An RNA-seq computational statistics pipeline to gain insights of differential gene expression pathways on exposure to dexamethasone in breast cancer cells

Abstract

Author(s): Abhishek Narain Singh

As we turn our attention towards Third Generation Sequencing (TGS), it might be worthwhile to see what the Second Generation Sequencing (SGS) had to offer in terms of bioinformatics pipeline to study certain cells, such as cancerous cells in this study this analysis using SGS can act as a template for improved research using TGS technology. Results of the Differentially Expressed (DE) analysis showed that Dexamethasone (Dex) treatment causes both up and downregulation of genes in breast cancer cells. Comparison of the number of DE genes showed that longer exposure to Dex induces the transcription of greater number of DE genes compared to the normal state, i.e., non-treated cells. Treatment of studied breast cancer cells with Dex for 4h almost doubled the number of DE genes compared to 2h Dex treatment. Since Dex is a synthetic corticosteroid and binds to the Glucocortisol Receptor in place of natural glucocorticoids, it can be expected to induce the activation of GR, and thereby, cause changes in the transcriptional state of GR-regulated genes leading to the activation of pathways that are regulated by GCs. Few diseases are also possibly related to Dex treatment. The examination of enriched diseases is particularly important from a clinical perspective, since it can give indications of risks that exposure to Dex may bear, before exploring Dex or its homologous forms as a treatment option for cancer.

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