scRNA-seq Exploration of Alternative Promoters and EMT in TNBC

Objective:

Jit, S., Jain, K., Dhingra, L. et al. Alternative Promoters Drive Transcriptomic Reprogramming and Prognostic Stratification in TNBC. npj Syst Biol Appl 11, 121 (2025). https://doi.org/10.1038/s41540-025-00599-7

This paper discovered the role of alternative promoters in TNBC. Genes: HUWE1, SEC31A & AKAP9 showed promoter switching with no change in gene level expression. As an extended study, I wanted to explore in which tumor programs these genes are more active.

Since TNBC is highly metastatic, I wanted to see the correlation of the expression of these with EMT phenotypic state at single cell level. This is a practice project to explore single-cell RNA sequencing analysis. After reading a recent paper by Jit et al. on alternative promoter switching in Triple-Negative Breast Cancer (TNBC), I wanted to test if the overall expression of some of those specific target genes correlates with the Epithelial-Mesenchymal Transition (EMT) state at a single-cell resolution.

Workflow

• Dataset: Downloaded 32 TNBC samples from the GEO database (GSE305078).
• Processing: Used Seurat for QC, filtering, and dimensionality reduction.
• Integration: Applied Harmony to correct for batch effects across the 32 patients.
• Cell Annotation: Used SingleR with the HumanPrimaryCellAtlasData to isolate the epithelial cancer cells from the tumor microenvironment.
• EMT Scoring: Calculated EMT score using a 76-gene signature method

Results

I ran a correlation analysis between the EMT score and the overall expression of three specific genes within the isolated cancer cells.

• HUWE1: R = 0.196 | p < 0.001
• SEC31A: R = 0.214 | p < 0.001
• AKAP9: R = 0.338 | p < 0.001

The results show a statistically significant, weak-to-moderate positive correlation, with AKAP9 showing the strongest shift alongside the EMT-High phenotype.

Expression across EMT States

Cells were grouped into EMT-High (top 25%) and EMT-Low (bottom 25%) based on their scores.

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Note: The 76-gene EMT signature used in this analysis is based on the metric evaluated by Chakraborty et al. (2020). The specific gene list file (EMT_signature_76GS.xlsx) was obtained from the EMT_score_calculation repository.