A serum microRNA panel as potential biomarkers for hepatocellular carcinoma related with hepatitis B virus
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ABSTRACT: 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: 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.
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:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
Project description:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
Project description:Purpose: In this study, we performed RNA-seq analysis as a screening strategy to identify EV-miRNAs derived from serum of well clinically annotated breast cancer (BC) patients from South of Brazil. Methods: EVs from three groups of samples, healthy controls (CT), luminal A (LA), and triple negative (TNBC), were isolated from serum using a precipitation method and analyzed by RNA-seq (screening phase). Subsequently, four EV-miRNAs (miR-142-5p, miR-150-5p, miR-320a, and miR-4433b-5p) were selected to be quantified by RT-qPCR in individual samples (test phase). Results: A panel composed of miR-142-5p, miR-320a, and miR-4433b-5p discriminated BC patients from CT with an AUC of 0.8387 (93.33% sensitivity, 68.75% specificity). In addition, the combination of miR-142-5p and miR-320a, presented an AUC of 0.941 (100% sensitivity, 93.80% specificity) in distinguishing LA patients from CT. Interestingly, decrease expression of miR-142-5p and miR-150-5p were significantly associated with more advanced tumor grades (grade III), while the decrease expression of miR-142-5p and miR-320a with larger tumor size. Conclusion: These results provide insights into the potential application of EVs-miRNAs from serum as novel specific markers for early diagnosis of BC.
Project description:Introduction: MicroRNAs (miRNAs) in circulation have emerged as promising biomarkers. In this study we aimed to identify a circulating miRNA signature for osteoarthritis (OA) patients. Methods: Serum samples were collected from 12 primary OA patients and 12 healthy individuals and were screened using the Agilent Human miRNA Microarray. Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic performance of the deregulated miRNAs. Expression levels of selected miRNAs were validated by quantitative Real-time PCR (qRT-PCR) in all serum samples and in articular cartilage samples from OA patients (n=12) and healthy individuals (n=7). Bioinformatics analysis was used to investigate the involved pathways and target genes of the above miRNAs. Results: We identified 279 differentially expressed miRNAs in the serum of OA patients compared to healthy controls. 205 (73.5%) were up-regulated and 74 (26.5%) down-regulated. ROC analysis revealed that 77 miRNAs had area under the curve (AUC)> 0.8 and p<0.05. Bioinformatics analysis in 7 out of the 77 selected miRNAs (hsa-miR-33b-3p, hsa-miR-4284, hsa-miR-671-3p, hsa-miR-663a, hsa-miR-140-3p, hsa-miR-150-5p and hsa-miR-1233-3p) revealed that their target genes were involved in multiple signaling pathways, among which FOXO, mTOR, pI3K/akt, lipid metabolism and TGF-β. A serum miRNA signature including three down-regulated miRNAs (hsa-miR-33b-3p, hsa-miR-671-3p and hsa-miR-140-3p) were also verified by qRT-PCR in OA patients. Furthermore, we found that hsa-miR-140-3p, hsa-miR-671-3p and potentially hsa-miR-33b-3p expression levels were consistently down-regulated in articular cartilage of OA patients compared to healthy individuals. Conclusions: A global miRNA serum signature was revealed in OA patients. We identified a three- miRNA signature in peripheral serum which could be potential osteoarthritis biomarkers.
Project description:The objective of this study was the identification of serum microRNAs that can differentiate osteoporotic fracture patients with and without type-2 diabetes from healthy control subjects. For that purpose circulating microRNAs were profiled by real-time quantitative PCR using a custom 384-well panel in 200 µl serum samples. Univariate and multivariate statistical tools were used in order to identify single as well as combinations of circulating microRNas that were characteristic of patients with prevalent osteoporotic fractures: a qRT-PCR-based classifier consisting of miR-550a-5p, miR-96-5p, miR-32-3p and miR-486-5p can distinguish T2D women with (DMFx) and without fragility fractures (DM) with high specifitiy and sensitivity (AUC = 0.93). A classifier consisting of miR-188-3p, miR-382-3p, miR-942 and miR-155-5p was capable of differentiating between postmenopausal women with osteoporotic fractures and fracture-free controls with an AUC of 0.98.
Project description:We conducted this study to determine whether exosome regulation underlies the antimigraine mechanisms of acupuncture. By comparing serum samples from patients with migraine and healthy controls using high-throughput small RNA sequencing technology , we identified 705 exosomal microRNAs that are differentially expressed in patients with migraine, and this set of 705 microRNAs included five that are particularly well characterised (hsa-miR-369-5p, hsa-miR-1268b, hsa-miR-145-5p, hsa-miR-222-5p, and hsa-miR-4488). By comparing serum samples collected from patients with migraine before and after acupuncture treatment, we showed that acupuncture normalised the expression levels of those five well-characterised exosomal microRNAs.
Project description:MicroRNAs (miRNAs) and their target genes are aberrantly expressed in many cancers and are linked to carcinogenesis and metastasis, especially among hepatocellular carcinoma (HCC) patients. This study sought to identify new biomarkers related to HCC prognosis using small RNA sequencing from the tumor and matched normal adjacent tissue of 32 patients with HCC. Eight miRNAs were downregulated and 61 were upregulated more than twofold. Of these, five miRNAs, hsa-miR-3180, hsa-miR-5589-5p, hsa-miR-490-5p, hsa-miR-137, and hsa-miR-378i, were significantly associated with 5-year overall survival (OS) rates. Differential upregulation of hsa-miR-3180 and downregulation of hsa-miR-378i in tumor samples supported the finding that low and high concentrations of hsa-miR-3180 (p = 0.029) and hsa-miR-378i (p = 0.047), respectively, were associated with higher 5-year OS. Cox regression analyses indicated that hsa-miR‑3180 (HR = 0.08; p = 0.013) and hsa-miR‑378i (HR = 18.34; p = 0.045) were independent prognostic factors of poor survival. However, high hsa-miR‑3180 expression obtained larger AUCs for OS and progression-free survival (PFS) and had better nomogram prediction than hsa-miR‑378i. These findings indicate that hsa-miR‑3180 may be associated with HCC progression and could serve as a potential biomarker for this disease.
Project description:Objective: The expression pattern of exosomal miRNAs derived from parathyroid tumor is still unknown. In the present work, we aimed to examine the differences on microRNA (miRNA) expression, present in serum exosomes, by comparing parathyroid carcinoma (PC) and parathyroid adenoma (PA). Methods: MiRNA expression profile of serum exosomes, derived from 4 PC patients and 4 PA patients, were analyzed by next-generation sequencing technology. The differential expressions of target miRNAs were further verified in both serum exosomes and tissues of PC/PA patients by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Lastly, receiver operating characteristic (ROC) curves were plotted to investigate the efficiency of target exosomal miRNAs in distinguishing PC patients from PA controls. Results: Multiple differentially expressed miRNAs of serum exosomes were screened out by sequencing. Based on this screening, hsa-miR-146b-5p (p=0.0846), hsa-miR-27a-5p (p=0.0412), hsa-miR-93-5p (p=0.73), hsa-miR-381-3p (p=0.1239) and hsa-miR-134-5p (p=0.0694) were up-regulated in the serum exosomes of PC patients. These results were validated by qPCR, where the trend on differential miRNA expression was consistent with the sequencing results. Specifically, the expression of exosomal hsa-miR-27a-5p was able to clearly distinguish PC patients from PA controls, and related analysis indicated that the area under the ROC curve was 0.8594 (p=0.0157). Conclusion: Here we present, for the first time, the miRNA expression profile of serum exosomes derived from PC patients. Based on this result, we presently suggest that the exosomal hsa-miR-27a-5p may serve as a putative tumor marker for preoperative identification of PC and PA subjects.