Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

Background Neurogenic heterotopic ossification is a disorder of aberrant bone formation affecting one in five patients sustaining a spinal cord injury or traumatic brain injury (SCI-TBI-HO). However, the underlying mechanisms of SCI-TBI-HO have proven difficult to elucidate. The aim of the present study is to identify the most promising candidate genes and biological pathways for SCI-TBI-HO. Methods In this study, we used text mining to generate potential explanations for SCI-TBI-HO. Moreover, we employed several additional datasets, including gene expression profile data, drug data and tissue-specific gene expression data, to explore promising genes that associated with SCI-TBI-HO. Results We identified four SCI-TBI-HO-associated genes, including GDF15, LDLR, CCL2, and CLU. Finally, using enrichment analysis, we identified several pathways, including integrin signaling, insulin pathway, internalization of ErbB1, urokinase-type plasminogen activator and uPAR-mediated signaling, PDGFR-beta signaling pathway, EGF receptor (ErbB1) signaling pathway, and class I PI3K signaling events, which may be associated with SCI-TBI-HO. Conclusions These results enhance our understanding of the molecular mechanisms of SCI-TBI-HO and offer new leads for researchers and innovative therapeutic strategies

Analysis of temporal expression profiles after sciatic nerve injury by bioinformatic method

Abstract After Peripheral nerve injuries (PNI), many complicated pathophysiologic processes will happen. A global view of functional changes following PNI is essential for the looking for the adequate therapeutic approaches. In this study, we performed an in-depth analysis on the temporal expression profiles after sciatic nerve injury by bioinformatic methods, including (1) cluster analysis of the samples; (2) identification of gene co-expression modules(CEMs) correlated with the time points; (3) analysis of differentially expressed genes at each time point (DEGs-ET); (4) analysis of differentially expressed genes varying over time (DEGs-OT); (5) creating Pairwise Correlation Plot for the samples; (6) Time Series Regression Analysis; (7) Determining the pathway, GO (gene ontology) and drug by enrichment analysis. We found that at a 3 h “window period” some specific gene expression may exist after PNI, and responses to lipopolysaccharide (LPS) and TNF signaling pathway may play important roles, suggesting that the inflammatory microenvironment exists after PNI. We also found that troglitazone was closely associated with the change of gene expression after PNI. Therefore, the further evaluation of the precise mechanism of troglitazone on PNI is needed and it may contribute to the development of new drugs for patients with PNI

Using formalin fixed paraffin embedded tissue to characterize the microbiota in p16-positive and p16-negative tongue squamous cell carcinoma: a pilot study

Abstract Background Tongue squamous cell carcinoma (TSCC) is the most common oral cavity cancer, and p16 immunohistochemistry is an exact and available tool in the prognostic and predictive characterization of squamous cell cancers in the head and neck. Microorganisms have a close relationship with the development of TSCC. However, the association between oral bacteria and p16 status has not been well defined in the case of TSCC. Compared with traditional clinical microbial collection methods, formalin-fixed paraffin-embedded (FFPE) tissue samples have several advantages. Methods To compare the microbiota compositions between p16-positive and p16-negative patients with TSCC, we performed a small pilot study of microbiological studies of TSCC by paraffin tissue. DNA from FFPE tissue blocks were extracted and microbiomes were profiled by sequencing the 16 S-rRNA-encoding gene (V1–V2/V3-V4/V4 regions). Alterations in the functional potential of the microbiome were predicted using PICRUSt, Tax4Fun, and BugBase. Results A total of 60 patients with TSCC were enrolled in the study, however, some challenges associated with DNA damage in FFPE tissues existed, and only 27 (15 p16-positive and 12 p16-negative) passed DNA quality control. Nevertheless, we have tentatively found some meaningful results. The p16 status is associated with microbiota diversity, which is significantly increased in p16-positive patients compared with p16-negative patients. Desulfobacteria, Limnochordia, Phycisphaerae, Anaerolineae, Saccharimonadia and Kapabacteria had higher abundances among participants with p16-positive. Moreover, functional prediction revealed that the increase of these bacteria may enhance viral carcinogenesis in p16-positive TSCC. Conclusions Bacterial profiles showed a significant difference between p16-positive TSCC and p16-negative TSCC. These findings may provide insights into the relationship between p16 status and the microbial taxa in TSCC, and these bacteria may provide new clues for developing therapeutic targets for TSCC

Meta-analysis of 125 rheumatoid arthritis-related single nucleotide polymorphisms studied in the past two decades.

OBJECTIVE: Candidate gene association studies and genome-wide association studies (GWAs) have identified a large number of single nucleotide polymorphisms (SNPs) loci affecting susceptibility to rheumatoid arthritis (RA). However, for the same locus, some studies have yielded inconsistent results. To assess all the available evidence for association, we performed a meta-analysis on previously published case-control studies investigating the association between SNPs and RA. METHODS: Two hundred and sixteen studies, involving 125 SNPs, were reviewed. For each SNP, three genetic models were considered: the allele, dominant and recessive effects models. For each model, the effect summary odds ratio (OR) and 95% CIs were calculated. Cochran's Q-statistics were used to assess heterogeneity. If the heterogeneity was high, a random effects model was used for meta-analysis, otherwise a fixed effects model was used. RESULTS: The meta-analysis results showed that: (1) 30, 28 and 26 SNPs were significantly associated with RA (P<0.01) for the allele, dominant, and recessive models, respectively. (2) rs2476601 (PTPN22) showed the strongest association for all the three models: OR = 1.605, 95% CI: 1.540-1.672, P<1.00E-15 for the T-allele; OR = 1.638, 95% CI: 1.565-1.714, P<1.00E-15 for the T/T+T/C genotype and OR = 2.544, 95% CI: 2.173-2.978, P<1.00E-15 for the T/T genotype. (3) Only 23 (18.4%), 13 (10.4%) and 15 (12.0%) SNPs had high heterogeneity (P<0.01) for the three models, respectively. (4) For some of the SNPs, there was no publication bias according to Funnel plots and Egger's regression tests (P<0.01). For the other SNPs, the associations were tested in only a few studies, and may have been subject to publication bias. More studies on these loci are required. CONCLUSION: Our meta-analysis provides a comprehensive evaluation of the RA association studies from the past two decades. The detailed meta-analysis results are available at: http://210.46.85.180/DRAP/index.php/Metaanalysis/index

Meta-analysis results of subgroups.

a<p>Model: If the <i>Q-</i>statistic was significant (P<0.01), we selected random effects model for a meta-analysis, otherwise we selected fixed effects model.</p>b<p><i>OR</i> = Combined odds ratio.</p

Meta-analysis results under the dominant model.

a<p>Model: If the <i>Q-</i>statistic was significant (P<0.01), we selected random effects model for a meta-analysis, otherwise we selected fixed effects model.</p>b<p><i>OR</i> = Combined odds ratio.</p

Meta-analysis results under the allele model.

a<p>Model: If the <i>Q-</i>statistic was significant (<i>P</i><0.01), we selected random effects model for a meta-analysis, otherwise we selected fixed effects model.</p>b<p><i>OR</i> = Combined odds ratio.</p

Meta-analysis results of special phenotypes.

<p>RF+: RF positive; RF-: RF negative; CPP+: anti-CCP positive; CPP-: anti-CCP negative.</p>a<p>Model: If the <i>Q-</i>statistic was significant (P<0.01), we selected random effects model for a meta-analysis, otherwise we selected fixed effects model.</p>b<p><i>OR</i> = Combined odds ratio.</p