Via Research Recognition Day Program VCOM-Carolinas 2025

Biomedical Research

Differential Gene Expression and GSEA in Colorectal Cancer Cells Treated with Atorvastatin. Brianna Dyar and Christopher Farrell Clemson University, School of Nursing, Clemson, SC

ABSTRACT The American Cancer Society reports that colorectal cancer is the third leading cause of death in men and the fourth leading cause of death in women, and estimated that around 52,900 people will perish due to colon cancer in the year 2025. Effective treatment with chemotherapy is crucial to survival, yet chemoresistance is an ever-present threat in oncology. Statins, a popularly prescribed class of cholesterol-lowering drugs, may contribute to a cancer’s chemoresistance even prior to chemotherapy exposure by altering the expression profile of cells over time, shifting concentrations of key metabolic proteins such that the desired effects of chemotherapy are mitigated. This study aims to discover expression differences between both individual genes and biological systems in statin-treated and non-treated colorectal cancer cells, as well as identify candidate genes for further study. A line of colorectal cancer cells, Caco2, was split into three groups; each group was treated twice a week with either DMSO (negative vehicle control), doxorubicin (positive control), or 10uM of atorvastatin, and after four months of treatment, cells were harvested for RNA sequencing. Differential gene expression was conducted using the R package DESeq2, and gene set enrichment analysis (GSEA) was conducted using the browser tool WebGestalt and the KEGG pathway database. In Caco2 cells treated with 10uM of atorvastatin, when compared to the vehicle control, a total of 57 genes were significantly differentially expressed (FDR < 0.05) and seven pathways were significantly enriched (FDR < 0.05). In Caco2 cells treated with 10uM of atorvastatin, when compared to the positive control, a total of 103 genes were significantly differentially expressed (FDR < 0.05) and ten pathways were significantly enriched (FDR < 0.05). INTRODUCTION Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in the United States (American Cancer Society), and for those diagnosed with multidrug-resistant (MDR) strains, the prognoses are poor. A variety of mechanisms govern MDR development; altered metabolism, enhanced efflux of a drug, genetic and epigenetic variations in key genes (Bukowski, 2020), and these factors can be either intrinsic or extrinsic. In extrinsic, or acquired, MDR, exposure to chemotherapeutics over time exerts a selection pressure on populations of tumor cells such that successive generations of cells consist primarily of the offspring of drug resistant predecessors. Intrinsic MDR on the other hand is resistant from the moment of first exposure and is found more often in colorectal cancers than other types of cancers (Ashique et al, 2024). One well-documented mechanism of MDR development in colorectal cancer is increased efflux of xenobiotics by cell membrane transport proteins, specifically P-glycoprotein (PGP). PGP is a member of the ATP-binding cassette family of proteins and is encoded by the gene ABCB1. It is expressed primarily in areas of the body whose foremost function is mediation of substance exchange such as the kidneys, liver, blood-brain barrier, and the intestines. It is its increased presence in the intestine that makes it a potent driver of intrinsic MDR in colorectal cancer (Ashique et al, 2024); however, the pre-existing elevated level of PGP may be exacerbated by other factors such as medication with certain drugs. Statins, a class of cholesterol-lowering drugs, is commonly prescribed for management of diseases like hyperlipidemia and diabetes, and it is typically targeted at patients aged between 40-75 years of age (Matyori et al , 2023). Coincidentally, this age group is also more susceptible to colorectal cancer than younger groups (American Cancer Society). In a study performed by Björkhem-Bergman et al (2013), human liver tissue from patients receiving atorvastatin treatments measured higher for levels of ABCB1 and ABCG2 mRNA transcripts than tissue from patients treated with a placebo. As stated earlier, ABCB1, and ABCG2, are efflux transporters responsible for ferrying xenobiotics out of a cell, including chemotherapeutic agents. If prolonged statin use increases PGP expression in intestinal cells as well, then it is possible that patients taking statins face a higher risk of MDR colorectal cancer than is typical for non-users. While the study by Björkhem-Bergman et al (2013) focused on a set of genes selected a priori , this study aims to broaden observations to the entire transcriptome via gene set enrichment analysis and identify other MDR culpable genes as well as any signature expression profiles in colorectal cancer cells treated with atorvastatin over a prolonged period of time.

METHODS Human colorectal cell lines, Caco2, were obtained through the American Type Culture Collection (ATCC, Gaithersburg, MD). Cells were cultured in complete EMEM media containing 10% fetal bovine serum and 5% penicillin/streptomycin. Incubation conditions were maintained at 37 ℃ with5%CO 2. Cells were passaged as per the methods described in the ATCC protocols. Cells were divided into three groups: a positive control, a negative control, and treatment group. Negative controls were given 10µM of DMSO twice a week, the positive controls were given 10nM of doxorubicin twice a week, and the treatment group was given 10µM of atorvastatin twice a week. After four months of treatment (approximately 30-40 passages), cells were harvested for RNA sequencing using the RNeasy Micro Kit (Qiagen, Valencia, CA). The RNA was then prepared for library synthesis using TruSeq RNA Sample Prep Kit (Illumina, San Diego, CA) as per the manufacturer protocol. Clustered RNA-seq libraries were paired-end sequenced with 2x150 cycles on an Illumina Sequencing instrument (Illumina, San Diego, CA). Fastq files were used for downstream analysis. Raw fastq files were assessed for quality using FastQC. Poor reads and adapters were removed using fastp, and post-trimming quality testing was performed to ensure integrity of the files. Ribosomal RNA was removed using bbduk. The quality, processed reads were then aligned against the human reference genome GRChg38 sourced from the UCSC Genome Browser using GSNAP. SAMtools was used to discern the integrity of the .sam files, to convert the .sam files to .bam files, and to sort and index the .bam files for streamlined analysis. The featureCounts tool from the Subread package was used to quantify the number of reads for each gene from each of the samples. EdgeR and DeSeq2 were used to generate differential gene expression files for further analysis. Pathway enrichment analysis was performed using the WebGestalt Gene Set Analysis Toolkit. Genes were checked against the KEGG pathways database using a cutoff value of FDR <0.05.

RESULTS CONT.

A) Volcano plot of KEGG pathways with significant (FDR < 0.05) enrichment scores for atorvastatin treated Caco2 cells when compared to the positive control.

A)

B) Volcano plot of KEGG pathways with significant (FDR < 0.05) enrichment scores for atorvastatin treated Caco2 cells when compared to the positive control.

B)

CONCLUSION When comparing pharmacokinetic pathways for atorvastatin against chemotherapies, a few differentially expressed genes in cells treated with atorvastatin stand out. Firstly are ABCB1 (LFC = 2.306114, FDR = 0.071) and ABCG2 (LFC = 1.461527, FDR = 0.421) whose protein products are PGP and an ATP-Binding cassette of subfamily G respectively. Both are responsible for transporting a variety of xenobiotics out of the cell, and their upregulation in Caco2 populations treated with atorvastatin may confer drug resistance against medications to which these cells are presently naïve such as chemotherapeutics. CYP51A1 codes for a subunit belonging to the cytochrome (CYP) P450 family of proteins and is upregulated significantly in the treatment group (LFC = 1.534, FDR = 0.016). This dovetails with results from the GSEA analysis where the KEGG pathway representing metabolism of xenobiotics by cytochrome P450 is significantly overrepresented (NES = -2.14, FDR < 2.2e -16 ). CYP enzymes are responsible for activation and inactivation of many anticancer drugs, and each enzyme’s impact varies based on its polymorphism status (Rodriguez-Antona & Ingelman-Sundberg, 2006). While this study points to these three genes as candidates for MDR induction via atorvastatin treatment, further research will be required to confirm chemoresistance in these cells as well as the exact molecular mechanisms of MDR attainment.

RESULTS When compared to both the negative and positive controls, colorectal cells treated with atorvastatin exhibited a 2.31 log-fold change (FDR = 0.071) and 0.332 log-fold change (FDR = 0.745). Additionally, CYP51A1 , a member of the cytochrome P450 family, was significantly upregulated by a log-fold change of 1.534 (FDR = 0.016) in atorvastatin-treated cells when compared with the negative control while not showing any significant changes when compared to the positive control.

Number of Downregulated Genes

Total Number of Significantly Differentially Expressed Genes (FDR < 0.05)

Number of Upregulated Genes

Group

REFERENCES

Negative (DMSO) v Atorvastatin

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American Cancer Society. (2023). American Cancer Society. Colorectal Cancer Facts & Figures 2023-2025. . https://www.cancer.org/content/dam/cancer org/research/cancer-facts-and-statistics/colorectal-cancer-facts-and-figures/colorectal-cancer-facts-and-figures-2023.pdf Ashique, S., Bhowmick, M., Pal, R., Khatoon, H., Kumar, P., Sharma, H., Garg, A., Kumar, S., & Das, U. (2024). Multi drug resistance in Colorectal Cancer approaches to overcome, advancements and future success. Advances in Cancer Biology - Metastasis, 10, 100114. 10.1016/j.adcanc.2024.100114 Björkhem-Bergman, L., Bergström, H., Johansson, M., Parini, P., Eriksson, M., Rane, A., & Ekström, L. (2013). Atorvastatin Treatment Induces Uptake and Efflux Transporters in Human Liver. Drug Metabolism and Disposition, 41(9), 1610–1615. 10.1124/dmd.113.051698 Bukowski, K., Kciuk, M., & Kontek, R. (2020). Mechanisms of Multidrug Resistance in Cancer Chemotherapy. International Journal of Molecular Sciences, 21(9), 3233. 10.3390/ijms21093233 Rodriguez-Antona, C., & Ingelman-Sundberg, M. (2006). Cytochrome P450 pharmacogenetics and cancer. Oncogene, 25(11), 1679–1691. 10.1038/sj.onc.1209377

Positive Control (Doxorubicin) v Atorvastatin

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2025 Research Recognition Day

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