Integrative meta-analysis of differentially expressed genes in osteoarthritis using microarray technology

THU0494 Meta-Analysis of Differentially Expressed Genes in Ankylosing Spondylitis

Our aim was to identify differentially expressed (DE) genes and biological processes that may help predict patient response to biologic agents for rheumatoid arthritis (RA). Using the INMEX (integrative meta-analysis of expression data) software tool, we performed a meta-analysis of publicly available microarray Gene Expression Omnibus (GEO) datasets that examined patient response to biologic therapy for RA. Three GEO datasets, containing 79 responders and 34 non-responders, were included in the meta-analysis. We identified 1,374 genes that were consistently differentially expressed in responders vs. non-responders (651 up-regulated and 723 down-regulated). The up-regulated gene with the smallest p value (p=0.000192) was ASCC2 (Activating Signal Cointegrator 1 Complex Subunit 2), and the up-regulated gene with the largest fold change (average log fold change=-0.75869, p=0.000206) was KLRC3 (Killer Cell Lectin-Like Receptor Subfamily C, Member 3). The down-regulated gene with the smallest p value (p=0.000195) was MPL (Myeloproliferative Leukemia Virus Oncogene). Among the 236 GO terms associated with the set of DE genes, the most significantly enriched was "CTP biosynthetic process" (GO:0006241; p=0.000454). Our meta-analysis identified genes that were consistently DE in responders vs. non-responders, as well as biological pathways associated with this set of genes. These results provide insight into the molecular mechanisms underlying responsiveness to biologic therapy for RA.

The immunogenic cell death (ICD) pathway plays a crucial prognostic role in lung adenocarcinoma (LUAD) therapy. The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway is an upstream mechanism that drives ICD activation, but the interaction of hub genes remains unclear. The present study aimed to investigate the hub genes involved in ICD and the cGAS-STING pathway. The prognostic performance for hub genes and related Gene Ontology (GO) terms were also investigated. Gene expression data of ICD induction and cGAS-STING pathway activation samples were extracted from the Gene Expression Omnibus (GEO) database, and gene expression as well as clinical data of LUAD patients who received pharmaceutical therapy were extracted from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified, and protein-protein interaction (PPI) analysis was used to identify hub genes. Hazard risk (HR) scores were identified using Kaplan-Meier (K-M) and COX analyses. Gene set enrichment analysis (GSEA) was performed to identify the related GO terms, and receiver operating characteristic (ROC) analysis was used to evaluate the prognosis performance of the related gene sets. A total of 22 DEGs were identified in the two GEO datasets, and six hub genes were identified by PPI analysis. Keratin 6A (KRTA6A) and fatty acid 2-hydroxylase (FA2H) were selected as the hub genes after survival analysis. GSEA and ROC analysis indicated that there was no difference between the KRTA6A and FA2H gene sets on prognosis performance. KRTA6A and FA2H are hub genes associated with the induction of cGAS-STING-related ICD in LUAD.

The study aimed to identify common differentially expressed lncRNAs from manually curated ulcerative colitis (UC) gene expression omnibus (GEO) datasets. Nine UC transcriptomic datasets of clearly annotated human colonic biopsies were included in the study. The datasets were manually curated to select active UC samples and controls. R packages geneknitR, gprofiler, clusterProfiler were used for gene symbol annotation. The R EdgeR package was used to analyze differential expression. This resulted in a total of nineteen lncRNAs that were differentially expressed in at least three datasets of the nine GEO datasets. Several of the differentially expressed lncRNAs found in UC were associated with promoting colorectal cancer (CRC) through regulating gene expression, epithelial to mesenchymal transition (EMT), cell cycle progression, and by promoting tumor proliferation, invasion, and migration. The expression of several lncRNAs varied between disease states and tissue locations within the same disease state. The identified differentially expressed lncRNAs may function as general markers for active UC independent of biopsy location, age, gender, or treatment, thereby representing a comparative resource for future comparisons using available GEO UC datasets.

Molecular fingerprinting of the podocyte reveals novel gene and protein regulatory networks

Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: ‘extracellular matrix organization’, ‘collagen catabolic process’ and ‘cell adhesion’. These three KEGG pathways were significantly enriched in the categories: ‘ECM-receptor interaction’, ‘protein digestion and absorption’, and ‘focal adhesion’. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

BackgroundThe function of miR-31-5p in lung squamous cell carcinoma (LUSC) remains unclear, therefore, a systematic study was performed for the clinical significance and molecular mechanism of miR-31-5p in LUSC.MethodsQuantitative real-time reverse transcription PCR (qRT-PCR) was utilized to test the expression level of miR-31-5p in 88 LUSC tissue samples and their matching normal tissues. Data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were also utilized to confirm the expression level and clinical value of miR-31-5p in LUSC. The potential target genes of miR-31-5p were predicted by several online predicted software. Gene ontology (GO), protein-protein interaction (PPI) and pathway analysis were utilized to investigate the underlying molecular mechanism of miR-31-5p in LUSC.ResultsThe result from qRT-PCR found that there was significant difference of miR-31-5p between LUSC and normal tissues (P<0.001). Meanwhile, Data from TCGA also showed a higher expression of miR-31-5p in LUSC tissues than that in the normal tissues (P<0.001). on the basis of the data of GEO database, five GEO datasets indicated that the expression of miR-31-5p in LUSC tissues was significantly higher than that in normal lung tissues, include GSE51858 (P=0.025), GSE74190 (P<0.000), GSE16025 (P=0.031), GSE25508 (P=0.0.01), and GSE47525 (P=0.049). Moreover, in consideration of the meta-analysis, 1,012 clinical specimens were systematically analyzed via meta-analysis, clinical specimens were systematically analyzed via meta-analysis, and the results showed that the expression of miR-31-5p in LUSC was significantly higher than in the adjacent lung tissues (SMD =0, CI: 1.08–1.45, Z=13.30, P=0.000). In addition, result from GO and pathway analyses showed that potential target genes of miR-31-5p were significantly associated with 20 GO terms and 5 pathways, such as signal transduction, transmembrane receptor protein tyrosine kinase activity, plasma membrane and Rap1 signaling pathway. Meanwhile, we also found thatmiR-31-5p target genes were related to the Rap1 signaling pathway, Oxytocin signaling pathway and Proteoglycans in cancer. Furthermore, six hub genes were identified from PPI and three hub genes, including ADCY6, ADCY9 and EGFR, proved to coexist in the Rap1 signaling pathway, oxytocin signaling pathway and Melanogenesis simultaneously.ConclusionsAccording to what has been discussed above, we speculated that miR-31-5p may play a vital role in the occurrence and development of LUSC.

The metabolic syndrome, a combination of interrelated metabolic risk factors, is associated with insulin resistance and promotes the development of cardiovascular diseases and type 2 diabetes mellitus. There is a close link between inflammation and metabolic disease, but the responsible mechanisms remain elusive. The aim of this study was to identify differentially expressed genes in insulin-resistant skeletal muscle tissue of women with the metabolic syndrome compared with healthy control women. Women with the metabolic syndrome (n = 19) and healthy control women (n = 20) were extensively phenotyped, insulin sensitivity was measured using a hyperinsulinaemic euglycaemic clamp, and a skeletal muscle biopsy was obtained. Gene expression levels were compared between the two groups by microarrays. The upregulated genes in skeletal muscle of the women with the metabolic syndrome were primarily enriched for inflammatory response-associated genes. The three most significantly upregulated of this group, interleukin 6 receptor (IL6R), histone deacetylase 9 (HDAC9) and CD97 molecule (CD97), were significantly correlated with insulin resistance. Taken together, these findings suggest an important role for a number of inflammatory-related genes in the development of skeletal muscle insulin resistance.

Osteoarthritis (OA) is more common and severe in females than in males. This study aimed to identify key genes and pathways underlying this gender disparity. Three microarray datasets (GSE12021, GSE55457, and GSE55584) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein-protein interaction (PPI) networks were constructed and analyzed in Cytoscape, and hub genes were identified using nine topological methods in CytoHubba. Selected hub genes were further validated by RT-qPCR. We identified 90 and 375 DEGs in female and male OA patients compared to controls. Among them, 24 genes (9 downregulated and 15 upregulated) were identified in both genders. The PPI network analysis identified 9 hub genes in females and 5 hub genes in males. RT-qPCR validated the expression levels of CTGF, CX3CR1, TGBR2, and KDR in female OA patients and those of ISG15, STAT1, MYC, and IL6 in male OA patients. Additionally, the expression level of JUN was validated in both genders. A total of 90 DEGs were identified in female OA patients and 375 in male patients, compared to controls. Twenty-four genes (15 upregulated, 9 downregulated) were common to both genders. PPI analysis revealed nine hub genes in females and five in males. RT-qPCR confirmed the expression of CTGF, CX3CR1, TGBR2, and KDR in female OA patients, and ISG15, STAT1, MYC, and IL6 in male OA patients. JUN expression was validated in both genders. The identified genes and pathways may contribute to the higher incidence and severity of OA in females. These findings provide potential gender-specific diagnostic markers and therapeutic targets for OA. Key Points • This study identified gender-specific differentially expressed genes (DEGs) in osteoarthritis using integrated analysis of three GEO datasets. • Distinct hub genes were discovered in male and female OA patients through PPI network analysis and CytoHubba algorithms. • RT-qPCR validated key genes including CTGF, CX3CR1, and IL6, revealing gender-specific gene expression profiles in OA. • Findings provide insights into potential gender-specific diagnostic biomarkers and therapeutic targets for osteoarthritis.

BackgroundAcute myocardial infarction (AMI) is a fatal disease that causes high morbidity and mortality. It has been reported that AMI is associated with immune cell infiltration. Now, we aimed to identify the potential diagnostic biomarkers of AMI and uncover the immune cell infiltration profile of AMI.MethodsFrom the Gene Expression Omnibus (GEO) data set, three data sets (GSE48060, GSE60993, and GSE66360) were downloaded. Differentially expressed genes (DEGs) from AMI and healthy control samples were screened. Furthermore, DEGs were performed via gene ontology (GO) functional and kyoto encyclopedia of genes and genome (KEGG) pathway analyses. The Gene set enrichment analysis (GSEA) was used to analyze GO terms and KEGG pathways. Utilizing the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) database, a protein–protein interaction (PPI) network was constructed, and the hub genes were identified. Then, the receiver operating characteristic (ROC) curves were constructed to analyze the diagnostic value of hub genes. And, the diagnostic value of hub genes was further validated in an independent data set GSE61144. Finally, CIBERSORT was used to represent the compositional patterns of the 22 types of immune cell fractions in AMI.ResultsA total of 71 DEGs were identified. These DEGs were mainly enriched in immune response and immune-related pathways. Toll-like receptor 2 (TLR2), interleukin-1B (IL1B), leukocyte immunoglobulin-like receptor subfamily B2 (LILRB2), Fc fragment of IgE receptor Ig (FCER1G), formyl peptide receptor 1 (FPR1), and matrix metalloproteinase 9 (MMP9) were identified as diagnostic markers with the value of p < 0.05. Also, the immune cell infiltration analysis indicated that TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 were correlated with neutrophils, monocytes, resting natural killer (NK) cells, gamma delta T cells, and CD4 memory resting T cells. The fractions of monocytes and neutrophils were significantly higher in AMI tissues than in control tissues.ConclusionTLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 are involved in the process of AMI, which can be used as molecular biomarkers for the screening and diagnosis of AMI. In addition, the immune system plays a vital role in the occurrence and progression of AMI.

BackgroundPrevious studies found that rats subjected to carrageenan injection develop hyperalgesia, and despite complete recovery in several days, they continue to have an enhanced hyperalgesic response to a new noxious challenge for more than 28d. The study's aim was to identify candidate genes that have a role in the formation of the long-term hyperalgesia-related imprint in the spinal cord. This objective was undertaken with the understanding that the long-lasting imprint of acute pain in the central nervous system may contribute to the transition of acute pain to chronicity.ResultsTo analyze changes in gene expression when carrageenan-induced hyperalgesia has disappeared but propensity for the enhanced hyperalgesic response is still present, we determined the gene expression profile using oligo microarray in the lumbar part of the spinal cord in three groups of rats: 28d after carrageenan injection, 24h after injection (the peak of inflammation), and with no injection (control group). Out of 17,000 annotated genes, 356 were found to be differentially expressed compared with the control group at 28d, and 329 at 24h after carrageenan injection (both groups at p < 0.01). Among differentially expressed genes, 67 (39 in 28d group) were identified as being part of pain-related pathways, altered in different models of pain, or interacting with proteins involved in pain-related pathways. Using gene ontology (GO) classification, we have identified 3 functional classes deserving attention for possible association with pain memory: They are related to cell-to-cell interaction, synaptogenesis, and neurogenesis.ConclusionDespite recovery from inflammatory hyperalgesia, persistent changes in spinal cord gene expression may underlie the propensity for the enhanced hyperalgesic response. We suggest that lasting changes in expression of genes involved in the formation of new synapses and neurogenesis may contribute to the transition of acute pain to chronicity.

identify robust and strain-, sex-, and strain*sex-dependent responses to the diet. Global analysis of the expression data by ANOVA revealed extensive impact of both strain and sex on dietary responses in liver. The main effect of diet was apparent in the differential expression of almost 57% of genes on the arrays. Among the top co-regulated of these, the authors showed using Gene Ontology (GO) enrichment analyses that the expected effects of diet-mediated suppression of cholesterol biosynthesis occurred in all strains and that other specific biological systems functional involvements were also strongly implicated. Valuable data emerge from the ability to identify sets of genes that are reflective of strain-, sex-, and strain*sexdependent effects of diet, attributable in part from the statistical power of the study design in which the underlying genetic variations imparted systematic effects at the level of biological pathways and processes. When strain is taken into account, the majority of significantly enriched GO categories reflected a powerful correlation between effects of diet on immune function, such as antigen processing and presentation. The authors suggest that these changes may be related to diet-induced liver damage, which was indicated by changes expression of damage-related genes and by an increase in circulating levels of glutamate dehydrogenase. However, these changes may also broadly reflect genetic correlation between pathways that mediate immune responses and the development of diet-induced atherogenic plaques, the formation and propagation of which are driven by immune responses. Integrating the expression data with information about the incidence, severity, and phenotypic details of atherosclerotic lesions in this same set of strains should shed additional light on this possibility. Other important follow-on studies may also determine if strainspecific patterns of immune gene expression in liver are paralleled by qualitatively similar effects on lymphocyte populations, atherogenic lesions, and insulin pathway-associated tissues. Importantly, the authors have also provided a publicly accessible database that can be mined in a number of ways. For example, this resource could be used to select strains with divergent responses to diet (e.g., sensitive and resistant to liver damage) or to identify coexpression networks centered around a gene(s) of specific interest. It could also be used to extract supportive evidence for genes implicated in processes related

Teratozoospermia, characterized by abnormal sperm morphology, is a major contributor to male infertility. Kinases, enzymes that catalyze the transfer of phosphate groups to proteins, are crucial regulators of cellular signaling pathways and play significant roles in sperm development and maturation. The purpose of the current study was to identify differentially expressed genes (DEGs) between teratozoospermia and normozoospermia samples and to investigate the role of kinases in these expression changes. An integrated analysis of transcriptome data was conducted from teratozoospermia and normozoospermia samples using publicly available gene expression omnibus (GEO) datasets. Three gene expression series (GSE) profiles of teratozoospermia from one superseries were selected and combined. Differential expression analysis was performed using the limma package in R, applying linear modeling and empirical Bayes statistics to identify DEGs with a threshold of adjusted p<0.05. A comprehensive list of human kinase genes was obtained from the KinHub database, and differentially expressed kinases between the two conditions were identified. Functional enrichment analyses including gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathways were conducted. Additionally, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic potential of identified kinases. Our analysis identified 1,292 DEGs. Among these, 34 kinases were identified (10 upregulated and 24 downregulated). ROR1 and STK39 showed the most significant changes. ROC analysis demonstrated strong diagnostic values for these kinases. This study is the first comprehensive analysis integrating transcriptomic data and kinase-focused gene expression profiling specifically in teratozoospermia, suggesting that kinase dysregulation may contribute to teratozoospermia and male infertility.

Rationale: Rhinovirus (RV) infection in preterm-born children is associated with higher rates of hospitalization and development of chronic respiratory symptoms. The mechanisms driving RV-induced respiratory morbidity in preterm-born children remain unclear. We hypothesized that preterm-born children hospitalized with RV infection exhibit gene expression profiles that differ from term-born children with RV. Methods: We collected nasal swabs from children (10 preterm-born and 9 term-born) hospitalized with RV infection. Control swabs were taken from 5 preterm-born and 5 term-born uninfected children in outpatient settings. Participants’ age ranged from 6 months to 16 years; those with presumed bacterial co-infections or asthma diagnosis were excluded. RNA was extracted and RNA integrity confirmed. Samples were processed with low input RNA-Seq Kit. Sequences were aligned and annotated. Differentially expressed genes for preterm-RV and term-RV compared to their respective controls and the differential response to RV in preterm relative to term-born were determined using DESeq2 design. Significance was based on fold change &amp;gt; 1.5 and adjusted P &amp;lt;0.05. Gene Ontology (GO) analysis of the differentially expressed genes determined enriched GO terms. Results: Two preterm-RV and two term-RV samples were excluded based on low RNA integrity. Additionally, 1 term-RV and 1 preterm control outliers were excluded based on principal component analysis. 5183 genes were differentially expressed in term-RV compared to term control (2433 upregulated, 2750 downregulated). In contrast, only 1433 genes (476 upregulated, 957 downregulated) were differentially expressed in preterm-RV relative to preterm control. GO terms enriched in the upregulated genes for preterm-RV were different from those enriched in term-RV and included chromosome segregation, cell cycle, ATP hydrolysis, and growth factor binding for the former and leukocyte migration and activation, cytokine binding and receptor activity, NADPH oxidase activity for the latter. GO terms enriched in the downregulated genes for preterm-RV and term-RV were similar and included cilium organization and movement. Statistical analysis of the difference in response to RV in preterm relative to term identified only 124 differentially expressed genes. GO terms enriched among those were cilium assembly and organization, epithelial cell proliferation, morphogenesis, nitric oxide signaling. Top differentially expressed genes in preterm relative to term in response to RV were immune-related ACOD1, NFATC2, LEF1, IL12RB2, NLRC3, SDC3. Conclusion: This study demonstrates that compared to term, preterm-born children respond to RV infection with fewer differentially expressed genes and distinct GO term enrichment. Future research is needed to determine the specific mechanisms of RV-induced morbidity in preterm-born children.

Hyperinflammation is related to the development of COVID-19. Resveratrol is considered an anti-inflammatory and antiviral agent. Herein, we used a network pharmacological approach and bioinformatic gene analysis to explore the pharmacological mechanism of Resveratrol in COVID-19 therapy. Potential targets of Resveratrol were obtained from public databases. SARS-CoV-2 differentially expressed genes (DEGs) were screened out via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE147507, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis; then, protein-protein interaction network was constructed. The common targets, GO terms, and KEGG pathways of Resveratrol targets and SARS-CoV-2 DEGs were confirmed. KEGG Mapper queried the location of common targets in the key pathways. A notable overlap of the GO terms and KEGG pathways between Resveratrol targets and SARS-CoV-2 DEGs was revealed. The shared targets between Resveratrol targets and SARS-CoV-2 mainly involved the IL-17 signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway. Our study uncovered that Resveratrol is a promising therapeutic candidate for COVID-19 and we also revealed the probable key targets and pathways involved. Ultimately, we bring forward new insights and encourage more studies on Resveratol to benefit COVID-19 patients.