RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

Sep 21, 2026

Breast cancer detection faces major barriers from the high dimensionality and complexity of gene expression data, which makes classification difficult and can delay intervention. This study develops an advanced deep learning model using RNA-Seq gene expression to identify breast cancer accurately. A hybrid gene selection strategy combines Harris Hawk Optimization and Whale Optimization with deep learning to improve feature selection. The approach is benchmarked against five conventional optimization methods using paired normal and cancer samples, achieving strong classification accuracy.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

Abstract

Problem

Aim

Methods

Results

Conclusion

Introduction

Breast cancer burden and need for early diagnosis

Limitations of traditional diagnostics

RNA-Seq and computational challenges

Journal of Cancer Research and Clinical Oncology (2024) 150:455 [https://doi.org/10.1007/s00432-024-05968-z](https://doi.org/10.1007/s00432-024-05968-z) RNA‑Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques Abrar Yaqoob1 · Navneet Kumar Verma1 · Rabia Musheer Aziz2 · Mohd Asif Shah3,4,5 Received: 23 August 2024 / Accepted: 21 September 2024 / Published online: 10 October 2024 © The Author(s) 2024 Abstract Problem Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process. Aim This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity. Methods We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India. Results The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls. Conclusion Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes. Keywords Breast cancer · Harris Hawk algorithm · Whale optimization algorithm · Deep learning * Abrar Yaqoob [abraryaqoob77@gmail.com](abraryaqoob77@gmail.com) * Mohd Asif Shah [m.asif@kardan.edu.af](m.asif@kardan.edu.af) 1 School of Advanced Science and Language, VIT Bhopal University, Kothrikalan, Sehore, Bhopal 466114, India 2 Planning Department, State Planning Institute (New Division), Lucknow, Utter Pradesh 226001, India 3 Department of Economics, Kardan University, Parwane Du, 1001 Kabul, Afghanistan 4 Division of Research and Development, Lovely Professional University, Phagwara, Punjab 144001, India 5 Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, India Introduction Breast cancer is one of the most common cancers globally, particularly among women, and remains the leading cause of cancer-related mortality (Dewangan 2022; Yaqoob et al. 2024a) . Despite significant advancements in medical research and treatment, breast cancer continues to pose a major global health challenge. In 2020 alone, there were approximately 2.3 million new cases of breast cancer, resulting in 685,000 deaths worldwide (Dewangan et al. 2022 ; Yaqoob 2024). These alarming statistics highlight the pressing need for improved diagnostic tools that can detect breast cancer at its earliest stages, enabling timely intervention and enhancing survival rates. Early detection is key to reducing mortality and improving treatment efficacy. Traditional diagnostic techniques such as mammography, ultrasound, and biopsy, while widely used, have limitations in sensitivity, s

FAQs

What problem does the study address in breast cancer detection?

It addresses the difficulty of early detection caused by the high dimensionality and complexity of RNA-Seq gene expression data, which complicates accurate classification.

How does the proposed method select genes?

It uses a hybrid gene selection approach combining Harris Hawk Optimization (HHO) and Whale Optimization (WO) with deep learning to improve feature selection and classification accuracy.

What results does the model achieve on paired tissue samples?

The model reaches a mean classification accuracy of 99.0% and consistently outperforms GA, ABC, CS, and PSO when combined with deep learning.

Sep 21, 2026

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