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Updated: May 28, 2025
16:24
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
Published on: February 21, 2014
20.1K
Profiling Precursor microRNAs of Breast Cancer From Total RNA Sequencing Data to Gain Insights Into Their Roles and
Sen Wu1, Jia-Wern Pan2, Marimuthu Citartan1
1Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia.
Genes, Chromosomes & Cancer
|February 12, 2025
Summary
We developed a new algorithm to detect precursor microRNAs (pre-miRNAs) from RNA sequencing data. This method identified pre-miRNAs that can predict breast cancer prognosis, offering new insights into breast cancer development.
Area of Science:
- Molecular Biology
- Genomics
- Cancer Research
Background:
- Breast cancer is a heterogeneous disease with distinct subtypes, including luminal breast cancer (LBC) and triple-negative breast cancer (TNBC).
- Precursor microRNAs (pre-miRNAs) are implicated in cancer development but are often overlooked in standard RNA sequencing analysis.
- Profiling pre-miRNAs from raw RNA sequencing data presents a technical challenge.
Purpose of the Study:
- To develop and validate a novel algorithm for profiling pre-miRNAs from raw total RNA sequencing data.
- To identify differentially expressed pre-miRNAs between TNBC and LBC subtypes.
- To establish a pre-miRNA-based prognostic signature for breast cancer.
Main Methods:
- Development of a novel algorithm to profile pre-miRNAs from total RNA sequencing data.
- Profiling 907 breast cancer samples from the Malaysian Breast Cancer Genetic Study (MyBrCa) cohort.
- Comparison of pre-miRNA profiles with mature miRNA profiles from The Cancer Genome Atlas (TCGA).
- Differential expression analysis between TNBC and LBC, followed by functional analysis of target genes.
- Construction and validation of a prognostic signature using LASSO-Cox regression.
Main Results:
- Identification of 10 common differentially expressed pre-miRNAs between TNBC and LBC.
- Functional analysis linked these pre-miRNAs to aggressive TNBC characteristics.
- A prognostic signature comprising 4 pre-miRNAs demonstrated significant prognostic capability in both internal (MyBrCa) and external (TCGA) validation cohorts.
- The pre-miRNA signature was independent of conventional prognostic factors.
Conclusions:
- The novel algorithm enables pre-miRNA profiling from raw RNA sequencing data, facilitating cross-platform comparisons with mature miRNA data.
- Pre-miRNAs play a significant role in breast cancer carcinogenesis.
- The identified pre-miRNA signature serves as a robust and independent prognostic biomarker for breast cancer patients.
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