MicroRNA expression profiles of whole blood in lung adenocarcinoma.
ABSTRACT: The association of lung cancer with changes in microRNAs in plasma shown in multiple studies suggests a utility for circulating microRNA biomarkers in non-invasive detection of the disease. We examined if presence of lung cancer is reflected in whole blood microRNA expression as well, possibly because of a systemic response. Locked nucleic acid microarrays were used to quantify the global expression of microRNAs in whole blood of 22 patients with lung adenocarcinoma and 23 controls, ten of whom had a radiographically detected non-cancerous lung nodule and the other 13 were at high risk for developing lung cancer because of a smoking history of >20 pack-years. Cases and controls differed significantly for age with a mean difference of 10.7 years, but not for gender, race, smoking history, blood hemoglobin, platelet count, or white blood cell count. Of 1282 quantified human microRNAs, 395 (31%) were identified as expressed in the study's subjects, with 96 (24%) differentially expressed between cases and controls. Classification analyses of microRNA expression data were performed using linear kernel support vector machines (SVM) and top-scoring pairs (TSP) methods, and classifiers to identify presence of lung adenocarcinoma were internally cross-validated. In leave-one-out cross-validation, the TSP classifiers had sensitivity and specificity of 91% and 100%, respectively. The values with SVM were both 91%. In a Monte Carlo cross-validation, average sensitivity and specificity values were 86% and 97%, respectively, with TSP, and 88% and 89%, respectively, with SVM. MicroRNAs miR-190b, miR-630, miR-942, and miR-1284 were the most frequent constituents of the classifiers generated during the analyses. These results suggest that whole blood microRNA expression profiles can be used to distinguish lung cancer cases from clinically relevant controls. Further studies are needed to validate this observation, including in non-adenocarcinomatous lung cancers, and to clarify upon the confounding effect of age.
Project description:Radon is the number one cause of lung cancer in non-smokers. microRNA expression in human bronchial epithelium cells is altered by radon, with particular reference to upregulation of miR-16, miR-15, miR-23, miR-19, miR-125, and downregulation of let-7, miR-194, miR-373, miR-124, miR-146, miR-369, and miR-652. These alterations alter cell cycle, oxidative stress, inflammation, oncogene suppression, and malignant transformation. Also DNA methylation is altered as a consequence of miR-29 modification induced by radon. Indeed miR-29 targets DNA methyltransferases causing inhibition of CpG sites methylation. Massive microRNA dysregulation occurs in the lung due to radon expose and is functionally related with the resulting lung damage. However, in humans this massive lung microRNA alterations only barely reflect onto blood microRNAs. Indeed, blood miR-19 was not found altered in radon-exposed subjects. Thus, microRNAs are massively dysregulated in experimental models of radon lung carcinogenesis. In humans these events are initially adaptive being aimed at inhibiting neoplastic transformation. Only in case of long-term exposure to radon, microRNA alterations lead towards cancer development. Accordingly, it is difficult in human to establish a microRNA signature reflecting radon exposure. Additional studies are required to understand the role of microRNAs in pathogenesis of radon-induced lung cancer.
Project description:Circulating microRNAs carried by exosomes have emerged as promising diagnostic biomarkers for cancer because of their abundant amount and remarkable stability in body fluids. Exosomal microRNAs in blood are typically quantified using the RNA isolation-qRT-PCR workflow, which cannot distinguish circulating microRNAs secreted by cancer cells from those released by non-tumor cells, making it potentially less sensitive in detecting cancer-specific microRNA biomarkers. We have developed a sensitive and simple tethered cationic lipoplex nanoparticles (tCLN) biochip to detect exosomal microRNAs in human sera. The tCLN biochip allows the discrimination of tumor-derived exosomes from their non-tumor counterparts, and thus achieves higher detection sensitivity and specificity than qRT-PCR. We have demonstrated the clinical utility of the tCLN biochip in lung cancer diagnosis using sera from normal controls, therapy-naive early stage and late stage non-small cell lung cancer (NSCLC) patients. Total five microRNAs (miR-21, miR-25, miR-155, miR-210, and miR-486) were selected as the biomarkers. Each microRNA biomarker measured by tCLN assay showed higher sensitivity and specificity in lung cancer detection than that measured by qRT-PCR. When all five microRNAs were combined, the tCLN assay distinguished normal controls from all NSCLC patients with sensitivity of 0.969, specificity of 0.933 and AUC of 0.970, and provided much better diagnostic accuracy than qRT-PCR (sensitivity = 0.469, specificity = 1.000, AUC = 0.791). Remarkably, the tCLN assay achieved absolute sensitivity and specificity in discriminating early stage NSCLC patients from normal controls, demonstrating its great potential as a liquid biopsy assay for lung cancer early detection.
Project description:Exosomes are nano-sized extracellular vesicles released by many cells that contain molecules characteristic of their cell of origin, including microRNA. Exosomes released by glioblastoma cross the blood-brain barrier into the peripheral circulation and carry molecular cargo distinct to that of "free-circulating" miRNA. In this pilot study, serum exosomal microRNAs were isolated from glioblastoma (n?=?12) patients and analyzed using unbiased deep sequencing. Results were compared to sera from age- and gender-matched healthy controls and to grade II-III (n?=?10) glioma patients. Significant differentially expressed microRNAs were identified, and the predictive power of individual and subsets of microRNAs were tested using univariate and multivariate analyses. Additional sera from glioblastoma patients (n?=?4) and independent sets of healthy (n?=?9) and non-glioma (n?=?10) controls were used to further test the specificity and predictive power of this unique exosomal microRNA signature. Twenty-six microRNAs were differentially expressed in serum exosomes from glioblastoma patients relative to healthy controls. Random forest modeling and data partitioning selected seven miRNAs (miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p, and miR-543) as the most stable for classifying glioblastoma. Strikingly, within this model, six iterations of these miRNA classifiers could distinguish glioblastoma patients from controls with perfect accuracy. The seven miRNA panel was able to correctly classify all specimens in validation cohorts (n?=?23). Also identified were 23 dysregulated miRNAs in IDHMUT gliomas, a partially overlapping yet distinct signature of lower-grade glioma. Serum exosomal miRNA signatures can accurately diagnose glioblastoma preoperatively. miRNA signatures identified are distinct from previously reported "free-circulating" miRNA studies in GBM patients and appear to be superior.
Project description:<h4>Background</h4>The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.<h4>Results</h4>We developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasets<h4>Conclusions</h4>The k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis.
Project description:Introduction: Circulating microRNAs (miRNAs) are promising molecular biomarkers for the early detection of esophageal squamous cell carcinoma (ESCC). We investigated the serum miRNA expression profiles from microarray-based technologies and evaluated the diagnostic value of serum miRNAs as potential biomarkers for ESCC by using feature selection algorithms. Methods: Serum miRNA expression profiles were obtained from 52 ESCC patients and 52 age- and sex-matched controls via performing a high-throughput microarray assay. Five representative feature selection algorithms including the false discovery rate procedure, family-wise error rate procedure, Lasso logistic regression, hybrid huberized support vector machine (SVM), and SVM using the squared-error loss with the elastic-net penalty were jointly carried out to select the significantly differentially expressed miRNAs based on the miRNA profiles. Results: Three miRNAs including miR-16-5p, miR-451a, and miR-574-5p were identified as the powerful biomarkers for the diagnosis of ESCC. The diagnostic accuracy of the combination of these three miRNAs was evaluated by using logistic regression and the SVM. The averages of the area under the receiver operating curve and classification accuracies based on different classifiers were more than 0.80 and 0.79, respectively. The cross-validation results suggested that the three-miRNA-based classifiers could clearly distinguish ESCC patients from healthy controls. Moreover, the classifying performance of the miRNA panel persisted in discriminating the healthy group from patients with ESCC stage I-II (AUC > 0.76) and patients with ESCC stage III-IV (AUC > 0.80). Conclusions: These results in this study have moved forward the identification of novel biomarkers for the diagnosis of ESCC.
Project description:BACKGROUND:Metastasis is the main cause of lung cancer mortality. Bone marrow-derived mesenchymal stem cells (BMSCs) are a component of the cancer microenvironment and contribute to cancer progression. Intratumoral hypoxia affects both cancer and stromal cells. Exosomes are recognized as mediators of intercellular communication. Here, we aim to further elucidate the communication between BMSC-derived exosomes and cancer cells in the hypoxic niche. METHODS:Exosomal miRNA profiling was performed using a microRNA array. Lung cancer cells and an in vivo mouse syngeneic tumor model were used to evaluate the effects of select exosomal microRNAs. Hypoxic BMSC-derived plasma exosomal miRNAs were assessed for their capacity to discriminate between cancer patients and non-cancerous controls and between cancer patients with or without metastasis. RESULTS:We demonstrate that exosomes derived from hypoxic BMSCs are taken by neighboring cancer cells and promote cancer cell invasion and EMT. Exosome-mediated transfer of select microRNAs, including miR-193a-3p, miR-210-3p and miR-5100, from BMSCs to epithelial cancer cells activates STAT3 signaling and increases the expression of mesenchymal related molecules. The diagnostic accuracy of individual microRNA showed that plasma exosomal miR-193a-3p can discriminate cancer patients from non-cancerous controls. A panel of these three plasma exosomal microRNAs showed a better diagnostic accuracy to discriminate lung cancer patients with or without metastasis than individual exosomal microRNA. CONCLUSIONS:Exosome-mediated transfer of miR-193a-3p, miR-210-3p and miR-5100, could promote invasion of lung cancer cells by activating STAT3 signalling-induced EMT. These exosomal miRNAs may be promising noninvasive biomarkers for cancer progression.
Project description:BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by varying degrees of emphysematous lung destruction and small airway disease, each with distinct effects on clinical outcomes. There is little known about how microRNAs contribute specifically to the emphysema phenotype. We examined how genome-wide microRNA expression is altered with regional emphysema severity and how these microRNAs regulate disease-associated gene expression networks. METHODS: We profiled microRNAs in different regions of the lung with varying degrees of emphysema from 6 smokers with COPD and 2 controls (8 regions?×?8 lungs?=?64 samples). Regional emphysema severity was quantified by mean linear intercept. Whole genome microRNA and gene expression data were integrated in the same samples to build co-expression networks. Candidate microRNAs were perturbed in human lung fibroblasts in order to validate these networks. RESULTS: The expression levels of 63 microRNAs (P?<?0.05) were altered with regional emphysema. A subset, including miR-638, miR-30c, and miR-181d, had expression levels that were associated with those of their predicted mRNA targets. Genes correlated with these microRNAs were enriched in pathways associated with emphysema pathophysiology (for example, oxidative stress and accelerated aging). Inhibition of miR-638 expression in lung fibroblasts led to modulation of these same emphysema-related pathways. Gene targets of miR-638 in these pathways were amongst those negatively correlated with miR-638 expression in emphysema. CONCLUSIONS: Our findings demonstrate that microRNAs are altered with regional emphysema severity and modulate disease-associated gene expression networks. Furthermore, miR-638 may regulate gene expression pathways related to the oxidative stress response and aging in emphysematous lung tissue and lung fibroblasts.
Project description:At least seven studies have suggested that microRNA levels in whole blood can be diagnostic for lung cancer. We conducted a large bi-institutional study to validate this. Qiagen® PAXgene™ Blood miRNA System was used to collect blood and extract RNA from it for 85 pathologic stage I-IV non-small cell lung cancer (NSCLC) cases and 76 clinically-relevant controls who had a benign pulmonary mass, or a high risk of developing lung cancer because of a history of cigarette smoking or age >60 years. Cases and controls were similar for age, gender, race, and blood hemoglobin and leukocyte but not platelet levels (0.23 and 0.26 million/?l, respectively; t test P = 0.01). Exiqon® MiRCURY™ microarrays were used to quantify microRNAs in RNA isolates. Quantification was also performed using Taqman™ microRNA reverse transcription (RT)-PCR assays for five microRNAs whose lung cancer-diagnostic potential had been suggested in seven published studies. Of the 1,941 human mature microRNAs detectable with the microarray platform, 598 (31%) were identified as expressed and reliably quantified among the study's subjects. However, none of the microRNAs was differentially expressed between cases and controls (P >0.05 at false discovery rate <5% in test using empirical Bayes-moderated t statistics). In classification analyses with leave-one-out internal cross-validation, cases and controls could be identified by microRNA expression with 47% and 50% accuracy with support vector machines and top-scoring pair methods, respectively. Cases and controls did not differ for RT-PCR-based measurements of any of the five microRNAs whose biomarker potential had been suggested by seven previous studies. Additionally, no difference for microRNA expression was noticed in microarray-based microRNA profiles of whole blood of 12 stage IA-IIIB NSCLC cases before and three-four weeks after tumor resection. These findings show that whole blood microRNA expression profiles lack diagnostic value for high-risk screening of NSCLC, though such value may exist for selective sub-groups of NSCLC and control populations.
Project description:To understand the process of cardiac aging, it is of crucial importance to gain insight into the age-related changes in gene expression in the senescent failing heart. Age-related cardiac remodeling is known to be accompanied by changes in extracellular matrix (ECM) gene and protein levels. Small noncoding microRNAs regulate gene expression in cardiac development and disease and have been implicated in the aging process and in the regulation of ECM proteins. However, their role in age-related cardiac remodeling and heart failure is unknown. In this study, we investigated the aging-associated microRNA cluster 17-92, which targets the ECM proteins connective tissue growth factor (CTGF) and thrombospondin-1 (TSP-1). We employed aged mice with a failure-resistant (C57Bl6) and failure-prone (C57Bl6?×?129Sv) genetic background and extrapolated our findings to human age-associated heart failure. In aging-associated heart failure, we linked an aging-induced increase in the ECM proteins CTGF and TSP-1 to a decreased expression of their targeting microRNAs 18a, 19a, and 19b, all members of the miR-17-92 cluster. Failure-resistant mice showed an opposite expression pattern for both the ECM proteins and the microRNAs. We showed that these expression changes are specific for cardiomyocytes and are absent in cardiac fibroblasts. In cardiomyocytes, modulation of miR-18/19 changes the levels of ECM proteins CTGF and TSP-1 and collagens type 1 and 3. Together, our data support a role for cardiomyocyte-derived miR-18/19 during cardiac aging, in the fine-tuning of cardiac ECM protein levels. During aging, decreased miR-18/19 and increased CTGF and TSP-1 levels identify the failure-prone heart.
Project description:Colorectal cancer (CRC) transcriptional subtypes have been recently identified by gene expression profiling. Here we describe an analytical pipeline, microRNA master regulator analysis (MMRA), developed to search for microRNAs potentially driving CRC subtypes. Starting from a microRNA-mRNA tumour expression data set, MMRA identifies candidate regulator microRNAs by assessing their subtype-specific expression, target enrichment in subtype mRNA signatures and network analysis-based contribution to subtype gene expression. When applied to a CRC data set of 450 samples, assigned to subtypes by 3 different transcriptional classifiers, MMRA identifies 24 candidate microRNAs, in most cases downregulated in the stem/serrated/mesenchymal (SSM) poor prognosis subtype. Functional validation in CRC cell lines confirms downregulation of the SSM subtype by miR-194, miR-200b, miR-203 and miR-429, which share target genes and pathways mediating this effect. These results show that, by combining statistical tests, target prediction and network analysis, MMRA effectively identifies microRNAs functionally associated to cancer subtypes.