ArticlePeerJ2023
Identification of key biomarkers based on the proliferation of secondary hyperparathyroidism by bioinformatics analysis and machine learning.
Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Pooled it
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Secondary hyperparathyroidism (SHPT) is a frequent complication of chronic kidney disease (CKD) associated with morbidity and mortality. This study aims to identify potential biomarkers that may be used to predict the progression of SHPT and to elucidate the molecular mechanisms of SHPT pathogenesis at the transcriptome level. Methods: We analyzed differentially expressed genes (DEGs) between diffuse and nodular parathyroid hyperplasia of SHPT patients from the GSE75886 dataset, and then verified DEG levels with the GSE83421 data file of primary hyperparathyroidism (PHPT) patients. Candidate gene sets were selected by machine learning screens of differential genes and immune cell infiltration was explored with the CIBERSORT algorithm. RcisTarget was used to predict transcription factors, and Cytoscape was used to construct a lncRNA-miRNA-mRNA network to identify possible molecular mechanisms. Immunohistochemistry (IHC) staining and quantitative real-time polymerase chain reaction (qRT-PCR) were used to verify the expression of screened genes in parathyroid tissues of SHPT patients and animal models. Results: A total of 614 DEGs in GSE75886 were obtained as candidate gene sets for further analysis. Five key genes (USP12, CIDEA, PCOLCE2, CAPZA1, and ACCN2) had significant expression differences between groups and were screened with the best ranking in the machine learning process. These genes were shown to be closely related to immune cell infiltration levels and play important roles in the immune microenvironment. Transcription factor ZBTB6 was identified as the master regulator, alongside multiple other transcription factors. Combined with qPCR and IHC assay of hyperplastic parathyroid tissues from SHPT patients and rats confirm differential expression of USP12, CIDEA, PCOLCE2, CAPZA1, and ACCN2, suggesting that they may play important roles in the proliferation and progression of SHPT. Conclusion: USP12, CIDEA, PCOLCE2, CAPZA1, and ACCN2 have great potential both as biomarkers and as therapeutic targets in the proliferation of SHPT. These findings suggest novel potential targets and future directions for SHPT research.
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