GEO series

Single-Cell Transcriptomics Reveal Distinct Biological and Immunometabolic Landscapes in Renal Medullary Carcinoma Compared to Clear Cell Renal Cell Carcinoma

GSE289672 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2026/03/28 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 10 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: Kidney cancerThis series is part of our curated briefing on it.
Samples: 10 samples: 10 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.

Summary (written by the submitters)

Renal medullary carcinoma (RMC) is a rare, highly aggressive kidney malignancy primarily affecting young individuals of African descent with sickle cell trait. The currently available targeted therapies and immunotherapies approved for clear cell renal cell carcinoma (ccRCC) are ineffective against RMC, highlighting the urgent need for novel treatment approaches specifically tailored to the distinct biological and immune hallmarks of RMC.

Read the full summary ↓

Here, we performed single-cell RNA-sequencing (scRNA-seq) of tumor samples from five patients with RMC and five patients with ccRCC to define their immunometabolic and transcriptomic landscapes. Differential expression analysis identified ENPP3 and CA9 as distinct cell surface markers upregulated in ccRCC, whereas MUC16, TROP2, and EPCAM were upregulated in RMC tumors cells. RMC displayed a more immunosuppressive tumor microenvironment (TME), with lower infiltration of T and NK cells but an enrichment of immunosuppressive SPP1+ macrophages, contrasting with the more immune-rich TME in ccRCC. Ligand-receptor interaction analyses revealed stronger VEGF-VEGFR signaling in ccRCC and enhanced APP-CD74 signaling in RMC. Nephron anatomy mapping indicated that RMC originates in the loop of Henle, whereas ccRCC arises from proximal tubule cells. We uncovered distinct metabolic reprogramming, with RMC demonstrating higher oxidative phosphorylation (OXPHOS) and lower glycolysis compared to ccRCC. These findings were validated in vitro with RMC cell lines showing elevated oxygen consumption and diminished extracellular acidification compared with ccRCC cell lines. Overall, this study underscores the distinct origins, gene expression patterns, and immunometabolic features of RMC versus ccRCC, offering insights for future tailored therapeutic strategies against RMC.

This dataset

Open GSE289672 in finder → Find more human RNA-seq datasets → Kidney 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 GSE289672_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 PRJNA1223925. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

10 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 10 sample titles: 10 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

GSM8796240 RMC46, replicate1, scRNA-seq
GSM8796241 RMC49, replicate1, scRNA-seq
GSM8796242 RMC53, replicate1, scRNA-seq
GSM8796243 RMC66, replicate1, scRNA-seq
GSM8796244 RMC71, replicate1, scRNA-seq
GSM8796245 ccRCC1,replicate1, scRNA-seq
GSM8796246 ccRCC2,replicate1, scRNA-seq
GSM8796247 ccRCC3,replicate1, scRNA-seq
GSM8796248 ccRCC4,replicate1, scRNA-seq
GSM8796249 ccRCC5,replicate1, scRNA-seq

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GSE214635 The E3 ligase RBCK1 reduces the sensitivity of ccRCC to sunitinib through the ANKRD35-MITD1-ANXA1 axis 18 samples same disease briefing
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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.