Special issue: Cilia and ciliopathies

Zhao, Chengtian; Roy, Sudipto

doi:10.1002/jcp.30816

Cited by 1 publication

(1 citation statement)

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“…To more accurately screen the m 7 G-regulators associated with the occurrence of OA, significant m 7 G-regulators and OA were extracted using the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest algorithm (RF) [ 33 ], and the two analysis results were intersected using the Venn diagram. Finally, to determine the best prognostic characteristic regulators, the LASSO Cox regression analysis was performed on the basis of the abovementioned results [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Construction of a Diagnostic m7G Regulator-Mediated Scoring Model for Identifying the Characteristics and Immune Landscapes of Osteoarthritis

Liang

Shang

et al. 2023

Biomolecules

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With the increasingly serious burden of osteoarthritis (OA) on modern society, it is urgent to propose novel diagnostic biomarkers and differentiation models for OA. 7-methylguanosine (m7G), as one of the most common base modification forms in post transcriptional regulation, through which the seventh position N of guanine (G) of messenger RNA is modified by methyl under the action of methyltransferase; it has been found that it plays a crucial role in different diseases. Therefore, we explored the relationship between OA and m7G. Based on the expression level of 18 m7G-related regulators, we identified nine significant regulators. Then, via a series of methods of machine learning, such as support vector machine recursive feature elimination, random forest and lasso-cox regression analysis, a total of four significant regulators were further identified (DCP2, EIF4E2, LARP1 and SNUPN). Additionally, according to the expression level of the above four regulators, two different m7G-related clusters were divided via consensus cluster analysis. Furthermore, via immune infiltration, differential expression analysis and enrichment analysis, we explored the characteristic of the above two different clusters. An m7G-related scoring model was constructed via the PCA algorithm. Meanwhile, there was a different immune status and correlation for immune checkpoint inhibitors between the above two clusters. The expression difference of the above four regulators was verified via real-time quantitative polymerase chain reaction. Overall, a total of four biomarkers were identified and two different m7G-related subsets of OA with different immune microenvironment were obtained. Meanwhile, the construction of m7G-related Scoring model may provide some new strategies and insights for the therapy and diagnosis of OA patients.

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Section: Methodsmentioning

confidence: 99%