MicroRNA profiling data of fresh lung adenocarcinoma and adjacent normal tissues obtained from ten Korean patients using miRNA-seq
Description
MicroRNA transcriptomes from fresh tumor and adjacent normal tissues of ten Korean patients who were diagnosed with lung adenocarcinoma were profiled using a next-generation sequencing (NGS) technique called miRNA-seq. The quality of sequencing was measured using FastQC, and low-quality and/or adapter-contaminated portions of reads were removed with Trim Galore. Quality-assured reads were analyzed using miRDeep2 with Bowtie. The abundance of known miRNAs was estimated by the read per million (RPM) normalization method. Then, differentially expressed miRNAs and potential miRNA biomarkers for lung adenocarcinoma tissues compared to adjacent normal tissues were identified using DESeq2 and Wx, respectively. Finally, reliable miRNA biomarkers for lung adenocarcinoma were defined as those detected by both methods. The miRNA-seq data are available in the gene expression omnibus (GEO) database with accession number GSE196633.
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Sequenced reads (GSE196633; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE196633) were trimmed by the sequencing quality and/or adopter contaminations using Cutadapt (https://cutadapt.readthedocs.io/en/stable/) with the following parameters: --overlap=6 -f fastq -a TGGAATTCTCGGGTGCCAAGG -m 18 -M 26. The sequencing quality of trimmed reads were checked using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Trimmed reads were aligned to the reference human genome using the mapper (mapper.pl; the following parameters were used: -e -h -j -m -s) function of miRDeep2 (https://github.com/rajewsky-lab/mirdeep2) with Bowtie (http://bowtie-bio.sourceforge.net/index.shtml). Expression levels of all known miRNAs were estimated using the quantifier (quantifier.pl; the following parameters were used: -t has -g 2 -e 2 -f 5) function of miRDeep2. Three-dimensional PCA plot were generated with 581 miRNAs, showing expression values greater than 1 read per million (RPM) on the average of all samples. Differentially expressed miRNAs between lung adenocarcinoma and adjacent normal tissues were identified using DESeq2 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html). Potential miRNA biomarkers were also identified using a deep learning-based biomarker algorithm called Wx (https://www.nature.com/articles/s41598-019-47016-8).