Dataset Information

Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma.

ABSTRACT: The roles of miRNAs in lung cancer have not yet been explored systematically at the genome scale despite their important regulatory functions. Here, we report an integrative analysis of miRNA and mRNA sequencing data for matched tumor-normal samples from 109 Korean female patients with non-small-cell lung adenocarcinoma (LUAD). We produced miRNA sequencing (miRNA-Seq) and RNA-Seq data for 48 patients and RNA-Seq data for 61 additional patients. Subsequent differential expression analysis with stringent criteria yielded 44 miRNAs and 2322 genes. Integrative gene set analysis of the differentially expressed miRNAs and genes using miRNA-target information revealed several regulatory processes related to the cell cycle that were targeted by tumor suppressor miRNAs (TSmiR). We performed colony formation assays in A549 and NCI-H460 cell lines to test the tumor-suppressive activity of downregulated miRNAs in cancer and identified 7 novel TSmiRs (miR-144-5p, miR-218-1-3p, miR-223-3p, miR-27a-5p, miR-30a-3p, miR-30c-2-3p, miR-338-5p). Two miRNAs, miR-30a-3p and miR-30c-2-3p, showed differential survival characteristics in the Tumor Cancer Genome Atlas (TCGA) LUAD patient cohort indicating their prognostic value. Finally, we identified a network cluster of miRNAs and target genes that could be responsible for cell cycle regulation. Our study not only provides a dataset of miRNA as well as mRNA sequencing from the matched tumor-normal samples, but also reports several novel TSmiRs that could potentially be developed into prognostic biomarkers or therapeutic RNA drugs.

SUBMITTER: Yu N

PROVIDER: S-EPMC6547618 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma.

Yu Namhee N Yong Seunghui S Kim Hong Kwan HK Choi Yoon-La YL Jung Yeonjoo Y Kim Doyeon D Seo Jihae J Lee Ye Eun YE Baek Daehyun D Lee Jinseon J Lee Seungjae S Lee Jong Eun JE Kim Jaesang J Kim Jhingook J Lee Sanghyuk S

Molecular oncology 20190418 6

The roles of miRNAs in lung cancer have not yet been explored systematically at the genome scale despite their important regulatory functions. Here, we report an integrative analysis of miRNA and mRNA sequencing data for matched tumor-normal samples from 109 Korean female patients with non-small-cell lung adenocarcinoma (LUAD). We produced miRNA sequencing (miRNA-Seq) and RNA-Seq data for 48 patients and RNA-Seq data for 61 additional patients. Subsequent differential expression analysis with st ...[more]

PMID: 30913346

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Project description:BackgroundObservations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual's inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a "normal" sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.ResultsWe evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43-60%) but lesser precision (20-21%) to current matched tumor/normal approaches (recall 43-73%, precision 30-82%) when compared to a "gold-standard" tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.ConclusionsA detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

Dataset Information

Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma.

Publications

Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma.

Similar Datasets

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