Differential Transcriptomic Responses to MRQ 74 and MRQ 76 Rice Extracts in HT-29 Cells; Implications for Apoptosis and Cell Cycle Regulation
RNA-seq (gene expression) · human · 8 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)
- Topic
- Colorectal cancerThis series is part of our curated briefing on it.
- Samples
- 8 samples: 8 samples, each with a distinct title — no replicated groupsGroups 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.
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE337619_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1490505 and SRA study SRP715469. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 8 sample titles: 8 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.
- GSM9858822 control, HT-29 cells, 24 h
- GSM9858823 control, HT-29 cells, 48 h
- GSM9858824 5-FU drug, HT-29 cells, 24 h
- GSM9858825 5-FU drug, HT-29 cells, 48 h
- GSM9858826 74 3NP extract, HT-29 cells, 24 h
- GSM9858827 74 3NP extract, HT-29 cells, 48 h
- GSM9858828 76 3P extract, HT-29 cells, 24 h
- GSM9858829 76 3P extract, HT-29 cells, 48 h
- GSE250000 Bulk RNA-sequencing of transduced colorectal cancer cell line HCT-116 and breast cancer cell line MCF-7 overexpressing full-lenght p53β or p53β-stop-lost. 4 samples same disease briefing
- GSE12032 Inherent gene signature of stage II and III colorectal cancer leads in individual recurrence 198 samples same disease briefing
- GSE109308 Single-cell transcriptomic survey of adenomatous polyposis patients with germline APC mutations 97 samples same disease briefing
- GSE110009 Single cell transcriptome landscape of primary and metastatic tumor in colon cancer 76 samples same disease briefing
- GSE301142 Multiple mechanisms lead to differential loss-of-function effects of pathogenic SARS2 variants: implications for genotype-phenotype analyses 18 samples shares differential, implications
- GSE284658 HT-29 and SW620 24 samples shares ht-29
- GSE287043 Unique Mycobacterium tuberculosis-specific blood immune signatures in sarcoidosis: implications for clinical diagnosis 542 samples shares implications
- GSE290313 Differentially expressed transcripts associated with depressive symptoms during pregnancy and postpartum 301 samples shares differential
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.