Melanoma RNA Seq Data commitee
Ontology highlight
ABSTRACT:
PROVIDER: EGAC00001002928 | EGA |
REPOSITORIES: EGA
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Project description:Using a chromatin regulator-focused shRNA library, we found that suppression of sex determining region Y-box 10 (SOX10) in melanoma causes resistance to BRAF and MEK inhibitors. To investigate how SOX10 loss leads to drug resistance, we performed transcriptome sequencing (RNAseq) of both parental A375 (Ctrl. PLKO) and A375-SOX10KD (shSOX10-1, shSOX10-2) cells. To ask directly whether SOX10 is involved indrug resistance in BRAF(V600E) melanoma patients, we isolated RNA from paired biopsies from melanoma patients (pre- and post- treatment) , that had gained BRAF or MEK inhibitor resistance . We performed RNAseq analysis to determine changes in transcriptome upon drug resistance. Investigate genes regulated by SOX10 and differntial gene expression between pre- and post-treatment biopsies. We use short hairpin RNA to suppression SOX10 in A375 cells and cells were harvested with trizol reagent for RNA isolation. For paired biopsies (patient samples) we collected the first biopsy before the initiation of treatment and the second biopsy after drug resistance developed. RNA was isolated from FFPE samples and subjected for RNA sequencing.
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Project description:We studied the transcriptome landscape of skin cutaneous melanoma (SKCM) using 103 primary tumor samples from TCGA, and measured the expression levels of both protein coding genes and non-coding RNAs (ncRNAs). In particular, we emphasized pseudogenes potentially relevant to this cancer. While cataloguing the profiles based on the known biotypes, all the employed RNA-Seq methods generated just a small consensus of significant biotypes. We thus designed an approach to reconcile the profiles from all methods following a simple strategy: we selected genes that were confirmed as differentially expressed by the ensemble predictions obtained in a regression model. The main advantages of this approach are: 1) Selection of a high-confidence gene set identifying relevant pathways; 2) Use of a regression model whose covariates embed all method-driven outcomes to predict an averaged profile; 3) Method-specific assessment of prediction power and significance. Furthermore, the approach can be generalized to any biological system for which noisy RNA-Seq profiles are computed. As our analyses concerned bio-annotations of both high-quality protein coding genes and ncRNAs, we considered the associations between pseudogenes and parental genes (targets). Among the candidate targets that were validated, we identified PINK1, which is studied in patients with Parkinson and cancer (especially melanoma).
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