Differential Expression and Pathway Analysis in Drug-Resistant Triple-Negative Breast Cancer Cell Lines Using RNASeq Analysis

Abstract

Triple-negative breast cancer (TNBC) is among the most notorious types of breast cancer, the treatment of which does not give consistent results due to the absence of the three receptors (estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) as well as high amount of molecular variability. Drug resistance also contributes to treatment unresponsiveness. We studied differentially expressed genes, their biological roles, as well as pathways from RNA-Seq datasets of two different TNBC drug-resistant cell lines of Basal B subtype SUM159 and MDA-MB-231 treated with drugs JQ1 and Dexamethasone, respectively, to elucidate the mechanism of drug resistance. RNA sequencing(RNA-Seq) data analysis was done using edgeR which is an efficient program for determining the most significant Differentially Expressed Genes (DEGs), Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. iPathway analysis was further used to obtain validated results using analysis that takes into consideration type, function, and interactions of genes in the pathway. The significant similarities and differences throw light into the molecular heterogeneity of TNBC, giving clues into the aspects that can be focused to overcome drug resistance. From this study, cytokine-cytokine receptor interaction pathway appeared to be a key factor in TNBC drug resistance.

Keywords: RNASeq; Triple-negative breast cancer; basal b; cytokine-cytokine receptor interaction; drug resistance.

Figure 1

Multidimensional scaling plots (a) for JQ1 treatment; (b) for Dexamethasone treatment. s1—JQ1 sensitive-3 h treatment, s2—JQ1 sensitive-3 h treatment, s3—JQ1 sensitive-24 h treatment, s4—JQ1 sensitive-24 h treatment, r1—JQ1 resistant-3 h treatment, r2—JQ1 resistant-3 h treatment, r3—JQ1 resistant-24 h treatment, r4—JQ1 resistant-24 h treatment, ctrl1—control 1, ctrl2—control 2, dex2_1—Dexamethasone-2 h treatment, dex2_2—Dexamethasone-2 h treatment, dex4_1—Dexamethasone-4 h treatment and dex4_2—Dexamethasone-4 h treatment.

Figure 2

Biological Coefficient of Variation plots: (a) For JQ1 treatment; (b) For Dexamethasone treatment. CPM- counts per million.

Figure 2

Biological Coefficient of Variation plots: (a) For JQ1 treatment; (b) For Dexamethasone treatment. CPM- counts per million.

Figure 3

Heat maps: (a) For 24 h of JQ1 treatment; (b) For four hours of Dexamethasone treatment.

Figure 4

Volcano plots: (a) For 24 h of JQ1 treatment. DE thresholds: Fold change 0.6, p-value 0.05.; (b) For four hours of Dex treatment. x-axis: log fold change (log FC), y-axis: negative logarithm of adjusted p-value -log10 (adjPVal). DE thresholds: Fold change 0.6, p-value 0.05.

Figure 5

Perturbation (y-axis) vs. Overrepresentation (x-axis) reveals disrupted pathways: (a) At 24 h of JQ1 treatment; (b) At four hours of Dexamethasone treatment. The size of each dot denotes the number of genes in the pathway. The x-axis measures p-values obtained using the classical over-representation analysis (pORA). The y-axis represents the p-values obtained from total perturbation accumulation (pAcc) in the pathway. Yellow: Cytokine-cytokine receptor interaction pathway, Red: Significantly enriched pathways, Black: Non-significantly enriched pathways.