Evidence map›Paper›PMID 37418137›Full record

ArticleCellular and molecular neurobiology2023

A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics.

Jian Li, Wenjia Liu, Wen Sun, Xin Rao, Xiaodong Chen, Liyang Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cellular and molecular neurobiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
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  3. Article
  4. Article
  5. Article
  6. Review
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

6 authors.

Jian Li *School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China.
Wenjia Liu *School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China.
Wen SunSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China.
Xin RaoSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China. raoxin1992@hdu.edu.cn.
Xiaodong ChenSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China. xiaodong.chen@qmul.ac.uk.
Liyang YuSchool of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China. yuliyang@hdu.edu.cn.

Funding

China Postdoctoral Science Foundation 2022M720976National Natural Science Foundation of China 62101171Natural Science Foundation of Zhejiang Province LQ22F010015
6 · The paper itself

Abstract

Alzheimer's disease (AD) is a neurodegenerative disease with an annual incidence increase that poses significant health risks to people. However, the pathogenesis of AD is still unclear. Autophagy, as an intracellular mechanism can degrade damaged cellular components and abnormal proteins, which is closely related to AD pathology. The goal of this work is to uncover the intimate association between autophagy and AD, and to mine potential autophagy-related AD biomarkers by identifying key differentially expressed autophagy genes (DEAGs) and exploring the potential functions of these genes. GSE63061 and GSE140831 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of AD expression profiles. A total of 259 autophagy-related genes were discovered through the autophagy gene databases ATD and HADb. The differential genes of AD and autophagy genes were integrated and analyzed to screen out DEAGs. Then the potential biological functions of DEAGs were predicted, and Cytoscape software was used to detect the key DEAGs. There were ten DEAGs associated with the AD development, including nine up-regulated genes (CAPNS1, GAPDH, IKBKB, LAMP1, LAMP2, MAPK1, PRKCD, RAB24, RAF1) and one down-regulated gene (CASP1). The correlation analysis reveals the potential correlation among 10 core DEAGs. Finally, the significance of the detected DEAGs expression was verified, and the value of DEAGs in AD pathology was detected by the receiver operating characteristic curve. The area under the curve values indicated that ten DEAGs are potentially valuable for the study of the pathological mechanism and may become biomarkers of AD. This pathway analysis and DEAG screening in this study found a strong association between autophagy-related genes and AD, providing new insights into the pathological progression of AD. Exploring the relationship between autophagy and AD: analysis of genes associated with autophagy in pathological mechanisms of AD using bioinformatics. 10 autophagy-related genes play an important role in the pathological mechanisms of AD.

Indexed as

Alzheimer DiseaseNeurodegenerative DiseasesAutophagyBiomarkersComputational BiologyHumansBiomarkersAlzheimer’s diseaseAutophagyBiomarkerDifferentially expressed autophagy genesPathological mechanism

Identifiers

PMID37418137
PMCPMC11409956

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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.