Identification of Potential Biomarkers for Hypertensive Nephropathy by Bioinformatics Analysis

Ariski Fajarido; Fadilah Fadilah; Wawaimuli Arozal

doi:10.24036/eksakta/vol25-iss04/537

EKSAKTA: Berkala Ilmiah Bidang MIPA

Open Access

Identification of Potential Biomarkers for Hypertensive Nephropathy by Bioinformatics Analysis

Ariski Fajarido ¹

, Fadilah Fadilah ²

, Wawaimuli Arozal ³

¹ Master Programs in Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia

² Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia

³ Department of Pharmacology and Therapeutics, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia

Published 2024-12-30

Vol. 25 No. 04 (2024) PAGES 484-497

DOI 10.24036/eksakta/vol25-iss04/537

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Abstract

Hypertensive nephropathy (HN) is a common complication of chronic hypertension that leads to kidney damage. This study aimed to identify potential biomarkers and key pathways associated with HN using bioinformatics tools. Gene data related to HN were retrieved from GeneCards and the Comparative Toxicogenomics Database (CTD), resulting in 89 genes from GeneCards and 10,898 genes from CTD. A Venn diagram revealed 58 overlapping genes, which were then analyzed using Protein-Protein Interaction (PPI) networks and the CytoHubba plugin in Cytoscape. The Maximal Clique Centrality (MCC) algorithm identified 10 hub genes, including ACE, AGT, ACE2, AGTR1, and AGTR2, integral to the renin- angiotensin-aldosterone system (RAAS). Functional enrichment analysis using Gene Ontology (GO) and KEGG pathways revealed that the most significant biological process was regulating systemic arterial blood pressure by the Renin-Angiotensin system, with the renin-angiotensin system pathway being the most highly enriched. Further visualization using ShinyGo highlighted the involvement of key genes in the RAAS pathway. These findings provide valuable insights into the molecular mechanisms underlying HN and suggest that bioinformatics approaches can aid in the identification of specific biomarkers for early diagnosis, non-invasive monitoring, and targeted treatments for HN in the future.

References

1

Wang, Z., Liu, Z., Yang, Y., & Kang, L. (2020). Identification of biomarkers and pathways in hypertensive nephropathy based on the ceRNA regulatory network. BMC nephrology, 21, 1-10.

2

Lucero, C. M., Prieto-Villalobos, J., Marambio-Ruiz, L., Balmazabal, J., Alvear, T. F., Vega, M., ... & GÃ³mez, G. I. (2022). Hypertensive nephropathy: unveiling the possible involvement of hemichannels and pannexons. International Journal of Molecular Sciences, 23(24), 15936.

3

Ameer, O. Z. (2022). Hypertension in chronic kidney disease: What lies behind the scene. Frontiers in pharmacology, 13, 949260.

4

Kostovska, I., Tosheska Trajkovska, K., LabudoviÄ‡, D., Kostovski, O., & Spasovski, G. (2023). Urinary Nephrin as an Early Biomarker of Hypertensive Nephropathy. Acta clinica Croatica, 62(4), 635-643.

5

Kostovska, I., Tosheska Trajkovska, K., Labudovikj, D., Cekovska, S., Kostovski, O., & Spasovski, G. (2023). Assessment of urinary podocalyxin as a biomarker of early diagnosis of hypertensive nephropathy. The Ukrainian Biochemical Journal.

6

Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., ... & Von Mering, C. (2023). The STRING database in 2023: proteinâ€“protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic acids research, 51(D1), D638-D646.

7

Ali, A., & Sheng-Chang, C. (2020). Characterization of well logs using K-mean cluster analysis. Journal of Petroleum Exploration and Production Technology, 10, 2245-2256.

8

Doncheva, N. T., Morris, J. H., Gorodkin, J., & Jensen, L. J. (2018). Cytoscape StringApp: network analysis and visualization of proteomics data. Journal of proteome research, 18(2), 623-632.

9

Oti, E. U., Olusola, M. O., Eze, F. C., & Enogwe, S. U. (2021). Comprehensive review of K-Means clustering algorithms. criterion, 12, 22-23.

10

Rauf, S., Ullah, S., Abid, M. A., Ullah, A., Khan, G., Khan, A. U., ... & Faisal, S. (2024). A computational study of gene expression patterns in head and neck squamous cell carcinoma using TCGA data. Future Science OA, 10(1), 2380590.

11

Ge, S. X., Jung, D., & Yao, R. (2020). ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics, 36(8), 2628-2629.

12

Barshir, R., Fishilevich, S., Iny-Stein, T., Zelig, O., Mazor, Y., Guan-Golan, Y., ... & Lancet, D. (2021). GeneCaRNA: a comprehensive gene-centric database of human non-coding RNAs in the GeneCards suite. Journal of molecular biology, 433(11), 166913.

13

Davis, A. P., Wiegers, T. C., Johnson, R. J., Sciaky, D., Wiegers, J., & Mattingly, C. J. (2023). Comparative toxicogenomics database (CTD): update 2023. Nucleic acids research, 51(D1), D1257-D1262.

14

Hallee, L., & Gleghorn, J. P. (2023). Protein-Protein Interaction Prediction is Achievable with Large Language Models. bioRxiv, 2023-06.

15

Ramos, R. H., Ferreira, C. D. O. L., & Simao, A. (2024). Human proteinâ€“protein interaction networks: A topological comparison review. Heliyon.

16

Hall, J. E., do Carmo, J. M., da Silva, A. A., Wang, Z., & Hall, M. E. (2019). Obesity, kidney dysfunction and hypertension: mechanistic links. Nature reviews nephrology, 15(6), 367-385.

17

De Bhailis, Ã. M., & Kalra, P. A. (2022). Hypertension and the kidneys. British journal of hospital medicine, 83(5), 1-11.

18

Ames, M. K., Atkins, C. E., & Pitt, B. (2019). The reninâ€angiotensinâ€aldosterone system and its suppression. Journal of veterinary internal medicine, 33(2), 363-382.

19

Guo, L., Lin, M., Cheng, Z., Chen, Y., Huang, Y., & Xu, K. (2019). Identification of key genes and multiple molecular pathways of metastatic process in prostate cancer. PeerJ, 7, e7899.

20

A. Asadzadeh, N. Ghorbani, and K. Dastan (2023). Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study, Int J Reprod Biomed, vol. 21, no. 10, pp. 809â€“818.

21

Kunvariya, A. D., Dave, S. A., Modi, Z. J., Patel, P. K., & Sagar, S. R. (2023). Exploration of multifaceted molecular mechanism of angiotensin-converting enzyme 2 (ACE2) in pathogenesis of various diseases. Heliyon, 9(5).

22

Costa, L. B., Perez, L. G., Palmeira, V. A., Macedo e Cordeiro, T., Ribeiro, V. T., Lanza, K., & Simoes e Silva, A. C. (2020). Insights on SARS-CoV-2 molecular interactions with the renin-angiotensin system. Frontiers in Cell and Developmental Biology, 8, 559841.

23

Bhushan, S., Xiao, Z., Gao, K., Mao, L., Chen, J., Ping, W., ... & Zhang, Z. (2023). Role and interaction between ACE1, ACE2 and their related genes in cardiovascular disorders. Current Problems in Cardiology, 48(8), 101162.

24

Bode, M., Herrnstadt, G. R., Dreher, L., Ehnert, N., Kirkerup, P., Lindenmeyer, M. T., ... & Wenzel, U. O. (2024). Deficiency of complement C3a and C5a receptors does not prevent angiotensin IIâ€“induced hypertension and hypertensive end-organ damage. Hypertension, 81(1), 138-150.

25

Peng, Z., Xu, Q., Hu, W., & Cheng, Y. (2023). Review on Molecular Mechanism of Hypertensive Nephropathy. Current Pharmaceutical Design, 29(32), 2568-2578.

26

Costantino, V. V., Gil Lorenzo, A. F., Bocanegra, V., & VallÃ©s, P. G. (2021). Molecular mechanisms of hypertensive nephropathy: renoprotective effect of losartan through Hsp70. Cells, 10(11), 3146.

27

Evangelista, J. E., Xie, Z., Marino, G. B., Nguyen, N., Clarke, D. J., & Maâ€™ayan, A. (2023). Enrichr-KG: bridging enrichment analysis across multiple libraries. Nucleic acids research, 51(W1), W168-W179.

28

Zeng, Y., Jiang, Y., Huang, Z., Li, K., & Zhou, Y. (2023). Association between AGTR1 (c. 1166 A> C) Polymorphisms and Kidney Injury in Hypertension. Frontiers in Bioscience-Landmark, 28(7), 146.

29

Han, W., Wang, M., Zhai, X., Gan, Q., Guan, S., & Qu, X. (2020). Chemical renal denervation-induced upregulation of the ACE2/Ang (1-7)/Mas axis attenuates blood pressure elevation in spontaneously hypertensive rats. Clinical and Experimental Hypertension, 42(7), 661-668.

30

Wu, C., Ye, D., Mullick, A. E., Li, Z., Danser, A. J., Daugherty, A., & Lu, H. S. (2020). Effects of renin-angiotensin inhibition on ACE2 and TMPRSS2 expression: insights into COVID-19. bioRxiv, 2020-06.