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Time series integrative analysis of RNA-Seq and miRNA expression data reveals key biologic pathways during keloid formation [RNA-seq]

GSE113619 Homo sapiens (human) RNA-seq (gene expression) 64 samples Submitted 2018/06/29 Platform GPL21290Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 64 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
64 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Keloids represent a common form of exaggerated wound scarring that cause considerable morbidity. Moreover, there are limited data on molecular mechanisms underlying keloids and effective therapies are lacking. To gain new insight in the transcriptomic alterations of wound healing in keloid-prone individuals, we followed an integrative approach of RNA-Seq and miRNA expression data analysis in serial skin biopsies of the same site (baseline and six weeks after wounding) in keloid-prone (n=8) and healthy matched control individuals (n=6).
Read the full summary ↓
Bioinformatic analysis identified 37 miRNAs and 1449 genes that are differentially expressed specifically in keloid-prone individuals during wound healing. Pathway enrichment analysis was undertaken in the RNA-Seq data and identified NOTCH signaling, MAPK signaling, and Toll-like receptor pathways to be altered in keloid-prone individuals after wounding. In addition, dysregulation of DNA repair, p53 signalling and metabolic pathways (RNA, protein, fructose, mannose and glycerophospholipid metabolism) was highlighted during keloid formation. Gene association network analysis demonstrated divergent average expression profiles of cytokine signaling genes, as well as lipid metabolism genes between keloid-prone and healthy individuals during wound healing. In summary, our study provides a comprehensive and integrative analysis of the keloid transcriptome and miRNAome and highlights biological pathways that feature during keloid formation.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE113619_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA448679 and SRA study SRP137071. Searching any of these in the dataset finder brings you back here.

Samples in this study

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