Evidence map›Paper›PMID 38712372›Full record

ArticleCurrent diabetes reviews2024

Bioinformatics to Identify Biomarkers of Diabetic Nephropathy based on Sphingolipid Metabolism and their Molecular Mechanisms.

Yaxian Ning, Xiaochun Zhou, Gouqin Wang, Lili Zhang, Jianqin Wang

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Article in Current diabetes reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Yaxian NingDepartment of Nephrology, Second Hospital of Lanzhou University, Lanzhou 730030, Gansu, China.
Xiaochun ZhouDepartment of Nephrology, Second Hospital of Lanzhou University, Lanzhou 730030, Gansu, China.
Gouqin WangDepartment of Nephrology, Second Hospital of Lanzhou University, Lanzhou 730030, Gansu, China.
Lili ZhangDepartment of Nephrology, Second Hospital of Lanzhou University, Lanzhou 730030, Gansu, China.
Jianqin WangDepartment of Nephrology, Second Hospital of Lanzhou University, Lanzhou 730030, Gansu, China.

Funding

Clinical Medical Research Center of Gansu Provincial 21JR7RA436Foundation of Cui Ying Technology Innovation from the Second Hospital of Lanzhou University 2020QN-20Gansu Province Longyuan Young Talent Project 2020RCXM127Lanzhou City Science and Technology Talent Innovation Project 2021-RC-94National Natural Science Foundation of China 81960142
6 · The paper itself

Abstract

backgroundDiabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life of diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship with DN is unclear. Therefore, screening biomarkers related to sphingolipid metabolism is crucial for treating DN.

methodsTo identify Differentially Expressed Genes (DEGs) in the GSE142153 dataset, we conducted a differential expression analysis (DN samples versus control samples). The intersection genes were obtained by overlapping DEGs and Sphingolipid Metabolism-Related Genes (SMRGs). Furthermore, The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to filter biomarkers. We further analyzed the Gene Set Enrichment analysis (GSEA) and the immunoinfiltrational analysis based on biomarkers.

resultsWe identified 2,186 DEGs associated with DN. Then, five SMR-DEGs were obtained. Subsequently, biomarkers associated with sphingolipid metabolism (

conclusionOverall, we obtained two sphingolipid metabolism-related biomarkers (

Indexed as

BiomarkersComputational BiologyDiabetic NephropathiesSphingolipidsGene Expression ProfilingHumansBiomarkersSphingolipidsbiomarkersDiabetic nephropathyhyperinsulinemia.machine learningRASsphingolipid metabolism

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PMID38712372

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.