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Coordinated regulation by lncRNAs results in tight lncRNA–target couplings

GSE263343 Homo sapiens (human) RNA-seq (gene expression) 80 samples Submitted 2024/04/05 Platform GPL18573Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 80 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
80 samples: 22 conditions, mostly in duplicateGroups 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
Coordinated regulation by lncRNAs results in tight lncRNA-target couplingsCell genomics · 2025
Summary (written by the submitters)
The determination of long non-coding RNA (lncRNA) function is a major challenge in RNA biology with applications to basic, translational, and medical research. Our efforts to improve the accuracy of lncRNA-target inference identified lncRNAs that coordinately regulate both the transcriptional and post-transcriptional processing of their targets.
Read the full summary ↓
Namely, these lncRNAs may regulate the transcription of their target and chaperone the resulting message until its translation, leading to tightly coupled lncRNA and target abundance. Our analysis suggested that hundreds of cancer genes are coordinately and tightly regulated by lncRNAs and that this unexplored regulatory paradigm may propagate the effects of non-coding alterations to effectively dysregulate gene expression programs. As a proof-of-principle we studied the regulation of DICER1—a cancer gene that controls microRNA biogenesis—by the lncRNA ZFAS1, showing that ZFAS1 activates DICER1 transcription and blocks its post-transcriptional repression to phenomimic and regulate DICER1 and its target microRNAs.
Published in
Coordinated regulation by lncRNAs results in tight lncRNA-target couplings
Chiu HS, Somvanshi S, Chang CT et al. · Cell genomics 2025 · PMID 40628267 · doi:10.1016/j.xgen.2025.100927
This dataset
Open GSE263343 in finder → Find more human RNA-seq datasets →
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 GSE263343_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 PRJNA1096299 and SRA study SRP499755. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40628267) ↗
Study design
22 conditions, mostly in duplicate
CRISPRi_HEK293T_PoolCTL_RepA_LysisPlate ×6 CRISPRi_HEK293T_PoolCTL_RepB_LysisPlate ×6 CRISPRi_HEK293T_NEAT1_RepA_LysisPlate ×6 CRISPRi_HEK293T_NEAT1_RepB_LysisPlate ×6 siRNA_ECC-1_NT_Rep1_L ×2 siRNA_ECC-1_NT_Rep2_L ×2 siRNA_ECC-1_siDICER1_Rep1_L ×2 siRNA_ECC-1_siDICER1_Rep2_L ×2 +34 more

Supports a between-group comparison across 60 samples.

How we read this: 22 replicated groups read from 80 sample titles; they account for 60 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

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