Gene expression profiling and clinical relevance unravel the role hypoxia and immune signaling genes and pathways in breast cancer

Role of hypoxia and immune signaling genes in breast cancer

  • Mohammad Azhar Kamal
  • Mohiuddin Khan Warsi
  • Afnan Alnajeebi
  • Haytham A Ali
  • Nawal Helmi
  • Mohammad Asrar Izhari
  • Saad Mustafa
  • Mohammad Mobashir Karolinska Institute
Keywords: Hypoxic genes; immune signaling pathways; breast cancer; gene expression profiling; survival analysis; inferred functions, hallmarks of molecular signatures

Abstract

Hypoxia most often occurs in cancer and the occurrence of hypoxia helps the cells in adapting different responses than the normal such as the activation of of those signaling pathways which regulate proliferation, angiogenesis, and cell death. There are large number of genes which are known to be associated with diverse biological processes and their control and coordination and in different cancers, the hypoxia-response differs. In this study our goal is to understand the impact of alteration in expression of hypoxia and immune systems related genes and its survival in breast cancer and analyzed the hallmarks of molecular signatures. For this purpose we have collected the hypoxia-associated genes based on the literature related with diverse biological processes and functions. For all these genes, we have studied the survival analysis, breast cancer gene expression profiling, and relevant hypoxic genes alterations. Based on our study, we conclude that there are 17 critical pathways and 40 genes from hypoxic gene list appear to play the major roles in case of breast cancer and overall we observe that immune signaling pathways and its components are highly altered in case of breast cancer. Among the top raked hallmarks of molecular signatures are apoptosis, hypoxia, DNA repair, E2F targets, MYC targets, androgen and estrogen response, and TNFa signaling.

References

1. Wyatt AW, Mo F, Wang K, et al. Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer. August 2014:1–14. doi:10.1186/s13059-014-0426-y.
2. Liu AY, Roudier MP, True LD. Heterogeneity in Primary and Metastatic Prostate Cancer as Defined by Cell Surface CD Profile. The American Journal of Pathology. 2010;165(5):1543–1556. doi:10.1016/S0002-9440(10)63412-8.
3. Hu J, Locasale JW, Bielas JH, et al. HeterogeneityOfTumorInducedGeneExpressionChanges2013NatBiotech. Nat Biotechnol. 2013;31(6):522–529. doi:10.1038/nbt.2530.
4. Chapman A, del Ama LF, Ferguson J, Kamarashev J, Wellbrock C, Hurlstone A. Heterogeneous Tumor Subpopulations Cooperate to Drive Invasion. CellReports. 2014;8(3):688–695. doi:10.1016/j.celrep.2014.06.045.
5. Harris AL. HYPOXIA — A KEY REGULATORY FACTOR IN TUMOUR GROWTH. Nature Reviews Cancer. 2002;2(1):38–47. doi:10.1038/nrc704.
6. Bristow RG, Hill RP. Hypoxia and metabolism: Hypoxia, DNA repair and genetic instability. Nature Reviews Cancer. 2008;8(3):180–192. doi:10.1038/nrc2344.
7. Masson N, Ratcliffe PJ. Hypoxia signaling pathways in cancer metabolism: the importance of co-selecting interconnected physiological pathways. Cancer Metab. 2014;2(1):3. doi:10.1038/nature06734.
8. Vogelstein B, Kinzler KW. Cancer genes and the pathways they control. Nature Medicine. 2004;10(8):789–799. doi:10.1038/nm1087.
9. Klinke DJ II. An evolutionary perspective on anti-tumor immunity. January 2013:1–13. doi:10.3389/fonc.2012.00202/abstract.
10. Emilsson V, Thorleifsson G, Zhang B, et al. Genetics of gene expression and its effect on disease. Nature. 2008;452(7186):423–428. doi:10.1038/nature06758.
11. Bristow RG, Hill RP. Hypoxia and metabolism: Hypoxia, DNA repair and genetic instability. Nature Reviews Cancer. 2008;8(3):180–192. doi:10.1038/nrc2344.
12. Pouysségur J, Dayan F, Mazure NM. Hypoxia signalling in cancer and approaches to enforce tumour regression. Nature Cell Biology. 2006;441(7092):437–443. doi:10.1038/nature04871.
13. Emily F, Christoph L, Schofield CJ. Hypoxic Response and Associated Diseases. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2007. doi:10.1002/9780470048672.wecb232.
14. Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011;144(5):646–674. doi:10.1016/j.cell.2011.02.013.
15. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database Hallmark Gene Set Collection. Cell Systems. 2015;1(6):417–425. doi:10.1016/j.cels.2015.12.004.
16. Morris DS, Tomlins SA, Rhodes DR, Mehra R, Shah RB, Chinnaiyan AM. Integrating biomedical knowledge to model pathways of prostate cancer progression. Cell Cycle. 2007;6(10):1177–1187. doi:10.4161/cc.6.10.4247.
17. Clarke C, Madden SF, Doolan P, et al. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis. 2013;34(10):2300–2308. doi:10.1093/carcin/bgt208.
18. Restifo NP, Smyth MJ, Snyder A. Acquired resistance to immunotherapy and future challenges. Nature Reviews Cancer. 2016;16(2):121–126. doi:10.1038/nrc.2016.2.
19. Bui JD, Schreiber RD. Cancer immunosurveillance, immunoediting and inflammation: independent or interdependent processes? Current Opinion in Immunology. 2007;19(2):203–208. doi:10.1016/j.coi.2007.02.001.
20. McNutt M. Cancer Immunotherapy. Science. 2013;342(6165):1417–1417. doi:10.1126/science.1249481.
21. Kroemer G, Galluzzi L, Kepp O, Zitvogel L. Immunogenic Cell Death in Cancer Therapy. Annu Rev Immunol. 2013;31(1):51–72. doi:10.1146/annurev-immunol-032712-100008.
22. Quackenbush J. Microarray data normalization and transformation. Nature Genetics. 2002;32(Supp):496–501. doi:10.1038/ng1032.
23. Simon R. Microarray-based expression profiling and informatics. Current Opinion in Biotechnology. 2008;19(1):26–29. doi:10.1016/j.copbio.2007.10.008.
24. Ideker T, Thorsson V, Siegel AF, Hood LE. Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. Journal of Computational Biology. 2000;7(6):805–817. doi:10.1089/10665270050514945.
25. Reimers M. Making Informed Choices about Microarray Data Analysis. Lewitter F, ed. PLoS Comput Biol. 2010;6(5):e1000786. doi:10.1371/journal.pcbi.1000786.s001.
26. Chen K-H, Wang K-J, Tsai M-L, et al. Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. BMC Bioinformatics. 2014;15(1):1–10. doi:10.1186/1471-2105-15-49.
27. Bild AH, Parker JS, Gustafson AM, et al. An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer. Breast Cancer Res. 2009;11(4):R55. doi:10.1186/bcr2344.
28. Salomonis N, Hanspers K, Zambon AC, et al. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics. 2007;8(1):217. doi:10.1186/1471-2105-8-217.
29. Lapointe J, Li C, Higgins JP, et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA. 2004;101(3):811–816. doi:10.1073/pnas.0304146101.
30. Subramanian A, Tamayo P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. In:; 2005.
31. Mi H, Poudel S, Muruganujan A, Casagrande JT, Thomas PD. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Research. 2016;44(D1):D336–D342. doi:10.1093/nar/gkv1194.
32. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Research. 2011;40(D1):D109–D114. doi:10.1093/nar/gkr988.
33. Alexeyenko A, Sonnhammer ELL. Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Research. 2009;19(6):1107–1116. doi:10.1101/gr.087528.108.
34. Okawa S, Angarica VE, Lemischka I, Moore K, del Sol A. A differential network analysis approach for lineagespeci. Nature Publishing Group. November 2015:1–8. doi:10.1038/npjsba.2015.12.
35. Alexeyenko A, Sonnhammer ELL. Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Research. 2009;19(6):1107–1116. doi:10.1101/gr.087528.108.
36. Wang E, Zaman N, Mcgee S, Milanese J-S, Masoudi-Nejad A, O’Connor-McCourt M. Seminars in Cancer Biology. Seminars in Cancer Biology. 2015;30:4–12. doi:10.1016/j.semcancer.2014.04.002.
Published
2020-06-07
How to Cite
Kamal, M. A., Warsi, M. K., Alnajeebi, A., Ali, H. A., Helmi, N., Izhari, M. A., Mustafa, S., & Mobashir, M. (2020). Gene expression profiling and clinical relevance unravel the role hypoxia and immune signaling genes and pathways in breast cancer: Role of hypoxia and immune signaling genes in breast cancer. Journal of Internal Medicine: Science & Art, 1(1), 2 - 10. https://doi.org/10.36013/jimsa.v1i1.3
Section
Articles