Dataset Information

Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis.

ABSTRACT: Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatics method to analyze the dysregulated interaction network of microRNA-mRNA in NSCLC, based on both the gene expression data and the microRNA-gene regulation network. Considering the properties of the substructure and their biological functions, we identified the putative diagnostic biomarker microRNAs, some of which have been reported on the PubMed citations while the rest, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were predicted as the putative novel microRNA biomarker for the diagnosis of NSCLC adenocarcinoma. They were further validated by functional enrichment analysis of their target genes. These findings deserve further experimental validations for future clinical application.

SUBMITTER: Shao Y

PROVIDER: S-EPMC5494096 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis.

Shao Yang Y Liang Bin B Long Fei F Jiang Shu-Juan SJ

BioMed research international 20170615

Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatic ...[more]

PMID: 28698868

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Project description:BackgroundSmall cell lung cancer (SCLC) has an exceptionally poor prognosis; as most of the cases are initially diagnosed as extensive disease with hematogenous metastasis. Therefore, the early diagnosis of SCLC is very important and may improve its prognosis.MethodsTo investigate the feasibility of early diagnosis of SCLC, we examined exosomal microRNAs (miRNAs) present in serum obtained from patients with SCLC. First, exosomes were isolated in serum from patients with SCLC and healthy individuals and were characterized using particle size and protein markers. Additionally, miRNA array was performed to define SCLC-specific exosomal miRNAs. Second, the obtained miRNAs were further validated employing a large cohort. Finally, the ability to diagnose SCLC was estimated by area under the curve (AUC), and intracellular mRNA change patterns were verified through validated miRNAs.ResultsFrom the miRNA array results, we selected 51-miRNAs based on p-values and top 10 differentially expressed genes, and 25-miRNAs were validated using quantitative reverse transcription-polymerase chain reaction. The 25-miRNAs were further validated employing a large cohort. Among them, 7-miRNAs showed significant differences. Furthermore, 6-miRNAs (miR-3565, miR-3124-5p, miR-200b-3p, miR-6515, miR-3126-3p and miR-9-5p) were up-regulated and 1-miRNA (miR-92b-5p) was down-regulated. The AUC value of each miRNA sets between 0.64 and 0.76, however the combined application of 3-miRNAs (miR-200b-3p, miR-3124-5p and miR-92b-5p) remarkably improved the diagnostic value (AUC = 0.93). Gene ontology analysis revealed that the 3-miRNA panel is linked to various oncogene pathways and nervous system development. When the 3-miRNAs were introduced to cells, the resulting changes in total mRNA expression strongly indicated the presence of lung diseases, including lung cancer. In addition, the 3-miRNA panel was significantly associated with a poorer prognosis, although individual miRNAs have not been validated as prognostic markers.ConclusionOur study identified SCLC-specific exosomal miRNAs, and the 3-miRNAs panel (miR-200b-3p, miR-3124-5p and miR-92b-5p) may serve as a diagnostic and prognostic marker for SCLC.

Project description:We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di.uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett's esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients' group was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100 (p value = 0.0231; AUROC = 0.71); BE versus EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9 (p value = 0.0001; AUROC = 0.85); healthy versus EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin (p value = 0.0014; AUROC = 0.80). A panel of 8 glycoforms showed an improved AUROC of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by lectin magnetic bead array-immunoblotting, confirming the validity of the relative quantitation approach. Thus, we have identified candidate biomarkers, which, following large-scale clinical evaluation, can be developed into diagnostic blood tests. A key feature of the pipeline is the potential for rapid translation of the candidate biomarkers to lectin-immunoassays.

Dataset Information

Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis.

Publications

Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis.

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