VCOM Carolinas Research Day 2023

Biomedical Studies

Identification of Hypoxic Response Intermediates With Relevance to Breast Cancer Survival by Analysis of Hif-1 Alpha Target Genes in Mammary Gland Tumorigenesis Anna C. Deal, OMS-II, Luciana Schwab, Ph.D. Edward Via College of Osteopathic Medicine, Biomedical Department, Spartanburg, S.C.

Abstract # BIOM-5




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• Breast cancer (BC) is the second most common type of cancer among women. Breast cancer refers to tumors that originate in various structures of the mammary gland. These are very heterogeneous solid tumors. Several BC subtypes have been identified based on histological characteristics, gene expression, and the presence or absence of specific markers. Each subtype exhibits a different prognosis. • Breast tumors, like many other solid tumors, develop areas of low oxygenation or hypoxia . It has been shown that hypoxia, primarily through the action of the Hypoxia Inducible Factor (HIF), significantly enhances tumor progression and dissemination. Since modulation of HIF by drugs has been difficult to achieve, efforts have been focused on identifying HIF targets that could be affected by pharmaceuticals. • At a cellular level: o Cancer cells adapt to hypoxic stress by activating the HIF-1 pathway o HIF-1 is a heterodimeric transcription factor that regulates the transcription of multiple genes mediating hypoxic response.

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Discussion and Conclusions

References • We have previously shown that mice transplanted with Hif1- α wt MTEC develop larger, faster-growing, and more metastatic tumors than mice transplanted with Hif1- α ko MTEC. Here, we hypothesize that genes upregulated in the wt cells might be the effectors carrying out the hypoxia response and could be more suitable for therapeutic targeting than HIF itself. • By applying the survival analysis tool (Kaplan-Meier plotter), we have successfully narrowed a list of 200 genes upregulated in Hif1- α wt MTEC (microarray results) down to 14. • The 14 genes support our hypothesis for the role of Hif1- α in mammary gland tumorigenesis and their prognostic and predictive value make them relevant to human disease. • By applying the Gene Set Enrichment Analysis tools (Metascape and Enrichr), we have identified pathways and biological processes that we will further study to elucidate the role of these 14 genes in breast cancer. Future directions : • Validate selected genes by RT-PCR, Western Blot, and IHC. • Design the experiments to test the role of the 14 genes in the identified pathways. 1.Microarray data: Krutilina, Raisa I., Hilaire Playa, Danielle L. Brooks, Luciana P. Schwab, Deanna N. Parke, Damilola Oluwalana, Douglas R. Layman, et al. “HIF -Dependent CKB Expression Promotes Breast Cancer Metastasis, Whereas Cyclocreatine Therapy Impairs Cellular Invasion and Improves Chemotherapy Efficacy. ” Cancers 14, no. 1 (2021): 27. 2. K-M Plotter: Győrffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer, Computational and Structural Biotechnology Journal, 2021;19:4101-4109 3. ROC plotter: Fekete J & Gyorffy B: Validating predictive biomarkers of chemotherapy/hormonal therapy/anti-HER2 therapy using transcriptomic data of 3,104 breast 5. Enrichr: Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 128(14). • Steven Enkemann, Ph.D., for engaging in valuable discussions about microarray analysis. • Tiffany Seagroves, Ph.D., for lending advice on the overall project and providing the cells. • VCOM Research Department for providing funding through REAP grant. Acknowledgements cancer patients, Int J Cancer , 2019 Dec 1;145(11):3140-3151 4.Metascape: Zhou et al. Nature Commun. 2019 10(1):1523

Figure 1 . Level of gene expression and correlation with survival and response to treatment. Representative examples of the K-M (top panels) and ROC (bottom panels) plots that were run for each gene. K-M plotter provides prognostic (survival) and ROC plotter predictive (response to therapy) information. All available datasets were used and the response to all chemotherapies was analyzed.


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Objective: The goal of this project is to identify HIF1- α target genes with biological significance based on survival and gene ontology analysis. Hypothesis: Since HIF1- α is not easily druggable, we will focus on finding HIF1- α target genes with potential for pharmaceutical targeting.

Figure 2 . Visualization of functional enrichment results . Metascape application was used to find enrichment terms and networks for the 200 genes analyzed in this study. A ) Bar graph of top 20 enriched terms across input gene lists, colored by p-values. B ) Network of enriched terms colored by cluster ID.





1. Microarray: A list of genes from a microarray study performed in the Polyoma Middle T (PyMT+) transgenic animal model of breast cancer was the starting point of this project. In that study, mammary tumor epithelial cells (MTEC) harboring the Hif1- α wt (wild type) gene were compared to Hif-1 α ko (knockout) cells. 2. Survival Analysis: The Kaplan-Meier (K-M) plotter was used to analyze the relationship between the level of expression of 200 genes from the microarray study and patient survival from all the available datasets for breast cancer mRNA. We focused on genes upregulated in the wt versus the ko cells. 3. ROC Plotter: After identifying 14 genes whose high level of expression correlated with lower patient survival, the ROC plotter was used to investigate the link between gene expression and response to chemotherapy therapy. 4. GSEA: Gene Set Enrich Analysis tools (Metascape and Enrichr) were used to identify pathways and cellular processes related to these 14 genes.

Figure 3. Gene Ontology (G.O.) and KEGG analysis of 200 genes upregulated in Hif-1 α wt MTEC. Enrichr application was used to group genes based on enriched terms and allow for comparisons. A ) G.O. Biological processes. B ) G.O. Molecular process. C ) KEGG.


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