GEO series
Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer
GSE245535
Homo sapiens (human)
Single-cell RNA-seq
43 samples
Submitted 2024/06/28
Platform GPL20301
At a glance
Single-cell RNA-seq · human · 43 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Topic
- Pancreatic cancerThis series is part of our curated briefing on it.
- Samples
- 43 samples: 43 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
- Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancerNature medicine · 2024
Summary (written by the submitters)
Metastasis occurs frequently after resection of pancreatic cancer (PC). We hypothesized that multiparametric analysis of pre-metastatic liver biopsies will classify patients according to their metastatic risk, timing, and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PC and 18 control patients with non-cancerous pancreatic lesions were analyzed combining metabolomic, tissue and single cell transcriptomics, and multiplex imaging approaches.
Read the full summary ↓
Patients were followed prospectively (median three years) and classified into four recurrence groups; early (<6 months post resection) or late (>6 months post resection) liver metastasis (LM), extrahepatic (EH) metastasis, and disease-free survivors (NED). Overall, PC livers exhibited signs of augmented inflammation, compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation, and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LM were characterized by scant T cell lobular infiltration, less steatosis, and higher levels of citrullinated H3, compared to patients who developed EH metastasis, who had overexpression of interferon target genes (MX1, NR1D1) and an increase of CD11B+ NK cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset from those with late-onset LM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multiparametric profiling of liver biopsies at the time of PC diagnosis may determine metastatic risk and organotropism, and guide clinical stratification for optimal treatment selection.
Published in
Bojmar L, Zambirinis CP, Hernandez JM et al.
· Nature medicine 2024
· PMID 38942992
· doi:10.1038/s41591-024-03075-7
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 GSE245535_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1028956 and SRA study SRP466723. Searching any of these in the dataset finder brings you back here.
Study design
43 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 43 sample titles: 43 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
- GSM7844642 pre-metastatic liver, pancreatic cancer, patient #49 [49_IGO_06756_I_1]
- GSM7844643 Liver from patient with benign pancreatic lesion, #86 [86_IGO_06756_I_10]
- GSM7844644 pre-metastatic liver, pancreatic cancer, patient #53 [53_IGO_06756_I_3]
- GSM7844645 Liver from patient with benign pancreatic lesion, #55 [55_IGO_06756_I_4]
- GSM7844646 pre-metastatic liver, pancreatic cancer, patient #57 [57_IGO_06756_I_5]
- GSM7844647 pre-metastatic liver, pancreatic cancer, patient #58 [58_IGO_06756_I_6]
- GSM7844648 pre-metastatic liver, pancreatic cancer, patient #64 [64_IGO_06756_I_7]
- GSM7844649 pre-metastatic liver, pancreatic cancer, patient #67 [67_IGO_06756_I_8]
- GSM7844650 pre-metastatic liver, pancreatic cancer, patient #78 [78_IGO_06756_I_9]
- GSM7844651 pre-metastatic liver, pancreatic cancer, patient #10 [10FC_IGO_06756_G_6]
- GSM7844652 pre-metastatic liver, pancreatic cancer, patient #11 [11DB_IGO_06756_G_7]
- GSM7844653 pre-metastatic liver, pancreatic cancer, patient #13 [13RB_IGO_06756_G_8]
- GSM7844654 pre-metastatic liver, pancreatic cancer, patient #14 [14JR_IGO_06756_G_9]
- GSM7844655 pre-metastatic liver, pancreatic cancer, patient #15 [15MS_IGO_06756_G_10]
- GSM7844656 Liver from patient with benign pancreatic lesion, #17 [17LD_IGO_06756_G_12]
- GSM7844657 pre-metastatic liver, pancreatic cancer, patient #18 [18EB_IGO_06756_G_13]
- GSM7844658 pre-metastatic liver, pancreatic cancer, patient #1 [1RB_IGO_06756_G_1]
- GSM7844659 pre-metastatic liver, pancreatic cancer, patient #2 [2NS_re_IGO_06756_G_2]
- GSM7844660 Liver from patient with benign pancreatic lesion, #31 [31VK_IGO_06756_G_14]
- GSM7844661 pre-metastatic liver, pancreatic cancer, patient #34 [34RB_IGO_06756_G_16]
- GSM7844662 pre-metastatic liver, pancreatic cancer, patient #37 [37IL_IGO_06756_G_17]
- GSM7844663 Liver from patient with benign pancreatic lesion, #39 [39WD_IGO_06756_G_18]
- GSM7844664 Liver from patient with benign pancreatic lesion, #40 [40BM_IGO_06756_G_19]
- GSM7844665 pre-metastatic liver, pancreatic cancer, patient #41 [41JA_IGO_06756_G_20]
- GSM7844666 pre-metastatic liver, pancreatic cancer, patient #42 [42BS_IGO_06756_G_21]
- GSM7844667 pre-metastatic liver, pancreatic cancer, patient #43 [43JW_IGO_06756_G_22]
- GSM7844668 pre-metastatic liver, pancreatic cancer, patient #44 [44LJ_IGO_06756_G_23]
- GSM7844669 Liver from patient with benign pancreatic lesion, #45 [45ID_IGO_06756_G_24]
- GSM7844670 pre-metastatic liver, pancreatic cancer, patient #46 [46FM_IGO_06756_G_25]
- GSM7844671 pre-metastatic liver, pancreatic cancer, patient #48 [48MS_IGO_06756_G_26]
- GSM7844672 pre-metastatic liver, pancreatic cancer, patient #50 [50SO_IGO_06756_G_27]
- GSM7844673 pre-metastatic liver, pancreatic cancer, patient #6 [6PW_IGO_06756_G_3]
- GSM7844674 pre-metastatic liver, pancreatic cancer, patient #7 [7KA_IGO_06756_G_4]
- GSM7844675 pre-metastatic liver, pancreatic cancer, patient #9 [9CF_IGO_06756_G_5]
- GSM7844676 pre-metastatic liver, pancreatic cancer, patient #3 [3JD_21CS_IGO_06756_D_7]
- GSM7844677 Liver from patient with benign pancreatic lesion, #5 [5VS_IGO_06756_D_1]
- GSM7844678 pre-metastatic liver, pancreatic cancer, patient #21 [21CS_23SS_IGO_06756_D_8]
- GSM7844679 Liver from patient with benign pancreatic lesion, #22 [22EB_IGO_06756_D_3]
- GSM7844680 pre-metastatic liver, pancreatic cancer, patient #23 [23SS-2_IGO_06756_D_10]
- GSM7844681 pre-metastatic liver, pancreatic cancer, patient #24 [24RF-2_IGO_06756_D_12]
+ 3 more — browse all 43 samples with per-sample file links →
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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.