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Immunomodulatory mechanisms of Bojungikki-tang through systematic transcriptomic analysis_NK-92MI

GSE272090 Homo sapiens (human) RNA-seq (gene expression) 34 samples Submitted 2026/08/01 Platform GPL30209
At a glance

RNA-seq (gene expression) · human · 34 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
34 samples: 12 conditions, mostly in triplicateGroups 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.
Summary (written by the submitters)
This study aimed to identify the modes of action (MOA) of water extract of Bojungikki-tang (BJIKT) and major four ingredients in immune cell lines. The four main ingredients were designated as Panax ginseng C. A. Meyer, Astragalus mongholicus Bunge, Atractylodes japonica Koidzumi, and Glycyrrhiza uralensis Fisher, which are the main active herbs of BJIKT.
Read the full summary ↓
The immune-related cell lines used for transcriptome production are Jurkat (CD4+ T cells), RPMI8226 (B cells), NK-92MI (NK cells), M1MO (macrophages, differentiated from THP-1), and Mature DC (dendritic cells, differentiated from THP-1), respectively. This dataset contains transcriptome expression information for the NK-92MI cell line.
This dataset
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.

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 GSE272090_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1135097 and SRA study SRP519568. Searching any of these in the dataset finder brings you back here.

Study design
12 conditions, mostly in triplicate
NK-92MI_Vehicle(PBS) ×3 NK-92MI_Water extract of BJIKT (High dose) ×3 NK-92MI_Water extract of Panax ginseng C. A. … ×3 NK-92MI_Water extract of Astragalus mongholic… ×3 NK-92MI_Water extract of Astragalus mongholic… ×3 NK-92MI_Water extract of Atractylodes japonic… ×3 NK-92MI_Water extract of Atractylodes japonic… ×3 NK-92MI_Water extract of Glycyrrhiza uralensi… ×3 +4 more

Supports a between-group comparison across 34 samples.

How we read this: 12 replicated groups read from 34 sample titles; they account for 34 of them. 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
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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.