WWTR1-AS1 LncRNA as a Novel Potential Diagnostic and Prognostic Biomarker in Breast Cancer

Document Type : Research Article

Authors

1 Department of Biology, Faculty of Basic Sciences, Gonbad Kavous University, Gonbad Kavous, Golestan, Iran

2 Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran

3 Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

4 Department of Mathematics and Statistics, Faculty of Basic Sciences, Gonbad Kavous University, Gonbad Kavous, Golestan, Iran

Abstract

Breast cancer is the most lethal form of cancer in women, and patients face serious health risks. Long non-coding RNAs are involved in a variety of regulatory processes and can influence cancers development at different levels. This study aimed to introduce a central regulatory lncRNA based on differentially expressed proteins in breast cancer and evaluate its expression level in breast tissues. In this study, proteomic data was obtained from ProteomeXchange and then differentially expressed proteins were detected. The Enrichr database was used to identify the regulatory factors of differentially expressed proteins, and functional enrichment analysis was used to demonstrate key signaling pathways and biological processes. Eventually, a lncRNA with the highest rank in the central hub was chosen, and its expression level was measured by RT-qPCR in 15 breast cancer tissues and their adjacent nontumor tissues. Proteomic analysis recognized 1149 differential expressed proteins in breast cancer with regulatory agents consisting of 76 TFs, 61 kinases, 366 miRNAs, and 162 lncRNAs. A multi-regulatory network with 1811 nodes and 4022 edges was constructed based on differentially expressed proteins and their associated elements. In addition, these regulatory elements were related to three biological functions and 11 pathways. Finally, bioinformatic analysis identified lncRNA WWTR1-AS1 as having the highest node score involved in different mechanisms. Functional experiments confirmed that the expression level of the lncRNA WWTR1-AS1 was significantly increased in breast cancer patients.  ROC analysis suggested that this lncRNA can be used as a reliable biomarker. Our data provide evidence that the lncRNA WWTR1-AS1 is an effective factor in the regulation of DEPs, is associated with malignant features in breast cancer, and might be useful as a prognostic marker in Breast cancer.

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References

Augoff, K., McCue, B., Plow, E. F., & Sossey-Alaoui, K. (2012). miR-31 and its host gene lncRNA LOC554202 are regulated by promoter hypermethylation in triple-negative breast cancer. Molecular Cancer, 11(1), 5. https://doi.org/10.1186/1476-4598-11-5
Bindea, G., Galon, J., & Mlecnik, B. (2013). CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics, 29(5), 661-663. https://doi.org/10.1093/bioinformatics/btt019
Choi, H., Noh, H., Kim, K. M., Cho, I. J., Lim, S., & Han, A. (2021). Increasing mortality in korean patients with breast cancer: high mortality rate in elderly breast cancer population due to suboptimal treatment and other diseases. Cancer Control, 28, 10732748211037914. https://doi.org/10.1177/10732748211037914
Choudhuri, S. (2023). Long noncoding RNAs: biogenesis, regulation, function, and their emerging significance in toxicology. Toxicology Mechanisms and Methods, 33(7), 541-551. https://doi.org/10.1080/15376516.2023.2197489
Civelek, M., & Lusis, A. J. (2014). Systems genetics approaches to understand complex traits. Nature Reviews Genetics, 15(1), 34-48. https://doi.org/10.1038/nrg3575
Dinno, A. (2015). Nonparametric pairwise multiple comparisons in independent groups using Dunn's test. The Stata Journal, 15(1), 292-300. https://doi.org/10.1177/1536867x1501500117
García-Aranda, M., & Redondo, M. (2017). Protein kinase targets in breast cancer. International Journal of Molecular Sciences, 18(12), 2543. https://doi.org/10.3390/ijms18122543
Gomig, T. H. B., Cavalli, I. J., de Souza, R. L. R., Lucena, A. C. R., Batista, M., Machado, K. C., ... & Ribeiro, E. M. D. S. F. (2019). High-throughput mass spectrometry and bioinformatics analysis of breast cancer proteomic data. Data in Brief, 25, 104125. https://doi.org/10.1016/j.dib.2019.104125
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., ... & Ma'ayan, A. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research, 44(W1), W90-W97. https://doi.org/10.1093/nar/gkw377
Lehmann, B. D., Colaprico, A., Silva, T. C., Chen, J., An, H., Ban, Y., ... & Chen, X. S. (2021). Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes. Nature Communications, 12(1), 6276. https://doi.org/10.1038/s41467-021-26502-6
Li, J., Li, Z., Wu, Y., Diao, P., Zhang, W., Wang, Y., ... & Cheng, J. (2019). Overexpression of lncRNA WWTR1‐AS1 associates with tumor aggressiveness and unfavorable survival in head‐neck squamous cell carcinoma. Journal of Cellular Biochemistry, 120(10), 18266-18277. https://doi.org/10.1002/jcb.29132
Loh, H. Y., Norman, B. P., Lai, K. S., Rahman, N. M. A. N. A., Alitheen, N. B. M., & Osman, M. A. (2019). The regulatory role of microRNAs in breast cancer. International Journal of Molecular Sciences, 20(19), 4940. https://doi.org/10.3390/ijms20194940
Lu, C., Wei, D., Zhang, Y., Wang, P., & Zhang, W. (2021). Long non-coding RNAs as potential diagnostic and prognostic biomarkers in breast cancer: progress and prospects. Frontiers in Oncology, 11, 710538. https://doi.org/10.3389/fonc.2021.710538
Molendijk, J., & Parker, B. L. (2021). Proteome-wide systems genetics to identify functional regulators of complex traits. Cell Systems, 12(1), 5-22. https://doi.org/10.1016/j.cels.2020.10.005
Mon‐López, D., & Tejero‐González, C. M. (2019). Validity and reliability of the TargetScan ISSF Pistol & Rifle application for measuring shooting performance. Scandinavian Journal of Medicine and Science in Sports, 29(11), 1707-1712. https://doi.org/10.1111/sms.13515
Neagu, A. N., Jayathirtha, M., Whitham, D., Mutsengi, P., Sullivan, I., Petre, B. A., & Darie, C. C. (2022). Proteomics-based identification of dysregulated proteins in breast cancer. Proteomes, 10(4), 35. https://doi.org/10.3390/proteomes10040035
Pessoa, J., Martins, M., Casimiro, S., Pérez-Plasencia, C., & Shoshan-Barmatz, V. (2022). Altered expression of proteins in cancer: function and potential therapeutic targets. Frontiers in Oncology, 12, 949139. https://doi.org/10.3389/978-2-88976-581-2
Qin, J., Zhou, Z., Chen, W., Wang, C., Zhang, H., Ge, G., ... & Chen, C. (2015). BAP1 promotes breast cancer cell proliferation and metastasis by deubiquitinating KLF5. Nature Communications, 6(1), 8471. https://doi.org/10.1038/ncomms9471
Sanchez-Vega, F., Mina, M., Armenia, J., Chatila, W. K., Luna, A., La, K. C., ... & Marra, M. A. (2018). Oncogenic signaling pathways in the cancer genome atlas. Cell, 173(2), 321-337. https://doi.org/10.1016/j.cell.2018.03.035
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), 2498-2504. https://doi.org/10.1101/gr.1239303
Sideris, N., Dama, P., Bayraktar, S., Stiff, T., & Castellano, L. (2022). LncRNAs in breast cancer: a link to future approaches. Cancer Gene Therapy, 29(12), 1866-1877. https://doi.org/10.1038/s41417-022-00487-w
Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022). Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, 72(1). https://doi.org/10.3322/caac.21708
Supek, F., Bošnjak, M., Škunca, N., & Šmuc, T. (2011). REVIGO summarizes and visualizes long lists of gene ontology terms. Plos One, 6(7), e21800. https://doi.org/10.1371/journal.pone.0021800
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., ... & Mering, C. V. (2019). STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607-D613. https://doi.org/10.1093/nar/gky1131
Thomassen, M., Tan, Q., & Kruse, T. A. (2008). Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer. BMC Cancer, 8, 1-12. https://doi.org/10.1186/1471-2407-8-394
Tyanova, S., Temu, T., & Cox, J. (2016a). The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nature Protocols, 11(12), 2301-2319. https://doi.org/10.1038/nprot.2016.136
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., ... & Cox, J. (2016b). The Perseus computational platform for comprehensive analysis of (prote) omics data. Nature Methods, 13(9), 731-740. https://doi.org/10.1038/nmeth.3901
Vizcaíno, J. A., Deutsch, E. W., Wang, R., Csordas, A., Reisinger, F., Ríos, D., ... & Hermjakob, H. (2014). ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nature Biotechnology, 32(3), 223-226. https://doi.org/10.1038/nbt.2839
Yu, S., Hu, C., Liu, L., Cai, L., Du, X., Yu, Q., ... & Li, W. (2020). Comprehensive analysis and establishment of a prediction model of alternative splicing events reveal the prognostic predictor and immune microenvironment signatures in triple negative breast cancer. Journal of Translational Medicine, 18, 1-17. https://doi.org/10.1186/s12967-020-02454-1
ZadehRashki, N., Shahmohammadi, Z., Damrodi, Z., Boozarpour, S., Negahdari, A., Mansour Moshtaghi, N., ... & Ghalandarayeshi, S. (2022). Lncrnas as regulators of the stat3 signaling pathway in cancer. Journal of Cell and Molecular Research, 13(2), 137-150. https://doi.org/10.31579/2640-1053/020
Zhang, R., Zhu, Z., Shen, W., Li, X., Dhoomun, D. K., & Tian, Y. (2019a). Golgi membrane protein 1 (GOLM1) promotes growth and metastasis of breast cancer cells via regulating matrix metalloproteinase-13 (MMP13). Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 25, 847. https://doi.org/10.12659/msm.911667
Zhang, Y., Chen, J., Wang, Y., Wang, D., Cong, W., Lai, B. S., & Zhao, Y. (2019b). Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients. Plos One, 14(4), e0202311. https://doi.org/10.1101/384768
Zhou, X., Li, Z., & Li, M. (2024). LncRNA WWTR1-AS1 upregulates Notch3 through miR-136 to increase cancer cell stemness in cervical squamous cell carcinoma. BMC Women's Health, 24(1), 104. https://doi.org/10.1186/s12905-024-02905-7
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., ... & Chanda, S. K. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 10(1), 1523. https://doi.org/10.1038/s41467-019-09234-6
Journal of Genetic Resources
Volume 10, Issue 1
2024
Pages 66-75

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