npj Systems Biology and ApplicationsGenomicsPeer-reviewed journal article
Alternative Promoters Drive Transcriptomic Reprogramming and Prognostic Stratification in TNBC
Simran JitiD, Kirti JainiD, Leepakshi Dhingra, Rahul KumariD, Sherry BhallaiD
Abstract
Transcriptional regulation frequently involves alternative promoters, yet the distinct regulatory mechanisms governing alternative versus reference promoters remain poorly understood in Triple-Negative Breast Cancer (TNBC), a high-risk breast tumor subtype. It is worth emphasizing that, despite the availability of extensive short-read sequencing data, the impact of alternative promoter usage on TNBC transcriptome dynamics and patient survival remains underexplored. The current study leverages RNA sequencing data from the publicly available TNBC tumor samples (360) and adjacent normal samples (88) to identify TNBC-specific and subtype-specific active alternative promoters (AAPs). To further validate these findings, we integrated H3K4me3 ChIP-seq data, confirming key promoter switching events. We found that HDAC9, RPS14, and EPN1 exhibit tumor-specific AAP expression, while AKAP9 and SEC31A show basal subtype-specific promoter activity in TNBC. Beyond their transcriptional impact, we also explored the prognostic significance of AAPs. The alternative promoters of HUWE1 and FTX were recognized as independent survival predictors in TNBC. Notably, multivariate analysis demonstrated that prognostic AAPs remained significant in predicting relapse-free survival (RFS) even after adjusting for copy number alterations (CNA) and mRNA-based subtypes. Our AAP-based prognostic model achieved C-index of 0.73 in training and 0.72 in validation, with AUROC of 0.72 and integrated Brier score of 0.09 in validation dataset. Our findings indicate that AAP activity serves as a crucial prognostic marker beyond traditional clinical parameters, enhancing patient stratification and risk assessment in TNBC. Understanding promoter switching events may further unveil novel therapeutic targets, paving the way for precision oncology strategies in highly aggressive breast tumors.
- organism: human
- assay: bulk RNA-seq
- assay: ChIP-seq
- research focus: differential expression
- research focus: protein structure prediction
- approach: statistical model
- Citations
- 1
- FWCI
- 0.45
- Field percentile
- top 37%
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