GEO series

Single-cell transcriptome analysis deciphers stem cell-like epithelial cells as metastatic phenotype and immune characteristics of tumor deposition positive in Colorectal Cancer

GSE299737 Homo sapiens (human) Single-cell RNA-seq 11 samples Submitted 2025/09/17 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 11 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Colorectal cancerThis series is part of our curated briefing on it.
Samples: 11 samples: 3 × tumor deposits vs 3 × lymph node metastases vs 2 × mucosal side vs 2 × serosal sideGroups 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.

Summary (written by the submitters)

Background: Intratumoral heterogeneity (ITH) limits the accurate diagnosis and treatment of colorectal cancer (CRC). Currently, diagnoses, treatment decisions, and prognoses for CRC are determined through multiple superficial endoscopic biopsies. However, there is still a lack of understanding regarding the immune microenvironment in samples obtained from multiple biopsy sites.

Read the full summary ↓

Additionally, CRC cases with positive tumor deposition (TD) are associated with a higher risk of distant metastasis and a worse prognosis. Methods: A total of 11 tissue specimens from three TD-positive CRC patients were annotated to identify the primary tumor (T), the mucosal side (Ti) of primary loci, the serosal side (To) of primary loci, TD, and lymph nodes (LN). For each subregion, we analyzed the transcriptome profile using single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing, Hotspot gene module analysis, and validated our findings with the NanoString ‘PanCancer Progression Panel’. Immunohistochemistry (IHC), Western blotting, Transwell functional experiments, and data from public cohort databases were employed to verify. Results: Cancercells 1/2/4/5, mainly composed of stem-like cells, had a higher risk of metastasis than CancerCells 6/8. CancerCells 6/8 with weak metastatic ability were located only in the Ti side, while CancerCells 1/2/4/5 tumor cells with strong metastatic ability were located in To and metastasis. Bulk sequencing revealed that immune and metastasis-related genes such as SPP1, CD44, and FXYD5 were significantly upregulated in To and/or metastasis than Ti side. Notably, the immune suppression characteristics are more pronounced in To than Ti, which is reflected in the exhaustion of T cell function, more recruitment of tumor-associated macrophages, and reduction of plasma cells. The TD sample had fewer T cells than T and LNm, which present an "immune desert" state. Thus, multiple endoscopic superficial biopsies are limited, and endoscopists and pathologists must consider investigating deeper sites of T, TD, or metastatic LN for the assessment of patient treatment. Furthermore, considering that FXYD5 promotes the expression/stability of the membrane protein CD44, we identified that CancerCells 1/2/4/5 co-expressing FXYD5 and CD44 were more likely to metastasize. Conclusions: This study provides new insights and references for the understanding of the heterogeneity of CRC, as well as for the clinical value of precise diagnosis and future precise treatment provided by in-depth sampling.

This dataset

Open GSE299737 in finder → Find more human RNA-seq datasets → Colorectal cancer landscape →

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 GSE299737_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: 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 PRJNA1276338 and SRA study SRP592017. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × tumor deposits vs 3 × lymph node metastases vs 2 × mucosal side vs 2 × serosal side

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 11 sample titles; they account for 10 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

GSM9045324 primary colon tumor1
GSM9045325 tumor deposits 1 tumor deposits
GSM9045326 lymph node metastases 1 lymph node metastases
GSM9045327 mucosal side 2 mucosal side
GSM9045328 serosal side 2 serosal side
GSM9045329 tumor deposits 2 tumor deposits
GSM9045330 lymph node metastases 2 lymph node metastases
GSM9045331 mucosal side 3 mucosal side
GSM9045332 serosal side 3 serosal side
GSM9045333 tumor deposits 3 tumor deposits
GSM9045334 lymph node metastases 3 lymph node metastases

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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.