Evidence map›Paper›PMID 35958496›Full record

ArticleAmerican journal of translational research2022

Classifying mild cognitive impairment and Alzheimer's disease by constructing a 14-gene diagnostic model.

Jing Han, Gang-Hua Feng, Hua-Wu Liu, Ji-Ping Yi, Ji-Bao Wu, Xiao-Xi Yao

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 47% of its field
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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Jing HanSchool of Basic Medical Sciences, Xiangnan University Chenzhou 423000, Hunan, China.
Gang-Hua FengDepartment of Neurology, Chenzhou First People's Hospital Chenzhou 423000, Hunan, China.
Hua-Wu LiuSchool of Basic Medical Sciences, Xiangnan University Chenzhou 423000, Hunan, China.
Ji-Ping YiDepartment of Neurology, Chenzhou First People's Hospital Chenzhou 423000, Hunan, China.
Ji-Bao WuDepartment of Neurology, Chenzhou First People's Hospital Chenzhou 423000, Hunan, China.
Xiao-Xi YaoDepartment of Neurology, Chenzhou First People's Hospital Chenzhou 423000, Hunan, China.
Chenzhou First People's Hospital · CNXiangnan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) and mild cognitive impairment (MCI) are two neurodegenerative diseases. Most patients with MCI will develop AD. Early detection of AD and MCI is a crucial issue in terms of secondary prevention. Therefore, more diagnostic models need to be developed to distinguish AD patients from MCI patients.

methodsIn our research, the expression matrix and were screened from Gene Expression Omnibus (GEO) databases. A 14-gene diagnostic model was constructed with lasso logistic analysis. The efficiency and accuracy of diagnostic model have also been validated. In order to clarify the expression differences of 14 genes in health donor, AD and MCI, the blood samples of patients and healthy individuals were collected. The mRNA expression of the 14 genes in blood sample were detected. The SH-SY5Y cell injury model was constructed and biological function of POU2AF1 and ANKRD22 in SH-SY5Y have been proved.

resultsWe obtained 16 genes which have an area under curve (AUC) ≥0.6. After that, a diagnostic model based on 14 genes was constructed. Validation in independent cohort showed that the diagnostic model has a good diagnostic efficiency. The expressions of 6 genes in AD patients were significantly lower than those in healthy individuals and MCI patients, while the expressions of 8 genes in AD patients were significantly higher than those in healthy individuals and MCI patients. In in vitro experiments, we found that two key genes POU2AF1 and ANKRD22 could regulate neuronal development by regulating cell viability and IL-6 expression.

conclusionThe diagnostic model established in this study has a good diagnose efficiency. Most of these genes in diagnostic model also showed diagnostic value in AD patients. This research also can help doctors make better diagnosis for the treatment and prevention of AD.

Indexed as

Alzheimer’s diseasemild cognitive impairmentrisk factorsSH-SY5Y cellsunsupervised clustering

Identifiers

PMID35958496
PMCPMC9360837
OpenAlexW4291675038

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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.