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Pharmacological Downregulation of De Novo Active and Essential lncRNA SMILO via Inhibition of FLI1 Transcription Factor in Multiple Myeloma. [RNA-seq]

GSE260562 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/02/12 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 12 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
BET inhibitors downregulate the expression of the essential lncRNA SMILO in multiple myeloma through regulation of the transcription factor FLI1Haematologica · 2025
Summary (written by the submitters)
Background: Despite the advances in therapeutic strategies, Multiple Myeloma (MM) remains incurable. Our recent research has detected 1556 de novo active chromatin regions in MM in comparison to healthy B cells. These regions were associated to 1059 coding genes and 89 lncRNAs, which play important roles in MM pathobiology (Ordoñez. Genome Research 2020; Carrasco-León.
Read the full summary ↓
Leukemia 2021). The modulation of these genes could be essential for the development of novel therapeutic strategies to improve the response of MM patients. Aims: We aimed to pharmacologically regulate these de novo active genes in an attempt to modulate the abnormal transcriptome of MM. Methods: Given that de novo chromatin activation marks were mainly located at acetylated promoter and enhancer regions, JQ1 Bromodomain and Extraterminal Domain Inhibitor (BETi) was used. Three MM cell lines (KMS-11, RPMI8226 and KMS-12) were treated at 1µM during 72h and then, RNA-seq, ChIP-seq of H3K27ac and ATAC-seq were performed. For the identification of the regulatory proteins of de novo active genes Reverse-ChIP was carried out. Protein inhibitions were conducted using CRISPR-Cas9, and protein and mRNA levels were analyzed by Western Blot and RT-qPCR, respectively. Results: After JQ1 treatment, the RNA-seq revealed a significant downregulation of more than ten percent of de novo active genes, indicating that JQ1 can negatively modify the expression of de novo active genes in MM. Interestingly, SMILO, a de novo active and essential lncRNA in MM, presented one of the greatest inhibitions, surpassing even described BETi targets (Fig. 1A). Considering that pharmacological regulation of the lncRNAs has been widely proposed as a therapeutic approach, we decided to use JQ1 to further explore the mechanism of regulation of this lncRNA. Through ChIP-seq and ATAC-seq analysis, we verified that this decrease was not due to changes in chromatin activation and accessibility produced by JQ1 in MM cells, suggesting that JQ1 may displace the transcriptional machinery, such as Transcription Factors (TFs), from de novo active regions and specifically from those related to SMILO. Interestingly, the Reverse-ChIP study revealed the binding of 16 proteins in the SMILO region. Among these, we selected as our candidate the TF FLI1, because FLI1: 1) was overexpressed in MM in comparison to healthy B cells; 2) presented a positive correlation of expression with SMILO; 3) MM patients with higher expression of FLI1 presented worse Progression Free Survival (PFS) and Overall Survival (OS) both in univariate and multivariate analyses considering classical genetic alterations of MM (Fig. 1B) and 4) FLI1 showed a significant decrease in H3K27ac at the promoter region after JQ1 treatment (Fig. 1C), which correlated with a great decrease in FLI1 protein levels. To ascertain that FLI1 is causally involved in the expression of SMILO, FLI1 was silenced using CRISPR-Cas9 technology, which substantially depleted FLI1 protein levels giving rise to a significant reduction of the expression of SMILO (Fig. 1D). All these results explain the mechanism by which JQ1 downregulates SMILO via the downregulation of the TF FLI1 (Fig. 1E). Summary/Conclusion: Our study highlights the innovative ability of BETi molecules to modulate the expression of lncRNAs. We underscore the involvement of FLI1 in the regulation of SMILO, a de novo active and essential lncRNA in MM, opening the door to the development of novel therapeutic strategies based on RNA directed therapies.
Published in
BET inhibitors downregulate the expression of the essential lncRNA SMILO in multiple myeloma through regulation of the transcription factor FLI1
Gómez-Echarte N, José-Enériz ES, Carrasco-León A et al. · Haematologica 2025 · PMID 39323408 · doi:10.3324/haematol.2024.285966
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 GSE260562_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 PRJNA1082240 and SRA study SRP492549. Searching any of these in the dataset finder brings you back here.

Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 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
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