Dataset Information

Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer.

ABSTRACT: Studies on the accuracy of microRNAs (miRNAs) in diagnosing non-small cell lung cancer (NSCLC) have still controversial. Therefore, we conduct to systematically identify miRNAs related to NSCLC, and their target genes expression changes using microarray data sets.We screened out five miRNAs and six genes microarray data sets that contained miRNAs and genes expression in NSCLC from Gene Expression Omnibus.Our analysis results indicated that fourteen miRNAs were significantly dysregulated in NSCLC. Five of them were up-regulated (miR-9, miR-708, miR-296-3p, miR-892b, miR-140-5P) while nine were down-regulated (miR-584, miR-218, miR-30b, miR-522, miR486-5P, miR-34c-3p, miR-34b, miR-516b, miR-592). The integrating diagnosis sensitivity (SE) and specificity (SP) were 82.6% and 89.9%, respectively. We also found that 4 target genes (p < 0.05, fold change > 2.0) were significant correlation with the 14 discovered miRNAs, and the classifiers we built from one training set predicted the validation set with higher accuracy (SE = 0.987, SP = 0.824).Our results demonstrate that integrating miRNAs and target genes are valuable for identifying promising biomarkers, and provided a new insight on underlying mechanism of NSCLC. Further, our well-designed validation studies surely warrant the investigation of the role of target genes related to these 14 miRNAs in the prediction and development of NSCLC.

SUBMITTER: Hu L

PROVIDER: S-EPMC4890978 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer.

Hu Ling L Ai Junmei J Long Hui H Liu Weijun W Wang Xiaomei X Zuo Yi Y Li Yan Y Wu Qingming Q Deng Youping Y

Oncotarget 20160201 8

<h4>Background</h4>Studies on the accuracy of microRNAs (miRNAs) in diagnosing non-small cell lung cancer (NSCLC) have still controversial. Therefore, we conduct to systematically identify miRNAs related to NSCLC, and their target genes expression changes using microarray data sets.<h4>Methods</h4>We screened out five miRNAs and six genes microarray data sets that contained miRNAs and genes expression in NSCLC from Gene Expression Omnibus.<h4>Results</h4>Our analysis results indicated that fourt ...[more]

PMID: 26870998

Similar Datasets

Project description:BACKGROUND: Transforming growth factor beta (TGFbeta), a multifunctional cytokine, plays a crucial role in the accumulation of extracellular matrix components in lung fibrosis, where lung fibroblasts are considered to play a major role. Even though the effects of TGFbeta on the gene expression of several proteins have been investigated in several lung fibroblast cell lines, the global pattern of response to this cytokine in adult lung fibroblasts is still unknown. METHODS: We used Affymetrix oligonucleotide microarrays U95v2, containing approximately 12,000 human genes, to study the transcriptional profile in response to a four hour treatment with TGFbeta in control lung fibroblasts and in fibroblasts from patients with idiopathic and scleroderma-associated pulmonary fibrosis. A combination of the Affymetrix change algorithm (Microarray Suite 5) and of analysis of variance models was used to identify TGFbeta-regulated genes. Additional criteria were an average up- or down- regulation of at least two fold. RESULTS: Exposure of fibroblasts to TGFbeta had a profound impact on gene expression, resulting in regulation of 129 transcripts. We focused on genes not previously found to be regulated by TGFbeta in lung fibroblasts or other cell types, including nuclear co-repressor 2, SMAD specific E3 ubiquitin protein ligase 2 (SMURF2), bone morphogenetic protein 4, and angiotensin II receptor type 1 (AGTR1), and confirmed the microarray results by real time-PCR. Western Blotting confirmed induction at the protein level of AGTR1, the most highly induced gene in both control and fibrotic lung fibroblasts among genes encoding for signal transduction molecules. Upregulation of AGTR1 occurred through the MKK1/MKK2 signalling pathway. Immunohistochemical staining showed AGTR1 expression by lung fibroblasts in fibroblastic foci within biopsies of idiopathic pulmonary fibrosis. CONCLUSIONS: This study identifies several novel TGFbeta targets in lung fibroblasts, and confirms with independent methods the induction of angiotensin II receptor type 1, underlining a potential role for angiotensin II receptor 1 antagonism in the treatment of lung fibrosis.

Project description:Background: Lung cancer has the highest mortality rate of all the cancers in the world and asbestos-related lung cancer is one of the leading occupational cancers. The identification of the asbestos-related molecular changes has been a topic of major research interest over the years. The aim of the current study was to identify novel asbestos-related molecular correlates by integrating miRNA expression profiling with previously obtained microarray data (aCGH and mRNA expression) from the same patient material. Results: Twelve novel asbestos-related miRNAs (over-expressed: miR-148b, miR-374a, miR-24-1*, Let-7d, Let-7e, miR-199b-5p, miR-331-3p and under-expressed: miR-939, miR-671-5p, miR-605, miR-1224-5p, miR-202) which inversely correlated with target genes (e.g. GADD45A, LTBP1, FOSB, NCALD, CACNA2D2, MTSS1, EPB41L3) were identified. In addition, several known and new lung cancer-associated miRNAs were identified. DNA copy number alterations were also correlated with the deregulation of four miRNAs in lung cancer. The importance of integrated analysis was highlighted by the interesting finding of linking of the over-expression of well known squamous cell carcinoma-associated miR-205 with the down-regulation of the gene DOK4. Conclusions: Novel deregulated miRNAs and inversely correlating expression of target genes associated with lung cancer etiology and histology were identified. In addition, DNA copy number alterations correlated with the deregulation of some of the miRNAs. The results of the current study could have potential diagnostic implications, but require further investigation. Methods: The patient material used in the study consisted of 26 tumour and normal lung tissue from highly asbestos-exposed (13 samples) and non-exposed (13 samples) patients. Control lung tissue samples were obtained from 7 patients without cancer as well as from a commercial lung tissue RNA. Data analysis on miRNA expression and integration of miRNA and mRNA data were performed using Chipster (http://chipster.csc.fi/). In addition, miRNA and aCGH data were integrated in a separate analysis. *** This submission represents the microRNA component of the study

Project description:Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with. This is a potentially powerful approach because it allows a large number of expression profiling studies to be used but needs refinement because mRNAs can be targeted by multiple miRNAs and not all intronic miRNAs are co-expressed with their host genes.Here we introduce InMiR, a new computational method that uses a linear-Gaussian model to predict the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. Our method recovers nearly twice as many true positives at the same fixed false positive rate as a comparable method that only considers correlations. Through an analysis of 140 Affymetrix datasets from Gene Expression Omnibus, we build a network of 19,926 interactions among 57 intronic miRNAs and 3,864 targets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs. These prediction are supported both by sequence features and expression. By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.

Dataset Information

Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer.

Publications

Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets