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Identifying protein biomarkers in predicting disease severity of dengue virus infection using immune-related protein microarray.

ABSTRACT: Dengue virus is one of the most widespread flaviviruses that re-emerged throughout recent decades. The progression from mild dengue to severe dengue (SD) with the complications such as vascular leakage and hemorrhage increases the fatality rate of dengue. The pathophysiology of SD is not entirely clear. To investigate potential biomarkers that are suggestive of pathogenesis of SD, a small panel of serum samples selected from 1 healthy individual, 2 dengue patients without warning signs (DWS-), 2 dengue patients with warning signs (DWS+), and 5 patients with SD were subjected to a pilot analysis using Sengenics Immunome protein array. The overall fold changes of protein expressions and clustering heat map revealed that PFKFB4, TPM1, PDCL3, and PTPN20A were elevated among patients with SD. Differential expression analysis identified that 29 proteins were differentially elevated greater than 2-fold in SD groups than DWS- and DWS+. From the 29 candidate proteins, pathways enrichment analysis also identified insulin signaling and cytoskeleton pathways were involved in SD, suggesting that the insulin pathway may play a pivotal role in the pathogenesis of SD.

SUBMITTER: Soe HJ

PROVIDER: S-EPMC5805423 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Identifying protein biomarkers in predicting disease severity of dengue virus infection using immune-related protein microarray.

Soe Hui Jen HJ Yong Yean K YK Al-Obaidi Mazen M Jamil MMJ Raju Chandramathi Samudi CS Gudimella Ranganath R Manikam Rishya R Sekaran Shamala Devi SD

Medicine 20180201 5

Dengue virus is one of the most widespread flaviviruses that re-emerged throughout recent decades. The progression from mild dengue to severe dengue (SD) with the complications such as vascular leakage and hemorrhage increases the fatality rate of dengue. The pathophysiology of SD is not entirely clear. To investigate potential biomarkers that are suggestive of pathogenesis of SD, a small panel of serum samples selected from 1 healthy individual, 2 dengue patients without warning signs (DWS-), 2 ...[more]

PMID: 29384851

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Project description:BackgroundThyroid cancer (TC) tends to be a common malignancy worldwide and results in various outcomes due to its different subtypes. The tumor microenvironment (TME) was demonstrated to play crucial roles in various malignancies, including thyroid cancer. This study combined the ESTIMATE and CIBERSORT algorithms, identified four TME-related genes, and evaluated their correlation with clinical characteristics. These findings revealed the malignant performance of TME in TC, and the TME-related DEGs might serve as prognostic biomarkers, which can be utilized for the prediction of immunotherapy effects in patients with TC.MethodsThe clinical and gene expression profiles of TC patients were collected from the TCGA dataset. The ESTIMATE algorithm was utilized to estimate stromal and immune scores and predict the level of stromal and immune cell infiltration. The differential expressed genes related to TME were filtered by the "limma" package in R, and the PPI network was constructed by a string website. KEGG pathway and GO analyses were performed to investigate the biological progression and molecular functions of TME-related DEGs. Then, univariate Cox regression analysis was employed to screen four genes correlated with clinical characteristics. GSEA was conducted to assess their roles in the TME of TC. To further investigate the association between TME-related genes and tumor-infiltrating immune cells (TIICs), the CIBERSORT algorithm was performed. Finally, the malignancy behaviors of the two genes were verified by RT-qPCR, IHC, MTT, colony formation, and transwell assays.ResultsFour TME-related DEGs, LRRN4CL, HS3ST3A1, PCOLCE2, and CAPN8, were identified and were significantly predictive of poor overall survival. KEGG and GO pathway analysis established that the TME-related DEGs were involved in immune responses and pathways in cancer. Furthermore, the malignancy behaviors of HS3ST3A1 and CAPN8 were verified by cellular functional experiments. These results revealed that the TME-related genes HS3ST3A1 and CAPN8 were able to serve as predictors of prognosis in patients with TC.ConclusionHS3ST3A1 and CAPN8 may serve as valuable prognostic biomarkers and TME indicators, which can be utilized for the prediction of immunotherapy effects and provide novel treatment strategies for patients with TC.

Project description:This pilot study, aimed to profile the host transcriptome as a potential strategy for identifying specific biomarkers for dengue prediction and detection. High-throughput RNA-sequencing (RNA-seq) was employed to generate host transcriptome profiles in 16 dengue patients and 10 healthy controls. Differentially expressed genes (DEGs) were identified in patients with severe dengue and those with dengue with warning signs compared to healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the functions of upregulated and downregulated genes. Compared to healthy controls, 6466 genes were significantly differentially expressed (P < 0.05) in the dengue with warning signs group and 3082 genes in the severe dengue group, with over half being upregulated. The major KEGG pathways implicated included transport and catabolism (14.4%-16.3%), signal transduction (6.6%-7.3%), global and overview maps (6.7%-7.1%), viral diseases (4.6%-4.8%), and the immune system (4.4%-4.6%). Several genes exhibited consistent and significant upregulation across all dengue patients, regardless of severity: Interferon alpha inducible protein 27 (IFI27), Potassium Channel Tetramerisation Domain Containing 14 (KCTD14), Syndecan 1 (SDC1), DCC netrin 1 receptor (DCC), Ubiquitin C-terminal hydrolase L1 (UCHL1), Marginal zone B and B1 cell specific protein (MZB1), Nestin (NES), C-C motif chemokine ligand 2 (CCL2), TNF receptor superfamily member 17 (TNFSF17), and TNF receptor superfamily member 13B (TNFRSF13B). Further analysis revealed potential biomarkers for severe dengue prediction, including TNF superfamily member 15 (TNFSF15), Plasminogen Activator Inhibitor-2 (SERPINB2), motif chemokine ligand 7 (CCL7), aconitate decarboxylase 1 (ACOD1), Metallothionein 1G (MT1G) and Myosin Light Chain Kinase (MYLK2), which were expressed 3.5 times, 2.9 times, 2.3 times, 2.1 times, 1.7 times, and 1.4 times greater, respectively, than dengue patients exhibiting warning signs. The identification of these host biomarkers through RNA-sequencing holds promising implications and potential to augment existing dengue detection algorithms, contributing significantly to improved diagnostic and prognostic capabilities.

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Identifying protein biomarkers in predicting disease severity of dengue virus infection using immune-related protein microarray.

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Identifying protein biomarkers in predicting disease severity of dengue virus infection using immune-related protein microarray.

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