Jia Mi¹, Zhikang Liu¹, Chang Li², Jing Wan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4089-4106, 2025, DOI:10.32604/cmes.2025.074364 - 23 December 2025

Abstract Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies. Despite notable progress, existing computational techniques struggle to represent the interplay between sequence-derived and structure-dependent protein features. To overcome this limitation, we introduce GLM-EP, a unified framework that fuses protein language models with equivariant graph neural networks. By merging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations, GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification. Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional More >

Somayah Albaradei^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2937-2970, 2025, DOI:10.32604/cmes.2025.072584 - 23 December 2025

Abstract Cancer deaths and new cases worldwide are projected to rise by 47% by 2040, with transitioning countries experiencing an even higher increase of up to 95%. Tumor severity is profoundly influenced by the timing, accuracy, and stage of diagnosis, which directly impacts clinical decision-making. Various biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, contribute to cancer development. The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers. This integrative approach supports the notion that cancer is fundamentally driven by such alterations, enabling the discovery of molecular signatures… More > Graphic Abstract

C. B. Cui^a,b, G. C. Yang^b, Z. Zhang^a,, X. J. Wang^c,

Chalcogenide Letters, Vol.22, No.6, pp. 507-520, 2025, DOI:10.15251/CL.2025.226.507

Abstract This research introduces an innovative aptasensor for detecting alpha-fetoprotein with exceptional sensitivity and specificity, employing a novel MoS₂/Fe₃O₄ composite fabricated through an advanced in-situ growth methodology. The composite exhibited a hierarchical flower-like structure with uniformly distributed Fe3O4 nanoparticles, confirmed by SEM, XRD, and Raman spectroscopy. The MoS₂/Fe₃O₄ composite demonstrated a 66% increase in surface area (7.16 m^²/g) compared to pristine MoS2, enhancing aptamer immobilization and electron transfer efficiency. Electrochemical characterization revealed a significant increase in interfacial resistance upon AFP binding, with a detection limit of 0.3 pg/mL and a dual linear range of 0.001–0.1 ng/mL and 0.1–100 More >

Oncology Research Editorial Offfce

Oncology Research, Vol.33, No.12, pp. 4159-4159, 2025, DOI:10.32604/or.2025.075992 - 27 November 2025

Abstract This article has no abstract. More >

Yuri A. Piven¹, Danila V. Sorokin², Nastassia A. Varabyeva¹, Alexandra L. Mikhaylova², Fedor B. Bogdanov², Elena V. Shafranovskaya¹, Raman M. Puzanau³, Fedor A. Lakhvich¹, Alexander M. Scherbakov^2,4,*

Oncology Research, Vol.33, No.12, pp. 4049-4072, 2025, DOI:10.32604/or.2025.067832 - 27 November 2025

Abstract Background: The most aggressive forms of breast cancer are characterized by independence from steroid hormones but a strong dependence on growth factors. In such cancer cells, oncogenic receptors, including human epidermal growth factor receptor 2 (HER2), are activated, and their targeted inhibition represents an attractive therapeutic strategy. The study aimed to develop small-molecule potential dual heat shock protein 90 (HSP90)-HER2 inhibitors and evaluate them as anticancer agents in HER2-positive cells. Methods: The research project involved obtaining a series of compounds with potential dual inhibitory activity against HSP90 and HER2 by targeted organic synthesis, which was… More > Graphic Abstract

Fangyuan Li^1,2,#, Xiaoyuan Hu^1,#, Xiaoge Gao³, Ling Liu³, Tao Li¹, Dan He¹, Jiaxing Cheng¹, Xiaobiao Ma¹, Li Li^1,*, Chunlei Ge^1,*, Hong Yao^1,*

Oncology Research, Vol.33, No.12, pp. 4029-4048, 2025, DOI:10.32604/or.2025.067628 - 27 November 2025

Abstract Background: Liver cancer stem cells (LCSCs) are recognized as pivotal drivers of hepatocellular carcinoma (HCC) progression; however, the molecular mechanisms maintaining their stem-like phenotype remain largely unresolved. This work investigates the role of prefoldin subunit 6-like protein (PFDN6L) in shaping LCSC traits and promoting or restraining HCC progression. Methods: PFDN6L, a cytoskeleton-associated chaperone, was studied using multiple in vitro assays—cell growth evaluation, cell cycle profiling, and spheroid culture—alongside analyses of stemness-associated markers (SOX2, CD133, CD44). Tumorigenic capacity was assessed in xenograft mouse models, and signaling pathway interrogation was performed to define underlying mechanisms. Results: In patient samples, More >

Yi Tang^1,2,3,4,#, Minyi Situ^1,2,3,4,#, Zhiming Wu^1,2,3,4,#, Yanjun Wang^1,2,3,4,#, Xinpei Deng^1,2,3,4, Zhicheng Liu^1,2,3,4, Shengjie Guo^1,2,3,4, Qianghua Zhou^1,2,3,4,*, Gangjun Yuan^5,*, Xingliang Tan^1,2,3,4,*, Kai Yao^1,2,3,4,*

Oncology Research, Vol.33, No.12, pp. 3973-3989, 2025, DOI:10.32604/or.2025.066184 - 27 November 2025

Abstract Background: Current chemotherapy treatments, including the TIP (Taxol, Ifosfamide, Cisplatin) regimen, have shown limited effects but strong side effects in advanced Penile squamous cell carcinoma (PSCC) patients. Trophoblast cell-surface antigen-2 (TROP-2) is a novel target for antibody-drug conjugate (ADC) drugs and has been proven to be effective in several human cancers. This study aimed to explore the biological function and potential of the ADC target in PSCC cells. Methods: A total of 196 PSCC tumor tissue specimens and clinicopathological data were collected. TROP-2 expression was detected by IHC, and the correlation between TROP-2 expression and… More > Graphic Abstract

Ranran Yang^1,2,3,#, Dan Yuan^2,#, Chaohan Liang^4,#, Siying Zhu⁴, Jie Huang², Yingqi Zhang⁴, Weiling He⁵, Qinghai Li^1,*, Hong Zhang^2,*

Oncology Research, Vol.33, No.12, pp. 3945-3971, 2025, DOI:10.32604/or.2025.064463 - 27 November 2025

Abstract Background: Colorectal cancer (CRC) is a predominant contributor to global cancer-associated mortality worldwide. Oxaliplatin (OXP), a foundational chemotherapeutic agent for CRC, often exhibits limited efficacy due to the emergence of drug resistance. Although endothelin-1 (EDN1) has been implicated in tumor drug resistance, its role in oxaliplatin resistance in CRC remains poorly defined. This work aimed to define how EDN1 contributes to oxaliplatin resistance and to explore its potential as a therapeutic target. Methods: Public genomic datasets were analyzed to confirm EDN1 upregulation in colorectal cancer (CRC) and its association with poor prognosis. EDN1 expression was… More >

Yong Huang¹, Xiandeng Li¹, Qingling Jing¹, Qin Zhang¹, Chungui Huang^2,*

BIOCELL, Vol.49, No.11, pp. 2195-2216, 2025, DOI:10.32604/biocell.2025.072716 - 24 November 2025

Abstract Objective: Mesenchymal stem cells (MSCs) are important cells in bone tissue engineering. Bone morphogenetic protein-2 (BMP-2) effectively treats bone defects and nonunion. The purpose of this study is to investigate whether BMP-2 promotes bone formation and femoral fracture healing by inhibiting inflammation and promoting osteogenic differentiation of MSCs, in order to provide an experimental basis for developing more efficient fracture treatment strategies. Methods: Bone marrow-derived MSCs (BMSCs) were isolated from rats and treated with OE-BMP-2, the 5^′-adenosine monophosphate-activated protein kinase (AMPK) signal agonist 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), and the inhibitor Compound C. Osteogenic differentiation was evaluated through… More >

Mujeebu Rehman¹, Qinghua Liu¹, Ali Ghulam², Tariq Ahmad³, Jawad Khan^4,*, Dildar Hussain^5,*, Yeong Hyeon Gu⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 779-805, 2025, DOI:10.32604/cmes.2025.067412 - 30 October 2025

Abstract Identifying druggable proteins, which are capable of binding therapeutic compounds, remains a critical and resource-intensive challenge in drug discovery. To address this, we propose CEL-IDP (Comparison of Ensemble Learning Methods for Identification of Druggable Proteins), a computational framework combining three feature extraction methods Dipeptide Deviation from Expected Mean (DDE), Enhanced Amino Acid Composition (EAAC), and Enhanced Grouped Amino Acid Composition (EGAAC) with ensemble learning strategies (Bagging, Boosting, Stacking) to classify druggable proteins from sequence data. DDE captures dipeptide frequency deviations, EAAC encodes positional amino acid information, and EGAAC groups residues by physicochemical properties to generate… More >