1
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Aggarwal N, Janjua D, Chaudhary A, Joshi U, Tripathi T, Chandra Keshavam C, Yadav J, Chhokar A, Chandra Bharti A. Insights into expression and localization of HPV16 LCR-associated transcription factors and association with LCR activity in HNSCC. MOLECULAR THERAPY. ONCOLOGY 2025; 33:200926. [PMID: 39886356 PMCID: PMC11780949 DOI: 10.1016/j.omton.2024.200926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 10/29/2024] [Accepted: 12/18/2024] [Indexed: 02/01/2025]
Abstract
Human papillomavirus (HPV)-positive head and neck squamous cell carcinoma (HNSCC) encompasses a heterogeneous group of malignancies characterized by diverse clinical manifestations. Notably, HPV-positive HNSCC exhibits a more favorable prognosis, particularly when the virus is transcriptionally active. This study aimed to elucidate the role of key transcription factors in activating the HPV long control region (LCR), responsible for its oncogenic potential. Utilizing immunoblotting and immunofluorescence techniques, we analyzed the expression and nuclear localization of LCR-associated transcription factors in HPV-negative and HPV-positive HNSCC cell lines. High expression of JunB and low expression of Fra-1, pSTAT3(S727), SP1, and SOX2 were observed in HPV-positive HNSCC cells. Transcriptomic analysis corroborated these findings, revealing differential expression of transcription factors in HPV-positive lesions. Moreover, the study identified strong correlation of LCR-specific transcription factors with HNSCC patient survival. Evaluation of HPV16 LCR reporter activity further underscored the heterogeneous nature of HNSCC, with some HPV-negative cell lines exhibiting comparable LCR activity to HPV-positive counterparts. These findings elucidate the intricate regulatory mechanisms underlying HPV-associated HNSCC and provide insights into potential prognostic markers and therapeutic targets.
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Affiliation(s)
- Nikita Aggarwal
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
| | - Divya Janjua
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
| | - Apoorva Chaudhary
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
| | - Udit Joshi
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
| | - Tanya Tripathi
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
| | - Chetkar Chandra Keshavam
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
| | - Joni Yadav
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
| | - Arun Chhokar
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
- Department of Zoology, Deshbandu College, University of Delhi, Delhi, India
| | - Alok Chandra Bharti
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), New Delhi, India
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2
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Dey L, Chakraborty S. Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions. Gene 2025; 942:149228. [PMID: 39828063 DOI: 10.1016/j.gene.2025.149228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Revised: 12/04/2024] [Accepted: 01/07/2025] [Indexed: 01/22/2025]
Abstract
The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Since there are very limited databases available on the Ebola virus; we have prepared a comprehensive database of all the PPIs between the Ebola virus and human proteins (EbolaInt). Our work focuses on the finding of some new protein-protein interactions between humans and the Ebola virus using some state- of-the-arts machine learning techniques. However, it is basically a two-class problem with a positive interacting dataset and a negative non-interacting dataset. These datasets contain various sequence-based human protein features such as structure of amino acid and conjoint triad and domain-related features. In this research, we have briefly discussed and used some well-known supervised learning approaches to predict PPIs between human proteins and Ebola virus proteins, including K-nearest neighbours (KNN), random forest (RF), support vector machine (SVM), and deep feed-forward multi-layer perceptron (DMLP) etc. We have validated our prediction results using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Our goal with this prediction is to compare all other models' accuracy, precision, recall, and f1-score for predicting these PPIs. In the result section, DMLP is giving the highest accuracy along with the prediction of 2655 potential human target proteins.
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Affiliation(s)
- Lopamudra Dey
- Department of Biomedical and Clinical Sciences, Linköping University, Sweden; Department of Computer Science & Engineering, Meghnad Saha Institute of Technology, Kolkata, India
| | - Sanjay Chakraborty
- Department of Computer and Information Science (IDA), REAL, AIICS, Linköping University, Sweden; Department of Computer Science & Engineering, Techno International New Town, Kolkata, India.
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3
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Mattern S, Hollfoth V, Bag E, Ali A, Riemenschneider P, Jarboui MA, Boldt K, Sulyok M, Dickemann A, Luibrand J, Fusco S, Franz-Wachtel M, Singer K, Goeppert B, Schilling O, Malek N, Fend F, Macek B, Ueffing M, Singer S. An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine. EMBO Mol Med 2025:10.1038/s44321-025-00194-7. [PMID: 39901020 DOI: 10.1038/s44321-025-00194-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/14/2025] [Accepted: 01/14/2025] [Indexed: 02/05/2025] Open
Abstract
Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of routine diagnostic FFPE biopsy specimens (n = 55) representing controls, Reflux- (GERD), Eosinophilic-(EoE), Crohn's-(CD), Herpes simplex (HSV) and Candida (CA)-esophagitis by LC-MS/MS (DIA), we identified distinct signatures and functional networks (e.g. mitochondrial translation (EoE), immunoproteasome, complement and coagulations system (CD), ribosomal biogenesis (GERD)), and pathogen-specific proteins for HSV and CA. Moreover, combining these signatures with histological parameters in a machine learning model achieved high diagnostic accuracy (100% training set, 93.8% test set), and supported diagnostic decisions in borderline/challenging cases. Applied to a young patient representing a use case, the external GERD diagnosis could be revised to CD and ICAM1 was identified as highly abundant therapeutic target. This resulted in CyclosporinA as a personalized treatment recommendation by the local multidisciplinary molecular inflammation board. Our integrated AI-assisted morphoproteomic approach allows deeper insights in disease-specific molecular alterations and represents a promising tool in esophagitis-related precision medicine.
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Affiliation(s)
- Sven Mattern
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
- Center for Personalized Medicine (ZPM), Tübingen, Germany
| | - Vanessa Hollfoth
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | - Eyyub Bag
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | - Arslan Ali
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | | | - Mohamed A Jarboui
- Core Facility for Medical Proteomics, Institute for Ophthalmic Research, Center for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Karsten Boldt
- Core Facility for Medical Proteomics, Institute for Ophthalmic Research, Center for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Mihaly Sulyok
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | - Anabel Dickemann
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | - Julia Luibrand
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | - Stefano Fusco
- Center for Personalized Medicine (ZPM), Tübingen, Germany
- Department of Internal Medicine I, University of Tübingen, Tübingen, Germany
| | | | - Kerstin Singer
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | - Benjamin Goeppert
- Institute of Pathology and Neuropathology, Hospital RKH Kliniken Ludwigsburg, Ludwigsburg, Germany
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Oliver Schilling
- Institute of Pathology, University Medical Center Freiburg, Faculty of Medicine - University of Freiburg, Freiburg, Germany
- Center for Personalized Medicine (ZPM), Freiburg, Germany
| | - Nisar Malek
- Center for Personalized Medicine (ZPM), Tübingen, Germany
- Department of Internal Medicine I, University of Tübingen, Tübingen, Germany
| | - Falko Fend
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany
| | - Boris Macek
- Proteome Center Tübingen, University of Tübingen, Tübingen, Germany
| | - Marius Ueffing
- Core Facility for Medical Proteomics, Institute for Ophthalmic Research, Center for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Stephan Singer
- Department of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany.
- Center for Personalized Medicine (ZPM), Tübingen, Germany.
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4
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Saha D, Talukdar D, Mukherjee P, Mitra D, Mukherjee R, Guha S, Bhattacharjee A, Naskar R, Kumar Sahu S, Alam N, Das G, Murmu N. Green synthesis of gold nano-particles using Madhuca indica flower extract and their anticancer activity on head and neck cancer: Characterization and mechanistic study. Eur J Pharm Biopharm 2025; 207:114625. [PMID: 39756711 DOI: 10.1016/j.ejpb.2025.114625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Revised: 12/12/2024] [Accepted: 01/02/2025] [Indexed: 01/07/2025]
Abstract
Complete eradication of aggressive head and neck squamous cell carcinoma (HNSCC) still remains a major challenging problem due to numerous resistance properties of cancer stem cells (CSC) which is crucially responsible for tumor recurrence and metastasis. This challenge causes a high demand for the emergence of novel targeted treatment modalities for improved therapeutic efficacies. Phytochemicals derived from plants proves to be a wide reservoir of important drug candidates which have the potential to impede multiple aspects of malignant growth and progression. In the present study, we aimed to synthesize gold nanoparticles in a rapid and cost-effective manner by utilizing Madhuca indica flower extract and to evaluate its anticancer efficacy on head and neck cancer model via targeting cancer stemness and EMT. The phytochemicals present in the Madhuca indica flower extract acted as an effective reducing agent helping in the green synthesis of gold nanoparticles. The generated AuNPs were characterized by UV-Vis spectroscopy, XRD, FTIR, TEM, FE-SEM, DLS, EDX. Anti cancer potential of synthesized AuNPs were evaluated by in vitro and ex vivo HNSCC model. In vivo toxicity was assessed in Swiss albino mice model. The gold nanoparticles were characterized using UV-Vis spectroscopy which revealed unique wavelength maxima at 550 nm and its crystalline nature was confirmed by XRD. AuNPs were observed to be spherical in shape with the mean diameter of 20.34 ± 4.36 nm and zeta potential of nearly -50 mV. The FTIR spectral shift indicated the incorporation of various functional groups. MI-AuNP depicted strong anticancer attributes against HNSCC cell lines SCC154 and FaDu through significant inhibition of cancer stemness and EMT as evident from decreased tumor sphere forming efficiency and CD44+/CD24- subpopulation along with dose dependent downregulated expression of relevant CSC markers and EMT markers both in vitro and ex vivo HNSCC model. Additionally, no evidence of in vivo toxicity has been observed with MI-AuNP administration. In conclusion, this study reported for the first time that the MI-AuNP synthesized by novel green chemistry can efficiently prevent the self-renewal capability of HNSCC by targeting Cancer stemness. The scientific significance of this study lies in the fact that MI-AuNP might be a novel and potential therapeutic candidate against aggressive and metastatic HNSCC. The findings in this study unravels the way for developing a novel therapeutic candidate against aggressive and metastatic HNSCC with a much higher prognostic potential and significantly reduced off target toxicity.
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Affiliation(s)
- Depanwita Saha
- Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, 37, S.P. Mukherjee Road, Kolkata 700026, India
| | - Debojit Talukdar
- Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, 37, S.P. Mukherjee Road, Kolkata 700026, India
| | - Poulami Mukherjee
- Department of Chemistry and Chemical Biology, Indian Institute of Technology (ISM), Dhanbad 826004, Jharkhand, India
| | - Debarpan Mitra
- Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, 37, S.P. Mukherjee Road, Kolkata 700026, India
| | - Rimi Mukherjee
- Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, 37, S.P. Mukherjee Road, Kolkata 700026, India
| | - Subhabrata Guha
- Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, 37, S.P. Mukherjee Road, Kolkata 700026, India
| | | | - Rahul Naskar
- Department of Chemistry, Jadavpur University, Kolkata, 700032, India
| | - Sumanta Kumar Sahu
- Department of Chemistry and Chemical Biology, Indian Institute of Technology (ISM), Dhanbad 826004, Jharkhand, India
| | - Neyaz Alam
- Department of Surgical Oncology, Chittaranjan National Cancer Institute, 37, S. P. Mukherjee Road, Kolkata 700026, India
| | - Gaurav Das
- Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, 37, S.P. Mukherjee Road, Kolkata 700026, India.
| | - Nabendu Murmu
- Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, 37, S.P. Mukherjee Road, Kolkata 700026, India.
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5
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Sun X, Chen X, Ni Y, Li X, Song J, Wang J, Xu S, Shu W, Chen M. Latexin (LXN) enhances tumor immune surveillance in mice by inhibiting Treg cells through the macrophage exosome pathway. Int J Biol Macromol 2025; 289:138822. [PMID: 39694381 DOI: 10.1016/j.ijbiomac.2024.138822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Revised: 12/12/2024] [Accepted: 12/14/2024] [Indexed: 12/20/2024]
Abstract
Latexin (LXN) is a secreted protein with a molecular weight of 29 KD, which is considered a tumor suppressor and plays an important role in the inflammatory immune response. LXN is highly expressed in macrophages and regulates macrophage polarity and tumor immune escape, demonstrating excellent clinical potential. However, its mechanism is still unclear. In this study, a macrophage-T cell co-culture system is established to clarify the secretion of macrophage LXN into the extracellular through exosomes. The results indicate that LXN in macrophage-derived exosomes is functional, that is, LXN-enriched exosome inhibits CD4+T cell differentiation into Treg cells in vitro and in vivo, and exhibits good tumor suppressive effects. Based on this discovery, a biomimetic nanoparticle loaded with LXN protein (MØ@LXN-NPS) is designed and synthesized. Furthermore, the MØ@LXN-NPS shows excellent performance in both in vivo and in vitro, especially in enhancing tumor immune surveillance by inhibiting Treg cells in tumor microenvironment, thus exhibiting excellent anti-tumor activity. This study provides a demonstration for the transition of biomolecules from functional research to engineering applications. The excellent performance of MØ@LXN-NPS in tumor immune regulation suggests that the engineered biomimetic nanomedicine has good clinical application prospects.
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Affiliation(s)
- Xuchen Sun
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China
| | - Xuanming Chen
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China
| | - Yuanting Ni
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China
| | - Xiuzhen Li
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China
| | - Jiaqi Song
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China
| | - Jingzhu Wang
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China
| | - Shaohua Xu
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China
| | - Wei Shu
- College of Intelligent Medicine and Biotechnology, Guilin Medical University, Guilin, China
| | - Ming Chen
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China.
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6
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Balaraman AK, Moglad E, Afzal M, Babu MA, Goyal K, Roopashree R, Kaur I, Kumar S, Kumar MR, Chauhan AS, Hemalatha S, Gupta G, Ali H. Liquid biopsies and exosomal ncRNA: Transforming pancreatic cancer diagnostics and therapeutics. Clin Chim Acta 2025; 567:120105. [PMID: 39706249 DOI: 10.1016/j.cca.2024.120105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2024] [Revised: 12/17/2024] [Accepted: 12/17/2024] [Indexed: 12/23/2024]
Abstract
Pancreatic cancer is a highly fatal malignancy due to poor early detection rate and resistance to conventional therapies. This review examines the potential for liquid biopsy as a transformative technology to identify diagnostic and therapeutic targets in pancreatic cancer. Specifically, we explore emerging biomarkers such as exosomal non-coding RNAs (ncRNAs), circulating tumor DNA (ctDNA), and circulating tumor cells (CTCs). Tumor-derived exosomes contain nucleic acid and protein that reflect the unique molecular landscape of the malignancy and can serve as an alternative diagnostic approach vs traditional biomarkers like CA19-9. Herein we highlight exosomal miRNAs, lncRNAs, and other ncRNAs alongside ctDNA and CTC-based strategies, evaluating their combined ability to improve early detection, disease monitoring and treatment response. Furthermore, the therapeutic implications of ncRNAs such as lncRNA UCA1 and miR-3960 in chemoresistance and progression are also discussed via suppression of EZH2 and PTEN/AKT pathways. Emerging therapeutic strategies that target the immune response, epithelial-mesenchymal transition (EMT) and drug resistance are explored. This review demonstrates a paradigm shift in pancreatic cancer management toward personalized, less invasive and more effective approaches.
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Affiliation(s)
- Ashok Kumar Balaraman
- Research and Enterprise, University of Cyberjaya, Persiaran Bestari, Cyber 11, Cyberjaya, Selangor 63000, Malaysia
| | - Ehssan Moglad
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Muhammad Afzal
- Department of Pharmaceutical Sciences, Pharmacy Program, Batterjee Medical College, P.O. Box 6231, Jeddah 21442, Saudi Arabia
| | - M Arockia Babu
- Institute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh, India
| | - Kavita Goyal
- Department of Biotechnology, Graphic Era (Deemed to be University), Clement Town, Dehradun 248002, India
| | - R Roopashree
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India
| | - Irwanjot Kaur
- Department of Allied Healthcare and Sciences, Vivekananda Global University, Jaipur, Rajasthan 303012, India
| | - Sachin Kumar
- NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, India
| | - MRavi Kumar
- Department of Chemistry, Raghu Engineering College, Visakhapatnam, Andhra Pradesh 531162, India
| | - Ashish Singh Chauhan
- Uttaranchal Institute of Pharmaceutical Sciences, Division of Research and Innovation, Uttaranchal University, India
| | - S Hemalatha
- Sri Ramachandra Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Porur, Chennai, India
| | - Gaurav Gupta
- Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab 140401, India; Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | - Haider Ali
- Centre for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India.
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7
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S D, D S, P VK, K S. Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach. Comput Methods Biomech Biomed Engin 2025; 28:145-169. [PMID: 37982236 DOI: 10.1080/10255842.2023.2281277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 11/21/2023]
Abstract
The significant research carried out on medical healthcare networks is giving computing innovations lots of space to produce the most recent innovations. Pancreatic cancer, which ranks among of the most common tumors that are thought to be fatal and unsuspected since it is positioned in the region of the abdomen beyond the stomach and can't be adequately treated once diagnosed. In radiological imaging, such as MRI and CT, computer-aided diagnosis (CAD), quantitative evaluations, and automated pancreatic cancer classification approaches are routinely provided. This study provides a dynamic weighted ensemble framework for pancreatic cancer classification inspired by game theory. Grey Level Co-occurrence Matrix (GLCM) is utilized for feature extraction, together with Gaussian kernel-based fuzzy rough sets theory (GKFRST) for feature reduction and the Random Forest (RF) classifier for categorization. The ResNet50 and VGG16 are used in the transfer learning (TL) paradigm. The combination of the outcomes from the TL paradigm and the RF classifier paradigm is suggested using an innovative ensemble classifier that relies on the game theory method. When compared with the current models, the ensemble technique considerably increases the pancreatic cancer classification accuracy and yields exceptional performance. The study improves the categorization of pancreatic cancer by using game theory, a mathematical paradigm that simulates strategic interactions. Because game theory has been not frequently used in the discipline of cancer categorization, this research is distinctive in its methodology.
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Affiliation(s)
- Dhanasekaran S
- Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
| | - Silambarasan D
- Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Tamil Nadu, India
| | - Vivek Karthick P
- Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu, India
| | - Sudhakar K
- Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
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8
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Yadav J, Tripathi T, Chaudhary A, Janjua D, Joshi U, Aggarwal N, Chhokar A, Keshavam CC, Senrung A, Bharti AC. Influence of head and neck cancer exosomes on macrophage polarization. Cytokine 2025; 186:156831. [PMID: 39700664 DOI: 10.1016/j.cyto.2024.156831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2024] [Revised: 12/05/2024] [Accepted: 12/08/2024] [Indexed: 12/21/2024]
Abstract
BACKGROUND Tumor cells within the tumor microenvironment (TME) release exosomes that influence macrophage phenotypes, either pro-tumorigenic or anti-tumorigenic. This mechanism, especially in head and neck squamous cell carcinoma (HNSCC), remains poorly understood. This study investigates the role of HNSCC exosomes in macrophage polarization. METHODOLOGY Exosomes were isolated from HPV16-positive (93VU147T, UDSCC2) and HPV-negative (OCT1) HNSCC cell lines. These exosomes were characterized for their potential to modulate macrophage polarization. Uptake of PKH-26 labeled exosomes by macrophages was monitored via confocal microscopy. Changes in macrophage polarization were assessed using quantitative real-time PCR and immunoblotting. Exosomal transcripts and proteome cargo was examined for polarization associated mediators. RESULTS HPV-negative exosomes showed higher uptake by THP1 resting macrophages (M0). Exosomes from HPV-positive cells induced a mixed macrophage phenotype (M1 and M2), whereas HPV-negative exosomes favored M1 polarization. Immunoblotting analysis revealed that this polarization was driven by the activation of transcription factors STAT1, NF-κB, and AP1. Transcriptomic analysis of HNSCC exosomes revealed reads for AP1 (c-Jun, c-Fos, FosB, Fra1, Fra2) and NF-κB (p50/105, p52/100, RelA, RelB, c-Rel), along with their known upstream mediators MEK1--7, JNK1-3, JAK1-3, TYK2, IKKα, and IKKβ. Splice variants of macrophage polarization markers, including iNOS and TGFβ, were also identified, though none of the exosomal proteome component corresponded to these factors. CONCLUSION HPV-negative exosomes are efficiently internalized by macrophages, promoting M1 polarization likely via modulation of STAT1, NF-κB, and AP1 signaling. These findings provide novel insights into role of tumor exosomes in modulation of macrophage-mediated TME dynamics in HNSCC.
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Affiliation(s)
- Joni Yadav
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India
| | - Tanya Tripathi
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India
| | - Apoorva Chaudhary
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India
| | - Divya Janjua
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India
| | - Udit Joshi
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India
| | - Nikita Aggarwal
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India
| | - Arun Chhokar
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India; Department of Zoology, Deshbandhu College, University of Delhi, Delhi, India
| | - Chetkar Chandra Keshavam
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India
| | - Anna Senrung
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India; Department of Zoology, Daulat Ram College, University of Delhi, Delhi, India
| | - Alok Chandra Bharti
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi (North Campus), Delhi 110007, India.
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9
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Sun L, Yin Z, Lu L. ISLRWR: A network diffusion algorithm for drug-target interactions prediction. PLoS One 2025; 20:e0302281. [PMID: 39883675 PMCID: PMC11781719 DOI: 10.1371/journal.pone.0302281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/01/2024] [Indexed: 02/01/2025] Open
Abstract
Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. In this study, we used multi-source heterogeneous network information to build a network model, learn the network topology through multiple network diffusion algorithms, and obtain compressed low-dimensional feature vectors for predicting drug-target interactions (DTIs). We applied the metropolis-hasting random walk (MHRW) algorithm to improve the performance of the random walk with restart (RWR) algorithm, forming the basis by which the self-loop probability of the current node is removed. Additionally, the propagation efficiency of the MHRW was improved using the improved metropolis-hasting random walk (IMRWR) algorithm, facilitating network deep sampling. Finally, we proposed a correction of the transfer probability of the entire network after increasing the self-loop rate of isolated nodes to form the ISLRWR algorithm. Notably, the ISLRWR algorithm improved the area under the receiver operating characteristic curve (AUROC) by 7.53 and 5.72%, and the area under the precision-recall curve (AUPRC) by 5.95 and 4.19% compared to the RWR and MHRW algorithms, respectively, in predicting DTIs performance. Moreover, after excluding the interference of homologous proteins (popular drugs or targets may lead to inflated prediction results), the ISLRWR algorithm still showed a significant performance improvement.
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Affiliation(s)
- Lu Sun
- School of Mathematics, Physics and Statistics, Institute for Frontier Medical Technology, Center of Intelligent Computing and Applied Statistics, Shanghai University of Engineering Science, Shanghai, China
| | - Zhixiang Yin
- School of Mathematics, Physics and Statistics, Institute for Frontier Medical Technology, Center of Intelligent Computing and Applied Statistics, Shanghai University of Engineering Science, Shanghai, China
| | - Lin Lu
- Shanghai Xinhao Information Technology Co., Ltd., Shanghai, China
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10
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Zhang B, Quan L, Zhang Z, Cao L, Chen Q, Peng L, Wang J, Jiang Y, Nie L, Li G, Wu T, Lyu Q. MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph. J Chem Inf Model 2025; 65:1009-1026. [PMID: 39812134 DOI: 10.1021/acs.jcim.4c02073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2025]
Abstract
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views. Tested under various conditions on benchmark data sets, including varying positive-to-negative sample ratios, conducting hard negative sampling experiments, and masking known DTIs with different ratios, as well as redundant DTIs with various similarity metrics, MVCL-DTI exhibits strong robust generalization. The model is then employed to predict novel DTIs, with a particular focus on COVID-19-related drugs, highlighting its practical applicability. Ultimately, through features visualization and computational properties analysis, we've pinpointed critical elements, including Gene Ontology and substituent nodes, along with a proper initialization strategy, underscoring their vital role in DTI prediction tasks.
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Affiliation(s)
- Bei Zhang
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
- China Mobile (Suzhou) Software Technology Company Limited, Suzhou 215163, China
| | - Lijun Quan
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu 210000, China
| | - Zhijun Zhang
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Lexin Cao
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Qiufeng Chen
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Liangchen Peng
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Junkai Wang
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Yelu Jiang
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Liangpeng Nie
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Geng Li
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
| | - Tingfang Wu
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu 210000, China
| | - Qiang Lyu
- School of Computer Science and Technology, Soochow University, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu 210000, China
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11
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Kiouri DP, Batsis GC, Chasapis CT. Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning. Biomolecules 2025; 15:141. [PMID: 39858535 PMCID: PMC11763140 DOI: 10.3390/biom15010141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2024] [Revised: 01/11/2025] [Accepted: 01/14/2025] [Indexed: 01/27/2025] Open
Abstract
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques. These methods not only improve predictive accuracy but also provide insights into functional sites, such as binding and catalytic residues. However, challenges such as limited high-resolution structural data and the need for effective negative sampling persist. Through the integration of experimental and computational tools, structure-based prediction paves the way for comprehensive proteomic network analysis, holding promise for advancements in drug discovery, biomarker identification, and personalized medicine. Future directions include enhancing scalability and dataset reliability to expand these approaches across diverse proteomes.
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Affiliation(s)
- Despoina P. Kiouri
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece; (D.P.K.); (G.C.B.)
- Laboratory of Organic Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, 15772 Athens, Greece
| | - Georgios C. Batsis
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece; (D.P.K.); (G.C.B.)
| | - Christos T. Chasapis
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece; (D.P.K.); (G.C.B.)
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12
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Zueva E, Burbage M. Pogo transposons provide tools to restrict cancer growth. Mol Oncol 2025. [PMID: 39814373 DOI: 10.1002/1878-0261.13801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Accepted: 12/19/2024] [Indexed: 01/18/2025] Open
Abstract
Transposable elements provide material for novel gene formation. In particular, DNA transposons have contributed several essential genes involved in various physiological or pathological conditions. Here, we discuss recent findings by Tu et al. in Molecular Cell that identify Pogo transposon-derived gene POGK as tumor suppressor in triple-negative breast cancer (TNBC) by regulating ribosome biogenesis and restricting cell growth. An isoform-switch in TNBC results in the loss of POGK capacity to recruit the epigenetic corepressor TRIM28 and to exert its repressive functions. These findings shed light on the potential for TE-derived genes in providing new therapeutic opportunities for highly malignant TNBC.
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Affiliation(s)
- Elina Zueva
- Institut Curie, Inserm U932 - Immunity and Cancer, Paris, France
- PSL Research University, Paris, France
| | - Marianne Burbage
- Institut Curie, Inserm U932 - Immunity and Cancer, Paris, France
- PSL Research University, Paris, France
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13
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Yang G, Liu Y, Wen S, Chen W, Zhu X, Wang Y. DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks. BMC Bioinformatics 2025; 26:11. [PMID: 39800678 PMCID: PMC11726937 DOI: 10.1186/s12859-024-06021-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Accepted: 12/20/2024] [Indexed: 01/16/2025] Open
Abstract
Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates on key features throughout the encoding-decoding phase. Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. Subsequently, principal component analysis is applied to distill the most informative features, with the random forest algorithm employed for the final decoding of the integrated data. Our method outperforms six baseline models in terms of accuracy, as demonstrated by extensive experimentation. Comprehensive ablation studies, visualization of results, and in-depth case analyses further validate our framework's efficacy and interpretability, providing a novel tool for drug discovery that integrates multimodal features.
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Affiliation(s)
- Guang Yang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China
| | - Yinbo Liu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China
| | - Sijian Wen
- School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China
| | - Wenxi Chen
- School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China
| | - Xiaolei Zhu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China
| | - Yongmei Wang
- School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China.
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14
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Rawal O, Turhan B, Peradejordi IF, Chandrasekar S, Kalayci S, Gnjatic S, Johnson J, Bouhaddou M, Gümüş ZH. PhosNetVis: A web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data. PATTERNS (NEW YORK, N.Y.) 2025; 6:101148. [PMID: 39896259 PMCID: PMC11783894 DOI: 10.1016/j.patter.2024.101148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 11/12/2024] [Accepted: 12/11/2024] [Indexed: 02/04/2025]
Abstract
Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate, and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers by rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at https://gumuslab.github.io/PhosNetVis/.
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Affiliation(s)
- Osho Rawal
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Berk Turhan
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Türkiye
| | - Irene Font Peradejordi
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Cornell Tech, Cornell University, New York, NY 10044, USA
| | - Shreya Chandrasekar
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Cornell Tech, Cornell University, New York, NY 10044, USA
| | - Selim Kalayci
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sacha Gnjatic
- Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Immunology and Immunotherapy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jeffrey Johnson
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mehdi Bouhaddou
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zeynep H. Gümüş
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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15
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Chen X, Zeng Q, Yin L, Yan B, Wu C, Feng J, Wu Y, He J, Ding W, Zhong J, Shen Y, Zu X. Enhancing immunotherapy efficacy in colorectal cancer: targeting the FGR-AKT-SP1-DKK1 axis with DCC-2036 (Rebastinib). Cell Death Dis 2025; 16:8. [PMID: 39788945 PMCID: PMC11718245 DOI: 10.1038/s41419-024-07263-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 11/17/2024] [Accepted: 11/26/2024] [Indexed: 01/12/2025]
Abstract
This research demonstrates that DCC-2036 (Rebastinib), a potent third-generation tyrosine kinase inhibitor (TKI), effectively suppresses tumor growth in colorectal cancer (CRC) models with functional immune systems. The findings underscore the capacity of DCC-2036 to enhance both the activation and cytotoxic functionality of CD8+ T cells, which are crucial for facilitating anti-tumor immune responses. Through comprehensive multi-omics investigations, significant shifts in both gene and protein expression profiles were detected, notably a marked decrease in DKK1 levels. This reduction in DKK1 was linked to diminished CD8+ T cell effectiveness, correlating with decreased FGR expression. Moreover, our findings identify FGR as a pivotal modulator that influences DKK1 expression via the PI3K-AKT-SP1 signaling cascade. Correlative analysis of clinical specimens supports the experimental data, showing that increased levels of FGR and DKK1 in CRC tissues are associated with inferior clinical outcomes and reduced efficacy of immunotherapeutic interventions. Consequently, targeting the FGR-AKT-SP1-DKK1 pathway with DCC-2036 could potentiate immunotherapy by enhancing CD8+ T cell functionality and their tumor infiltration. This strategy may contribute significantly to the refinement of therapeutic approaches for CRC, potentially improving patient prognoses.
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Affiliation(s)
- Xiguang Chen
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
- Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Gastrointestinal Surgery Department, Hengyang, Hunan, 421001, PR China
| | - Qiting Zeng
- The First Affiliated Hospital, Department of Clinical Laboratory Medicine, Hengyang, Hunan, 421001, PR China
| | - Liyang Yin
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
| | - Bingru Yan
- Central Hospital of Hengyang City, Oncology Department, Hengyang, Hunan, 421001, PR China
| | - Chen Wu
- The First Affiliated Hospital, Department of Ultrasound Imaging, Hengyang Medical School, University of South China, Hengyang, 421001, China
| | - Jianbo Feng
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
| | - Ying Wu
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
| | - Jun He
- The Nanhua Affiliated Hospital, Department of Spine Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Wenjun Ding
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
| | - Jing Zhong
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
- Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, University of South China, Hengyang, Hunan, 421001, China
| | - Yingying Shen
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China.
- Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
- Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, University of South China, Hengyang, Hunan, 421001, China.
| | - Xuyu Zu
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China.
- Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
- Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, University of South China, Hengyang, Hunan, 421001, China.
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16
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Farahani RM. Neural differentiation in perspective: mitochondria as early programmers. Front Neurosci 2025; 18:1529855. [PMID: 39844856 PMCID: PMC11751005 DOI: 10.3389/fnins.2024.1529855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2024] [Accepted: 12/23/2024] [Indexed: 01/24/2025] Open
Abstract
Neural differentiation during development of the nervous system has been extensively studied for decades. These efforts have culminated in the generation of a detailed map of developmental events that appear to be associated with emergence of committed cells in the nervous system. In this review the landscape of neural differentiation is revisited by focusing on abiotic signals that play a role in induction of neural differentiation. Evidence is presented regarding a chimeric landscape whereby abiotic signals generated by mitochondria orchestrate early events during neural differentiation. This early stage, characterised by mitochondrial hyperactivity, in turn triggers a late stage of differentiation by reprogramming the activity of biotic signals.
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Affiliation(s)
- Ramin M. Farahani
- IDR/Research and Education Network, Westmead, NSW, Australia
- Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
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17
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Xu Y, Zhang Y, Song K, Liu J, Zhao R, Zhang X, Pei L, Li M, Chen Z, Zhang C, Wang P, Li F. ScDrugAct: a comprehensive database to dissect tumor microenvironment cell heterogeneity contributing to drug action and resistance across human cancers. Nucleic Acids Res 2025; 53:D1536-D1546. [PMID: 39526387 PMCID: PMC11701732 DOI: 10.1093/nar/gkae994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 09/27/2024] [Accepted: 10/15/2024] [Indexed: 11/16/2024] Open
Abstract
The transcriptional heterogeneity of tumor microenvironment (TME) cells is a crucial factor driving the diversity of cellular response to drug treatment and resistance. Therefore, characterizing the cells associated with drug treatment and resistance will help us understand therapeutic mechanisms, discover new therapeutic targets and facilitate precision medicine. Here, we describe a database, scDrugAct (http://bio-bigdata.hrbmu.edu.cn/scDrugAct/), which aims to establish connections among drugs, genes and cells and dissect the impact of TME cellular heterogeneity on drug action and resistance at single-cell resolution. ScDrugAct is curated with drug-cell connections between 3838 223 cells across 34 cancer types and 13 857 drugs and identifies 17 274 drug perturbation/resistance-related genes and 276 559 associations between >10 000 drugs and 53 cell types. ScDrugAct also provides multiple flexible tools to retrieve and analyze connections among drugs, genes and cells; the distribution and developmental trajectories of drug-associated cells within the TME; functional features affecting the heterogeneity of cellular responses to drug perturbation and drug resistance; the cell-specific drug-related gene network; and drug-drug similarities. ScDrugAct serves as an important resource for investigating the impact of the cellular heterogeneity of the TME on drug therapies and can help researchers understand the mechanisms of action and resistance of drugs, as well as discover therapeutic targets.
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Affiliation(s)
- Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Yifang Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Kaiyue Song
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Jiaqi Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Rui Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Xiaomeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Liying Pei
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Mengyue Li
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Zhe Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Peng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Harbin 150081, China
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18
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Zou H, Li S, Guo J, Wen L, Lv C, Leng F, Chen Z, Zeng M, Xu J, Li Y, Li X. Pan-cancer analysis reveals age-associated genetic alterations in protein domains. Am J Hum Genet 2025; 112:44-58. [PMID: 39708814 PMCID: PMC11739924 DOI: 10.1016/j.ajhg.2024.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 11/26/2024] [Accepted: 11/26/2024] [Indexed: 12/23/2024] Open
Abstract
Cancer incidence and mortality differ among individuals of different ages, but the functional consequences of genetic alterations remain largely unknown. We systematically characterized genetic alterations within protein domains stratified by affected individual's age and showed that the mutational effects on domains varied with age. We further identified potential age-associated driver genes with hotspots across 33 cancers. The candidate drivers involved numerous cancer-related genes that participate in various oncogenic pathways and play central roles in human protein-protein interaction (PPI) networks. We found widespread age biases in protein domains and identified the associations between hotspots and age. Age-stratified PPI networks perturbed by hotspots were constructed to illustrate the function of mutations enriched in domains. We found that hotspots in young adults were associated with premature senescence. In summary, we provided a catalog of age-associated hotspots and their perturbed networks, which may facilitate precision diagnostics and treatments for cancer.
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Affiliation(s)
- Haozhe Zou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Si Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, China
| | - Jiyu Guo
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, China
| | - Luan Wen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chongwen Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Feng Leng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Zefeng Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Mengqian Zeng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
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19
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Xie Y, Wang X, Wang P, Bi X. A pseudo-label supervised graph fusion attention network for drug–target interaction prediction. EXPERT SYSTEMS WITH APPLICATIONS 2025; 259:125264. [DOI: 10.1016/j.eswa.2024.125264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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20
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Wright SN, Colton S, Schaffer LV, Pillich RT, Churas C, Pratt D, Ideker T. State of the interactomes: an evaluation of molecular networks for generating biological insights. Mol Syst Biol 2025; 21:1-29. [PMID: 39653848 PMCID: PMC11697402 DOI: 10.1038/s44320-024-00077-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Revised: 11/07/2024] |