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Dual Roles of SBSN in Renal Cell Carcinoma Progression and Tumor Thrombus Formation: Cell-Autonomous NF-κB-CD44 Activation and Paracrine Angiogenesis via IFI6

GSE327867 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/08/06 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
Kidney cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × OSRC2-OESBSN vs 3 × OSRC2-VEC vs 3 × HUVEC-OSOESBSN vs 3 × HUVEC-OSVECGroups 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
Dual roles of SBSN in renal cancer progression and tumor thrombus: Cell-autonomous NF-κB-CD44 axis and IFI6-driven paracrine angiogenesisiScience · 2026
Summary (written by the submitters)
Renal cell carcinoma (RCC) is a highly vascularized malignancy with a propensity for venous invasion and tumor thrombus (TT) formation, yet the molecular mechanisms linking vascular aggression to tumor progression remain incompletely understood. In this study, we identify suprabasin (SBSN) as a key oncogenic mediator that is consistently upregulated in RCC tissues and further enriched in TT, correlating with poor patient prognosis.
Read the full summary ↓
Functional assays demonstrate that SBSN promotes RCC cell proliferation, migration, and invasion in vitro. Transcriptomic analysis reveals activation of the NF-κB signaling pathway in SBSN-overexpressing cells, with CD44 emerging as a critical downstream effector. We further delineate a novel SBSN-NF-κB-CD44 axis that drives malignant phenotypes in RCC cells. In vivo, SBSN knockdown attenuates tumor growth and induces a shift in epithelial-mesenchymal transition (EMT) markers. Clinically, SBSN expression in RCC tissue positively correlates with microvessel density (MVD). Mechanistically, tumor-secreted SBSN enhances angiogenesis through a paracrine pathway by upregulating the anti-apoptotic protein IFI6 in endothelial cells, thereby suppressing cleaved caspase-3 and Bax activation and promoting endothelial survival and tube formation. Collectively, our findings uncover a dual role for SBSN in RCC progression: intrinsically via the NF-κB-CD44 axis to drive tumor cell aggressiveness, and extrinsically via the SBSN-IFI6 axis to promote angiogenesis and TT formation. This study positions SBSN as a potential prognostic biomarker and therapeutic target to disrupt both tumor growth and vascular support in advanced RCC.
Published in
Dual roles of SBSN in renal cancer progression and tumor thrombus: Cell-autonomous NF-κB-CD44 axis and IFI6-driven paracrine angiogenesis
Ye Y, Xu J, Liu W et al. · iScience 2026 · PMID 42542584 · doi:10.1016/j.isci.2026.116905
This dataset
Open GSE327867 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 GSE327867_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: 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 PRJNA1452915 and SRA study SRP691456. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42542584) ↗
Study design
3 × OSRC2-OESBSN vs 3 × OSRC2-VEC vs 3 × HUVEC-OSOESBSN vs 3 × HUVEC-OSVEC

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
More Kidney cancer datasets

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