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Identification of Key lncRNAs in Gout Under Copper Death and Iron Death Mechanisms: A Study Based on ceRNA Network Analysis and Random Forest Algorithm

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Abstract

This study focused on identifying potential key lncRNAs associated with gout under the mechanisms of copper death and iron death through ceRNA network analysis and Random Forest (RF) algorithm, which aimed to provide new insights into the molecular mechanisms of gout, and potential molecular targets for future therapeutic strategies of gout. Initially, we conducted an in-depth bioinformatics analysis of gout microarray chips to screen the key cuproptosis-related genes (CRGs) and key ferroptosis-related genes (FRGs). Using these data, we constructed a key ceRNA network for gout. Finally, key lncRNAs associated with gout were identified through the RF algorithm combined with ROC curves, and validated using the Comparative Toxicogenomics Database (CTD). We successfully identified NLRP3, LIPT1, and DBT as key CRGs associated with gout, and G6PD, PRKAA1, LIG3, PHF21A, KLF2, PGRMC1, JUN, PANX2, and AR as key FRGs associated with gout. The key ceRNA network identified four downregulated key lncRNAs (SEPSECS-AS1, LINC01054, REV3L-IT1, and ZNF883) along with three downregulated mRNAs (DBT, AR, and PRKAA1) based on the ceRNA theory. According to CTD validation inference scores and biological functions of target mRNAs, we identified a potential gout-associated lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis. This study identified the key lncRNA ZNF883 in the context of copper death and iron death mechanisms related to gout for the first time through the application of ceRNA network analysis and the RF algorithm, thereby filling a research gap in this field and providing new insights into the molecular mechanisms of gout. We further found that lncRNA ZNF883 might function in gout patients by regulating PRKAA1, the mechanism of which was potentially related to uric acid reabsorption in the proximal renal tubules and inflammation regulation. The proposed lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis might represent a potential RNA regulatory pathway for controlling the progression of gout disease. This discovery offered new molecular targets for the treatment of gout, and had significant implications for future therapeutic strategies in managing the gout.

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Data Availability

The primary and processed data used in analysis can be downloaded from GEO public database (https://www.ncbi.nlm.nih.gov/geo/) (accessed on 20 June 2023).

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Acknowledgements

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 82060871); Jiangxi Provincial Natural Science Foundation (Grant No. 20232ACB206051); Key Research Laboratory and Clinical Research Base construction project of Jiangxi Provincial Administration of Traditional Chinese Medicine (Third Batch) [No. Gan Traditional Chinese Medicine Science and Education (2022) No. 8]; and Traditional Chinese Medicine Dominant Disease Specialty Project—Gout [No. Gan Traditional Chinese Medicine Comprehensive (2022) No. 4].

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Authors and Affiliations

  1. Affiliated Hospital of Jiangxi University of Chinese Medicine, No.445, Bayi Avenue, Nanchang, 330006, Jiangxi, China

    Hua-Nan Li

  2. Jiangxi University of Chinese Medicine, Nanchang, 330004, Jiangxi, China

    Zi-Chen Shao, Wei-Kang Sun, Qin-Qin Deng, Ling Cheng, Xin Huang & Lie-Kui Hu

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  1. Zi-Chen Shao
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Contributions

Zichen Shao contributed toward conceptualization, methodology, formal analysis, writing—Original Draft, and visualization. Weikang Sun contributed toward validation. Qinqin Deng contributed toward data Curation. Ling Cheng contributed toward writing—Review & Editing. Xin Huang contributed toward writing— review & editing. Liekui Hu contributed toward writing—review & editing. Huanan Li contributed toward funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hua-Nan Li.

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Shao, ZC., Sun, WK., Deng, QQ. et al. Identification of Key lncRNAs in Gout Under Copper Death and Iron Death Mechanisms: A Study Based on ceRNA Network Analysis and Random Forest Algorithm. Mol Biotechnol (2024). https://doi.org/10.1007/s12033-024-01099-5

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