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Carbonic Anhydrase 12 is a Novel Prognostic Biomarker and Therapeutic Target for High-Risk Follicular Thyroid Cancer

GSE305211 Homo sapiens (human) RNA-seq (gene expression) 19 samples Submitted 2026/01/14 Platform GPL24676Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 19 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
Thyroid cancerThis series is part of our curated briefing on it.
Samples
19 samples: 19 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
Carbonic Anhydrase 12 as a Novel Prognostic Biomarker and Therapeutic Target for High-Risk Follicular Thyroid CarcinomaCancer science · 2026
Summary (written by the submitters)
Identifying high-risk follicular thyroid cancer (FTC) is critical for stratified treatment and developing targeted therapies. This study performed RNA sequencing on high-risk FTC, identifying Carbonic Anhydrase 12 (CA12) as a promising biomarker. High CA12 expression predicted poor disease-free survival, surpassing conventional markers. In vitro, CA12 knockdown reduced proliferation and invasion, while a CA12 inhibitor showed synergistic anti-proliferative effects with Lenvatinib. These findings establish CA12 as both a novel prognostic biomarker and a potential therapeutic target for FTC.
Published in
Carbonic Anhydrase 12 as a Novel Prognostic Biomarker and Therapeutic Target for High-Risk Follicular Thyroid Carcinoma
Tanida M, Takashima T, Tahara S et al. · Cancer science 2026 · PMID 41208415 · doi:10.1111/cas.70248
This dataset
Open GSE305211 in finder → Find more human RNA-seq datasets → Thyroid cancer 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 GSE305211_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 PRJNA1304766 and SRA study SRP608322. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41208415) ↗
Study design
19 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 3 samples read as cases, 8 as controls.

How we read this: Read from 19 sample titles: 19 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
More Thyroid cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.