Evidence map›Paper›PMID 35966794›Full record

ArticleFrontiers in aging neuroscience2022

Identification of diagnostic signatures associated with immune infiltration in Alzheimer's disease by integrating bioinformatic analysis and machine-learning strategies.

Yu Tian, Yaoheng Lu, Yuze Cao, Chun Dang, Na Wang, Kuo Tian, Qiqi Luo, Erliang Guo, Shanshun Luo, Lihua Wang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 6% 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

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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

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  18. A glance through the effects of CD4Computational and structural biotechnology journal · 2023
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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

11 authors at 5 institutions in 1 country.

Yu TianDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Yaoheng LuDepartment of General Surgery, Chengdu Integrated Traditional Chinese Medicine and Western Medicine Hospital, Chengdu, China.
Yuze CaoDepartment of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Chun DangWest China Medical Publishers, West China Hospital, Sichuan University, Chengdu, China.
Na WangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Kuo TianDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Qiqi LuoDepartment of Gerontology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Erliang GuoDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Shanshun LuoDepartment of Gerontology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Lihua WangDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Qian LiDepartment of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
First Affiliated Hospital of Harbin Medical University · CNSecond Affiliated Hospital of Harbin Medical University · CNHarbin Medical University · CNPeking Union Medical College Hospital · CNWest China Medical Center of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: As a chronic neurodegenerative disorder, Alzheimer's disease (AD) is the most common form of progressive dementia. The purpose of this study was to identify diagnostic signatures of AD and the effect of immune cell infiltration in this pathology. Methods: The expression profiles of GSE109887, GSE122063, GSE28146, and GSE1297 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between AD and control brain samples. Functional enrichment analysis was performed to reveal AD-associated biological functions and key pathways. Besides, we applied the Least Absolute Shrinkage Selection Operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analysis to screen potential diagnostic feature genes in AD, which were further tested in AD brains of the validation cohort (GSE5281). The discriminatory ability was then assessed by the area under the receiver operating characteristic curves (AUC). Finally, the CIBERSORT algorithm and immune cell infiltration analysis were employed to assess the inflammatory state of AD. Results: A total of 49 DEGs were identified. The functional enrichment analysis revealed that leukocyte transendothelial migration, cytokine receptor interaction, and JAK-STAT signaling pathway were enriched in the AD group. MAF basic leucine zipper transcription factor F (MAFF), ADCYAP1, and ZFP36L1 were identified as the diagnostic biomarkers of AD with high discriminatory ability (AUC = 0.850) and validated in AD brains (AUC = 0.935). As indicated from the immune cell infiltration analysis, naive B cells, plasma cells, activated/resting NK cells, M0 macrophages, M1 macrophages, resting CD4 Conclusion: MAFF, ADCYAP1, and ZFP36L1 may become new candidate biomarkers of AD, which were closely related to the pathogenesis of AD. Moreover, the immune cells mentioned above may play crucial roles in disease occurrence and progression.

Indexed as

Alzheimer’s diseasebioinformatic analysisdiagnostic biomarkersimmune cell infiltrationmachine-learning strategies

Identifiers

PMID35966794
PMCPMC9372364
OpenAlexW4288711322

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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.