GEO series

S100A9 Overexpression Drives Chemoresistance and Poor Prognosis in AML with Adavivint as a Targeted Therapeutic

GSE283851 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2025/12/31 Platform GPL29480

At a glance

Single-cell RNA-seq · human · 18 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples: 18 samples
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.

Summary (written by the submitters)

Drug resistance and poor prognosis in acute myeloid leukemia (AML) are major challenges, underscoring the need for effective biomarkers and targeted therapies. In this study, we identified S100A9 as a critical biomarker for drug resistance and poor prognosis in AML through integrative bioinformatics analysis and experimental validation.

Read the full summary ↓

Using transcriptomic datasets, single-cell sequencing, and proteomics, we demonstrated that S100A9 overexpression is enriched in drug-resistant AML cells, particularly in the M5 subtype. High S100A9 expression correlates with shorter survival and higher risk scores, establishing its role as a prognostic marker. Functional studies revealed that S100A9 promotes AML cell proliferation and chemoresistance by enhancing mitochondrial oxidative phosphorylation and maintaining redox homeostasis through interaction with thioredoxin (TXN). This axis prevents excessive reactive oxygen species (ROS) accumulation, supports cell survival, and activates the WNT/β-catenin pathway to drive tumor growth. To explore therapeutic options, we identified Adavivint (Ada) through computational screening as a selective cytotoxic agent for S100A9-overexpressing AML cells. Ada demonstrated superior efficacy compared to standard drugs such as venetoclax and cytarabine, while showing reduced toxicity to normal bone marrow cells. In vivo, Ada significantly suppressed tumor progression and prolonged survival in murine models of S100A9-overexpressing AML. These findings highlight S100A9 as a promising therapeutic target and establish Adavivint as a potential agent to overcome chemoresistance in S100A9-positive AML, offering new avenues for improving outcomes in this challenging disease.

This dataset

Open GSE283851 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia landscape →

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 GSE283851_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1195898 and SRA study SRP550242. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 18 more — browse all 18 samples with per-sample file links →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.