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Bioinformatics Analysis Identifies p53 as a Candidate Prognostic Biomarker for Neuropathic Pain.

ABSTRACT: Neuropathic pain (NP) is a type of chronic pain that is different from the common type of pain. The mechanisms of NP are still poorly understood. Exploring the key genes and neurobiological changes in NP could provide important diagnostic and treatment tools for clinicians. GSE24982 is an mRNA-seq dataset that we downloaded from the Gene Expression Omnibus database to identify key genes in NP. Differentially expressed genes (DEGs) were identified using the BRB-ArrayTools software and R. Functional and pathway enrichment analyses of the DEGs were performed using Metascape. A protein-protein interaction network was created and visualized using Cytoscape. A total of 123 upregulated DEGs were obtained. Among these genes, p53 was the node with the highest degree; hence, we validated it experimentally using a chronic constriction injury mouse model. Our results showed that overexpression of the p53 gene, and the subsequent increase in caspase-3 expression, in dorsal root ganglion neurons led to increased apoptotic changes in these neurons. p53 may therefore be partly responsible for the development of chronic constriction injury-induced NP.

SUBMITTER: Gao Y

PROVIDER: S-EPMC6127677 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Bioinformatics Analysis Identifies p53 as a Candidate Prognostic Biomarker for Neuropathic Pain.

Gao Yibo Y Sun Na N Wang Lieju L Wu Ying Y Ma Longfei L Hong Juncong J Ren Jinxuan J Zhu Bin B Yu Lina L Yan Min M

Frontiers in genetics 20180831

Neuropathic pain (NP) is a type of chronic pain that is different from the common type of pain. The mechanisms of NP are still poorly understood. Exploring the key genes and neurobiological changes in NP could provide important diagnostic and treatment tools for clinicians. GSE24982 is an mRNA-seq dataset that we downloaded from the Gene Expression Omnibus database to identify key genes in NP. Differentially expressed genes (DEGs) were identified using the BRB-ArrayTools software and R. Function ...[more]

PMID: 30233637

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Project description:Background:Spinal cord injury (SCI) is one of the most devastating diseases with a high incidence rate around the world. SCI-related neuropathic pain (NeP) is a common complication, whereas its pathomechanism is still unclear. The purpose of this study is to identify key genes and cellular components for SCI-related NeP by an integrated transcriptome bioinformatics analysis. Methods:The gene expression profile of 25 peripheral blood samples from chronic phase SCI patients (E-GEOD-69901) and 337 normal peripheral blood samples were downloaded from ArrayExpress and Genotype-Tissue Expression Portal (GTEx), respectively. A total of 3,368 normal peripheral blood mononuclear cells (PBMC) were download from Sequence Read Archive (SRA713577). Non-parametric tests were used to evaluate the association between all of differential expression genes (DEGs) and SCI-related NeP. CellPhoneDB algorithm was performed to identify the ligand-receptor interactions and their cellular localization among single PBMCs. Transcription factor (TF) enrichment analysis and Gene Set Variation Analysis (GSVA) were used to identify the potential upstream regulatory TFs and downstream signaling pathways, respectively. Co-expression analysis among significantly enriched TFs, key cellular communication genes and differentially expressed signaling pathways were performed to identify key genes and cellular components for SCI-related NeP. Results:A total of 2,314 genes were identified as DEGs between the experimental and the control group. Five proteins (ADRB2, LGALS9, PECAM1, HAVCR2, LRP1) were identified in the overlap of proteins in the significant ligand-receptor interactions of PBMCs and protein-protein interaction (PPI) network based on the DEGs. Only HAVCR2 was significantly associated with NeP (P = 0.005). Besides, the co-expression analysis revealed that TF YY1 had significantly co-expression pattern with cellular communication receptor HAVCR2 (R = -0.54, P < 0.001) in NK cells while HAVCR2 was also co-expressed with mTOR signaling pathway (R = 0.57, P < 0.001). The results of RT-qPCR and external dataset validation supported the signaling axis with the most significant co-expression patterns. Conclusion:In peripheral blood of chronic SCI, HAVCR2 might act as a key receptor on the surface of NK cells and interact with ligand LGALS9 secreted by CD14+ monocytes, inhibiting NK cells through mTOR signaling pathway and ultimately predicting the occurrence of SCI-related NeP. This hypothetical signaling axis may provide prognostic biomarkers and therapeutic targets for SCI-related NeP.

Project description:Pyroptosis is defined as inflammation-induced programmed cell death. However, gene expression levels related to pyroptosis and their role in neuropathic pain (NP) remain unclear. The present study aimed to develop and validate an NP-predictive signature based on the genes associated with pyroptosis. Gene expression level profiles were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the pyroptotic genes most highly associated with NP. NP-related pyroptosis gene signature was constructed using multivariate logistic regression. A rat model of neuropathic pain was established through chronic constriction injury to analyse the inflammatory infiltration and myelin damage around the sciatic nerve, and examine the expression levels of macrophage markers S100 calcium-binding protein β (S100β) and ionized calcium-binding adapter molecule 1 (Iba-1). Finally, flow cytometry analysis was used to examine the lipopolysaccharide (LPS)-induced cell death ratio of RSC96 cells (Schwann cells), while the expression levels of LPS-induced pyroptosis-related genes in RSC96 cells were measured via reverse transcription-quantitative PCR. The results demonstrated that pyroptosis-related genes (gasdermin D, NLR family pyrin domain containing 3, neuronal apoptosis inhibitory protein and NLR family CARD domain containing 4) were identified to increase the risk of NP. NP-related pyroptosis signatures were constructed based on these four genes. Moreover, the high-risk group had a higher level of macrophage infiltration compared with the low-risk group, as determined by the CIBERSORT algorithm. H&E staining results showed that the myelin structure of the sciatic nerve tissue of chronic constriction injury (CCI) rats was destroyed and inflammatory cells infiltrated around neurons. The results of immunohistochemistry showed that compared with in the sham group, the expression levels of Iba-1 and sS100β in the sciatic nerve of the CCI group were increased. Furthermore, the expression levels of cell death and pyroptosis-related genes in Schwann cells induced by LPS were increased compared with in the control group. In conclusion, an NP-related pyroptosis gene signature was constructed based on four pyroptosis-related genes and it was found that the expression of pyroptosis-related genes was upregulated in the early steps of the neuroinflammatory process in RSC96 cells.

Project description:BACKGROUND:Duchenne muscular dystrophy (DMD) is a fatal disease for which no cure is available. Clinical trials have shown to be largely underpowered due to inter-individual variability and noisy outcome measures. The availability of biomarkers able to anticipate clinical benefit is highly needed to improve clinical trial design and facilitate drug development. METHODS:In this study, we aimed to appraise the value of protein biomarkers to predict prognosis and monitor disease progression or treatment outcome in patients affected by DMD. We collected clinical data and 303 blood samples from 157 DMD patients in three clinical centres; 78 patients contributed multiple blood samples over time, with a median follow-up time of 2 years. We employed linear mixed models to identify biomarkers that are associated with disease progression, wheelchair dependency, and treatment with corticosteroids and performed survival analysis to find biomarkers whose levels are associated with time to loss of ambulation. RESULTS:Our analysis led to the identification of 21 proteins whose levels significantly decrease with age and nine proteins whose levels significantly increase. Seven of these proteins are also differentially expressed in non-ambulant patients, and three proteins are differentially expressed in patients treated with glucocorticosteroids. Treatment with corticosteroids was found to partly counteract the effect of disease progression on two biomarkers, namely, malate dehydrogenase 2 (MDH2, P = 0.0003) and ankyrin repeat domain 2 (P = 0.0005); however, patients treated with corticosteroids experienced a further reduction on collagen 1 serum levels (P = 0.0003), especially following administration of deflazacort. A time to event analysis allowed to further support the use of MDH2 as a prognostic biomarker as it was associated with an increased risk of wheelchair dependence (P = 0.0003). The obtained data support the prospective evaluation of the identified biomarkers in natural history and clinical trials as exploratory biomarkers. CONCLUSIONS:We identified a number of serum biomarkers associated with disease progression, loss of ambulation, and treatment with corticosteroids. The identified biomarkers are promising candidate prognostic and surrogate biomarkers, which may support drug developers if confirmed in prospective studies. The serum levels of MDH2 are of particular interest, as they correlate with disease stage and response to treatment with corticosteroids, and are also associated with the risk of wheelchair dependency and pulmonary function.

Project description:Background:Lung squamous cell carcinoma (LUSC) is the second most common histological subtype of lung cancer (LC), and the prognoses of most LUSC patients are so far still very poor. The present study aimed at integrating lncRNA, miRNA and mRNA expression data to identify lncRNA signature in competitive endogenous RNA (ceRNA) network as a potentially prognostic biomarker for LUSC patients. Methods:Gene expression data and clinical characteristics of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database, and were integratedly analyzed using bioinformatics methods including Differentially Expressed Gene Analysis (DEGA), Weighted Gene Co-expression Network Analysis (WGCNA), Protein and Protein Interaction (PPI) network analysis and ceRNA network construction. Subsequently, univariate and multivariate Cox regression analyses of differentially expressed lncRNAs (DElncRNAs) in ceRNA network were performed to predict the overall survival (OS) in LUSC patients. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Gene expression profiling interactive analysis (GEPIA) was used to validate key genes. Results:WGCNA showed that turquoise module including 1,694 DElncRNAs, 2,654 DEmRNAs as well as 113 DEmiRNAs was identified as the most significant modules (cor=0.99, P<1e-200), and differentially expressed RNAs in the module were used to subsequently analyze. PPI network analysis identified FPR2, GNG11 and ADCY4 as critical genes in LUSC, and survival analysis revealed that low mRNA expression of FPR2 and GNG11 resulted in a higher OS rate of LUSC patients. A lncRNA-miRNA-mRNA ceRNA network including 121 DElncRNAs, 18 DEmiRNAs and 3 DEmRNAs was established, and univariate and multivariate Cox regression analysis of those 121 DElncRNAs showed a group of 3 DElncRNAs (TTTY16, POU6F2-AS2 and CACNA2D3-AS1) had significantly prognostic value in OS of LUSC patients. ROC analysis showed that the area under the curve (AUC) of the 3-lncRNA signature associated with 3-year survival was 0.629. Conclusions:The current study provides novel insights into the lncRNA-related regulatory mechanisms underlying LUSC, and identifying 3-lncRNA signature may serve as a potentially prognostic biomarker in predicting the OS of LUSC patients.

Project description:BackgroundThe BIRC5 gene encodes for the Survivin protein, which is a member of the inhibitor of apoptosis family. Survivin is found in humans during fetal development, but generally not in adult cells thereafter. Previous studies have shown that Survivin is abundant in most cancer cells, thereby making it a promising target for anti-cancer drugs and a potential prognostic tool.MethodsTo assess genetic alterations and mutations in the BIRC5 gene as well as BIRC5 co-expression with other genes, genomic and transcriptomic data were downloaded via cBioPortal for approximately 9000 samples from The Cancer Genome Atlas (TCGA) representing 33 different cancer types and 11 pan-cancer organ systems, and validated using the ICGC Data Portal and COSMIC. TCGA BIRC5 RNA sequencing data from 33 different cancer types and matching normal tissue samples for 16 cancer types were downloaded from Broad GDAC Firehose and validated using breast cancer microarray data from our previous work and data sets from the GENT2 web-based tool. Survival data were analyzed with multivariable Cox proportional hazards regression analysis and validated using KM plotter for breast-, ovarian-, lung- and gastric cancer.ResultsAlthough genetic alterations in BIRC5 were not common in cancer, BIRC5 expression was significantly higher in cancer tissue compared to normal tissue in the 16 different cancer types. For 14/33 cancer types, higher BIRC5 expression was linked to worse overall survival (OS, 4/14 after adjusting for both age and tumor grade and 10/14 after adjusting only for age). Interestingly, higher BIRC5 expression was associated with better OS in lung squamous cell carcinoma and ovarian serous cystadenocarcinoma. Higher BIRC5 expression was also linked to shorter progressive-free interval (PFI) for 14/33 cancer types (4/14 after adjusting for both age and tumor grade and 10/14 after adjusting only for age). External validation showed that high BIRC5 expression was significantly associated with worse OS for breast-, lung-, and gastric cancer.ConclusionsOur findings suggest that BIRC5 overexpression is associated with the initiation and progression of several cancer types, and thereby a promising prognostic biomarker.

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Bioinformatics Analysis Identifies p53 as a Candidate Prognostic Biomarker for Neuropathic Pain.

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Bioinformatics Analysis Identifies p53 as a Candidate Prognostic Biomarker for Neuropathic Pain.

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