Increased blood microRNA-320 expression associated with imminent and severe lung cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE188232 | GEO | 2022/11/04
REPOSITORIES: GEO
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Project description:Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The circulating transcriptome of LC patients represents a source of potential LC biomarkers. We used genome-wide RNA sequencing to identify LC candidate markers by comparing expression of >60,000 genes in whole blood specimens taken at LC diagnosis from cases (n=128) and controls (n=62). Further, we evaluated expression of these markers in two population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n=163 cases, 184 matched controls). We identified 14 candidate genes in whole blood associated with LC at diagnosis. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (adjusted p-values 0.009, 0.03, and 0.007, respectively). We observed strong association of ANXA3 and ARG1 expression with LC also in the pre-diagnostic blood specimens, and especially with late-stage LC within two years of diagnosis (odds ratios 3.47 and 5.00, respectively). Although blood neutrophils were elevated in LC cases both in the diagnostic and pre-diagnostic blood specimens, the observed associations of ANXA3, ARG1 and HP with LC were preserved also after adjusting for elevated blood neutrophils. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.
2023-11-29 | GSE198048 | GEO
Project description:Ηepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers. Early de-tection/diagnosis is vital for the prognosis of HCC whereas diagnosis at late stages is associated with very low survival rate. Early diagnosis is based on patient’s 6 month surveillance and use of at least two imaging modalities. The aim of this study was to investigate diagnostic markers for the detection of early HCC based on proteome analysis, microRNAs (miRNAs) and circulat-ing tumor cells (CTCs) in the blood of patients with cirrhosis, early and advanced HCC. We studied 89 patients with HCC of whom 33 had early HCC and 28 cirrhotic patients. CTCs were detected by Real-Time Quantitative Reverse Transcription PCR and immunofluorescence using the markers Epithelial cell adhesion molecule (EPCAM), Vimentin, A Fetoprotein (AFP) and surface major Vault protein (sMVP). Expression of the five most commonly HCC-involved miRNAs (miR-122, miR-200a, miR-200b, miR-221, miR-222) was examined in the serum by qRT-PCR. Finally patient serum was analyzed by whole proteome analysis (LC/MS). Twenty seven out of 53 (51%) patients with advanced HCC had detectable CTCs. Among them, 10/27 (37%) presented evidence of mesenchymal or intermediate stage cells (vimentin and/or sMVP positive). Moreover, 5/17 (29%) patients with early HCC and 6/28 (21%) cirrhotic patients had detectable CTCs. Patients with early and advanced HCC exhibited a significant increase of miR-200b when compared to cirrhotic patients. Our proteome analysis indicated that early HCC patients present a significant upregulation of APOA2, APOC3 proteins when compared to cir-rhotic patients, that when used in combination, cover the 100% of the patients with early HCC. In conclusion, miR-200b, APOA2 and APOC3 proteins are sensitive markers and can be poten-tially useful in combination for the early diagnosis of HCC.
2023-03-11 | PXD033306 | Pride
Project description:Hibernating American black bears have significantly different clotting parameters than their active summer counterparts, affording them innate protection against venous thromboembolism (VTE) despite prolonged periods of immobility. Physiologic changes that occur during hibernation are thought to result from differential gene expression, rather than novel genes, and there is increasing evidence miRNAs may play an important role this regulation. We propose that significant differences exist in miRNA expression in the plasma of hibernating black bears compared to their active counter parts (summer), which lead to critical gene regulation responsible for auto-anticoagulation during hibernation. Methods: Blood was collected from 21 American black bears in the Northern Michigan Peninsula in summer 2017 and winter 2018 (11 active, 10 hibernating). Plasma was extracted Results: Fifteen miRNAs were differentially expressed in the plasma of hibernating black bears. Nine miRNAs were significantly downregulated (miR10b-3p, miR-136-3p, miR-181c-5p , miR-200a-3p, miR-200b-5p, miR-200c-3p, miR-320b, miR-320c and miR-320d) and six miRNAs were significantly upregulated (miR-15a-5p, miR-15b-3p, miR-15b-5p, miR-16-5p, miR-92a-3p, miR-150-5p) during hibernation. Twelve miRNAs had no identifiable targets, but miR-200a-3p, miR-200b-5p and miR-200c-3p found to be targets of SERPINC1, the gene responsible for the production of antithrombin (AT). Conclusions: Several miRNAs were differentially expressed in hibernating bears (12). Most importantly miR-200a-3p, miR-200b-5p and miR-200c-3p were all downregulated in hibernation and associated with increased expression of SERPINC1 and production of AT. AT is a powerful anticoagulant and this finding may explain the hibernating black bears ability to achieve auto-anticoagulation and protection from VTE.
2020-09-01 | GSE124398 | GEO
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
Project description:Peritoneal carcinomatosis with malignant ascites is associated with dismal prognosis in gastric cancer. Malignant ascites is the most relevant body fluid in which to seek diagnostic biomarkers for peritoneal carcinomatosis. We aimed to identify and validate ascites-derived circulating microRNAs (miRNAs) that are differentially expressed between liver cirrhosis-associated benign ascites (LC-ascites) and gastric cancer-associated malignant ascites (GC-ascites). MiRNA expression levels were investigated in three independent cohorts. Overall, 165 ascites samples (73 LC-ascites and 92 GC-ascites) were obtained from the National Biobank of Korea. Initially, microarrays were used to screen the expression levels of 2,006 miRNAs in the discovery cohort (n = 22). Subsequently, quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analyses were used to validate the expression levels of selected miRNAs in the training (n = 70) and validation (n= 73) cohorts. In addition, the levels of carcinoembryonic antigen (CEA), a commonly used tumor marker, were determined in the ascites samples. Expression levels of miR-574-3p, miR-181b-5p, miR-4481, and miR-181d were significantly lower in the GC-ascites samples than in the LC-ascites samples, and miR-181b-5p showed the best diagnostic performance for GC-ascites (area under the curve [AUC] = 0.798 and 0.846 for the training and validation cohorts, respectively). The diagnostic performance of CEA for GC-ascites was improved if CEA and miR-181b-5p were analyzed together (AUC = 0.981 and 0.946 for the training and validation cohorts, respectively). Overall, we identified ascites-derived circulating miRNAs capable of differentiating non-malignant ascites and GC-ascites, and demonstrated that the combined use of miR-181b-5p and CEA produces the optimal diagnostic yield.
2019-02-12 | GSE126399 | GEO
Project description:Huntington’s disease (HD) is an autosomal-dominant neurodegenerative disorder caused by the CAG expansion in the Huntingtin (HTT) gene. Several studies have shown the potential of microRNAs as biomarkers in neurodegenerative diseases. However, no bona fide microRNAs have been identified as promising diagnostic biomarkers for HD. As part of the PREDICT-HD project, here we performed NanoString on blood samples of 115 pre-manifest HD cases and 111 controls in an effort to provide promising biomarkers for the diagnostic of HD or the disease processes tracking. The results showed that five miRNAs (miR-1252, miR-433, miR-553, miR-138-5p and miR-2053) are significantly differentially expressed in pre-manifest gene-positive HD cases vs. gene-negative controls. We also explored the miRNAs associated with HD severity by correlating the miRNA expression with CAP scores of 115 pre-manifest HD samples and found that 41 and 24 miRNAs are negatively and positively correlated with CAP scores significantly. Furthermore, target gene set of miR-138 and miR-433 , its co-expressed mRNAs in ROSMAP, and differentially expressed mRNAs of HD reveal four overlap genes (CASP7, CNOT6L, TMED and FERMT2). In summary, our study provides new insights into biomarker of Huntington’s disease by analyzing the cross-sectional miRNA expression data in blood.
2018-06-19 | GSE108396 | GEO
Project description:Huntington’s disease (HD) is an autosomal-dominant neurodegenerative disorder caused by the CAG expansion in the Huntingtin (HTT) gene. Several studies have shown the potential of microRNAs as biomarkers in neurodegenerative diseases. However, no bona fide microRNAs have been identified as promising diagnostic biomarkers for HD. As part of the PREDICT-HD project, here we performed NanoString on blood samples of 115 pre-manifest HD cases and 111 controls in an effort to provide promising biomarkers for the diagnostic of HD or the disease processes tracking. The results showed that five miRNAs (miR-1252, miR-433, miR-553, miR-138-5p and miR-2053) are significantly differentially expressed in pre-manifest gene-positive HD cases vs. gene-negative controls. We also explored the miRNAs associated with HD severity by correlating the miRNA expression with CAP scores of 115 pre-manifest HD samples and found that 41 and 24 miRNAs are negatively and positively correlated with CAP scores significantly. Furthermore, target gene set of miR-138 and miR-433 , its co-expressed mRNAs in ROSMAP, and differentially expressed mRNAs of HD reveal four overlap genes (CASP7, CNOT6L, TMED and FERMT2). In summary, our study provides new insights into biomarker of Huntington’s disease by analyzing the cross-sectional miRNA expression data in blood.
2018-06-19 | GSE108395 | GEO
Project description:We aimed at extending the repertoire of high-quality miRNA normalizers for reverse transcription-quantitative PCR (RT-qPCR) of human plasma with special emphasis on the extremely guanine-cytosine-rich portion of the miRNome. For high-throughput selection of stable candidates, microarray technology was preferred over small-RNA sequencing (sRNA-seq) since the latter underrepresented miRNAs with a guanine-cytosine (GC) content of at least 75% (p = 0.0002, n = 2). miRNA abundances measured on the microarray were ranked for consistency and uniformity using nine normalization approaches. The eleven most stable sequences included miRNAs of moderate, but also extreme GC content (45%–65%: miR-320d, miR-425-5p, miR-185-5p, miR-486-5p; 80%–95%: miR-1915-3p, miR-3656-5p, miR-3665-5p, miR-3960-5p, miR-4488-5p, miR-4497 and miR-4787-5p). In contrast, the seven extremely GC-rich miRNAs were not found in the two plasma miRNomes screened by sRNA-seq. Stem-loop RT-qPCR was employed for stability verification in 32 plasma samples of healthy male Caucasians (age range: 18–55 years). In general, inter-individual variance of miRNA abundance was low or very low as indicated by coefficient of variation (CV) values of 0.6%–8.2%. miR-3665 and miR-1915-3p outperformed in this analysis (CVs: 0.6 and 2.4%, respectively). The eight most stable sequences included four extremely GC-rich miRNAs (miR-1915-3p, miR-3665, miR-4787-5p and miR-4497). The best-performing duo normalization factor (NF) for the condition of human plasma, miR-320d and miR-4787-5p, also included a GC-extreme miRNA. In summary, the identification of extremely guanine-cytosine-rich plasma normalizers will help to increase accuracy of PCR-based miRNA quantification, thus raise the potential that miRNAs become markers for psychological stress reactions or early and precise diagnosis of clinical phenotypes. The novel miRNAs might also be useful for orthologous contexts considering their conservation in related animal genomes.
2022-07-25 | GSE209568 | GEO
Project description:A miRNA microarray was performed from HCV infected patient serum samples of bothe genotype 1b and genotype 3a, which are prevalent in India, with the aim of identifying a set of miRNAs which are uniquely differentially expressed during HCV infection. miR-320c, miR-483-5p, miR-134 and miR-198 were found to be upregulated in the patient samples as compared to the controls and are currenty being validated.
2013-11-01 | GSE41829 | GEO
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.
2018-10-01 | GSE104251 | GEO