Integrating Multi-Omics to Identify a Pyroptosis-Related Gene Signature in Triple-Negative Breas

07 Apr 20265 min read0 viewsJournal Feed

GIST

IntroductionTriple-negative breast cancer (TNBC) features significant heterogeneity and a complex tumor immune microenvironment (TIME). Pyroptosis strongly influences this environment, yet the roles of pyroptosis-related genes (PRGs) remain unclear.

Since single transcriptomic methods obscure the full clinical value of PRGs, multi-omics identification of PRGs in TNBC was used to predict the prognosis and immune landscape of TNBC.MethodsWe integrated TNBC transcriptomic data from the TCGA and GEO databases. We constructed a PRG prognostic signature using the LASSO algorithm.

This signature was validated in an independent GEO cohort. We used single-cell RNA sequencing (scRNA-seq) to analyze the expression heterogeneity of signature genes across different cell subpopulations.

We also evaluated their association with the TIME. Spatial transcriptomics (ST) was used to map the spatial distribution of these genes.

Finally, we performed immunohistochemistry (IHC) on 48 clinical TNBC samples. This step validated the protein expression of six core genes (PINK1, GZMB, PFKFB3, RSPO3, TREM1, and VEGFA) .ResultsThe PRG signature demonstrated robust prognostic predictive performance.

It effectively distinguished TNBC patients with different prognoses and immune phenotypes.

Clinical Editorial

Context and study objective

The work addresses triple-negative breast cancer (TNBC) characterized by heterogeneity and a complex tumor immune microenvironment (TIME).
Pyroptosis-linked genes are implicated in modulating TIME, but their precise roles remain uncertain.
The study employs a multi-omics approach to identify pyroptosis-related genes (PRGs) with prognostic and TIME relevance in TNBC, leveraging bulk, single-cell, and spatial transcriptomics.

Study design and analytical framework

Data sources: TNBC transcriptomic data aggregated from The Cancer Genome Atlas (TCGA) and multiple GEO datasets.
Prognostic modeling: A PRG-based signature was constructed using the LASSO algorithm and validated in an independent GEO cohort.
Multi-omics validation: Single-cell RNA sequencing (scRNA-seq) assessed expression heterogeneity of signature genes across cell subpopulations; spatial transcriptomics (ST) mapped spatial distribution; immunohistochemistry (IHC) validated protein expression in clinical samples.

Population and experimental scope

Population scope encompassed TNBC samples from TCGA, GEO cohorts, and 48 clinical TNBC specimens subjected to IHC.

Key findings and mechanistic insights

Prognostic utility: The PRG signature reliably stratified TNBC patients by prognosis and immune phenotype.
Cellular context: scRNA-seq indicated that signature genes are predominantly enriched in T cells.
Temporal dynamics: Pseudotime analysis portrayed a continuous T-cell state transition, marked by progressive upregulation of GZMB.
Intercellular communication: Cell communication analysis highlighted extensive crosstalk between T cells and macrophages, notably via the MIF-CD74-CXCR4 axis.
Spatial localization: ST supported concentrated expression of signature genes within immune cell–enriched regions.
Protein-level validation: IHC in 48 samples showed high GZMB and RSPO3 associated with lower recurrence risk and improved survival, while elevated PINK1, PFKFB3, TREM1, and VEGFA correlated with higher recurrence risk and worse survival outcomes.

Contextual interpretation and limitations

The signature demonstrated robust prognostic performance and reflects notable cellular and spatial heterogeneity within TIME.
The MIF pathway–mediated T cell–macrophage interactions emerged as a potential mechanistic context for TIME remodeling in relation to the PRG signature.
While informative, the study relies on retrospective cohorts and correlative analyses; explicit limitations and potential biases beyond the presented data were not detailed in the provided content.