics analysis.

METHODS

We analyzed the gene expression profiles of GSE15781 and GSE20842 from Gene Expression Omnibus database to identify differentially expressed genes between normal rectal tissue and rectal cancer tissue.

RESULTS

We searched for core genes, carried out survival analysis and analyzed the expressions of core genes. We found that 142 genes were significantly upregulated, and 229 genes were significantly downregulated in all 3 independent studies. In KEGG analysis, the upregulated genes were significantly enriched in cytokine-cytokine receptor interaction, IL-17 signaling pathway, cell cycle, etc. The downregulated genes were primarily enriched in nitrogen metabolism, mineral absorption and pentose and glucuronate interconversions. Inhibin subunit beta B (INHBB) expressed markedly higher in rectal cancer tissues compared with normal tissues, and claudins (CLDN) 23 expressed significantly lower in rectal cancer tissues.

CONCLUSION

In conclusion, we discovered that INHBB could provide a great significant diagnostic and prognostic values for rectal cancer.

Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method

Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exact pathogenesis has not been fully elucidated. Here, we used biological information method to explore and predict the major disease related genes of dermatomyositis and to find the underlying pathogenic molecular mechanism.The gene expression data of GDS1956, GDS2153, GDS2855, and GDS3417 including 94 specimens, 66 cases of dermatomyositis specimens and 28 cases of normal specimens, were obtained from the Gene Expression Omnibus database. The 4 microarray gene data groups were combined to get differentially expressed genes (DEGs). The gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were operated by the database for annotation, visualization and integrated discovery and KEGG orthology based annotation system databases, separately. The protein-protein interaction networks of the DEGs were built from the STRING website. A total of 4097 DEGs were extracted from the 4 Gene Expression Omnibus datasets, of which 2213 genes were upregulated, and 1884 genes were downregulated. Gene ontology analysis indicated that the biological functions of DEGs focused primarily on response to virus, type I interferon signaling pathway and negative regulation of viral genome replication. The main cellular components include extracellular space, cytoplasm, and blood microparticle. The molecular functions include protein binding, double-stranded RNA binding and MHC class I protein binding. KEGG pathway analysis showed that these DEGs were mainly involved in the toll-like receptor signaling pathway, cytosolic DNA-sensing pathway, RIG-I-like receptor signaling pathway, complement and coagulation cascades, arginine and proline metabolism, phagosome signaling pathway. The following 13 closely related genes, XAF1, NT5E, UGCG, GBP2, TLR3, DDX58, STAT1, GBP1, PLSCR1, OAS3, SP100, IGK, and RSAD2, were key nodes from the protein-protein interaction network.This research suggests that exploring for DEGs and pathways in dermatomyositis using integrated bioinformatics methods could help us realize the molecular mechanism underlying the development of dermatomyositis, be of actual implication for the early detection and prophylaxis of dermatomyositis and afford reliable goals for the curing of dermatomyositis.

Could CTSK and COL4A2 be specific biomarkers of poor prognosis for patients with gastric cancer in Asia?-a microarray analysis based on regional population

Background

In the purpose of identifying reliable biomarkers for evaluating prognosis, monitoring recurrence and exploring new therapeutic targets, it is quite necessary to screen for the genetic changes and potential molecular mechanisms of the occurrence and development of gastric cancer (GC) from the aspects of race and region.

Methods

Target datasets were retrieved from Gene Expression Omnibus (GEO) database with "gastric cancer" as the key word, and corresponding data was downloaded. The differentially expressed genes (DEGs) were obtained by using limma R package, and the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for DEGs were analyzed in Enirchr database. Protein-protein interaction (PPI) network and molecular module were also constructed through STRING database and Cytoscape software. Survival analyses were completed for DEGs in GEO and Kaplan-Meier plotter database via cross validation. Finally, the correlation between gene expression and the infiltration cell levels in tumor microenvironment (TME) was explored based on the tumor immune estimation resource (TIMER) database.

Results

Five GC-related microarray datasets were selected and used for differential analysis, and 222 DEGs were identified. GO analyses of DEGs were mainly involved in cell metabolism and the formation of extracellular matrix (ECM). The top enriched pathways of DEGs were protein digestion and absorption, ECM-receptor interaction, focal adhesion (FA), PI3K-Akt signaling pathway. Survival analyses of DEGs revealed that the expression levels of CTSK and COL4A2 were significantly associated with poor prognosis of GC patients in Asian. Specifically, the high expression of CTSK had a closely related to the infiltration level of inflammatory cell in TME.

Conclusions

CTSK and COL4A2 could play a critical role in the pathogenesis of GC and act as the promising prognostic biomarkers. CTSK could induce the formation of immunosuppressive TME and promote the immune escape of GC cells.

Down-regulated hsa_circ_0067934 facilitated the progression of gastric cancer by sponging hsa-mir-4705 to downgrade the expression of BMPR1B

Background

Gastric cancer is the third most lethal cancer worldwide. Finding a novel marker is essential to targeted therapy and the diagnosis of gastric cancer. As newly discovered markers, circRNAs have aroused widespread attention on a global scale. Our research aims to understand the role of circRNAs in gastric cancer and to explore the underlying pathogenesis.

Methods

Raw expression data of circRNAs were obtained from the GEO database. Integrated bioinformatics analysis was used to screen differentially expressed circRNAs (DECs) by RobustRankAggreg package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to predict the functions of DECs. Then, the miRNAs and mRNAs at the downstream of DECs were predicted. Expression data of miRNAs and mRNAs were downloaded from The Cancer Genome Atlas (TCGA). The aberrantly expressed miRNAs and mRNAs were selected using the edgeR package.

Results

Four datasets (GSE78092, GSE83521, GSE89143, and GSE93541) were downloaded from the GEO database. Among them, two DECs (hsa_circ_0007991 and hsa_circ_0067934) were screened. The functional analyses of DECs confirmed that they were cancer-related circRNAs. Furthermore, hsa-mir-4705 (miRNA) and BMPR1B (mRNA) at the downstream of hsa_circ_0067934 were found differentially expressed in gastric cancer by expression data from TCGA database.

Conclusions

Our study discovered the critical roles of hsa_circ_0007991 and hsa_circ_0067934 in the development of gastric cancer, and they could be novel markers for targeted therapy and assist the diagnosis of early-stage gastric cancer. Moreover, we discovered that the hsa_circ_0067934/hsa-mir-4705/BMPR1B axis might be involved in the carcinogenesis of gastric cancer.

Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis

Endometriosis is a common gynecological disease characterized by the presence and growth of endometrial tissue outside the uterus, including the pelvis and abdominal cavity. This condition causes various clinical symptoms, such as non-menstrual pelvic pain, dysmenorrhea and infertility, seriously affecting the health and quality of life of women. To date, the specific mechanism and the key molecules of endometriosis remain uncertain. The purpose of the present study was to elucidate the mechanisms involved in the development and persistence of the disease. A number of mRNA expression profile datasets (namely GSE11691, GSE23339, GSE25628 and GSE78851) were downloaded from the Gene Expression Omnibus (GEO) database. These gene expression profiles were normalized, and the differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis. A total of 103 DEGs were screened upon excluding the genes that exhibited inconsistency of expression (P<0.05). Furthermore, the Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and construction of protein-protein interaction networks of DEGs were performed using online software. The results revealed that the DEGs were closely associated with cell migration, adherens junction and hypoxia-inducible factor signaling. In addition, immunohistochemical assay results were found to be consistent with the bioinformatics results. The present study may help us understand underlying molecular mechanisms and the development of endometriosis, which has a great clinical significance for early diagnosis of the disease.

Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis

Background: Cervical carcinoma is one of the most common malignant gynecological tumors and is associated with high rates of morbidity and mortality. Early diagnosis and early treatment can reduce the mortality rate of cervical cancer. However, there is still no specific biomarkers for the diagnosis and detection of cervical cancer prognosis. Therefore, it is greatly urgent in searching biomarkers correlated with the diagnosis and prognosis of cervical cancer.

Results: The mRNA and microRNA expression profile datasets (GSE7803, GSE9750, GSE63514, and GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). The three microarray datasets were integrated to one via integrated bioinformatics. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained by R software. The protein–protein interaction (PPI) networks of the DEGs were performed from the STRING database and further visualized by Cytoscape software. A total of 83 DEGs and 14 DEMs were screened from the microarray expression profile datasets. The miRNAs validated to be associated with cervical cancer were obtained using HMDD online website and the target genes of DEMs were identified using the miRWalk2.0 online database. ESR1, PPP1R3C, NSG1, and TMPRSS11D were the gene targets of hsa-miR-21; the targets of hsa-miR-16 were GYS2, ENDOU, and KLF4. These targets were all downregulated in cervical cancer. Finally, we verified the expression of those targets in cervical tissues from TCGA and GTEx databases and analyzed their relationship with survival of cervical cancer patients. In the end, the expression of key genes in cervical cancer tissues was verified via experiment method, we found KLF4 and ESR1 were downregulated in tumor tissues.

Conclusion: This study indicates that KLF4 and ESR1 are downregulated by the upregulated miR21 and miRNA16 in cervical cancer, respectively, using bioinformatics analysis, and the lower expression of KLF4 and ESR1 is closely related to the poor prognosis. They might be of clinical significance for the diagnosis and prognosis of cervical cancer, and provide effective targets for the treatment of cervical cancer.

Identification of Potential Key Genes Associated with Adipogenesis through Integrated Analysis of Five Mouse Transcriptome Datasets

Adipose tissue is the most important energy metabolism and secretion organ, and these functions are conferred during the adipogenesis process. However, the cause and the molecular events underlying adipogenesis are still unclear. In this study, we performed integrated bioinformatics analyses to identify vital genes involved in adipogenesis and reveal potential molecular mechanisms. Five mouse high-throughput expression profile datasets were downloaded from the Gene Expression Omnibus (GEO) database; these datasets contained 24 samples of 3T3-L1 cells during adipogenesis, including 12 undifferentiated samples and 12 differentiated samples. The five datasets were reanalyzed and integrated to select differentially expressed genes (DEGs) during adipogenesis via the robust rank aggregation (RRA) method. Functional annotation of these DEGs and mining of key genes were then performed. We also verified the expression levels of some potential key genes during adipogenesis. A total of 386 consistent DEGs were identified, with 230 upregulated genes and 156 downregulated genes. Gene Ontology (GO) analysis showed that the biological functions of the DEGs primarily included fat cell differentiation, lipid metabolic processes, and cell adhesion. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these DEGs were mainly associated with metabolic pathways, the peroxisome proliferator-activated receptor (PPAR) signaling pathway, regulation of lipolysis in adipocytes, the tumor necrosis factor (TNF) signaling pathway, and the FoxO signaling pathway. The 30 most closely related genes among the DEGs were identified from the protein⁻protein interaction (PPI) network and verified by real-time quantification during 3T3-L1 preadipocyte differentiation. In conclusion, we obtained a list of consistent DEGs during adipogenesis through integrated analysis, which may offer potential targets for the regulation of adipogenesis and treatment of adipose dysfunction.

Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

Background

The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.

Results

The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein–protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network.

Conclusion

This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.