Identification of potential hub genes as biomarkers for breast, ovarian, and endometrial cancers

Abstract

Breast cancer (BC) and gynecological cancers have emerged as significant threats to women’s health and are known to be among the primary causes of cancer-related fatalities in women. Innovative treatments and early detection may significantly cut mortality rates for these diseases. In this study, potential hub genes were thoroughly evaluated in the contexts of BC, ovarian cancer (OC), and endometrial cancer (EC). Initially, a total of 374 overlapping differentially expressed genes (DEGs) were identified within the microarray datasets. The STRING database and Cytoscape software analyzed protein-protein interaction (PPI) network structure, whereas FunRich found hub genes. The five hub genes that were ultimately discovered are PTEN, SMAD2, FASN, CYCS, and KRAS. As a result, these genes may serve as potential biomarkers for the aforementioned diseases. Importantly, this study offers valuable insights into all three diseases based on recent molecular advancements. However, further investigation is required to precisely measure these biomarkers’ effectiveness.

Keywords

• Breast cancer
• DEGs
• endometrial cancer
• enriched pathway
• hub gene
• ovarian cancer
• potential biomarker

References

1. Arakal, N. G., Sharma, V., Kumar, A., Kavya, B., Devadath, N. G., Kumar, S. B., ... & Murahari, M. (2021). Ligand-based design approach of potential Bcl-2 inhibitors for cancer chemotherapy. Computer Methods and Programs in Biomedicine, 209, 106347.
2. Banno, K., Kisu, I., Yanokura, M., Tsuji, K., Masuda, K., Ueki, A., ... & Aoki, D. (2012). Biomarkers in endometrial cancer: Possible clinical applications. Oncology letters, 3(6), 1175-1180.
3. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300.
4. BCS, (2023). Breast Cancer Statistics, https://www.wcrf.org/cancer-trends/breast-cancer-statistics/, Last Accessed on December 16, 2023.
5. Chang, L., & Xia, J. (2022). MicroRNA regulatory network analysis using miRNet 2.0. In: Song Q., Tao Z. (eds) Transcription Factor Regulatory Networks (pp. 185-204). Springer, Humana, New York.
6. Chou, W. C., Cheng, A. L., Brotto, M., & Chuang, C. Y. (2014). Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer. BMC Genomics, 15(1), 1-12.
7. Davies, E. J., Marsh Durban, V., Meniel, V., Williams, G. T., & Clarke, A. R. (2014). PTEN loss and KRAS activation leads to the formation of serrated adenomas and metastatic carcinoma in the mouse intestine. The Journal of Pathology, 233(1), 27-38.
8. Du, Z., Yan, D., Li, Z., Gu, J., Tian, Y., Cao, J., & Tang, Z. (2020). Genes involved in the PD-L1 pathway might associate with radiosensitivity of patients with gastric cancer. Journal of Oncology, 2020.

9. Emmanuel, C., Gava, N., Kennedy, C., Balleine, R. L., Sharma, R., Wain, G., ... & deFazio, A. (2011). Comparison of expression profiles in ovarian epithelium in vivo and ovarian cancer identifies novel candidate genes involved in disease pathogenesis. PloS One, 6(3), e17617.
10. ECS, (2023). Endometrial Cancer Statistics, https://www.wcrf.org/cancer-trends/endometrial-cancer-statistics/, Last Accessed on December 16, 2023.
11. Enrichr, (2024). Enrichr Database, https://maayanlab.cloud/Enrichr/, Last Accessed on December 16, 2023.
12. Fernández, L. P., de Cedron, M., & de Molina, A. (2020). Alterations of lipid metabolism in cancer: Implications in prognosis and treatment. Frontiers in Oncology, 10, 577420.
13. Ferraldeschi, R., Rodrigues, D. N., Riisnaes, R., Miranda, S., Figueiredo, I., Rescigno, P., ... & de Bono, J. (2015). PTEN protein loss and clinical outcome from castration-resistant prostate cancer treated with abiraterone acetate. European Urology, 67(4), 795-802.
14. Fonseka, P., Pathan, M., Chitti, S. V, Kang, T., & Mathivanan, S. (2021). FunRich enables enrichment analysis of OMICs datasets. Journal of Molecular Biology, 433(11), 166747.
15. Gayther, S. A., & Pharoah, P. D. P. (2010). The inherited genetics of ovarian and endometrial cancer. Current Opinion in Genetics & Development, 20(3), 231-238.
16. GSE17025, (2023). National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025, Last Accessed on December 16, 2023.
17. GSE27651, (2023). National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27651, Last Accessed on December 16, 2023.
18. GSE42568, (2023). National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42568, Last Accessed on December 16, 2023.
19. Li, F. H., Shen, L., Li, Z. H., Luo, H. Y., Qiu, M. Z., Zhang, H. Z., ... & Xu, R. H. (2010). Impact of KRAS mutation and PTEN expression on cetuximab-treated colorectal cancer. World Journal of Gastroenterology: WJG, 16(46), 5881.
20. Li, Y., & Li, L. (2020). Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer. BMC Genetics, 21(1), 1-13.
21. Liu, N., Qi, D., Jiang, J., Zhang, J., & Yu, C. (2020). Expression pattern of p-Smad2/Smad4 as a predictor of survival in invasive breast ductal carcinoma. Oncology Letters, 19(3), 1789-1798.
22. Martinez-Ledesma, E., Verhaak, R. G. W., & Treviño, V. (2015). Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm. Scientific Reports, 5(1), 11966.
23. miRNet, (2024). miRNet Tutorial Starting with a List, https://www.mirnet.ca/miRNet/resources/data/tutorials/Start_with_list.pdf, Last Accessed on January 24, 2024.
24. OCS, (2023). Ovarian Cancer Statistics, https://www.wcrf.org/cancer-trends/ovarian-cancer-statistics/, Last Accessed on December 16, 2023.
25. Petrucelli, N., Daly, M. B., & Pal, T. (2022). BRCA1-and BRCA2-associated hereditary breast and ovarian cancer. GeneReviews.
26. Qian, F., Kong, W., & Wang, S. (2022). Exploring autophagy-related prognostic genes of Alzheimer’s disease based on pathway crosstalk analysis. Bosnian Journal of Basic Medical Sciences, 22(5), 751.
27. Rahman, M. F., Rahman, M. R., Islam, T., Zaman, T., Shuvo, M. A. H., Hossain, M. T., ... & Moni, M. A. (2019). A bioinformatics approach to decode core genes and molecular pathways shared by breast cancer and endometrial cancer. Informatics in Medicine Unlocked, 17, 100274.
28. Ramanathan, R., Olex, A. L., Dozmorov, M., Bear, H. D., Fernandez, L. J., & Takabe, K. (2017). Angiopoietin pathway gene expression associated with poor breast cancer survival. Breast Cancer Research and Treatment, 162, 191-198.
29. Sarkar, D., Chakraborty, S., Bhowmick, S., & Maiti, T. (2021). In-silico analysis: common biomarkers of NDs. BioRxiv, 2021-2029.
30. Scaglia, N. C., Chatkin, J. M., Pinto, J. A., Tsukazan, M. T. R., Wagner, M. B., & Saldanha, A. F. (2013). Role of gender in the survival of surgical patients with nonsmall cell lung cancer. Annals of Thoracic Medicine, 8(3), 142.
31. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., ... & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 24982504.
32. Smith, I. N., & Briggs, J. M. (2016). Structural mutation analysis of PTEN and its genotype-phenotype correlations in endometriosis and cancer. Proteins: Structure, Function, and Bioinformatics, 84(11), 1625-1643.
33. Stebbing, J., Lit, L. C., Zhang, H., Darrington, R. S., Melaiu, O., Rudraraju, B., & Giamas, G. (2014). The regulatory roles of phosphatases in cancer. Oncogene, 33(8), 939-953.
34. Sun, Y., Xu, Y., Che, X., & Wu, G. (2022). Development of a novel sphingolipid signaling pathway-related risk assessment model to predict prognosis in kidney renal clear cell carcinoma. Frontiers in Cell and Developmental Biology, 10, 881490.
35. Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A., Minguez, P., ... & Mering, C. V. (2010). The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Research, 39(suppl_1), D561-D568.
36. Toss, A., Tomasello, C., Razzaboni, E., Contu, G., Grandi, G., Cagnacci, A., ... & Cortesi, L. (2015). Hereditary ovarian cancer: not only BRCA 1 and 2 genes. BioMed Research İnternational, 2015.
37. Walsh, M. F., Nathanson, K. L., Couch, F. J., & Offit, K. (2016). Genomic biomarkers for breast cancer risk. Novel Biomarkers in the Continuum of Breast Cancer, 1-32.
38. Wang, Y., Wang, J., Hu, Y., Shangguan, J., Song, Q., Xu, J., ... & Zhang, Y. (2022). Identification of key biomarkers for STAD using filter feature selection approaches. Scientific Reports, 12(1), 19854.
39. Xue, H., Sun, Z., Wu, W., Du, D., & Liao, S. (2021). Identification of hub genes as potential prognostic biomarkers in cervical cancer using comprehensive bioinformatics analysis and validation studies. Cancer Management and Research, 117-131.
40. Yadav, G., Vashisht, M., Yadav, V., & Shyam, R. (2020). Molecular biomarkers for early detection and prevention of ovarian cancer—A gateway for good prognosis: A narrative review. International Journal of Preventive Medicine, 11.
41. Yndestad, S., Austreid, E., Knappskog, S., Chrisanthar, R., Lilleng, P. K., Lønning, P. E., & Eikesdal, H. P. (2017). High PTEN gene expression is a negative prognostic marker in human primary breast cancers with preserved p53 function. Breast Cancer Research and Treatment, 163, 177-190.
42. Zhang, S., Jiang, H., Gao, B., Yang, W., & Wang, G. (2022). Identification of diagnostic markers for breast cancer based on differential gene expression and pathway network. Frontiers in Cell and Developmental Biology, 9, 811585.
43. Zhang, W., Li, Z., Li, H., & Zhang, D. (2024). Identification of differentially expressed genes associated with ferroptosis in Crohn’s disease. Experimental and Therapeutic Medicine, 27(2), 1-12.
44. Zhang, X., & Wang, Y. (2019). Identification of hub genes and key pathways associated with the progression of gynecological cancer. Oncology Letters, 18(6), 6516-6524.
45. Zhou, C., Guo, H., & Cao, S. (2021). Gene Network Analysis of Alzheimer’s Disease Based on Network and Statistical Methods. Entropy, 23(10), 1365.