Developing precision medicine for remote liver cancer patients
RNA-seq (gene expression) · human · 38 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
- Hepatocellular carcinomaThis series is part of our curated briefing on it.
- Samples
- 38 samples: 38 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.
- Paper
- Transcriptomic Characterization of North Queensland Hepatocellular CarcinomaOncology · 2026
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 GSE304512_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 PRJNA1301410 and SRA study SRP606400. 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 38 sample titles: 38 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.
- GSM9151771 Patient 001, Normal Liver
- GSM9151772 Patient 002, Normal Liver
- GSM9151773 Patient 003, Normal Liver
- GSM9151774 Patient 004, Normal Liver
- GSM9151775 Patient 005, Normal Liver
- GSM9151776 Patient 006, Normal Liver
- GSM9151777 Patient 007, Normal Liver
- GSM9151778 Patient 008, Normal Liver
- GSM9151779 Patient 009, Normal Liver
- GSM9151780 Patient 010, Normal Liver
- GSM9151781 Patient 012, Normal Liver
- GSM9151782 Patient 014, Normal Liver
- GSM9151783 Patient 015, Normal Liver
- GSM9151784 Patient 017, Normal Liver
- GSM9151785 Patient 018, Normal Liver
- GSM9151786 Patient 019, Normal Liver
- GSM9151787 Patient 020, Normal Liver
- GSM9151788 Patient 021, Normal Liver
- GSM9151789 Patient 022, Normal Liver
- GSM9151790 Patient 001, Tumour Tissue
- GSM9151791 Patient 002, Tumour Tissue
- GSM9151792 Patient 003, Tumour Tissue
- GSM9151793 Patient 004, Tumour Tissue
- GSM9151794 Patient 005, Tumour Tissue
- GSM9151795 Patient 006, Tumour Tissue
- GSM9151796 Patient 007, Tumour Tissue
- GSM9151797 Patient 008, Tumour Tissue
- GSM9151798 Patient 009, Tumour Tissue
- GSM9151799 Patient 010, Tumour Tissue
- GSM9151800 Patient 012, Tumour Tissue
- GSM9151801 Patient 014, Tumour Tissue
- GSM9151802 Patient 015, Tumour Tissue
- GSM9151803 Patient 017, Tumour Tissue
- GSM9151804 Patient 018, Tumour Tissue
- GSM9151805 Patient 019, Tumour Tissue
- GSM9151806 Patient 020, Tumour Tissue
- GSM9151807 Patient 021, Tumour Tissue
- GSM9151808 Patient 022, Tumour Tissue
- GSE114564 Identification of novel serum biomarkers in early-stage HCC patients. 118 samples same disease briefing
- GSE105130 Comprehensive analysis of Transcriptome Profiles in Hepatocellular Carcinoma patients 52 samples same disease briefing
- GSE140228 Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma 41 samples same disease briefing
- GSE14520 Gene expression data of human hepatocellular carcinoma (HCC) 488 samples same disease briefing
- GSE10143 Gene Expression in Fixed Tissues and Outcome in Hepatocellular Carcinoma 387 samples same disease briefing
- GSE107170 Molecular Signature of Hepatitis D Virus-Associated Hepatocellular Carcinoma 307 samples same disease briefing
- GSE109211 Molecular predictors of prevention of recurrence in hepatocellular carcinoma with sorafenib as adjuvant treatment in the phase 3 STORM trial 140 samples same disease briefing
- GSE10186 Integrative Transcriptome Analysis Reveals Common Molecular Subtypes of Human Hepatocellular Carcinoma 118 samples same disease briefing
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.