Hailong Li^1,#, Wen Ouyang^2,#, Yiyin Long¹, Yun Peng¹, Ziyi Liu¹, Qi Zhou¹, Rong Xu^1,*, Wei Du^1,*

Oncology Research, Vol.33, No.9, pp. 2549-2571, 2025, DOI:10.32604/or.2025.065050 - 28 August 2025

Abstract Background: The study aimed to explore the clinical value of mitochondrial ribosomal protein L18 (MRPL18) in breast cancer. Methods: Multiple databases were used to validate the expression of MRPL18. The prognostic impact and predictive value of MRPL18 were evaluated by using predictive models. Protein-protein interaction (PPI) networks were constructed by using GeneMANIA. Enrichment analysis is used to explore the signaling pathway regulated by MRPL18. Cell counting kit-8 (CCK-8) assays, colony formation, migration assays, flow cytometry, and xenograft models were employed to evaluate the role of MRPL18 in tumor progression. The immune response of MRPL18 was More >

Xizhou Zhang^1,#, Zetao Zhang^1,#, Jianguang Lin², Jiarong Yi³, Xuxiazi Zou³, Jikun Feng³, Guangsheng Huang¹, Bingfeng Chen¹, Junxi Long¹, Fengjia Wu³, Feng Ye^3,*, Haoming Wu^1,*

Oncology Research, Vol.33, No.9, pp. 2529-2547, 2025, DOI:10.32604/or.2025.065029 - 28 August 2025

Abstract Background: Breast cancer remains a leading cause of morbidity and mortality among women worldwide, with significant geographic disparities in its impact. While human epidermal growth factor receptor 2 (HER2)-targeted therapies, such as trastuzumab, have improved outcomes for HER2-positive breast cancer, challenges like therapy resistance persist, highlighting the need for novel treatments. Recent developments in antibody-drug conjugates (ADCs), particularly disitamab vedotin (RC48), show promising efficacy in targeting both HER2-positive and HER2-low expression tumors, warranting further investigation through real-world studies to assess its broader clinical applicability. Method: This retrospective, multicenter observational study evaluated the real-world efficacy and… More >

Akari Fukumoto, Satoru Matsuda^*

Oncology Research, Vol.33, No.9, pp. 2205-2219, 2025, DOI:10.32604/or.2025.062810 - 28 August 2025

Abstract The non-coding RNAs (ncRNAs) are a family of single-stranded RNAs that have become recognized as crucial gene expression regulators in normal and cancer cell biology. The gut microbiota, which consists of several different bacteria, can actively contribute to the regulation of host metabolism, immunity, and inflammation. Roles of ncRNAs and gut microbiota could significantly interact with each other to regulate the growth of various types of cancer. In particular, a causal relationship among ncRNAs, gut microbiota, and immune cells has been shown for their potential importance in the development of breast cancer. Alteration of ncRNA More >

Alejandro Javier Español^1,2,*, Yamila Sanchez¹, Sofia Volpi²

BIOCELL, Vol.49, No.8, pp. 1449-1480, 2025, DOI:10.32604/biocell.2025.068034 - 29 August 2025

Abstract Background: Triple-negative (TN) breast cancer, the most aggressive subtype of breast cancer, is usually treated with high doses of paclitaxel (PX), which induces resistance. To prevent this adverse effect, metronomic chemotherapy based on administering low doses of PX plus carbachol (Carb), a muscarinic acetylcholine receptor (mAChR) agonist, has emerged as an alternative. Other acetylcholine receptors also present in breast tissue are nicotinic ones. When activated by nicotine (Nic), these receptors can decrease the effectiveness of conventional chemotherapy. However, whether metronomic chemotherapy with PX and Carb is affected by Nic has not yet been described. This… More >

Roseline Oluwaseun Ogundokun^*, Pius Adewale Owolawi, Chunling Tu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4869-4885, 2025, DOI:10.32604/cmc.2025.064944 - 30 July 2025

Abstract Breast cancer is among the leading causes of cancer mortality globally, and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification. Existing machine learning (ML) methods struggle with intra-class heterogeneity and inter-class similarity, necessitating more robust classification models. This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning (DL) and Bat Swarm Optimization (BSO) hyperparameter optimization to improve breast cancer histopathology (BCH) image classification. A dataset of 804 Hematoxylin and Eosin (H&E) stained images classified as Benign, in situ, Invasive, and Normal categories (ICIAR2018_BACH_Challenge) has… More >

Oncology Research Editorial Offfce

Oncology Research, Vol.33, No.8, pp. 2175-2175, 2025, DOI:10.32604/or.2025.070131 - 18 July 2025

Abstract This article has no abstract. More >

Zhun Yu^1,2, Jie Wang^1,2, Guoping Xu^1,2,*

Oncology Research, Vol.33, No.8, pp. 2013-2035, 2025, DOI:10.32604/or.2025.061757 - 18 July 2025

Abstract Objectives: Triple-negative breast cancer (TNBC) presents a major treatment challenge due to its aggressive behavior. The dysfunction of the Golgi apparatus (GA) contributes to the development of various cancers. This study aimed to utilize GA-related genes (GARGs) to forecast the prognosis and immune profile of TNBC. Methods: The data were downloaded from The Cancer Genome Atlas (TCGA) database, including 175 TNBC and 99 healthy samples. The differentially expressed GARGs (DEGARGs) were analyzed using the TCGA biolinks package. The patients with TNBC were classified into two clusters utilizing the ConsensusClusterPlus package according to prognosis-related DEGARGs, followed by… More >

Xiao Jian Tan^1,2,3,*, Wai Loon Cheor², Ee Meng Cheng^4,5, Chee Chin Lim^3,4, Khairul Shakir Ab Rahman⁶

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2015-2060, 2025, DOI:10.32604/cmc.2025.063407 - 03 July 2025

Abstract This study systematically reviews the applications of generative artificial intelligence (GAI) in breast cancer research, focusing on its role in diagnosis and therapeutic development. While GAI has gained significant attention across various domains, its utility in breast cancer research has yet to be comprehensively reviewed. This study aims to fill that gap by synthesizing existing research into a unified document. A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in the retrieval of 3827 articles, of which 31 were deemed eligible for analysis. The included studies were… More >

Khlood M. Mehdar¹, Toufique A. Soomro^2,3,*, Ahmed Ali⁴, Faisal Bin Ubaid⁵, Muhammad Irfan^6,*, Sabah Elshafie Mohammed Elshafie¹, Aisha M. Mashraqi⁷, Abdullah A. Asiri⁸, Nagla Hussien Mohamed Khalid⁸, Hanan T. Halawani⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3091-3132, 2025, DOI:10.32604/cmes.2025.065471 - 30 June 2025

Abstract Breast cancer remains one of the most pressing global health concerns, and early detection plays a crucial role in improving survival rates. Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities. However, existing methodologies face persistent challenges, including low image contrast, noise interference, and inaccuracies in segmenting regions of interest. To address these limitations, this study introduces a novel computational framework for analyzing mammographic images, evaluated using the Mammographic Image Analysis Society (MIAS) dataset comprising 322 samples. The proposed methodology follows a structured three-stage approach. Initially,… More >

MD. MAHMUDUL HASAN^1,2, SHAH MD. WASIN^2,3, MISHU RAHMAN^2,4, EVA AZME^1,2, MD. SAQLINE MOSTAQ⁵, MD. MAHEDI HASAN NAHID⁶, NOR MOHAMMAD⁷, FARJANA AFRIN TANJUM⁸, MD. ANAMUL HAQUE⁵, MD ASHIQ MAHMUD^5,*, MOHAMMAD NURUL AMIN^5,*

Oncology Research, Vol.33, No.7, pp. 1611-1632, 2025, DOI:10.32604/or.2025.062778 - 26 June 2025

Abstract Background: Breast cancer still stands to be the foremost contributor to cancer-related incidence and mortality in women globally accounting for about 14% of all female cancer-related deaths worldwide. This research seeks to illustrate the mechanisms and clinical findings of natural products against breast cancer treatment. Methodology: Required data for this review article was retrieved employing several readily obtainable search databases, including Web of Science^® (Thomson Reuters, USA), PubMed^® (U.S. National Library of Medicine, USA), and SciVerse Scopus^® (Elsevier Properties S.A., USA), taking into consideration certain search terms like “breast cancer,” “natural products against breast cancer,” and “Clinically… More > Graphic Abstract