Gene expression profiling of HIV-associated diffuse large B-cell lymphomas from Malawi
RNA-seq (gene expression) · human · 70 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
- Diffuse large B-cell lymphomaThis series is part of our curated briefing on it.
- Samples
- 70 samples: 70 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
- HIV infection and ART exposure affect tumor TCR repertoire of diffuse large B cell lymphomaJCI insight · 2024
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 GSE262621_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 PRJNA1092658. 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 70 sample titles: 70 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.
- GSM8172542 SMR1, DLBCL, Exp, Male
- GSM8172543 SMR10, DLBCL, Exp, Male
- GSM8172544 SMR11, DLBCL, HIVneg, Male
- GSM8172545 SMR13, DLBCL, HIVneg, Male
- GSM8172546 SMR14, EBV+ DLBCL, Exp, Male
- GSM8172547 SMR15, DLBCL, Exp, Male
- GSM8172548 SMR16, DLBCL, HIVneg, Male
- GSM8172549 SMR18, DLBCL, HIVneg, Male
- GSM8172550 SMR19, DLBCL, Naive, Female
- GSM8172551 SMR2, DLBCL, Exp, Female
- GSM8172552 SMR20, EBV+ DLBCL, HIVneg, Male
- GSM8172553 SMR22, DLBCL, Naive, Female
- GSM8172554 SMR23, DLBCL, Naive, Male
- GSM8172555 SMR24, DLBCL, HIVneg, Male
- GSM8172556 SMR25, DLBCL, HIVneg, Male
- GSM8172557 SMR26, DLBCL, HIVneg, Male
- GSM8172558 SMR27, DLBCL, Exp, Female
- GSM8172559 SMR28, FL/DLBCL, Exp, Male
- GSM8172560 SMR29, DLBCL, Naive, Male
- GSM8172561 SMR3, DLBCL, Exp, Male
- GSM8172562 SMR30, DLBCL, Naive, Female
- GSM8172563 SMR31, DLBCL, Naive, Male
- GSM8172564 SMR32, DLBCL, HIVneg, Female
- GSM8172565 SMR33, DLBCL, Naive, Female
- GSM8172566 SMR34, DLBCL, HIVneg, Female
- GSM8172567 SMR35, DLBCL, Exp, Male
- GSM8172568 SMR36, DLBCL, Naive, Female
- GSM8172569 SMR37, DLBCL, Exp, Female
- GSM8172570 SMR38, DLBCL, HIVneg, Female
- GSM8172571 SMR39, DLBCL, HIVneg, Female
- GSM8172572 SMR4, FL/DLBCL, HIVneg, Female
- GSM8172573 SMR40, DLBCL, HIVneg, Male
- GSM8172574 SMR41, DLBCL, Exp, Female
- GSM8172575 SMR43, DLBCL, HIVneg, Male
- GSM8172576 SMR45, DLBCL, Exp, Male
- GSM8172577 SMR46, DLBCL, Exp, Male
- GSM8172578 SMR48, DLBCL, Exp, Male
- GSM8172579 SMR50, DLBCL, Naive, Female
- GSM8172580 SMR51, DLBCL, Naive, Male
- GSM8172581 SMR52, DLBCL, HIVneg, Female
+ 30 more — browse all 70 samples with per-sample file links →
- GSE213555 Molecular and pathological characterisation of diffuse large B-cell lymphomas in children with Nijmegen breakage syndrome 41 samples same disease briefing · shares lymphomas, diffuse, b-cell, large
- GSE15127 Genomic profiles of diffuse large B-cell lymphomas 167 samples same disease briefing · shares lymphomas, diffuse, b-cell, large
- GSE278092 IRF4 contributes to chemoresistance in IGH-BCL2 positive diffuse large B-cell lymphomas by mediating BCL2 induced SOX9 expression 2 samples same disease briefing · shares lymphomas, diffuse, b-cell, large
- GSE283388 Gene Expression Profiling in Diffuse Large B-Cell Lymphoma (DLBCL) Cell Lines Treated with Selinexor 12 samples same disease briefing · shares diffuse, b-cell, large
- GSE218750 TRAF3 loss-of-function Reveals the Non-Canonical NF-κB Pathway as a Therapeutic Target in Diffuse Large B-cell Lymphoma 24 samples same disease briefing · shares diffuse, b-cell, large
- GSE287333 Myeloid-derived suppressor cells exhibit distinct characteristics in bone marrow and blood of individuals with diffuse large B-cell lymphoma 19 samples same disease briefing · shares diffuse, b-cell, large
- GSE279610 Cell-cycle deregulation in diffuse large B-cell lymphoma promotes critical dependence on the cyclin-G associated kinase 18 samples same disease briefing · shares diffuse, b-cell, large
- GSE139183 KDM4A/KDM4C lysine demethylases sustain an oncogenic transcriptional program in diffuse large B-cell lymphoma [RNA-Seq] 9 samples same disease briefing · shares diffuse, b-cell, large
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.