Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BackgroundIt is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.MethodsTwo expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.Results1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.ConclusionsIn summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.     

MiR-330-3p and miR-485-5p as biomarkers for glioblastoma: An integrated bioinformatics and experimental study

Glioblastoma Multiforme (GBM) is the most common, invasive, and malignant primary brain tumor with a poor prognosis and a median survival of 12–15 months. This study tried to identify the most significant miRNA biomarkers in both tissue and serum samples of GBM. GSE25632 was employed from gene expression omnibus and using WGCNA package, association of miRNA networks and clinical data was explored and brown and green modules identified as the most relevant modules. Independently, Limma package was utilized to identify differentially expressed miRNAs (DEMs) in GSE25632 by cutoff $\left|\text{l}\text{o}\text{g}\text{F}\text{C}\right|$ > 2 and P.value < 0.05. By merging the results of Limma and WGCNA, the miRNAs that were in brown and green modules and had mentioned cutoff were selected as hub miRNAs. Performing enrichment analysis, Pathways in cancer, Prostate cancer, Glioma, p53 signaling pathway, and Focal adhesion were identified as the most important signaling pathways. Based on miRNA- target genes, has-mir-330−3p and has-mir-485−5p were identified as core miRNAs. The expression level of core miRNAs was validated by GSE90604, GSE42657, and GSE93850. We evaluated the expression level of common target genes of two detected core genes based on GSE77043, GSE42656, GSE22891, GSE15824, and GSE122498. The ability of detected miRNAs to discriminate GBM from healthy controls was assessed by area under the curve (AUC) using the ROC curve analysis. Based on TCGA database, we tested the prognostic significance of miRNAs using overall survival analysis. We evaluated the expression level of the miRNAs in tissue of 83 GBM patients and also non-tumoral adjacent (as control) tissues. We used serum samples of 34 GBM patients to evaluate the expression levels of the hub miRNAs compare to the controls. Our results showed that has-mir-330−3p and has-mir-485−5p could be potential biomarkers in GBM.     

Insights regarding novel biomarkers and the pathogenesis of primary colorectal carcinoma based on bioinformatic analysis

BackgroundBiomarkers are important in the study of tumor processes for early detection and precise treatment. The biomarkers that have been previously detected are not useful for clinical application for primary colorectal carcinoma (PCRC). The aim of this study was to explore clinically valuable biomarkers of PCRC based on integrated bioinformatic analysis.Material and methodsGene expression data were acquired from the GSE41258 dataset, and the differentially expressed genes were determined between PCRC and normal colorectal samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were implemented via Gene Set Enrichment Analysis. A protein-protein interaction (PPI) network was constructed. The significant modules and hub genes were screened and identified in the PPI network.ResultsA total of 202 DEGs were identified, including 58 upregulated and 144 downregulated genes in PCRC samples compared to those in normal colorectal samples. Enrichment analysis demonstrated that the gene sets enriched in PCRC were significantly related to bicarbonate transport, regulation of sodium ion transport, potassium ion homeostasis, regulation of telomere maintenance, and other processes. A total of 10 hub genes was identified by cytoHubba: PYY, CXCL3, CXCL11, CXCL8, CXCL12, CCL20, MMP3, P2RY14, NPY1R, and CXCL1.ConclusionThe hub genes, such as NPY1R, P2RY14, and CXCL12, and the electrolyte disequilibrium resulting from the differential expression of genes, especially bicarbonate imbalance, may provide novel insights and evidence for the future diagnosis and targeted therapy of PCRC.     

Down regulation of lactotransferrin enhanced radio-sensitivity of nasopharyngeal carcinoma

IntroductionIt is reported that LTF had a radiation resistance effect, and its expression in nasopharyngeal carcinoma (NPC) was significantly down-regulated. However, the mechanism of down-regulated LTF affecting the sensitivity of radiotherapy has remained elusive.MethodsWe re-analyzed the microarray data GSE36972 and GSE48503 to find differentially expressed genes (DEGs) in NPC cell line 5−8 F transfected with LTF or vector control, and the DEGs between radio-resistant and radio-sensitive NPC cell lines. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and protein-protein interaction network (PPI) analysis of DEGs were performed to obtain the node genes. The target genes of miR-214 were also predicted to complement the mechanism associated with radiotherapy resistance because it could directly target LTF.ResultsThis study identified 1190 and 1279 DEGs, respectively. GO and KEGG analysis showed that apoptotic process and proliferation, PI3K-Akt signaling pathway were significantly enriched pathways. Four nodes (DUSP1, PPARGC1A, FOS and SMARCA1) associated with LTF were screened. And 42 target genes of miR-214 were cross-linked to radiotherapy sensitivity.ConclusionsThe present study demonstrates the possible molecular mechanism that the down-regulated LTF enhances the radiosensitivity of NPC cells through interaction with DUSP1, PPARGC1A, FOS and SMARCA1, and miR-214 as its superior negative regulator may play a role in regulating the radiotherapy effect.     

A Gibbs sampling method to determine biomarkers for asthma

PurposeTo identify potential biomarkers and to uncover the mechanisms underlying asthma based on Gibbs sampling.MethodsThe molecular functions (MFs) with genes greater than 5 were determined using AnnotationMFGO of BAGS package, and the obtained MFs were then transformed to Markov chain (MC). Gibbs sampling was conducted to obtain a new MC. Meanwhile, the average probabilities of MFs were computed via MC Monte Carlo (MCMC) algorithm, followed by identification of differentially expressed MFs based on the probabilities of MF more than 0.6. Moreover, the differentially expressed genes (DEGs) and their correlated genes were screened and merged, called as co-expressed genes. Pathways enrichment analysis was implemented for the co-expressed genes.ResultsBased on the gene set more than 5, overall 396 MFs were determined. After Gibbs sampling, 5 differentially expressed MF were acquired according to alfa.pi > 0.6. Moreover, the genes in these 5 differentially expressed MF were merged, and 110 DEGs were identified. Subsequently, 338 co-expressed genes were gained. Based on the P value < 0.01, the co-expressed genes were significantly enriched in 6 pathways. Among these, ubiquitin mediated proteolysis contained the maximum numbers of 35 co-expressed genes, and cell cycle were enriched by the second largest number of 11 co-expressed genes, respectively.ConclusionsThe identified pathways such as ubiquitin mediated proteolysis and cell cycle might play important roles in the development of asthma and may be useful for developing the credible therapeutic approaches for diagnosis and treatment of asthma in future.     

Identification of Key Candidate Genes Involved in the Progression of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a lethal, agnogenic interstitial lung disease with limited therapeutic options. To investigate vital genes involved in the development of IPF, we integrated and compared four expression profiles ({"type":"entrez-geo","attrs":{"text":"GSE110147","term_id":"110147"}}GSE110147, {"type":"entrez-geo","attrs":{"text":"GSE53845","term_id":"53845"}}GSE53845, {"type":"entrez-geo","attrs":{"text":"GSE24206","term_id":"24206"}}GSE24206, and {"type":"entrez-geo","attrs":{"text":"GSE10667","term_id":"10667"}}GSE10667), including 87 IPF samples and 40 normal samples. By reanalyzing these datasets, we managed to identify 62 upregulated genes and 20 downregulated genes in IPF samples compared with normal samples. Differentially expressed genes (DEGs) were analyzed by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to illustrate relevant pathways of IPF, biological processes, molecular function, and cell components. The DEGs were then subjected to protein–protein interaction (PPI) for network analysis, serving to find 11 key candidate genes (ANXA3, STX11, THBS2, MMP1, MMP9, MMP7, MMP10, SPP1, COL1A1, ITGB8, IGF1). The result of RT-qPCR and immunohistochemical staining verified our finding as well. In summary, we identified 11 key candidate genes related to the process of IPF, which may contribute to novel treatments of IPF.     

Attractors of hypertrophic cardiomyopathy using maximal cliques and attract methods

BackgroundOur study was designed to identify the differential attractor modules related with hypertrophic cardiomyopathy (HCM) by integrating clustering-based on maximal cliques algorithm and Attract method.MethodsWe firstly recruited the HCM-related microarray data from ArrayExpress database. Next, protein–protein interaction (PPI) networks of normal and HCM were constructed and re-weighted using spearman correlation coefficient (SCC). Then, maximal cliques were found from the PPI networks through the clustering-based on maximal cliques approach. Afterwards, highly overlapped cliques were eliminated or merged according to the interconnectivity, and then modules were obtained. Subsequently, we used Attract method to identify differential attractor modules, following by the pathway enrichment analyses for genes in differential attractor modules.ResultsAfter removing the cliques with nodes less than or equal to 4, 926 and 1118 maximal cliques in normal and HCM PPI networks were obtained for module analysis. Then, we obtained 32 and 55 modules from the PPI networks of normal and HCM, respectively. By comparing with normal condition, there were 5 module pairs with the same or similar gene composition. Significantly, based on attract method, we found that these 5 modules were differential attractors. Pathway enrichment analyses indicated that proteasome, ribosome and oxidative phosphorylation were the significant pathways.ConclusionsProteasome, ribosome and oxidative phosphorylation might play pathophysiological roles in HCM.     

Gastric cancer in proximal site exerts poorer survival outcome with divergent genetic features than distal site

BackgroundAnatomical subsites always harbor specific biological features in carcinogenesis. The divergent prognosis of proximal gastric cancer (PGC) and distal gastric cancer (DGC) has been reported. The current study aimed to comprehensively interpret anatomic subsite-specific genomic profiles, which may improve the effectiveness of personalized management.MethodsSurvival and genomic data from the online Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases were queried for prognostic and genetic analysis, respectively. Propensity score matching (PSM) analysis was performed to balance patient epidemiological factors. Differentially expressed genes (DEGs) were analyzed using the DESeq algorithm. Functional enrichment was performed by the clusterProfiler package. The protein-protein interaction network of DEGs was predicted by the online STRING database.ResultsA total of 3,955 patient pairs were assembled by PSM in SEER data with even background characteristics. Prognostic analysis indicated worse overall survival of PGC than DGC (17 vs 20 months, p = 0.0002). Genetic analysis of TCGA database identified 280 DEGs, 90 of which were upregulated in the DGC group and the remaining 190 were upregulated in the PGC group. Functional enrichment analysis indicated that kallikrein serine protease activity, ion channel (Na⁺/Cl⁻) activity, and cytoskeleton constituent might be attributed to the poor prognosis observed in PGC. Furthermore, alcohol, retinol, and lipoprotein metabolism were the features for DGC malignancy.ConclusionThe current study first demonstrated that PGC exerts poorer survival outcome than DGC based on the SEER database. Further bioinformatic investigation depicts the specific genetic features for PGC and DGC, which may generate differences in tumor malignancy. Our findings provide promising genetic targets for future specific and individualized gastric cancer therapy.     

Molecular Genomic Study of Inhibin Molecule Production through Granulosa Cell Gene Expression in Inhibin-Deficient Mice

Inhibin is a molecule that belongs to peptide hormones and is excreted through pituitary gonadotropins stimulation action on the granulosa cells of the ovaries. However, the differential regulation of inhibin and follicle-stimulating hormone (FSH) on granulosa cell tumor growth in mice inhibin-deficient females is not yet well understood. The objective of this study was to evaluate the role of inhibin and FSH on the granulosa cells of ovarian follicles at the premature antral stage. This study stimulated immature wild-type (WT) and Inhibin-α knockout (Inha−/−) female mice with human chorionic gonadotropin (hCG) and examined hCG-induced gene expression changes in granulosa cells. Also, screening of differentially expressed genes (DEGs) was performed in the two groups under study. In addition, related modules to external traits and key gene drivers were determined through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm. The results identified a number of 1074 and 931 DEGs and 343 overlapping DEGs (ODEGs) were shared in the two groups. Some 341 ODEGs had high relevance and consistent expression direction, with a significant correlation coefficient (r² = 0.9145). Additionally, the gene co-expression network of selected 153 genes showed 122 nodes enriched to 21 GO biological processes (BP) and reproduction and 3 genes related to genomic pathways. By using principal component analysis (PCA), the 14 genes in the regulatory network were fixed and the cumulative proportion of fitted top three principal components was 94.64%. In conclusion, this study revealed the novelty of using ODEGs for investigating the inhibin and FSH hormone pathways that might open the way toward gene therapy for granulosa cell tumors. Also, these genes could be used as biomarkers for tracking the changes in inhibin and FSH hormone from the changes in the nutrition pattern.     

Investigating ego modules involved in TGFβ3-induced chondrogenesis in mesenchymal stem cells based on ego network

ObjectiveThis paper aimed to investigate ego modules for TGFβ3-induced chondrogenesis in mesenchymal stem cells (MSCs) using ego network algorithm.MethodsThe ego network algorithm comprised three parts, extracting differential expression network (DEN) based on gene expression data and protein-protein interaction (PPI) data; exploring ego genes by reweighting DEN; and searching ego modules by ego gene expansions. Subsequently, permutation test was carried out to evaluate the statistical significance of the ego modules. Finally, pathway enrichment analysis was conducted to investigate ego pathways enriched by the ego modules.ResultsA total of 15 ego genes were obtained from the DEN, such as PSMA4, HNRNPM and WDR77. Starting with each ego genes, 15 candidate modules were gained. When setting the thresholds of the area under the receiver operating characteristics curve (AUC) ≥0.9 and gene size ≥4, three ego modules (Module 3, Module 8 and Module 14) were identified, and all of them had statistical significances between normal and TGFβ3-induced chondrogenesis in MSCs. By mapping module genes to confirmed pathway database, their ego pathways were detected, Cdc20:Phospho-APC/C mediated degradation of Cyclin A for Module 3, Mitotic G1-G1/S phases for Module 8, and mRNA Splicing for Module 14.ConclusionsWe have successfully identified three ego modules, evaluated their statistical significances and investigated their functional enriched ego pathways. The findings might provide potential biomarkers and give great insights to reveal molecular mechanism underlying this process.     

lncRNA-miRNA-mRNA interaction network for colorectal cancer; An in silico analysis

BackgroundColorectal cancer (CRC) is one of the most frequent and diagnosed diseases. Accumulating evidences showed that mRNAs and noncoding RNAs play important regulatory roles in tumorigenesis. Identification and determining the relationship between them can help diagnosis and treatment of cancer.MethodsHere we analyzed three microarray datasets; GSE110715, GSE32323 and GSE21510, to identify differentially expressed lncRNAs and mRNAs in CRC. The adjusted p-value ≤0.05 was considered statistically significant. Gene set enrichment analysis was carried out using DAVID tool. The miRCancer database was searched to obtain differentially expressed miRNAs in colorectal cancer, and the miRDB database was used to attain the targets of the obtained miRNAs. To predict the lncRNA-miRNA interactions we used DIANA-LncBase v2 and RegRNA 2.0. Finally the lncRNA-miRNA-mRNA-signaling pathway network was constructed using Cytoscape v3.1.ResultsBy analyzing the three datasets, a total of 21 mRNAs (15 up- and 6 down-regulated) and 24 lncRNAs (18 up- and 6 down-regulated) were identified as common differentially expressed genes between CRC tumor and marginal tissues. Nevertheless, the constructed lncRNA-miRNA-mRNA-signaling pathway network revealed a convergence on 6 lncRNAs (3 up- and 3 downregulated), 7 mRNAs (2 up- and 5 downregulated) and 6 miRNAs (3 up- and 3 downregulated). We found that dysregulation of lncRNAs such as PCBP1-AS1, UCA1 and SNHG16 could sequester several miRNAs such as hsa-miR-582-5p and hsa-miR-198 and promote the proliferation, invasion and drug resistance of colorectal cancer cells.ConclusionsWe introduced a set of lncRNAs, mRNAs and miRNAs differentially expressed in CRC which might be considered for further experimental research as potential biomarkers of CRC development.     

Clinicopathological value and underlying molecular mechanism of annexin A2 in 992 cases of thyroid carcinoma

BackgroundThyroid carcinoma (THCA) is one of the most frequent endocrine cancers and has increasing morbidity. Annexin A2 (ANXA2) has been found to be highly expressed in various cancers; however, its expression level and potential mechanism in THCA remain unknown. This study investigated the clinicopathological value and primary molecular machinery of ANXA2 in THCA.Material and MethodsPublic RNA-sequencing and microarray data were obtained and analyzed with ANXA2 expression in THCA and corresponding non-cancerous thyroid tissue. A Pearson correlation coefficient calculation was used for the acquisition of ANXA2 coexpressed genes, while edgR, limma, and Robust Rank Aggregation were employed for differentially expressed gene (DEG) in THCA. The probable mechanism of ANXA2 in THCA was predicted by gene ontology and pathway enrichment. A dual-luciferase reporter assay was employed to confirm the targeting relationships between ANXA2 and its predicted microRNA (miRNA).ResultsExpression of ANXA2 was significantly upregulated in THCA tissues with a summarized standardized mean difference of 1.09 (P < 0.0001) based on 992 THCA cases and 589 cases of normal thyroid tissue. Expression of ANXA2 was related to pathologic stage. Subsequently, 1442 genes were obtained when overlapping 4542 ANXA2 coexpressed genes with 2248 DEGs in THCA; these genes were mostly enriched in pathways of extracellular matrix-receptor interaction, cell adhesion molecules, and complement and coagulation cascades. MiR-23b-3p was confirmed to target ANXA2 by dual-luciferase reporter assay.ConclusionsUpregulated expression of ANXA2 may promote the malignant biological behavior of THCA by affecting the involving pathways or being targeted by miR-23b-3p.     

Gene Interaction Network Analysis Reveals IFI44L as a Drug Target in Rheumatoid Arthritis and Periodontitis

Simple SummaryIn spite of substantial investigation, the biological link between periodontitis and rheumatoid arthritis remains unexplained. This study intended to correlate periodontitis and rheumatoid arthritis gene expression patterns to find shared targets for both the disease. We identified the differentially expressed genes (DEGs) in periodontitis and rheumatoid arthritis. The network was built by integrating DEGs and ranking the genes using GeneMANIA. FINDSITEcomb2.0 was used to find a possible inhibitor for the top-ranked gene. Further, the binding effectiveness and protein-ligand complex stability were then determined by molecular docking and molecular dynamics. The network analysis showed IFI44L as a highly ranking gene implicated in most immunological pathways. A virtual screening of 6507 compounds revealed vemurafenib as the best candidate for the IFI44L target. Molecular docking and molecular dynamics modelling revealed the stability of the IFI44L-vemurafenib complex, which suggest IFI44L is potential target and vemurafenib could be the better candidate to treat both diseases.AbstractObjective: Despite extensive research on periodontitis and rheumatoid arthritis, the underlying molecular connectivity between these condition remains largely unknown. This research aimed to integrate periodontitis and rheumatoid arthritis gene expression profiles to identify interconnecting genes and focus to develop a common lead molecule against these inflammatory conditions. Materials and Methods: Differentially expressed genes (DEGs) of periodontitis and rheumatoid arthritis were identified from the datasets retrieved from the Gene Expression Omnibus database. The network was constructed by merging DEGs, and the interconnecting genes were identified and ranked using GeneMANIA. For the selected top ranked gene, the potential inhibitor was searched using FINDSITE^comb2.0. Subsequently, the molecular docking and molecular dynamics were performed to determine the binding efficiency and protein-ligand complex stability, respectively. Results: From the network analysis, IFN-induced protein 44-like (IFI44L) was identified as a top ranked gene involved in most of the immunological pathway. With further virtual screening of 6507 molecules, vemurafenib was identified to be the best fit against the IFI44L target. The binding energy and stability of IFI44L with vemurafenib were investigated using molecular docking and molecular dynamics simulation. Docking results show binding energy of −7.7 Kcal/mol, and the simulation results show stability till 100 ns. Conclusions: The identified IFI44L may represent a common drug target for periodontitis and rheumatoid arthritis. Vemurafenib could be a potent anti-inflammatory drug for both diseases.