Biological links in periodontitis and rheumatoid arthritis: Discovery via text-mining PubMed abstracts.

BACKGROUND AND OBJECTIVE: Primary research concerning molecular pathways that link rheumatoid arthritis with periodontitis is limited. Biomedical literature data mining can offer insights into putative linkage mechanisms toward hypothesis development, based on information discovery. The aim of this study was to explore potential Periodontitis-Rheumatoid Arthritis biological links by analysing "overlapping" genes reported in biomedical abstracts. MATERIAL AND METHODS: PubMed abstracts for terms: (a) "Periodontitis" or "Periodontal Diseases" (PD), (b) "Rheumatoid arthritis" (RA), and (c) their combination with "AND" (RA+PD), were each text-mined to extract genes using "Human Genome Nomenclature Committee" (HGNC) symbols. A gene-set common to RA and PD abstracts was determined (RA∩PD). Gene ontology (GO) profiles of RA∩PD and RA+PD were compared using "GoProfiler." Minimum order protein-protein interaction (PPI) and gene-miRNA networks of "differential genes" between RA∩PD and RA+PD were constructed with "networkAnalyst." RESULTS: Among 1676 genes documented in RA (10 5241 abstracts), and 893 genes in PD (80 982 abstracts), 535 genes were common (RA∩PD), from which 35 genes were also documented in RA+PD (415 abstracts). 41 GO-terms significantly different between RA∩PD and RA+PD GO profiles represented 38 biological processes including; nitric oxide metabolism, immunoglobulin production, hormonal regulation, catabolic process down-regulation, and leukocyte proliferation. The 500 differential genes' PPI and gene-miRNA networks showed REL, TRAF2, AQP1 genes, and miRNAs 335-5p, 17-5p, 93-5p with genes HMOX1 and SP1 as hub nodes. CONCLUSIONS: Text-mining biomedical abstracts revealed potentially shared but un-investigated links between PD and RA, meriting further research.

The molecular differences between human papillomavirus-positive and -negative oropharyngeal squamous cell carcinoma: A bioinformatics study.

OBJECTIVE: To investigate the genetic and epigenetic differences between human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) and HPV-negative OPSCC. METHODS: Microarray data of HPV-positive and -negative OPSCC were retrieved from NCBI GEO datasets. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) were identified by performing differential expression analysis. A functional enrichment analysis was performed to explore the biological processes and signaling pathways that DEGs and DE-miRNAs were involved in, respectively. A protein-protein interaction (PPI) network of DEGs was constructed to identify hub genes. miRNA-target network and miRNA-miRNA functional synergistic network were each constructed in order to identify risk-marker miRNAs. An miRNA-target-pathway network was constructed in order to explore the function of identified risk-marker miRNAs. RESULTS: Microarray data from 3 datasets (GSE39366, GSE40774, and GSE55550) was included and analyzed. The PPI network identified 3 hub genes (VCAM1, UBD, and RPA2). MiR-107 and miR-142-3p were found to play the most significant role in both the DE-miRNA-target network as well as in the miRNA-miRNA functional synergistic network. MiR-107 was involved in HPV-induced tumorigenesis by targeting many genes (CAV1, CDK6, MYB, and SERPINB5) and regulating the p53 signaling pathway, the PI3K-Akt signaling pathway, and the autophagy pathway. In addition, miR-142-3p was implicated in HPV-induced tumorigenesis by targeting the PPFIA1 gene and regulating transcriptional dysregulation and other cancerous pathways. CONCLUSION: Three genes (VCAM1, UBD, and RPA2), two miRNAs (miR-107 and miR-142-3p), and four pathways (p53, PI3K-Akt, autophagy, and transcription dysregulation in cancer) were identified to play critical roles in distinguishing HPV-positive OPSCC from HPV-negative OPSCC.

dbPHCC: a database of prognostic biomarkers for hepatocellular carcinoma that provides online prognostic modeling.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant cancers with a poor prognosis. For decades, more and more biomarkers were found to effect on HCC prognosis, but these studies were scattered and there were no unified identifiers. Therefore, we built the database of prognostic biomarkers and models for hepatocellular carcinoma (dbPHCC). METHODS: dbPHCC focuses on biomarkers which were related to HCC prognosis by traditional experiments rather than high-throughput technology. All of the prognostic biomarkers came from literatures issued during 2002 to 2014 in PubMed and were manually selected. dbPHCC collects comprehensive information of candidate biomarkers and HCC prognosis. RESULTS: dbPHCC mainly contains 567 biomarkers: 323 proteins, 154 genes, and 90 microRNAs. For each biomarker, the reference information, experimental conditions, and prognostic information are shown. Based on two available patient cohort data sets, an exemplified prognostic model was constructed using 15 phosphotransferases in dbPHCC. The web interface does not only provide a full range of browsing and searching, but also provides online analysis tools. dbPHCC is available at http://lifecenter.sgst.cn/dbphcc/ CONCLUSIONS: dbPHCC provides a comprehensive and convenient search and analysis platform for HCC prognosis research. GENERAL SIGNIFICANCE: dbPHCC is the first database to focus on experimentally verified individual biomarkers, which are related to HCC prognosis. Prognostic markers in dbPHCC have the potential to be therapeutic drug targets and may help in designing new treatments to improve survival of HCC patients. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Recurrence of uterine tissue residues after laparoscopic hysterectomy or myomectomy.

OBJECTIVE: To report a new complication after laparoscopic surgery i.e recurrence of endometrium and leiomyoma fragments from uterine tissue residues after laparoscopic hysterectomy or laparoscopic myomectomy. Methods : This study was carried out on three patients with the recurrence of endometrium or leiomyoma fragments from tissue residues after laparoscopic hysterectomy or laparoscopic myomectomy in the First Affiliated Hospital, Yangtze University, China. We also explored the possible reasons and corresponding preventative strategies. RESULTS: Small residues of endometrium and leiomyoma fragments could implant into normal tissue anywhere in the peritoneal cavity after laparoscopic myomectomy or laparoscopic hysterectomy. CONCLUSION: These cases emphasize the importance of removing every single fragment to prevent the recurrence of endometrium and leiomyoma from tissue residues.

Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses.

Objective: To identify the genetic linkage mechanisms underlying Parkinson's disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases. Methods: The gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated. Results: Overall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD. Conclusion: Five genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis.

BACKGROUND: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. METHODS: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. RESULTS: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three "master" immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. CONCLUSION: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.

The Genetic and Epigenetic Mechanisms Involved in Irreversible Pulp Neural Inflammation.

AIM: To identify the critical genetic and epigenetic biomarkers by constructing the long noncoding RNA- (lncRNA-) related competing endogenous RNA (ceRNA) network involved in irreversible pulp neural inflammation (pulpitis). MATERIALS AND METHODS: The public datasets regarding irreversible pulpitis were downloaded from the gene expression omnibus (GEO) database. The differential expression analysis was performed to identify the differentially expressed genes (DEGs) and DElncRNAs. Functional enrichment analysis was performed to explore the biological processes and signaling pathways enriched by DEGs. By performing a weighted gene coexpression network analysis (WGCNA), the significant gene modules in each dataset were identified. Most importantly, DElncRNA-DEmRNA regulatory network and DElncRNA-associated ceRNA network were constructed. A transcription factor- (TF-) DEmRNA network was built to identify the critical TFs involved in pulpitis. RESULT: Two datasets (GSE92681 and GSE77459) were selected for analysis. DEGs involved in pulpitis were significantly enriched in seven signaling pathways (i.e., NOD-like receptor (NLR), Toll-like receptor (TLR), NF-kappa B, tumor necrosis factor (TNF), cell adhesion molecules (CAMs), chemokine, and cytokine-cytokine receptor interaction pathways). The ceRNA regulatory relationships were established consisting of three genes (i.e., LCP1, EZH2, and NR4A1), five miRNAs (i.e., miR-340-5p, miR-4731-5p, miR-27a-3p, miR-34a-5p, and miR-766-5p), and three lncRNAs (i.e., XIST, MIR155HG, and LINC00630). Six transcription factors (i.e., GATA2, ETS1, FOXP3, STAT1, FOS, and JUN) were identified to play pivotal roles in pulpitis. CONCLUSION: This paper demonstrates the genetic and epigenetic mechanisms of irreversible pulpitis by revealing the ceRNA network. The biomarkers identified could provide research direction for the application of genetically modified stem cells in endodontic regeneration.

Protective Effect of Triphala against Oxidative Stress-Induced Neurotoxicity.

BACKGROUND: Oxidative stress is implicated in the progression of many neurological diseases, which could be induced by various chemicals, such as hydrogen peroxide (H2O2) and acrylamide. Triphala is a well-recognized Ayurvedic medicine that possesses different therapeutic properties (e.g., antihistamine, antioxidant, anticancer, anti-inflammatory, antibacterial, and anticariogenic effects). However, little information is available regarding the neuroprotective effect of Triphala on oxidative stress. MATERIALS AND METHODS: An in vitro H2O2-induced SH-SY5Y cell model and an in vivo acrylamide-induced zebrafish model were established. Cell viability, apoptosis, and proliferation were examined by MTT assay, ELISA, and flow cytometric analysis, respectively. The molecular mechanism underlying the antioxidant activity of Triphala against H2O2 was investigated dose dependently by Western blotting. The in vivo neuroprotective effect of Triphala on acrylamide-induced oxidative injury in Danio rerio was determined using immunofluorescence staining. RESULTS: The results indicated that Triphala plays a neuroprotective role against H2O2 toxicity in inhibiting cell apoptosis and promoting cell proliferation. Furthermore, Triphala pretreatment suppressed the phosphorylation of the mitogen-activated protein kinase (MARK) signal pathway (p-Erk1/2, p-JNK1/2, and p-p38), whereas it restored the activities of antioxidant enzymes (superoxide dismutase 1 (SOD1) and catalase) in the H2O2-treated SH-SY5Y cells. Consistently, similar protective effects of Triphala were observed in declining neuroapoptosis and scavenging free radicals in the zebrafish central neural system, possessing a critical neuroprotective property against acrylamide-induced oxidative stress. CONCLUSION: In summary, Triphala is a promising neuroprotective agent against oxidative stress in SH-SY5Y cells and zebrafishes with significant antiapoptosis and antioxidant activities.

Shared Genetic and Epigenetic Mechanisms between the Osteogenic Differentiation of Dental Pulp Stem Cells and Bone Marrow Stem Cells.

OBJECTIVE: To identify the shared genetic and epigenetic mechanisms between the osteogenic differentiation of dental pulp stem cells (DPSC) and bone marrow stem cells (BMSC). MATERIALS AND METHODS: The profiling datasets of miRNA expression in the osteogenic differentiation of mesenchymal stem cells from the dental pulp (DPSC) and bone marrow (BMSC) were searched in the Gene Expression Omnibus (GEO) database. The differential expression analysis was performed to identify differentially expressed miRNAs (DEmiRNAs) dysregulated in DPSC and BMSC osteodifferentiation. The target genes of the DEmiRNAs that were dysregulated in DPSC and BMSC osteodifferentiation were identified, followed by the identification of the signaling pathways and biological processes (BPs) of these target genes. Accordingly, the DEmiRNA-transcription factor (TFs) network and the DEmiRNAs-small molecular drug network involved in the DPSC and BMSC osteodifferentiation were constructed. RESULTS: 16 dysregulated DEmiRNAs were found to be overlapped in the DPSC and BMSC osteodifferentiation, including 8 DEmiRNAs with a common expression pattern (8 upregulated DEmiRNAs (miR-101-3p, miR-143-3p, miR-145-3p/5p, miR-19a-3p, miR-34c-5p, miR-3607-3p, miR-378e, miR-671-3p, and miR-671-5p) and 1 downregulated DEmiRNA (miR-671-3p/5p)), as well as 8 DEmiRNAs with a different expression pattern (i.e., miR-1273g-3p, miR-146a-5p, miR-146b-5p, miR-337-3p, miR-382-3p, miR-4508, miR-4516, and miR-6087). Several signaling pathways (TNF, mTOR, Hippo, neutrophin, and pathways regulating pluripotency of stem cells), transcription factors (RUNX1, FOXA1, HIF1A, and MYC), and small molecule drugs (curcumin, docosahexaenoic acid (DHA), vitamin D3, arsenic trioxide, 5-fluorouracil (5-FU), and naringin) were identified as common regulators of both the DPSC and BMSC osteodifferentiation. CONCLUSION: Common genetic and epigenetic mechanisms are involved in the osteodifferentiation of DPSCs and BMSCs.

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach.

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCC-related genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumor-infiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area.

Shared Molecular Mechanisms between Atherosclerosis and Periodontitis by Analyzing the Transcriptomic Alterations of Peripheral Blood Monocytes.

OBJECTIVE: This study investigated the nature of shared transcriptomic alterations in PBMs from periodontitis and atherosclerosis to unravel molecular mechanisms underpinning their association. METHODS: Gene expression data from PBMs from patients with periodontitis and those with atherosclerosis were each downloaded from the GEO database. Differentially expressed genes (DEGs) in periodontitis and atherosclerosis were identified through differential gene expression analysis. The disease-related known genes related to periodontitis and atherosclerosis each were downloaded from the DisGeNET database. A Venn diagram was constructed to identify crosstalk genes from four categories: DEGs expressed in periodontitis, periodontitis-related known genes, DEGs expressed in atherosclerosis, and atherosclerosis-related known genes. A weighted gene coexpression network analysis (WGCNA) was performed to identify significant coexpression modules, and then, coexpressed gene interaction networks belonging to each significant module were constructed to identify the core crosstalk genes. RESULTS: Functional enrichment analysis of significant modules obtained by WGCNA analysis showed that several pathways might play the critical crosstalk role in linking both diseases, including bacterial invasion of epithelial cells, platelet activation, and Mitogen-Activated Protein Kinases (MAPK) signaling. By constructing the gene interaction network of significant modules, the core crosstalk genes in each module were identified and included: for GSE23746 dataset, RASGRP2 in the blue module and VAMP7 and SNX3 in the green module, as well as HMGB1 and SUMO1 in the turquoise module were identified; for GSE61490 dataset, SEC61G, PSMB2, SELPLG, and FIBP in the turquoise module were identified. CONCLUSION: Exploration of available transcriptomic datasets revealed core crosstalk genes (RASGRP2, VAMP7, SNX3, HMGB1, SUMO1, SEC61G, PSMB2, SELPLG, and FIBP) and significant pathways (bacterial invasion of epithelial cells, platelet activation, and MAPK signaling) as top candidate molecular linkage mechanisms between atherosclerosis and periodontitis.

Shared Molecular Mechanisms between Alzheimer's Disease and Periodontitis Revealed by Transcriptomic Analysis.

OBJECTIVE: To investigate the genetic crosstalk mechanisms that link periodontitis and Alzheimer's disease (AD). BACKGROUND: Periodontitis, a common oral infectious disease, is associated with Alzheimer's disease (AD) and considered a putative contributory factor to its progression. However, a comprehensive investigation of potential shared genetic mechanisms between these diseases has not yet been reported. METHODS: Gene expression datasets related to periodontitis were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis was performed to identify differentially expressed genes (DEGs). Genes associated with AD were downloaded from the DisGeNET database. Overlapping genes among the DEGs in periodontitis and the AD-related genes were defined as crosstalk genes between periodontitis and AD. The Boruta algorithm was applied to perform feature selection from these crosstalk genes, and representative crosstalk genes were thus obtained. In addition, a support vector machine (SVM) model was constructed by using the scikit-learn algorithm in Python. Next, the crosstalk gene-TF network and crosstalk gene-DEP (differentially expressed pathway) network were each constructed. As a final step, shared genes among the crosstalk genes and periodontitis-related genes in DisGeNET were identified and denoted as the core crosstalk genes. RESULTS: Four datasets (GSE23586, GSE16134, GSE10334, and GSE79705) pertaining to periodontitis were included in the analysis. A total of 48 representative crosstalk genes were identified by using the Boruta algorithm. Three TFs (FOS, MEF2C, and USF2) and several pathways (i.e., JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity) were identified as regulators of these crosstalk genes. Among these 48 crosstalk genes and the chronic periodontitis-related genes in DisGeNET, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 were shared and identified as the most pivotal candidate links between periodontitis and AD. CONCLUSIONS: Exploration of available transcriptomic datasets revealed C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 as the top candidate molecular linkage genes between periodontitis and AD.

Shared genetic and epigenetic mechanisms between chronic periodontitis and oral squamous cell carcinoma.

OBJECTIVES: To analyze bioinformatic datasets for detecting genetic and epigenetic mechanisms shared by chronic periodontitis (CP) and oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS: Datasets from GEO and TCGA databases reporting mRNAs, miRNAs or methylation expression in human CP and OSCC tissues were analyzed. Differential expression, functional enrichment and protein-protein interaction (PPI) network analyses were performed. Differentially expressed miRNAs (DEmiRNAs) and genes (DEG) in CP and OSCC were determined. DEmiRNA-target and DEmiRNA-DEG networks were constructed. Directly and indirectly interacting cross-talk genes were screened, and their prediction accuracy and association with OSCC prognosis was determined. RESULTS: 3 DE-miRNAs (miR-375, miR-3609 and miR-3652) expressed in both CP and OSCC critically regulated most DEGs. Among 12 directly interacting cross-talk genes, NCAPH was significantly related with the prognosis of OSCC. NR2F2 had highest differential expression in CP and OSCC. Among 4 cross-talk genes (FN1, MPPED1, NDEL1, and NR2F2) differentially expressed in CP, 3 (FN1, MPPED1, NDEL1) were also expressed in OSCC. Among 12 indirectly interacting cross-talk genes differentially expressed in OSCC, 3 genes (CDCA8, HIST1H3J, and RAD51) were significantly related to its prognosis. Significant pathways involved in CP and OSCC included: chemokine receptors, class I PI3K signaling events, epithelial-to-mesenchymal transition and signaling events by VEGFR1 and VEGFR2, EGF receptor (ErbB1). CONCLUSION: Bioinformatic analysis of available datasets implicated 1 directly interacting cross-talk gene (NCAPH), 4 indirectly interacting cross-talk genes (NCAPH, NR2F2, FN1, and MPPED1) and 3 DE-miRNAs (hsa-miR-375, miR-3609 and miR-3652) as shared genetic and epigenetic expression patterns between CP and OSCC.

Complex integrated analysis of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma.

OBJECTIVES: This study aims to reveal regulatory network of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma (OSCC) through gene expression data. MATERIAL AND METHODS: Differentially expressed lncRNAs, miRNAs and mRNAs (cut-off: False discovery rate (FDR)<0.05 and |fold change|>1.5) were unveiled by package edgeR of R. Cox regression analysis was performed to screen prognostic factors in OSCC related with overall survival (OS) and relapse-free survival (RFS). Protein-protein interaction (PPI) network was constructed for differentially expressed mRNAs using BioGRID, HPRD and DIP. Key hub genes were identified from top 100 differentially expressed mRNAs ranked by betweenness centrality using recursive feature elimination. LncRNA-miRNA and miRNA-mRNA regulatory network were constructed and combined into ceRNAs regulatory network. Gene ontology biological terms and Kyoto Encyclopedia of Genes and Genomes pathways were identified using Fisher's exact test. RESULTS: A total of 929 differentially expressed mRNAs, 23 differentially expressed lncRNAs and 29 differentially expressed miRNAs were identified. 59 mRNAs, 6 miRNAs (hsa-mir-133a-1, hsa-mir-1-2, hsa-mir-486, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 6 lncRNAs (C10orf91, C2orf48, SFTA1P, FLJ41941,PART1,TTTY14) were related with OS; and 52 mRNAs, 4 miRNAs (hsa-mir-133a-1, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 2 lncRNAs (PART1, TTTY14) were associated with RFS. A support vector machine (SVM) classifier containing 37 key hub genes was obtained. A ceRNA regulatory network containing 417 nodes and 696 edges was constructed. ECM-receptor interaction, cytokine-cytokine receptor interaction, focal adhesion, arachidonic acid metabolism, and p53 signaling pathway were significantly enriched in the network. CONCLUSION: These findings uncover the pathogenesis of OSCC and might provide potential therapeutic targets.

Large-Scale Analysis of Drug Side Effects via Complex Regulatory Modules Composed of microRNAs, Transcription Factors and Gene Sets.

Identifying the occurrence mechanism of drug-induced side effects (SEs) is critical for design of drug target and new drug development. The expression of genes in biological processes is regulated by transcription factors(TFs) and/or microRNAs. Most of previous studies were focused on a single level of gene or gene sets, while studies about regulatory relationships of TFs, miRNAs and biological processes are very rare. Discovering the complex regulating relations among TFs, gene sets and miRNAs will be helpful for researchers to get a more comprehensive understanding about the mechanism of side reaction. In this study, a framework was proposed to construct the relationship network of gene sets, miRNAs and TFs involved in side effects. Through the construction of this network, the potential complex regulatory relationship in the occurrence process of the side effects was reproduced. The SE-gene set network was employed to characterize the significant regulatory SE-gene set interaction and molecular basis of accompanied side effects. A total of 117 side effects complex modules including four types of regulating patterns were obtained from the SE-gene sets-miRNA/TF complex regulatory network. In addition, two cases were used to validate the complex regulatory modules which could more comprehensively interpret occurrence mechanism of side effects.

[Gene regulatory network of hepatocellular carcinoma: a review].

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. HCC gene regulatory network (HCC GRN), whose nodes consist of genes, miRNAs or TFs and whose edges consist of interaction relationships of nodes, is one of the important ways to study molecular mechanism of HCC. Based on various experimental data, types of HCC GRNs could be conducted such as TF-miRNA regulatory network. Integrating the studies of HCC GRN, TF-miRNA transcriptional regulatory network performs better in identifying core genes which play important roles in network disturbances. It is a trend that gene variations and transcriptional regulatory networks should be combined, however the corresponding research is almost blank. This review summarizes the source of HCC data sources, the classification, character, and research program of HCC GRN. Finally, according to present analysis and discussion of progress and research status of HCC GRN, we provide a useful reference for researchers.

Identifying the causative proteins of similar side effect pairs to explore the common molecular basis of these side effects.

Drug side effects, or adverse drug reactions (ADRs), have become a major public health concern and often cause drug development failure and withdrawal. Some ADRs always occur concomitantly. Therefore, identifying these ADRs and their common molecular basis can better promote their prevention and treatment. In this paper we predicted the potential proteins for ADR pairs with similar mechanisms based on three layers of information: (i) the drug co-occurrence between a pair of ADRs; (ii) the correlation between a protein and an ADR pair based on the co-occurrence of drugs and (iii) the interaction between these proteins within the protein-protein interaction (PPI) network. The methods of randomization and functional annotation are used to investigate and analyze the relation between causative proteins and similar ADR pairs. The prediction accuracy of the relation between similar ADR pairs and related proteins reached 80%, and it increases with the number of drugs shared by the ADR pairs. From the ADR network made of single ADRs from predicted similar ADR pairs, we found that some ADRs are involved in multiple ADR pairs. The functional analysis of these ADR-related proteins suggests that a similar molecular basis is shared by multiple ADR pairs containing the same ADR, and these ADR pairs are almost caused by the same drug sets. The results of this study are reliable and provide a theoretical basis for the better prevention and treatment of ADRs that always occur concomitantly.

Establishment of an orthotopic transplantation tumor model in nude mice using a drug-resistant human ovarian cancer cell line with a high expression of c-Kit.

The resistance of ovarian cancer to platinum-based chemotherapy is a critical issue in the clinical setting. The present study aimed to establish animal models to replicate this clinical condition, as well as to investigate the resistance mechanisms of ovarian cancer. A cisplatin (DDP)-resistant human ovarian cancer cell line, SKOV3/DDP, was screened, validated and injected subcutaneously into the neck of female nude mice. Following tumor establishment, the tumor was collected and cut into small sections, which were subsequently implanted into the ovaries of other nude mice. The growth of the orthotopic tumors was observed and the tumor-bearing mice were sacrificed and dissected. The orthotopic and metastatic tumor tissues were collected, sectioned, stained with hematoxylin and eosin and analyzed. In the present study, 16 nude mice underwent orthotopic transplantation surgery and a tumor model was successfully established in 14/16 of the mice, with an in situ tumor formation rate of 87.5%. Following euthanasia, a laparotomy demonstrated the tumor formation at the site of transplantation, as well as varying degrees of metastasis to additional organs and tissues. Therefore, the present study successfully established an orthotopic tumor transplantation model in nude mice using a c-Kit-positive DDP-resistant human ovarian cancer cell line. This model may represent a useful tool for investigating the resistance mechanism of ovarian cancer, as well as evaluating the efficacy of therapeutic strategies.

Methodologies for the establishment of an orthotopic transplantation model of ovarian cancer in mice.

This study used different methods to establish an animal model of orthotopic transplantation for ovarian cancer to provide an accurate simulation of the mechanism by which tumor occurs and develops in the human body. We implanted 4T1 breast cancer cells stably-transfected with luciferase into BALB/c mice by using three types of orthotopic transplantation methodologies: (1) cultured cells were directly injected into the mouse ovary; (2) cell suspension was initially implanted under the skin of the mouse neck; after tumor mass formed, the tumor was removed and ground into cell suspension, which was then injected into the mouse ovary; and (3) a subcutaneous tumor mass was first generated, removed, and cut into small pieces, which were directly implanted into the mouse ovary. After these models were established, in vivo luminescence imaging was performed. Results and data were compared among groups. Orthotopic transplantation model established with subcutaneous tumor piece implantation showed a better simulation of tumor development and invasion in mice. This model also displayed negligible response to artificial factors. This study successfully established an orthotopic transplantation model of ovarian cancer with high rates of tumor formation and metastasis by using subcutaneous tumor pieces. This study also provided a methodological basis for future establishment of an animal model of ovarian cancer in humans.

Drug repositioning by applying 'expression profiles' generated by integrating chemical structure similarity and gene semantic similarity.

Drug repositioning, also known as drug repurposing or reprofiling, is the process of finding new indications for established drugs. Because drug repositioning can reduce costs and enhance the efficiency of drug development, it is of paramount importance in medical research. Here, we present a systematic computational method to identify potential novel indications for a given drug. This method utilizes some prior knowledge such as 3D drug chemical structure information, drug-target interactions and gene semantic similarity information. Its prediction is based on another form of 'expression profile', which contains scores ranging from -1 to 1, reflecting the consensus response scores (CRSs) between each drug of 965 and 1560 proteins. The CRS integrates chemical structure similarity and gene semantic similarity information. We define the degree of similarity between two drugs as the absolute value of their correlation coefficients. Finally, we establish a drug similarity network (DSN) and obtain 33 modules of drugs with similar modes of action, determining their common indications. Using these modules, we predict new indications for 143 drugs and identify previously unknown indications for 42 drugs without ATC codes. This method overcomes the instability of gene expression profiling derived from experiments due to experimental conditions, and predicts indications for a new compound feasibly, requiring only the 3D structure of the compound. In addition, the high literature validation rate of 71.8% also suggests that our method has the potential to discover novel drug indications for existing drugs.