MRNA profiling in immune mediated necrotizing myopathy
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ABSTRACT: Background: The therapeutic regimen of immune-mediated necrotizing myopathy (IMNM), a category of immune-related myopathies featuring progressive proximal muscle weakness and extremely high level of serum creatine kinase, remains a formidable barrier for clinicians. The proposed study intended to investigate new diagnostic biomarkers based on high-throughput sequencing data of IMNM and generate novel IMNM management strategies by bioinformatics technology. Methods: Eighteen IMNM patients were synthesized in a local sequencing cohort (high-throughput sequencing data) with Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis to pinpoint robust IMNM-related differentially expressed genes (DEGs). The definitive feature genes were categorized by employing the protein-protein interaction (PPI) network, Least Absolute Shrinkage Selection Operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE), and their sample discriminatory utility was appraised by obtaining the area under the curve (AUC) of the receiver operating curve (ROC) curve. The expression difference of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Enrichment analysis of predefined gene sets disclosed the hidden functions of diagnostic DEGs. The 28 immune cell abundance patterns in the IMNM cohort were measured using single-sample gene set enrichment analysis (ssGSEA). Results: We recognized 193 genes that were aberrantly upregulated in the IMNM population and were firmly affiliated with immune-inflammatory responses, regulation of skeletal and cardiac muscle contraction and lipidprotein metabolism. LTK, MYBPH, and MYL4 were authenticated as diagnostic markers for IMNM (all AUC = 1.00), and functional enrichment analysis highlighted that these genes were notably integrated with immune-inflammatory response, autophagy-lysosome pathway and myofiber differentiation and regeneration. Ultimately, ssGSEA revealed a pronounced immune cell infiltrative microenvironment in the IMNM population. The results revealed the most significant relationship among CD4 T cells, CD8 T cells, macrophages, natural killer cells, T helper cells, Th17 cells and dendritic cells. Conclusion: LTK, MYBPH, and MYL4 might be served as diagnostic molecules for IMNM and possibly yield innovative insights into the mechanisms of progression and therapeutic options for IMNM in the future.
Project description:Exosomal microRNAs have recently been studied as potential diagnostic marker for various malignancies, including hepatocellular carcinoma (HCC). The aim of this study was to investigate serum exosomal microRNA profiles as HCC diagnostic marker. Transmission electron microscopy and western blot were used to identify serum exosomes. Deep sequencing was performed to screen differentially expressed microRNAs between HCC (n=5) and liver cirrhosis (LC, n=5) group. Three upregulated and two downregulated microRNAs were selected for qPCR analysis. The levels of selected microRNAs were normalized to Caenorhabditis elegans miR-39 microRNA mimics. Serum exosomal level of miR-122, miR-148a, and miR-1246 were further analyzed and significantly higher in HCC than LC and normal control (NC) group (P<0.001), but not different from chronic hepatitis group(p>0.05). The receiver operating characteristic curve was used to evaluate diagnostic perfromance of candidate microRNAs. Area under the curve (AUC) of miR-148a was 0.891 [95 % confidence interval (CI), 0.809-0.947] in discriminating HCC from LC, remarkably higher than alpha fetoprotein (AFP) (AUC: 0.712, 95 % CI: 0.607-0.803). Binary logistic regression was adpoted to establish the diagnostic model for discriminating HCC from LC. And the combination of miR-122, miR-148a and AFP increased the AUC to 0.931 (95% CI, 0.857-0.973), which can also be applied for distinguishing early HCC from LC. miR-122 was the best for differentiating HCC from NC (AUC: 0.990, 95% CI, 0.945-1.000). These data suggests that serum exosomal microRNAs signature or their combination with traditional biomarker may be used as a suitable peripheral screening tool for HCC.
Project description:The pathogenesis of celiac disease (CeD) remains incompletely understood. Traditional diagnostic techniques for CeD include serological testing and endoscopic examination; however, they have limitations. Therefore, there is a need to identify novel noninvasive biomarkers for CeD diagnosis. We analyzed duodenal and plasma samples from CeD patients by four-dimensional data-dependent acquisition (4D-DIA) proteomics. Differentially expressed proteins (DEPs) were identified for functional analysis and to propose blood biomarkers associated with CeD diagnosis. In duodenal and plasma samples, respectively, 897 and 140 DEPs were identified. Combining weighted gene co-expression network analysis(WGCNA) with the DEPs, five key proteins were identified across three machine learning methods. FGL2 and TXNDC5 were significantly elevated in the CeD group, while CHGA expression showed an increasing trend, but without statistical significance. The receiver operating characteristic curve results indicated an area under the curve (AUC) of 0.7711 for FGL2 and 0.6978 for TXNDC5, with a combined AUC of 0.8944. Exploratory analysis using Mfuzz and three machine learning methods identified four plasma proteins potentially associated with CeD pathological grading (Marsh classification): FABP, CPOX, BHMT, and PPP2CB. We conclude that FGL2 and TXNDC5 deserve exploration as potential sensitive, noninvasive diagnostic biomarkers for CeD.
Project description:Background: Long noncoding RNAs (lncRNAs) have been confirmed to be associated with ischemic stroke (IS); however, their involvement still needs to be extensively explored. Therefore, we aimed to study the expression profile of lncRNAs and the potential roles and mechanisms of lncRNAs in the pathogenesis of acute ischemic stroke (AIS) in the Southern Chinese Han population. Methods: lncRNA and mRNA expression profiles in AIS were analyzed using high-throughput RNA sequencing (RNA-Seq) and validated using quantitative real-time polymerase chain reaction (qRT-PCR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and network analyses were performed to predict the functions and interactions of the aberrantly expressed genes. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of lncRNAs in AIS. Results: RNA-Seq analysis showed that 428 lncRNAs and 957 mRNAs were significantly upregulated, while 791 lncRNAs and 4263 mRNAs were downregulated in patients with AIS when compared with healthy controls. GO enrichment and KEGG pathway analyses of differentially expressed genes showed that the apoptosis, inflammatory, oxidative and calcium signaling pathways were potentially implicated in AIS pathology. The PCR results showed that the selected lncRNA-C14orf64 and lncRNA-AC136007.2 were significantly downregulated in AIS. ROC curve analysis showed that the area under the ROC curve (AUC) values of lncRNA-C14orf64 and lncRNA-AC136007.2 between AIS and healthy controls were 0.74 and 0.94, respectively. Conclusion: This study provides evidence of altered expression of lncRNAs and their potential functions in AIS. Our findings may facilitate pathological mechanistic studies of lncRNAs in AIS and provide potential diagnostic biomarkers and therapeutic targets for AIS.
Project description:Background: Palindromic rheumatism (PR) is a unique disease characterized by the intermittent inflammation of different joints that may progress to a variety of immune-related diseases. Unclear diagnostic criteria have limited the research on its pathogenesis and treatment options. Recently, microRNAs (miRNAs) have been used in the diagnosis of various diseases; however, the role of miRNAs in PR diagnosis remains unexplored. Using next-generation high-throughput sequencing (NGS), this study aimed to screen miRNAs specifically expressed in the serum of patients with PR to construct a diagnostic signature and verify its diagnostic efficacy. Methods: Patients with PR (N=4), patients with rheumatoid arthritis (RA; N=3), and healthy controls (N=3) were included in an exploration cohort. Differentially expressed miRNAs were screened using NGS to construct diagnostic signatures and bioinformatics tools were used to perform target gene enrichment analysis of the top 25 differentially expressed miRNAs, both upregulated and downregulated. RT-qPCR was used to verify the differential expression of the diagnostic signatures in the three validation cohorts of patients with PR (N=27) and RA (N=30), and healthy controls (N=31), and the diagnostic efficiency of the diagnostic signatures was evaluated using receiver operator characteristic (ROC) curves. Results: A total of 130 differentially expressed miRNAs–35 upregulated and 95 downregulated miRNAs–were found in the PR exploration cohort, which differed between both RA and healthy controls. miRNA-186-3p showed the largest upregulated difference, and miRNA-382-3p showed the largest downregulated difference, and consequently, were selected to construct the diagnostic signature. In the ROC analysis of the validation cohort, the diagnostic signature produced an area under the ROC curve (AUC) of 1 (95% CI 1.000-1.000) compared with healthy controls. For patients with RA, the diagnostic signature produced an AUC of 0.915 (95% CI 0.842-0.988). However, miRNA-186-3p and miRNA-382-3p levels were not associated with disease activity in patients with PR. Conclusion: A diagnostic signature comprising miRNA-186-3p and miRNA-382-3p can effectively diagnose and differentiate PR from RA. This study provides a basis for the creation of a clinical miRNA signature for the diagnosis of PR.
Project description:Background & Aims: MicroRNAs have been shown to offer great potential in the diagnosis of cancer. We aimed to identify microRNAs in peripheral blood mononuclear cells (PBMCs) for diagnosing pancreatic cancer (PC). Methods: PBMCs microRNA expression was investigated in three independent cohorts including 352 participants (healthy, benign pancreatic/peripancreatic diseases (BPD), and PC). First, we used sequencing technology to identify differentially expressed microRNAs in 60 PBMCs samples for diagnosing PC. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using an independent cohort. Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: We found that PBMCs miR-27a-3p could efficiently discriminate PC from BPD (AUC=0.840; 95% CI, 0.787 to 0.885; sensitivity=82.2%, specificity=76.7%). A panel composed of PBMCs miR-27a-3p and serum CA19-9 provided a high diagnostic accuracy in differentiating PC from BPD in the clinical setting (AUC=0.886; 95% CI, 0.837 to 0.923; sensitivity=85.3%, specificity=81.6%). The satisfactory diagnostic performance of the panel persisted regardless of disease status (AUCs for tumour-node-metastasis stages?,?, and ? were 0.881, 0.884, and 0.893, respectively). Conclusion: PBMCs miR-27a-3p could be a potential marker for PC screening. A panel composed of PBMCs miR-27a-3p and serum CA19-9 has considerable clinical value in diagnosing early-stage PC. Therefore, patients who would have otherwise missed the curative treatment window can benefit from optimal therapy. Examination of different MicroRNA profiles in 3 types of PBMCs samples
Project description:Background: Assessments of airways inflammation in patients with chronic obstructive pulmonary diseases (COPD) require semi-invasive procedures and specialized sample processing know-how. In this study we aimed to set up and validate a novel non-invasive processing-free method for RNA sequencing (RNAseq) of spontaneous sputum samples collected from COPD patients. Methods: Spontaneous sputum samples were collected and stabilized, with or without selection of plugs and with or without the use of a stabilizer specifically formulated for downstream diagnostic testing (PrimeStore® Molecular Transport Medium). After 8-day storage at ambient temperature RNA was isolated according to an optimized RNAzol® method. An average percentage of fragments longer than 200 nucleotides (DV200) >30% and an individual yield >50ng were required for progression of samples to sequencing. Finally, to assess if the transcriptome generated would reflect a true endotype of COPD inflammation, the outcome of single-sample gene-set enrichment analysis (ssGSEA) was validated using an independent set of processed induced sputum samples Results: RNA extracted from spontaneous sputum using a stabilizer showed an average DV200 higher than 30%. 70% of the samples had a yield >50ng and were submitted to downstream analysis. There was a straightforward correlation in terms of gene expression between samples handled with or without separation of plugs. This was also confirmed by principal component analysis and ssGSEA. The top ten enriched pathways resulting from spontaneous sputum ssGSEA were associated to features of COPD, namely, inflammation, immune responses and oxidative stress; up to 70% of these were in common within the top ten enriched pathways resulting from induced sputum ssGSEA. Conclusion: This analysis confirmed that the typical COPD endotype was represented within spontaneous sputum and supported the current method as a non-invasive processing-free procedure to assess the level of sputum cell inflammation in COPD patients by RNAseq analysis.
Project description:Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often 30 exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results 31 in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an Area Under the Curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 40 circRNA) enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.
Project description:Background: The identification of new high sensitivity and specificity markers for HCC are essential. We aimed to identify serum microRNAs for diagnosing hepatitis B virus (HBV) â??related HCC. Methods: Serum microRNA expression was investigated with four cohorts including 667 participants (261 HCC patients ,233 cirrhosi patients and 173 healthy controls), recruited between August 2010 and June 2013. First, An initial screening of miRNA expression by Illumina sequencing was performed using serum samples pooled from HCC patients and controls,respectively. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using a training cohort (n=357) and then validated using a cohort(n=241). The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: , We identified 8 miRNAs(hsa-miR-206, hsa-miR-141-3p, hsa-miR-433-3p, hsa-miR-1228-5p, hsa-miR-199a-5p, hsa-miR-122-5p, hsa-miR-192-5p and hsa-miR-26a-5p.) formed a miRNA panel that provided a high diagnostic accuracy of HCC (AUC=0.887 and 0.879 for training and validation data set, respectively). The microRNA panel can also differentiate HCC from healthy (AUC =0.894) and cirrhosis (AUC = 0.892), respectively. Conclusions:We found a serum microRNAs panel that has considerable clinical value in diagnosing HCC. 9 serum samples pooled from 3 healthy control donors and 3 HCC patients, 3 cirrhosi patients treated at The First Affiliated Hospital of Soochow University were subjected to Illumina HiSeq 2000 deep sequencing to identify the miRNAs that were significantly differentially expressed.
Project description:Background: Microglia plays complex and crucial roles in multiple sclerosis (MS). This study aimed to explore the biological significance of microglia-associated genes in experimental autoimmune encephalomyelitis (EAE) . Methods: Differentially expressed genes (DEGs) were screened with six machine learning (ML) methods, which were also utilized to validate the microglia-associated DEGs in three public databases. ceRNA and Protein–protein interaction (PPI) network analyses were utilized to identify the interaction of the 6 novel biomarkers with other molecules. Then, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were employed to quantify the relative abundance of each immune cell infiltration, respectively. qRT-PCR was performed to test the expression of key DEGs in murine models. Results: A total of 247 DEmRNA, 499 DElncRNAs and 269 DEcircRNAs were identified. With screening strategy of five ML algorithms, 6 DEmRNAs were obtained including NGP, HIST1H2BJ, PBLD1, MBLN3, CD180 and F10. Then the 6 DEmRNAs were used as a multigene signature to construct models to differentiate EAE from normal microglia, and AUC value for each model was greater than 0.8. The diagnostic value of these 6 DEmRNAs were identified and further verified by qRT-PCR. Then, differential expression for five out of these 6 DEmRNAs, namely NGP, HIST1H2BJ, PBLD1, MBLN3, and F10 were confirmed. Using PPI analysis, DEmRNAs frequently interacting with transcription factors (TFs), potential drugs and RBPs were identified. With immune cell infiltration analyses, we found EAE microglia presented high levels of immune infiltration, especially Nature Killer (NK) cells and CD8+ T cells. We also reported circRNA (circRNA_00638) was predicted to bind to 76 RBPs. Conclusions: We identified and validated 6 novel microglia related genes and developed a multigene signature with ML methods to confirm their ability to accurately diagnose and characterize biological alterations in EAE microglia. The six key DEmRNAs might also be latent targets for immunoregulatory therapy.
Project description:Background: The identification of new high sensitivity and specificity markers for HCC are essential. We aimed to identify serum microRNAs for diagnosing hepatitis B virus (HBV) –related HCC. Methods: Serum microRNA expression was investigated with four cohorts including 667 participants (261 HCC patients ,233 cirrhosi patients and 173 healthy controls), recruited between August 2010 and June 2013. First, An initial screening of miRNA expression by Illumina sequencing was performed using serum samples pooled from HCC patients and controls,respectively. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using a training cohort (n=357) and then validated using a cohort(n=241). The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: , We identified 8 miRNAs(hsa-miR-206, hsa-miR-141-3p, hsa-miR-433-3p, hsa-miR-1228-5p, hsa-miR-199a-5p, hsa-miR-122-5p, hsa-miR-192-5p and hsa-miR-26a-5p.) formed a miRNA panel that provided a high diagnostic accuracy of HCC (AUC=0.887 and 0.879 for training and validation data set, respectively). The microRNA panel can also differentiate HCC from healthy (AUC =0.894) and cirrhosis (AUC = 0.892), respectively. Conclusions:We found a serum microRNAs panel that has considerable clinical value in diagnosing HCC.