Increased LEF1 protein levels and isoform switching drive cell proliferation in chronic lymphocytic leukemia [RNA-Seq]
RNA-seq (gene expression) · human · 50 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
- 50 samples: 16 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
- Increased LEF1 protein levels and isoform switching drive cell proliferation in chronic lymphocytic leukemiaBlood · 2025
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 GSE299964_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 PRJNA1277728 and SRA study SRP592513. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 32 samples.
How we read this: 16 replicated groups read from 50 sample titles; they account for 32 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.
- GSM9051083 M-CLL3_Day 0 M-CLL3_Day
- GSM9051084 M-CLL5_Day 0 M-CLL5_Day
- GSM9051085 M-CLL9_Day 0 M-CLL9_Day
- GSM9051086 M-CLL4_Day 0 M-CLL4_Day
- GSM9051087 M-CLL3_Day 6 M-CLL3_Day
- GSM9051088 M-CLL5_Day 6 M-CLL5_Day
- GSM9051089 M-CLL9_Day 6 M-CLL9_Day
- GSM9051090 M-CLL4_Day 6 M-CLL4_Day
- GSM9051091 U-CLL11_Day 0 U-CLL11_Day
- GSM9051092 U-CLL12_Day 0 U-CLL12_Day
- GSM9051093 U-CLL2_Day 0 U-CLL2_Day
- GSM9051094 U-CLL1_Day 0 U-CLL1_Day
- GSM9051095 U-CLL11_Day 6 U-CLL11_Day
- GSM9051096 U-CLL12_Day 6 U-CLL12_Day
- GSM9051097 U-CLL2_Day 6 U-CLL2_Day
- GSM9051098 U-CLL1_Day 6 U-CLL1_Day
- GSM9051099 M-CLL10_NTC
- GSM9051100 M-CLL10_g1 LEF1
- GSM9051101 M-CLL10_g2 LEF1
- GSM9051102 M-CLL5_NTC
- GSM9051103 M-CLL5_g1 LEF1
- GSM9051104 M-CLL5_g2 LEF1
- GSM9051105 M-CLL9_NTC
- GSM9051106 M-CLL9_g1 LEF1
- GSM9051107 M-CLL9_g2 LEF1
- GSM9051108 U-CLL1_NTC
- GSM9051109 U-CLL1_g1 LEF1
- GSM9051110 U-CLL1_g2 LEF1
- GSM9051111 U-CLL2_NTC
- GSM9051112 U-CLL2_g1 LEF1
- GSM9051113 U-CLL2_g2 LEF1
- GSM9051114 U-CLL6_NTC
- GSM9051115 U-CLL6_g1 LEF1
- GSM9051116 U-CLL6_g2 LEF1
- GSM9051117 HG3_Stuffer 1 HG3_Stuffe
- GSM9051118 HG3_Stuffer 2 HG3_Stuffe
- GSM9051119 HG3_LEF1 FL overexpression 1 HG3_LEF1 FL overexpress…
- GSM9051120 HG3_LEF1 FL overexpression 2 HG3_LEF1 FL overexpress…
- GSM9051121 HG3_LEF1 Isoform 6 overexpression 1 HG3_LEF1 Isoform 6 over…
- GSM9051122 HG3_LEF1 Isoform 6 overexpression 2 HG3_LEF1 Isoform 6 over…
+ 10 more — browse all 50 samples with per-sample file links →
- GSE299960 Increased LEF1 protein levels and isoform switching drive cell proliferation in chronic lymphocytic leukemia [CUT&Run] 70 samples shares lef1, proliferation, lymphocytic, increased, switching, leukemia, protein, isoform
- GSE246521 Chronic Lymphocytic Leukemia in African Ancestry Population is Characterized by Increased Genomic Instability 350 samples shares lymphocytic, increased, leukemia
- GSE265940 Overexpression of extracellular matrix proteins, increased cell death and reduced cell proliferation contribute to the pathophysiology of microphthalmia across in vitro patient-derived models 48 samples shares proliferation, increased, protein
- GSE270169 GAPDH Controls Transcript Stability, Protein Translation and Drives Proliferation in Acute Myeloid Leukemia [GAPDH_KD_RNAseq] 12 samples shares proliferation, leukemia, protein
- GSE318878 Micro-RNA networks in T-cell prolymphocytic leukemia reflect T-cell activation and shape DNA damage response and survival pathways 56 samples shares lymphocytic, leukemia
- GSE305113 Immunopeptidome-guided personalized peptide vaccines induce potent T cell response in chronic lymphocytic leukemia 24 samples shares lymphocytic, leukemia
- GSE261917 The Follicular Lymphoma and Chronic Lymphocytic Leukemia Proliferative Microenvironment at Single-Cell Resolution 14 samples shares lymphocytic, leukemia
- GSE259253 Long-Read Single-Cell RNA Sequencing Enables Cell Genotyping to Refine Subclone Detection in Chronic Lymphocytic Leukemia 12 samples shares lymphocytic, leukemia
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.