Evidence map›Paper›PMID 39333211›Full record

ArticleScientific reports2024

Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms.

Jing Peng, Sha Yang, Chaomin Zhou, Chenguang Qin, Kaiyun Fang, Ying Tan, Jingjing Da, Jiqing Zhang, Yan Zha

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jing Peng *Guizhou University Medical College, Guiyang, 550002, China.
Sha Yang *Guizhou University Medical College, Guiyang, 550002, China.
Chaomin ZhouGuizhou University Medical College, Guiyang, 550002, China.
Chenguang QinDepartment of Anesthesiology, Guizhou Provincial People's Hospital, 83, Zhongshan Road, Nanming District, GuiyangGuizhou, 550002, China.
Kaiyun FangDepartment of Anesthesiology, Guizhou Provincial People's Hospital, 83, Zhongshan Road, Nanming District, GuiyangGuizhou, 550002, China.
Ying TanDepartment of Neurosurgery, Guizhou Provincial People's Hospital, 83, Zhongshan Road, Nanming District, GuiyangGuizhou, 550002, China.
Jingjing DaDepartment of Nephrology, Guizhou Provincial People's Hospital, 83, Zhongshan Road, Nanming District, GuiyangGuizhou, 550002, China. dajingjing0927@126.com.
Jiqing ZhangDepartment of Anesthesiology, Guizhou Provincial People's Hospital, 83, Zhongshan Road, Nanming District, GuiyangGuizhou, 550002, China. zt1724@yeah.net.
Yan ZhaGuizhou University Medical College, Guiyang, 550002, China. zhayan72@126.com.

Funding

Guizhou Science & Technology Department,China QKHCG2023-ZD010National Natural Science Foundation of China 82360148Science and Technology Fund project of Guizhou Provincial Health Commission gzwkj2022-126
6 · The paper itself

Abstract

Cognitive dysfunction caused by diabetes has become a serious global medical issue. Diabetic kidney disease (DKD) exacerbates cognitive dysfunction in patients, although the precise mechanism behind this remains unclear. Here, we conducted an investigation using RNA sequencing data from the Gene Expression Omnibus (GEO) database. We analyzed the differentially expressed genes in DKD and three types of neurons in the temporal cortex (TC) of diabetic patients with cognitive dysfunction. Through our analysis, we identified a total of 133 differentially expressed genes (DEGs) shared between DKD and TC neurons (62 up-regulated and 71 down-regulated). To identify potential common biomarkers, we employed machine learning algorithms (LASSO and SVM-RFE) and Venn diagram analysis. Ultimately, we identified 8 overlapping marker genes (ZNF564, VPS11, YPEL4, VWA5B1, A2ML1, KRT6A, SEC14L1P1, SH3RF1) as potential biomarkers, which exhibited high sensitivity and specificity in ROC curve analysis. Functional analysis using Gene Ontology (GO) revealed that these genes were primarily enriched in autophagy, ubiquitin/ubiquitin-like protein ligase activity, MAP-kinase scaffold activity, and syntaxin binding. Further enrichment analysis using Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) indicates that these biomarkers may play a crucial role in the development of cognitive dysfunction and diabetic nephropathy. Building upon these biomarkers, we developed a diagnostic model with a reliable predictive ability for DKD complicated by cognitive dysfunction. To validate the 8 biomarkers, we conducted RT-PCR analysis in the cortex, hippocampus and kidney of animal models. The results demonstrated the up-regulation of SH3RF1 in the cortex, hippocampus and kidney of mice, which was further confirmed by immunofluorescence and Western blot validation. Notably, SH3RF1 is a scaffold protein involved in cell survival in the JNK signaling pathway. Based on these findings, we support that SH3RF1 may be a common gene expression feature that influences DKD and cognitive dysfunction through the apoptotic pathway.

Indexed as

BiomarkersCognitive DysfunctionDiabetic NephropathiesMachine LearningAlgorithmsAnimalsGene Expression ProfilingHumansMiceNeuronsBiomarkersCognitive dysfunctionDiabetic kidney diseaseMachine learning algorithmsRNA sequencingSH3RF1

Identifiers

PMID39333211
PMCPMC11436791

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Registered trials

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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.