GEO series

Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's Disease

GSE282742 Homo sapiens (human) RNA-seq (gene expression) 116 samples Submitted 2024/11/30 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 116 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 116 samples: 115 distinct titles, 1 of them repeatedGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's diseaseAlzheimer's research & therapy · 2025

Summary (written by the submitters)

Background: Effective treatment for Alzheimer’s disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention. Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n=28), stable MCI (S-MCI, n=39), and AD patients (n=49).

Read the full summary ↓

Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models. Results: We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.

Published in

Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's disease

Huang YL, Tsai TH, Shen ZQ et al. · Alzheimer's research & therapy 2025 · PMID 39754267 · doi:10.1186/s13195-024-01651-0

This dataset

Open GSE282742 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE282742_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1190346. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39754267) ↗

Study design

115 distinct titles, 1 of them repeated

F, S-MCI, Subject ID: 427, Age: 62y ×2

How we read this: Read from 116 sample titles: 115 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM8649709 VGH0075: F, P-MCI, Subject ID: 289, Age: 69y
GSM8649710 VGH0089: F, AD, Subject ID: 91, Age: 78y
GSM8649711 VGH0146: M, P-MCI, Subject ID: 125, Age: 67y
GSM8649712 VGH0195: F, AD, Subject ID: 323, Age: 73y
GSM8649713 VGH0203: M, AD, Subject ID: 42, Age: 85y
GSM8649714 VGH0216: M, AD, Subject ID: 16, Age: 83y
GSM8649715 VGH0219: M, AD, Subject ID: 202, Age: 72y
GSM8649716 VGH0240: F, S-MCI, Subject ID: 338, Age: 67y
GSM8649717 VGH0252: F, S-MCI, Subject ID: 287, Age: 66y
GSM8649718 VGH0264: F, AD, Subject ID: 347, Age: 81y
GSM8649719 VGH0265: M, AD, Subject ID: 127, Age: 71y
GSM8649720 VGH0293: M, P-MCI, Subject ID: 198, Age: 81y
GSM8649721 VGH0309: M, AD, Subject ID: 256, Age: 84y
GSM8649722 VGH0310: F, AD, Subject ID: 79, Age: 83y
GSM8649723 VGH0321: M, S-MCI, Subject ID: 72, Age: 77y
GSM8649724 VGH0329: M, S-MCI, Subject ID: 369, Age: 78y
GSM8649725 VGH0402: M, S-MCI, Subject ID: 241, Age: 78y
GSM8649726 VGH0415: M, S-MCI, Subject ID: 389, Age: 60y
GSM8649727 VGH0459: F, S-MCI, Subject ID: 352, Age: 77y
GSM8649728 VGH0515: F, S-MCI, Subject ID: 411, Age: 73y
GSM8649729 VGH0516: M, AD, Subject ID: 260, Age: 62y
GSM8649730 VGH0536: M, AD, Subject ID: 152, Age: 82y
GSM8649731 VGH0539: F, P-MCI, Subject ID: 417, Age: 76y
GSM8649732 VGH0555: M, S-MCI, Subject ID: 426, Age: 63y
GSM8649733 VGH0556: F, S-MCI, Subject ID: 427, Age: 62y
GSM8649734 VGH0576: F, AD, Subject ID: 433, Age: 78y
GSM8649735 VGH0608: M, P-MCI, Subject ID: 233, Age: 77y
GSM8649736 VGH0614: M, S-MCI, Subject ID: 444, Age: 62y
GSM8649737 VGH0622: M, S-MCI, Subject ID: 447, Age: 71y
GSM8649738 VGH0627: F, S-MCI, Subject ID: 38, Age: 72y
GSM8649739 VGH0628: M, S-MCI, Subject ID: 448, Age: 73y
GSM8649740 VGH0644: M, S-MCI, Subject ID: 458, Age: 67y
GSM8649741 VGH0650: F, S-MCI, Subject ID: 461, Age: 64y
GSM8649742 VGH0687: M, P-MCI, Subject ID: 376, Age: 83y
GSM8649743 VGH0729: F, P-MCI, Subject ID: 407, Age: 74y
GSM8649744 VGH0748: F, P-MCI, Subject ID: 475, Age: 61y
GSM8649745 VGH0760: M, S-MCI, Subject ID: 478, Age: 66y
GSM8649746 VGH0770: M, P-MCI, Subject ID: 422, Age: 77y
GSM8649747 VGH0845: M, AD, Subject ID: TADN1-RA001, Age: 62y
GSM8649748 VGH0847: M, AD, Subject ID: 490, Age: 77y

+ 76 more — browse all 116 samples with per-sample file links →

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