Prioritizing potential diagnostic biomarkers of Alzheimer’s disease by investigating gene expression data: A network‐based approach
Bibliographic record
Abstract
Abstract Background Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder. The identification of differentially expressed genes (DEGs) across affected brain regions can provide new insights into the mechanisms of AD. Method Our study aims to identify potential biomarkers of AD across brain regions using gene expression and network‐based approaches. The gene expression data were downloaded from Gene Expression Omnibus (GEO) for the series GSE5281. The series comprises expression data from 6 brain regions Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX). Differentially expressed genes (DEGs) were identified using GeneSpring GX 12.6.1 software. A protein‐protein interaction network (PPIN) was constructed from high throughput experiments using Biosogenet. A subnetwork comprising common DEGs and their first neighbors was extracted from the complex PPIN, and the network centralities, including degree and betweenness, were calculated using NetworkAnalyzer plugin in Cytoscape 3.7.1. Result Our results identified 4748 non‐redundant DEGs, of which 1493 and 3255 constitute the up and down‐regulated genes, respectively. A total of 124 common DEGs were identified across more than four brain regions. The subnetwork comprised 5723 and 140625 nodes and edges, respectively. Topological analysis of the subnetwork identified 474/148 Hub/Bottleneck genes and 146 Hub‐Bottleneck genes. Two HB genes EGFR and FYN and two H genes NOTCH2NL and SRRM2 were identified as up‐regulated across four brain regions. Six HB genes CUL3, COPS5, HSP90AB1, YWHAZ, YWHAB, and CDC42, and two H genes SNCA, TUBA4A were identified down‐regulated across five brain regions. Furthermore, four HB genes UBC, CUL1, C1QBP, and UBQLN1 and 10 H genes TUBB, GAPDH, SSX2IP, AP2M1, PSMA1, SKP1, TERF2IP, ATP5A1, CCT7, and NDUFA4 are found to be down‐regulated across four brain‐region. Of these, SNCA, GAPDH, UBQLN1 were known to be associated with AD. Conclusion The identification of AD biomarkers across different brain regions integrating differential gene expression study and network‐based approach may provide new insights into the mechanisms of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".