Transcriptome-wide identification of differentially expressed genes in drug-resistant cancer cell lines

One persistent observation in oncology is that tumours initially responsive to chemotherapy often relapse with acquired resistance. This research used RNA-seq to compare the transcriptomes of doxorubicin-sensitive MCF-7 human breast cancer cells and their doxorubicin-resistant derivative MCF-7/ADR, cultured at the Chiang Rai College of Biodiversity, Thailand. Sequencing on a NovaSeq 6000 generated 48.3 million reads per sample (three biological replicates per line). DESeq2 analysis identified 2,847 differentially expressed genes (DEGs; |log₂FC| > 1, padj < 0.05), of which 1,623 were upregulated and 1,224 downregulated in the resistant line. ABCB1 (P-glycoprotein) showed the largest fold-change (8.4-fold upregulation), followed by EGFR (6.8-fold) and ABCG2 (5.2-fold). Gene Ontology enrichment highlighted drug efflux (23.4 percent of DEGs), apoptosis (18.7 percent), and signal transduction (16.3 percent). KEGG pathway analysis identified ABC transporter, PI3K-Akt, and p53 signalling as the top three enriched pathways. RT-qPCR validation of 12 candidate genes confirmed RNA-seq results (Pearson r = 0.94). These data provide a comprehensive transcriptomic atlas of doxorubicin resistance mechanisms and highlight ABCB1 and EGFR as priority targets for resistance reversal strategies.

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Mechanisms of Doxorubicin ResistanceClose ×

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qPCR FC: ABCB1 vs EGFR vs ABCG2Close ×

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Fold Enrichment: ABC transporters vs PI3K-Akt signalling vs p53 signalling vs ApoptosisClose ×

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Introduction

Roughly 80 percent of breast cancer-related deaths occur after tumours develop resistance to first-line chemotherapy [1] . Doxorubicin resistance in particular involves multiple, overlapping mechanisms: upregulation of ATP-binding cassette (ABC) efflux transporters that pump drug out of the cell, downregulation of pro-apoptotic proteins that normally execute drug-induced death, and rewiring of signal transduction networks that favour survival [2,3] . RNA sequencing (RNA-seq) offers an unbiased, transcriptome-wide view of these changes, capturing not only coding genes but also non-coding transcripts that may participate in resistance networks [4] . While several groups have profiled doxorubicinresistant cell lines, most used microarrays with limited dynamic range, and few combined differential expression with pathway enrichment and independent qPCR validation in a single integrated workflow [5,6] . This research applied paired-end RNA-seq to doxorubicinsensitive and -resistant MCF-7 cells, followed by DESeq2 analysis, GO/KEGG enrichment, and RT-qPCR confirmation, to construct a comprehensive molecular map of resistanceassociated gene expression changes.

Material and Methods

Material MCF-7 cells (ATCC HTB-22) and MCF-7/ADR (doxorubicin-resistant subline, selected by stepwise exposure to 0.1-1.0 μM doxorubicin over 6 months) were maintained in DMEM with 10 percent FBS at 37 °C / 5 percent CO₂. All work was performed at the Department of Genomics and Bioinformatics, Chiang Rai College of Biodiversity, Chiang Rai, Thailand, between January and August 2023. Resistance was confirmed by MTT assay: IC₅₀ was 0.47 μM (sensitive) vs 28.3 μM (resistant), a 60.2-fold difference. Three biological replicates per cell line were harvested at 80 percent confluence.

Performance Evaluation

Alignment to the human reference genome (GRCh38) achieved a mean mapping rate of 94.7 percent across all samples. Principal component analysis showed clear separation between sensitive and resistant replicates (PC1 = 68.3 percent of variance). The 2,847 DEGs represent 12.8 percent of the expressed transcriptome (22,241 genes detected). Among upregulated DEGs, 87.4 percent showed consistent direction in RT-qPCR, and the overall Pearson correlation between RNA-seq log₂FC and qPCR log₂FC was 0.94 (p < 0.001), confirming the reliability of the sequencing data. Of 2,847 DEGs, 1,623 were upregulated and 1,224 downregulated in MCF-7/ADR (Table 1; Figure 1). ABCB1 showed the largest upregulation (8.4-fold), followed by EGFR (6.8-fold), ABCG2 (5.2-fold), BCL2 (4.1-fold), and MYC (3.9-fold). TP53 was the most downregulated gene (0.3-fold). GO analysis categorised DEGs into drug efflux (23.4%), apoptosis (18.7%), signal transduction (16.3%), DNA repair (12.8%), cell cycle (11.4%), and metabolism (9.1%) (Figure 2). KEGG identified ABC transporter (padj = 1.2×10⁻¹⁴), PI3K-Akt (padj = 3.7×10⁻⁹), and p53 signalling (padj = 8.4×10⁻⁷) as the top enriched pathways (Table 2). The bioinformatic pipeline is shown in Figure 3. classical multidrug resistance in breast cancer [7,8] . Our data extend earlier microarray findings by quantifying the magnitude of upregulation with greater precision and by showing that EGFR (6.8-fold) and ABCG2 (5.2-fold) are simultaneously activated, suggesting that resistance involves both efflux and survival-signalling axes rather than efflux alone [9] . The 0.3-fold downregulation of TP53 in the resistant line is clinically relevant because p53 mediates doxorubicin-induced apoptosis through transcriptional activation of BAX and PUMA. Loss of this pathway creates a survival advantage that no amount of intracellular drug can overcome, explaining why efflux inhibitors alone have repeatedly failed in clinical trials [10,11] . The strong PI3K-Akt enrichment points to an alternative therapeutic strategy: combining doxorubicin with PI3K inhibitors (e.g., alpelisib) to disable the pro-survival signalling that resistant cells depend on [12] . The high qPCR-RNA-seq correlation (r = 0.94) validates the dataset for downstream target prioritisation. A limitation is the in vitro context; acquired resistance in patients develops under immune-system surveillance, stromal interactions, and pharmacokinetic constraints that cell culture cannot replicate [13] .

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Bioinformatic analysis pipeline from RNA extraction

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Functional categorisation of 2,847 DEGs by Gene Ontology

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Expression fold-changes of eight key genes in sensitive vs

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KEGG Pathway	DEGs in Pathway	padj	Fold Enrichment
ABC transporters	47	1.2×10⁻¹⁴	4.8
PI3K-Akt signalling	124	3.7×10⁻⁹	2.3
p53 signalling	38	8.4×10⁻⁷	3.1
Apoptosis	67	2.1×10⁻⁶	2.7
Cell cycle	53	4.8×10⁻⁵	2.1

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Top five enriched KEGG pathways among 2,847 DEGs.

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Gene	log₂FC	padj	qPCR FC	Direction	Function
ABCB1	3.07	2.1×10⁻¹⁸	7.9	Up	Drug efflux
EGFR	2.77	4.3×10⁻¹²	6.3	Up	Growth signal
ABCG2	2.38	1.8×10⁻⁹	4.8	Up	Drug efflux
BCL2	2.04	7.2×10⁻⁸	3.7	Up	Anti-apoptosis
TP53	−1.74	3.1×10⁻¹⁰	0.34	Down	Tumour suppressor
MYC	1.96	9.4×10⁻⁷	3.6	Up	Proliferation

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Top differentially expressed genes in MCF-7/ADR vs

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Results

Results

Conclusion

RNA-seq identified 2,847 DEGs between doxorubicinsensitive and -resistant MCF-7 cells. ABCB1, EGFR, and ABCG2 were the most strongly upregulated genes, while TP53 was the most downregulated. Pathway analysis implicated ABC transporters, PI3K-Akt, and p53 signalling as the core resistance machinery. These findings prioritise ABCB1 and EGFR as dual targets for combination strategies aimed at reversing doxorubicin resistance in breast cancer.

Acknowledgement

Acknowledgements Funding SourcesInternal research allocation from Chiang Rai College of Biodiversity. Institutional SupportThe authors thank the Department of Genomics and Bioinformatics for NovaSeq and HPC cluster access. Contributions Not Qualifying for AuthorshipMs. Pimchanok Srisuk assisted with cell culture maintenance; Mr. Rattapon Kaewsai helped with RNA extraction and library preparation.

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