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Florentino BR, Parmezan Bonidia R, Sanches NH, da Rocha UN, de Carvalho AC. BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning. Comput Struct Biotechnol J 2024; 23:2267-2276. [PMID: 38827228 PMCID: PMC11140557 DOI: 10.1016/j.csbj.2024.05.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Machine Learning (ML) algorithms have been important tools for the extraction of useful knowledge from biological sequences, particularly in healthcare, agriculture, and the environment. However, the categorical and unstructured nature of these sequences requiring usually additional feature engineering steps, before an ML algorithm can be efficiently applied. The addition of these steps to the ML algorithm creates a processing pipeline, known as end-to-end ML. Despite the excellent results obtained by applying end-to-end ML to biotechnology problems, the performance obtained depends on the expertise of the user in the components of the pipeline. In this work, we propose an end-to-end ML-based framework called BioPrediction-RPI, which can identify implicit interactions between sequences, such as pairs of non-coding RNA and proteins, without the need for specialized expertise in end-to-end ML. This framework applies feature engineering to represent each sequence by structural and topological features. These features are divided into feature groups and used to train partial models, whose partial decisions are combined into a final decision, which, provides insights to the user by giving an interpretability report. In our experiments, the developed framework was competitive when compared with various expert-created models. We assessed BioPrediction-RPI with 12 datasets when it presented equal or better performance than all tools in 40% to 100% of cases, depending on the experiment. Finally, BioPrediction-RPI can fine-tune models based on new data and perform at the same level as ML experts, democratizing end-to-end ML and increasing its access to those working in biological sciences.
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Affiliation(s)
- Bruno Rafael Florentino
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil
| | - Robson Parmezan Bonidia
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, 86300-000, Paraná, Brazil
| | - Natan Henrique Sanches
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil
| | - Ulisses N. da Rocha
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ GmbH, Leipzig, Saxony, Germany
| | - André C.P.L.F. de Carvalho
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil
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Liu Y, Xia H, Wang Y, Han S, Liu Y, Zhu S, Wu Y, Luo J, Dai J, Jia Y. Prognosis and immunotherapy in melanoma based on selenoprotein k-related signature. Int Immunopharmacol 2024; 137:112436. [PMID: 38857552 DOI: 10.1016/j.intimp.2024.112436] [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: 03/17/2024] [Revised: 05/26/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
Selenium and selenoproteins are closely related to melanoma progression. However, it is unclear how SELENOK affects lipid metabolism, endoplasmic reticulum stress (ERS), immune cell infiltration, survival, and prognosis in melanoma patients. Transcriptome data from melanoma patients was used to investigate SELENOK levels and their effect on prognosis, followed by an investigation of SELENOK's effects on immune cell infiltration. Furthermore, a risk model based on ERS, lipid metabolism, and immune-related genes was constructed, and its utility in melanoma prognosis was evaluated. Finally, the drug sensitivity of the risk model was analyzed to provide a reference for melanoma therapy. The results showed that melanoma with a high SELENOK level had a greater degree of immune cell infiltration and a better prognosis. Additionally, SELENOK was found to regulate ERS, lipid metabolism, and immune cell infiltration in melanoma. The risk model based on SELENOK signature genes successfully predicted the prognosis of melanoma, and the low-risk group exhibited a favorable immunological microenvironment. Furthermore, high-risk patients with melanoma were candidates for chemotherapy with RAS pathway inhibitors, whereas low-risk patients were more susceptible to routinely used chemotherapy medicines. In summary, SELENOK was shown to regulate ERS, lipid metabolism, and immune cell infiltration in melanoma, and SELENOK was positively associated with the prognosis of melanoma. The risk model based on SELENOK signature genes was valuable for melanoma prognosis and therapy.
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Affiliation(s)
- Yang Liu
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province, Cellular Immunotherapy Engineering Research Center of Guizhou Province, School of Biology and Engineering (School of Modern Industry for Health and Medicine)/School of Basic Medical Sciences, Guizhou Medical University, Guiyang 550025, China; Immune Cells and Antibody Engineering Research Center of Guizhou Province, Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China
| | - Huan Xia
- Department of Pathology, GuiZhou QianNan People's Hospital, Qiannan Pathology Research Center of Guizhou Province, QianNan 558000, China
| | - Yongmei Wang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province, Cellular Immunotherapy Engineering Research Center of Guizhou Province, School of Biology and Engineering (School of Modern Industry for Health and Medicine)/School of Basic Medical Sciences, Guizhou Medical University, Guiyang 550025, China; Immune Cells and Antibody Engineering Research Center of Guizhou Province, Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China
| | - Shuang Han
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province, Cellular Immunotherapy Engineering Research Center of Guizhou Province, School of Biology and Engineering (School of Modern Industry for Health and Medicine)/School of Basic Medical Sciences, Guizhou Medical University, Guiyang 550025, China; Immune Cells and Antibody Engineering Research Center of Guizhou Province, Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China
| | - Yongfen Liu
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province, Cellular Immunotherapy Engineering Research Center of Guizhou Province, School of Biology and Engineering (School of Modern Industry for Health and Medicine)/School of Basic Medical Sciences, Guizhou Medical University, Guiyang 550025, China; Immune Cells and Antibody Engineering Research Center of Guizhou Province, Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China
| | - Shengzhang Zhu
- Department of Pathology, GuiZhou QianNan People's Hospital, Qiannan Pathology Research Center of Guizhou Province, QianNan 558000, China
| | - Yongjin Wu
- Department of Clinical Laboratory, GuiZhou QianNan People's Hospital, QianNan 558000, China
| | - Jimin Luo
- Department of Pathology, GuiZhou QianNan People's Hospital, Qiannan Pathology Research Center of Guizhou Province, QianNan 558000, China
| | - Jie Dai
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province, Cellular Immunotherapy Engineering Research Center of Guizhou Province, School of Biology and Engineering (School of Modern Industry for Health and Medicine)/School of Basic Medical Sciences, Guizhou Medical University, Guiyang 550025, China; Immune Cells and Antibody Engineering Research Center of Guizhou Province, Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China.
| | - Yi Jia
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province, Cellular Immunotherapy Engineering Research Center of Guizhou Province, School of Biology and Engineering (School of Modern Industry for Health and Medicine)/School of Basic Medical Sciences, Guizhou Medical University, Guiyang 550025, China; Immune Cells and Antibody Engineering Research Center of Guizhou Province, Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China.
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3
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Koivu MKA, Chakroborty D, Airenne TT, Johnson MS, Kurppa KJ, Elenius K. Trans-activating mutations of the pseudokinase ERBB3. Oncogene 2024; 43:2253-2265. [PMID: 38806620 PMCID: PMC11245391 DOI: 10.1038/s41388-024-03070-9] [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: 02/09/2023] [Revised: 05/15/2024] [Accepted: 05/20/2024] [Indexed: 05/30/2024]
Abstract
Genetic changes in the ERBB family of receptor tyrosine kinases serve as oncogenic driver events and predictive biomarkers for ERBB inhibitor drugs. ERBB3 is a pseudokinase member of the family that, although lacking a fully active kinase domain, is well known for its potent signaling activity as a heterodimeric complex with ERBB2. Previous studies have identified few transforming ERBB3 mutations while the great majority of the hundreds of different somatic ERBB3 variants observed in different cancer types remain of unknown significance. Here, we describe an unbiased functional genetics screen of the transforming potential of thousands of ERBB3 mutations in parallel. The screen based on a previously described iSCREAM (in vitro screen of activating mutations) platform, and addressing ERBB3 pseudokinase signaling in a context of ERBB3/ERBB2 heterodimers, identified 18 hit mutations. Validation experiments in Ba/F3, NIH 3T3, and MCF10A cell backgrounds demonstrated the presence of both previously known and unknown transforming ERBB3 missense mutations functioning either as single variants or in cis as a pairwise combination. Drug sensitivity assays with trastuzumab, pertuzumab and neratinib indicated actionability of the transforming ERBB3 variants.
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Affiliation(s)
- Marika K A Koivu
- Institute of Biomedicine, and Medicity Research Laboratories, University of Turku, Turku, 20520, Finland
- Turku Doctoral Programme of Molecular Medicine, Turku, 20520, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, 20520, Finland
| | - Deepankar Chakroborty
- Institute of Biomedicine, and Medicity Research Laboratories, University of Turku, Turku, 20520, Finland
- Turku Doctoral Programme of Molecular Medicine, Turku, 20520, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, 20520, Finland
| | - Tomi T Airenne
- Structural Bioinformatics Laboratory and InFLAMES Research Flagship Center, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, 20520, Turku, Finland
| | - Mark S Johnson
- Structural Bioinformatics Laboratory and InFLAMES Research Flagship Center, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, 20520, Turku, Finland
| | - Kari J Kurppa
- Institute of Biomedicine, and Medicity Research Laboratories, University of Turku, Turku, 20520, Finland
| | - Klaus Elenius
- Institute of Biomedicine, and Medicity Research Laboratories, University of Turku, Turku, 20520, Finland.
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, 20520, Finland.
- Department of Oncology, Turku University Hospital, Turku, 20521, Finland.
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Li F, Wang X, Zhang J, Jing X, Zhou J, Jiang Q, Cao L, Cai S, Miao J, Tong D, Shyy JYJ, Huang C. AURKB/CDC37 complex promotes clear cell renal cell carcinoma progression via phosphorylating MYC and constituting an AURKB/E2F1-positive feedforward loop. Cell Death Dis 2024; 15:427. [PMID: 38890303 PMCID: PMC11189524 DOI: 10.1038/s41419-024-06827-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: 03/07/2024] [Revised: 06/07/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024]
Abstract
As the second most common malignant tumor in the urinary system, renal cell carcinoma (RCC) is imperative to explore its early diagnostic markers and therapeutic targets. Numerous studies have shown that AURKB promotes tumor development by phosphorylating downstream substrates. However, the functional effects and regulatory mechanisms of AURKB on clear cell renal cell carcinoma (ccRCC) progression remain largely unknown. In the current study, we identified AURKB as a novel key gene in ccRCC progression based on bioinformatics analysis. Meanwhile, we observed that AURKB was highly expressed in ccRCC tissue and cell lines and knockdown AURKB in ccRCC cells inhibit cell proliferation and migration in vitro and in vivo. Identified CDC37 as a kinase molecular chaperone for AURKB, which phenocopy AURKB in ccRCC. AURKB/CDC37 complex mediate the stabilization of MYC protein by directly phosphorylating MYC at S67 and S373 to promote ccRCC development. At the same time, we demonstrated that the AURKB/CDC37 complex activates MYC to transcribe CCND1, enhances Rb phosphorylation, and promotes E2F1 release, which in turn activates AURKB transcription and forms a positive feedforward loop in ccRCC. Collectively, our study identified AURKB as a novel marker of ccRCC, revealed a new mechanism by which the AURKB/CDC37 complex promotes ccRCC by directly phosphorylating MYC to enhance its stability, and first proposed AURKB/E2F1-positive feedforward loop, highlighting AURKB may be a promising therapeutic target for ccRCC.
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Affiliation(s)
- Fang Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China
| | - Xiaofei Wang
- Biomedical Experimental Center, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Jinyuan Zhang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China
| | - Xintao Jing
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China
| | - Jing Zhou
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China
| | - Qiuyu Jiang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China
| | - Li Cao
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China
| | - Shuang Cai
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China
| | - Jiyu Miao
- Department of Hematology, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, 710004, China
| | - Dongdong Tong
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China.
| | - John Y-J Shyy
- Division of Cardiology, Department of Medicine, University of California, San Diego, CA, USA
| | - Chen Huang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University School of Health Science Center, Xi'an, 710301, Shaanxi, China.
- Biomedical Experimental Center, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
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Zeng Q, Jiang T. The role of FHL1 in tumors. Gene 2024; 911:148347. [PMID: 38458365 DOI: 10.1016/j.gene.2024.148347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/01/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Affiliation(s)
- Qun Zeng
- Department of Biochemistry and Molecular Biology, Hengyang Medical School, University of South China, Hengyang 421001, Hunan, China
| | - Tingting Jiang
- Department of Clinical Laboratory, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang 421001, Hunan, China.
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Gauss C, Stone LD, Ghafouri M, Quan D, Johnson J, Fribley AM, Amm HM. Overcoming Resistance to Standard-of-Care Therapies for Head and Neck Squamous Cell Carcinomas. Cells 2024; 13:1018. [PMID: 38920648 PMCID: PMC11201455 DOI: 10.3390/cells13121018] [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: 05/29/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
Although there have been some advances during in recent decades, the treatment of head and neck squamous cell carcinoma (HNSCC) remains challenging. Resistance is a major issue for various treatments that are used, including both the conventional standards of care (radiotherapy and platinum-based chemotherapy) and the newer EGFR and checkpoint inhibitors. In fact, all the non-surgical treatments currently used for HNSCC are associated with intrinsic and/or acquired resistance. Herein, we explore the cellular mechanisms of resistance reported in HNSCC, including those related to epigenetic factors, DNA repair defects, and several signaling pathways. This article discusses these mechanisms and possible approaches that can be used to target different pathways to sensitize HNSCC to the existing treatments, obtain better responses to new agents, and ultimately improve the patient outcomes.
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Affiliation(s)
- Chester Gauss
- Carman and Ann Adams Department of Pediatrics, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (C.G.); (M.G.)
| | - Logan D. Stone
- Oral & Maxillofacial Surgery, School of Dentistry, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Mehrnoosh Ghafouri
- Carman and Ann Adams Department of Pediatrics, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (C.G.); (M.G.)
| | - Daniel Quan
- Department of Otolaryngology Head and Neck Surgery, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (D.Q.)
| | - Jared Johnson
- Department of Otolaryngology Head and Neck Surgery, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (D.Q.)
| | - Andrew M. Fribley
- Carman and Ann Adams Department of Pediatrics, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (C.G.); (M.G.)
- Department of Otolaryngology Head and Neck Surgery, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (D.Q.)
- Molecular Therapeutics Program, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48202, USA
| | - Hope M. Amm
- Oral & Maxillofacial Surgery, School of Dentistry, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
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Minvielle Moncla LH, Briend M, Sokhna Sylla M, Mathieu S, Rufiange A, Bossé Y, Mathieu P. Mendelian randomization reveals interactions of the blood proteome and immunome in mitral valve prolapse. COMMUNICATIONS MEDICINE 2024; 4:108. [PMID: 38844506 PMCID: PMC11156961 DOI: 10.1038/s43856-024-00530-x] [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: 02/17/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Mitral valve prolapse (MVP) is a common heart disorder characterized by an excessive production of proteoglycans and extracellular matrix in mitral valve leaflets. Large-scale genome-wide association study (GWAS) underlined that MVP is heritable. The molecular underpinnings of the disease remain largely unknown. METHODS We interrogated cross-modality data totaling more than 500,000 subjects including GWAS, 4809 molecules of the blood proteome, and genome-wide expression of mitral valves to identify candidate drivers of MVP. Data were investigated through Mendelian randomization, network analysis, ligand-receptor inference and digital cell quantification. RESULTS In this study, Mendelian randomization identify that 33 blood proteins, enriched in networks for immunity, are associated with the risk of MVP. MVP- associated blood proteins are enriched in ligands for which their cognate receptors are differentially expressed in mitral valve leaflets during MVP and enriched in cardiac endothelial cells and macrophages. MVP-associated blood proteins are involved in the renewal-polarization of macrophages and regulation of adaptive immune response. Cytokine activity profiling and digital cell quantification show in MVP a shift toward cytokine signature promoting M2 macrophage polarization. Assessment of druggability identify CSF1R, CX3CR1, CCR6, IL33, MMP8, ENPEP and angiotensin receptors as actionable targets in MVP. CONCLUSIONS Hence, integrative analysis identifies networks of candidate molecules and cells involved in immune control and remodeling of the extracellular matrix, which drive the risk of MVP.
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Affiliation(s)
| | - Mewen Briend
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Mame Sokhna Sylla
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Samuel Mathieu
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Anne Rufiange
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Yohan Bossé
- Department of Molecular Medicine, Laval University, Quebec City, QC, Canada
| | - Patrick Mathieu
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada.
- Department of Surgery, Laval University, Quebec City, QC, Canada.
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Avila S, Roberson ML, Rajagopal PS. Oncologists Must Consider Participant Data When Using Large-Scale Cancer Data Sets. JCO Clin Cancer Inform 2024; 8:e2300245. [PMID: 38959448 DOI: 10.1200/cci.23.00245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 04/16/2024] [Accepted: 05/10/2024] [Indexed: 07/05/2024] Open
Abstract
Primer that helps clarify large-scale clinical data sets and participant demographics for oncologists.
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Affiliation(s)
- Santiago Avila
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | - Mya L Roberson
- Department of Health Policy and Management, UNC Gillings School of Global Public Health, Chapel Hill, NC
| | - Padma Sheila Rajagopal
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD
- Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD
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Goldstein Y, Cohen OT, Wald O, Bavli D, Kaplan T, Benny O. Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning. SCIENCE ADVANCES 2024; 10:eadj4370. [PMID: 38809990 DOI: 10.1126/sciadv.adj4370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools, capable of classifying cancer cells based on their functions, may substantially enhance therapy and extend patient life span. The connection between cell biomechanics and cancer cell functions is used here to classify cells through mechanical measurements, via particle uptake. Machine learning (ML) was used to classify cells based on single-cell patterns of uptake of particles with diverse sizes. Three pairs of human cancer cell subpopulations, varied in their level of drug resistance or malignancy, were studied. Cells were allowed to interact with fluorescently labeled polystyrene particles ranging in size from 0.04 to 3.36 μm and analyzed for their uptake patterns using flow cytometry. ML algorithms accurately classified cancer cell subtypes with accuracy rates exceeding 95%. The uptake data were especially advantageous for morphologically similar cell subpopulations. Moreover, the uptake data were found to serve as a form of "normalization" that could reduce variation in repeated experiments.
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Affiliation(s)
- Yoel Goldstein
- Institute for Drug Research, The School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ora T Cohen
- Institute for Drug Research, The School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ori Wald
- Department of Cardiothoracic Surgery, Hadassah Medical Center, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Danny Bavli
- Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
| | - Tommy Kaplan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Department of Developmental Biology and Cancer Research, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ofra Benny
- Institute for Drug Research, The School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
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Li F, Han M, Gao X, Du X, Jiang C. APOA1 mRNA and serum APOA1 protein as diagnostic and prognostic biomarkers in gastric cancer. Transl Cancer Res 2024; 13:2141-2154. [PMID: 38881912 PMCID: PMC11170536 DOI: 10.21037/tcr-23-1966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/17/2024] [Indexed: 06/18/2024]
Abstract
Background Gastric cancer (GC) remains a formidable challenge in oncology, ranking as a leading cause of cancer mortality globally. This underscores an urgent need for innovative prognostic markers that can revolutionize patient management and outcomes. Recent insights into cancer biology have spotlighted the profound influence of lipid metabolism alterations on tumorigenesis, tumor progression, and the tumor microenvironment. These alterations not only fuel cancer cell growth and proliferation but also play a strategic role in evading immune surveillance and promoting metastasis. The intricate web of lipid metabolism in cancer cells, characterized by deregulated uptake, synthesis, and oxidation of fatty acids (FAs), opens new avenues for targeted therapeutic interventions and prognostic evaluations. Specifically, this study zeroes in on apolipoprotein A-I (APOA1), a key player in lipid metabolism, to unearth its prognostic value in GC. By delving into the role of lipid metabolism-related genes, particularly APOA1, we aim to unveil their potential as groundbreaking biomarkers for GC prognosis. This endeavor not only aims to enhance our understanding of the molecular underpinnings of GC but also to spearhead the development of lipid metabolism-based strategies for improved diagnostic, prognostic, and therapeutic outcomes. Methods Transcriptomic and clinical data from GC patients and healthy individuals were sourced from The Cancer Genome Atlas (TCGA) database, a comprehensive project that molecularly characterizes over 20,000 primary cancer and matched normal samples across 33 cancer types. Significantly differentially expressed lipid metabolism-related genes were identified using the "limma" package in R. Prognostic genes were selected via univariate Cox regression analysis. Differential gene enrichment analysis was performed using Metascape (http://www.metascape.org). The Human Protein Atlas (HPA, https://www.proteinatlas.org) provided information on APOA1 protein expression in GC and healthy tissues. Immune cell infiltration was analyzed using the CIBERSORT algorithm (http://cibersort.stanford.edu). Results Significant differences in lipid metabolism-related gene expression were observed between GC and normal tissues, closely linked to FA metabolism, oxidoreductase activity, and sphingolipid metabolism. APOA1 emerged as a potential prognostic biomarker by intersecting prognostic and differentially expressed lipid metabolism genes. Immunohistochemical analysis confirmed APOA1 downregulation in GC. The receiver operating characteristic (ROC) analysis demonstrated its predictive value, with the area under the curve (AUC) being 0.64 [95% confidence interval (CI): 0.52-0.76]. APOA1 expression correlated with immune cell infiltrations. Clinical serum APOA1 results revealed lower levels in GC patients (1.38 vs. 1.26; P<0.05), associated with poor prognosis (hazard ratio =1.50; P<0.001) and clinical characteristics. ROC analysis of serum APOA1 demonstrated good diagnostic ability (AUC: 0.63, 95% CI: 0.61-0.65). Serum APOA1 levels significantly increased after treatment. Conclusions This study highlights lipid metabolism reprogramming in GC and identifies APOA1 as a potential diagnostic and prognostic biomarker, suggesting its clinical utility in managing GC.
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Affiliation(s)
- Fangfei Li
- Department of Gastroenterology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Mei Han
- Department of Gastroenterology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Xiaoyun Gao
- Department of Geriatric, The Second Hospital of Dalian Medical University, Dalian, China
| | - Xuan Du
- Department of Gastroenterology, The Second Hospital of Dalian Medical University, Dalian, China
| | - Chunmeng Jiang
- Department of Gastroenterology, The Second Hospital of Dalian Medical University, Dalian, China
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Matsuoka T, Yashiro M. Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer. Int J Mol Sci 2024; 25:5880. [PMID: 38892067 PMCID: PMC11172243 DOI: 10.3390/ijms25115880] [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: 04/18/2024] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024] Open
Abstract
Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.
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Affiliation(s)
- Tasuku Matsuoka
- Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan;
- Institute of Medical Genetics, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan
| | - Masakazu Yashiro
- Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan;
- Institute of Medical Genetics, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan
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12
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Riba M, Sala C, Culhane AC, Flobak Å, Patocs A, Boye K, Plevova K, Pospíšilová Š, Gandolfi G, Morelli MJ, Bucci G, Edsjö A, Lassen U, Al-Shahrour F, Lopez-Bigas N, Hovland R, Cuppen E, Valencia A, Poirel HA, Rosenquist R, Scollen S, Arenas Marquez J, Belien J, De Nicolo A, De Maria R, Torrents D, Tonon G. The 1+Million Genomes Minimal Dataset for Cancer. Nat Genet 2024; 56:733-736. [PMID: 38702538 DOI: 10.1038/s41588-024-01721-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2024]
Affiliation(s)
- Michela Riba
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Cinzia Sala
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Aedin C Culhane
- Limerick Digital Cancer Research Centre, Health Research Institute, School of Medicine, University of Limerick, Limerick, Ireland
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St. Olav's University Hospital, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Attila Patocs
- Department of Molecular Genetics and the National Tumour Biology Laboratory, National Institute of Oncology, Budapest, Hungary
- Department of Oncology Biobank, National Institute of Oncology, Budapest, Hungary
- Hereditary Tumours Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary
| | - Kjetil Boye
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Karla Plevova
- Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czech Republic
- Department of Internal Medicine - Hematology and Oncology, Faculty of Medicine, Masaryk University and University Hospital, Brno, Czech Republic
- Department of Medical Genetics and Genomics, University Hospital and Faculty of Medicine, Brno, Czech Republic
| | - Šárka Pospíšilová
- Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czech Republic
- Department of Internal Medicine - Hematology and Oncology, Faculty of Medicine, Masaryk University and University Hospital, Brno, Czech Republic
- Department of Medical Genetics and Genomics, University Hospital and Faculty of Medicine, Brno, Czech Republic
| | - Giorgia Gandolfi
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco J Morelli
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Gabriele Bucci
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Anders Edsjö
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
- Division of Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Ulrik Lassen
- Department of Oncology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Fátima Al-Shahrour
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Nuria Lopez-Bigas
- Institución Catalana de Investigación y Estudios Avanzados (ICREA), Barcelona, Spain
| | - Randi Hovland
- Section of Cancer Genomics Haukeland University Hospital, Bergen, Norway
| | - Edwin Cuppen
- Center for Molecular Medicine, Oncode Institute, University Medical Center Utrecht, Utrecht, the Netherlands
- Hartwig Medical Foundation, Amsterdam, the Netherlands
| | - Alfonso Valencia
- Institución Catalana de Investigación y Estudios Avanzados (ICREA), Barcelona, Spain
| | | | - Richard Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Serena Scollen
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Jeroen Belien
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Arcangela De Nicolo
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ruggero De Maria
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario 'A. Gemelli' - Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - David Torrents
- Institución Catalana de Investigación y Estudios Avanzados (ICREA), Barcelona, Spain
| | - Giovanni Tonon
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Functional Genomics of Cancer Unit, Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
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13
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Li S, Li Z, Xue K, Zhou X, Ding C, Shao Y, Zhang S, Ruan T, Zheng M, Sun J. GC-CDSS: Personalized gastric cancer treatment recommendations system based on knowledge graph. Int J Med Inform 2024; 185:105402. [PMID: 38467099 DOI: 10.1016/j.ijmedinf.2024.105402] [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: 07/21/2023] [Revised: 02/25/2024] [Accepted: 03/05/2024] [Indexed: 03/13/2024]
Abstract
BACKGROUND Gastric cancer (GC) is one of the most common malignant tumors in the world, posing a serious threat to human health. Currently, gastric cancer treatment strategies emphasize a multidisciplinary team (MDT) consultation approach. However, there are numerous treatment guidelines and insights from clinical trials. The application of AI-based Clinical Decision Support System (CDSS) in tumor diagnosis and screening is increasing rapidly. OBJECTIVE The purpose of this study is to (1) summarize the treatment decision process for GC according to the treatment guidelines in China, and then create a knowledge graph (KG) for GC, (2) based on aforementioned KG, built a CDSS and conducted an initial feasibility evaluation for the current system. METHODS Firstly, we summarized the decision-making process for treatment of GC. Then, we extracted relevant decision nodes and relationships and utilized Neo4j to create the KG. After obtaining the initial node features for building the graph embedding model, graph embedding algorithm, such as Node2Vec and GraphSAGE, were used to construct the GC-CDSS. At last, a retrospective cohort study was used to compare the consistency between GC-CDSS and MDT in treatment decision making. RESULTS In current study, we introduce a GC-CDSS, which is constructed based on Chinese GC treatment guidelines knowledge graph (KG). In the KG, we define four types of nodes and four types of relationships, and it comprise a total of 207 nodes and 300 relationships. Regarding GC-CDSS, the system is capable of providing dynamic and personalized diagnostic and treatment recommendations based on the patient's condition. Furthermore, a retrospective cohort study is conducted to compare GC-CDSS recommendations with those of the MDT group, the overall consistency rate of treatment recommendations between the auxiliary decision system and MDT team is 92.96%. CONCLUSIONS We construct a GC treatment support system, GC-CDSS, based on KG. The GC-CDSS may help oncologists make treatment decisions more efficient and promote standardization in primary healthcare settings.
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Affiliation(s)
- Shuchun Li
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Zhiang Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Kui Xue
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Xueliang Zhou
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Chengsheng Ding
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Yanfei Shao
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Sen Zhang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Tong Ruan
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Minhua Zheng
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.
| | - Jing Sun
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.
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14
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Yu H, Wang Z, Dong Y, Li L, Fan X, Zheng N, Jiang J, Lin C, Lu C, Li K, Feng M. AXIN1/MYC Axis Mediated the Osimertinib Resistance in EGFR Mutant Non-Small Cell Lung Cancer Cells. TOHOKU J EXP MED 2024; 262:269-276. [PMID: 38233113 DOI: 10.1620/tjem.2024.j002] [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] [Indexed: 01/19/2024]
Abstract
Osimertinib, a promising and approved third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), is a standard strategy for EGFR-mutant non-small cell lung cancer (NSCLC) patients. However, developed resistance is unavoidable, which reduces its long-term effectiveness. In this study, RNA sequencing was performed to analyze differentially expressed genes (DEGs). The PrognoScan database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to identify the key genes for clinical prognosis and gene correlation respectively. Protein expression was determined by western blot analysis. Cell viability assay and Ki67 staining were used to evaluate the effect of osimertinib on tumor cells. Finally, we screened out two hub genes, myelocytomatosis oncogene (Myc) and axis inhibition protein 1 (Axin1), upregulated in three osimertinib-resistant cell lines through RNA sequencing and bioinformatics analysis. Next, cell experiment confirmed that expression of C-MYC and AXIN1 were elevated in different EGFR mutant NSCLC cell lines with acquired resistance to osimertinib, compared with their corresponding parental cell lines. Furthermore, we demonstrated that AXIN1 upregulated the expression of C-MYC and mediated the acquired resistance of EGFR mutant NSCLC cells to osimertinib in vitro. In conclusion, AXIN1 affected the sensitivity of EGFR mutant NSCLC to osimertinib via regulating C-MYC expression in vitro. Targeting AXIN1/MYC signaling may be a potential new strategy for overcoming acquired resistance to osimertinib.
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Affiliation(s)
- Haoyue Yu
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
- Department of Oncology, The First Affiliated Hospital of Dalian Medical University
| | - Zhiguo Wang
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Southwest Medical University
| | - Yan Dong
- Department of Oncology, The First Affiliated Hospital of Dalian Medical University
| | - Li Li
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
| | - Xianming Fan
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Southwest Medical University
| | - Nan Zheng
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
| | - Ji Jiang
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
| | - Caiyu Lin
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
| | - Conghua Lu
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
| | - Kunlin Li
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
| | - Mingxia Feng
- Department of Respiratory Disease, Daping Hospital, Army Medical University (Third Military Medical University)
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15
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Miao L, Wu D, Zhao H, Xie A. TIMM17A overexpression in lung adenocarcinoma and its association with prognosis. Sci Rep 2024; 14:8840. [PMID: 38632467 PMCID: PMC11024209 DOI: 10.1038/s41598-024-59526-1] [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: 07/24/2023] [Accepted: 04/11/2024] [Indexed: 04/19/2024] Open
Abstract
Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan-Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.
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Affiliation(s)
- Lili Miao
- Department of Respiration, YiZheng People's Hospital, YiZheng, Jiangsu, China
| | - Dejun Wu
- Department of Respiration, YiZheng People's Hospital, YiZheng, Jiangsu, China
| | - Hongyu Zhao
- Department of Respiration, YiZheng People's Hospital, YiZheng, Jiangsu, China
| | - Aiwei Xie
- Department of Nephrology, YiZheng People's Hospital, YiZheng, Jiangsu, China.
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16
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Neri P, Lee H, Bahlis NJ. Artificial Intelligence Individualized Risk Classifier in Multiple Myeloma. J Clin Oncol 2024; 42:1207-1210. [PMID: 38452316 DOI: 10.1200/jco.23.02781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/08/2024] [Accepted: 01/19/2024] [Indexed: 03/09/2024] Open
Affiliation(s)
- Paola Neri
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Holly Lee
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - Nizar J Bahlis
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
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17
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Cosentini I, Condorelli DF, Locicero G, Ferro A, Pulvirenti A, Barresi V, Alaimo S. Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks. PLoS One 2024; 19:e0301591. [PMID: 38593144 PMCID: PMC11003681 DOI: 10.1371/journal.pone.0301591] [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: 07/01/2023] [Accepted: 03/18/2024] [Indexed: 04/11/2024] Open
Abstract
Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome. We evaluated the multi-layer networks generated by COMBO on different TCGA cancer datasets (COAD, BLCA, BRCA, CESC, STAD) focusing on the effect of a specific chromosomal numerical aberration, broad gain in chromosome 20, on different cancer histotypes. In addition, the effect of chromosome 8q amplification was tested in the same TCGA cancer dataset. The results demonstrate the ability of COMBO to identify the chromosome 20 amplification cancer driver force in the different TCGA Pan Cancer project datasets.
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Affiliation(s)
- Ilaria Cosentini
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy
| | - Daniele Filippo Condorelli
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, Catania, Italy
| | - Giorgio Locicero
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy
| | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, Bioinformatics Unit, University of Catania, Catania, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, Bioinformatics Unit, University of Catania, Catania, Italy
| | - Vincenza Barresi
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, Catania, Italy
| | - Salvatore Alaimo
- Department of Clinical and Experimental Medicine, Bioinformatics Unit, University of Catania, Catania, Italy
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18
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da Silva Rosa SC, Barzegar Behrooz A, Guedes S, Vitorino R, Ghavami S. Prioritization of genes for translation: a computational approach. Expert Rev Proteomics 2024; 21:125-147. [PMID: 38563427 DOI: 10.1080/14789450.2024.2337004] [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: 05/26/2023] [Accepted: 02/21/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.
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Affiliation(s)
- Simone C da Silva Rosa
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
| | - Amir Barzegar Behrooz
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Electrophysiology Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Rui Vitorino
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
- Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
- UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Saeid Ghavami
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Faculty of Medicine in Zabrze, Academia of Silesia, Katowice, Poland
- Research Institute of Oncology and Hematology, Cancer Care Manitoba, University of Manitoba, Winnipeg, Canada
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19
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Liu G, Li X, Liu X, Lu W, Xue Y, Liu M. Cyclodextrin-conjugated low-molecular-weight polyethyleneimine as a macromolecular contrast agent for tumor-targeted magnetic resonance imaging. RSC Adv 2024; 14:10499-10506. [PMID: 38567319 PMCID: PMC10985534 DOI: 10.1039/d4ra00316k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/20/2024] [Indexed: 04/04/2024] Open
Abstract
Macromolecular contrast agents (CAs) usually possess excellent contrast ability and tumor-targeting ability in comparison with small-molecule CAs, especially for early tumor detection. Herein, cyclodextrin-conjugated low-molecular-weight polyethyleneimine was synthesized as a macromolecular backbone. Afterward, a linear polymer with adamantane terminal and Gd chelates was synthesized, followed by conjugating with the backbone via host-guest interaction. Finally, folic acid was conjugated onto the as-prepared CAs through bioorthogonal chemistry, which endowed the CAs with the capability to accumulate into the tumor region. Compared to Magnevist (r1 = 4.25 mM-1 s-1) used in clinic, the PC/Ad-PEG2000-PLL(DTPA-Gd)-FA exhibited higher longitudinal relaxivity (r1 = 11.62 mM-1 s-1) with excellent biocompatibility. Furthermore, in vivo experiments demonstrated that PC/Ad-PEG2000-PLL(DTPA-Gd)-FA could effectively accumulate in the tumor region and produce a brighter image than that of Magnevist. The H&E staining and metabolic data further illustrated that this CA possessed excellent biocompatibility in vivo. Finally, these results above suggest that this macromolecular CA could be a potential candidate as a MRI CA for tumor-targeted diagnosis.
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Affiliation(s)
- Guangkuo Liu
- Hubei Key Laboratory for Novel Reactor and Green Chemistry Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology Wuhan 430205 China
- School of Optoelectronic Materials & Technology, Institute for Interdisciplinary Research, Jianghan University Wuhan 430056 China
| | - Xinxin Li
- Hubei Key Laboratory for Novel Reactor and Green Chemistry Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology Wuhan 430205 China
- School of Optoelectronic Materials & Technology, Institute for Interdisciplinary Research, Jianghan University Wuhan 430056 China
| | - Xiaojie Liu
- School of Optoelectronic Materials & Technology, Institute for Interdisciplinary Research, Jianghan University Wuhan 430056 China
| | - Wangting Lu
- State Key Laboratory of Precision Blasting, Jianghan University Wuhan 430056 China
- School of Optoelectronic Materials & Technology, Institute for Interdisciplinary Research, Jianghan University Wuhan 430056 China
| | - Yanan Xue
- Hubei Key Laboratory for Novel Reactor and Green Chemistry Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology Wuhan 430205 China
| | - Min Liu
- State Key Laboratory of Precision Blasting, Jianghan University Wuhan 430056 China
- School of Optoelectronic Materials & Technology, Institute for Interdisciplinary Research, Jianghan University Wuhan 430056 China
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20
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Li Y, Gao W, Yang Z, Hu Z, Li J. Multi-omics pan-cancer analyses identify MCM4 as a promising prognostic and diagnostic biomarker. Sci Rep 2024; 14:6517. [PMID: 38499612 PMCID: PMC10948783 DOI: 10.1038/s41598-024-57299-1] [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: 10/04/2023] [Accepted: 03/16/2024] [Indexed: 03/20/2024] Open
Abstract
Minichromosome Maintenance Complex Component 4 (MCM4) is a vital component of the mini-chromosome maintenance complex family, crucial for initiating the replication of eukaryotic genomes. Recently, there has been a growing interest in investigating the significance of MCM4 in different types of cancer. Despite the existing research on this topic, a comprehensive analysis of MCM4 across various cancer types has been lacking. This study aims to bridge this knowledge gap by presenting a thorough pan-cancer analysis of MCM4, shedding light on its functional implications and potential clinical applications. The study utilized multi-omics samples from various databases. Bioinformatic tools were employed to explore the expression profiles, genetic alterations, phosphorylation states, immune cell infiltration patterns, immune subtypes, functional enrichment, disease prognosis, as well as the diagnostic potential of MCM4 and its responsiveness to drugs in a range of cancers. Our research demonstrates that MCM4 is closely associated with the oncogenesis, prognosis and diagnosis of various tumors and proposes that MCM4 may function as a potential biomarker in pan-cancer, providing a deeper understanding of its potential role in cancer development and treatment.
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Affiliation(s)
- Yanxing Li
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Wentao Gao
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Zhen Yang
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Zhenwei Hu
- Xi'an Jiaotong University Health Science Center, Xi'an, 710000, Shaanxi, People's Republic of China
| | - Jianjun Li
- Department of Cardiology, Jincheng People's Hospital Affiliated to Changzhi Medical College, Jincheng, Shanxi, People's Republic of China.
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21
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Jose A, Kulkarni P, Thilakan J, Munisamy M, Malhotra AG, Singh J, Kumar A, Rangnekar VM, Arya N, Rao M. Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine. Mol Cancer 2024; 23:50. [PMID: 38461268 PMCID: PMC10924370 DOI: 10.1186/s12943-023-01916-6] [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: 08/05/2023] [Accepted: 12/15/2023] [Indexed: 03/11/2024] Open
Abstract
Despite advancements in treatment protocols, cancer is one of the leading cause of deaths worldwide. Therefore, there is a need to identify newer and personalized therapeutic targets along with screening technologies to combat cancer. With the advent of pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, the scientific community has witnessed an improved molecular and metabolomic understanding of various diseases, including cancer. In addition, three-dimensional (3-D) disease models have been efficiently utilized for understanding disease pathophysiology and as screening tools in drug discovery. An integrated approach utilizing pan-omics technologies and 3-D in vitro tumor models has led to improved understanding of the intricate network encompassing various signalling pathways and molecular cross-talk in solid tumors. In the present review, we underscore the current trends in omics technologies and highlight their role in understanding genotypic-phenotypic co-relation in cancer with respect to 3-D in vitro tumor models. We further discuss the challenges associated with omics technologies and provide our outlook on the future applications of these technologies in drug discovery and precision medicine for improved management of cancer.
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Affiliation(s)
- Anmi Jose
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Pallavi Kulkarni
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Jaya Thilakan
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Murali Munisamy
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Anvita Gupta Malhotra
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Jitendra Singh
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Ashok Kumar
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Vivek M Rangnekar
- Markey Cancer Center and Department of Radiation Medicine, University of Kentucky, Lexington, KY, 40536, USA
| | - Neha Arya
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India.
| | - Mahadev Rao
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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22
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Nikolski M, Hovig E, Al-Shahrour F, Blomberg N, Scollen S, Valencia A, Saunders G. Roadmap for a European cancer data management and precision medicine infrastructure. NATURE CANCER 2024; 5:367-372. [PMID: 38321342 DOI: 10.1038/s43018-023-00717-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Affiliation(s)
- Macha Nikolski
- University of Bordeaux, CNRS-IBGC, UMR 5095, Bordeaux, France.
- University of Bordeaux, Bordeaux Bioinformatics Center CBiB, Bordeaux, France.
| | - Eivind Hovig
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Fatima Al-Shahrour
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | - Serena Scollen
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Alfonso Valencia
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Gary Saunders
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, UK
- EATRIS-ERIC, Amsterdam, the Netherlands
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23
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Mengzhen Z, Xinwei H, Zeheng T, Nan L, Yang Y, Huirong Y, Kaisi F, Xiaoting D, Liucheng Y, Kai W. Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma. J Cancer Res Clin Oncol 2024; 150:109. [PMID: 38427078 PMCID: PMC10907485 DOI: 10.1007/s00432-024-05630-8] [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: 12/20/2023] [Accepted: 01/20/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Neuroblastoma (NB), a prevalent pediatric solid tumor, presents formidable challenges due to its high malignancy and intricate pathogenesis. The role of disulfidptosis, a novel form of programmed cell death, remains poorly understood in the context of NB. METHODS Gaussian mixture model (GMM)-identified disulfidptosis-related molecular subtypes in NB, differential gene analysis, survival analysis, and gene set variation analysis were conducted subsequently. Weighted gene co-expression network analysis (WGCNA) selected modular genes most relevant to the disulfidptosis core pathways. Integration of machine learning approaches revealed the combination of the Least absolute shrinkage and selection operator (LASSO) and Random Survival Forest (RSF) provided optimal dimensionality reduction of the modular genes. The resulting model was validated, and a nomogram assessed disulfidptosis characteristics in NB. Core genes were filtered and subjected to tumor phenotype and disulfidptosis-related experiments. RESULTS GMM clustering revealed three distinct subtypes with diverse prognoses, showing significant variations in glucose metabolism, cytoskeletal structure, and tumor-related pathways. WGCNA highlighted the red module of genes highly correlated with disulfide isomerase activity, cytoskeleton formation, and glucose metabolism. The LASSO and RSF combination yielded the most accurate and stable prognostic model, with a significantly worse prognosis for high-scoring patients. Cytological experiments targeting core genes (CYFIP1, EMILIN1) revealed decreased cell proliferation, migration, invasion abilities, and evident cytoskeletal deformation upon core gene knockdown. CONCLUSIONS This study showcases the utility of disulfidptosis-related gene scores for predicting prognosis and molecular subtypes of NB. The identified core genes, CYFIP1 and EMILIN1, hold promise as potential therapeutic targets and diagnostic markers for NB.
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Affiliation(s)
- Zhang Mengzhen
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Hou Xinwei
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Tan Zeheng
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Li Nan
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Yang Yang
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Yang Huirong
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Fan Kaisi
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Ding Xiaoting
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China
| | - Yang Liucheng
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
| | - Wu Kai
- Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
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24
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Wang A. Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2024; 5:170-186. [PMID: 38464381 PMCID: PMC10918237 DOI: 10.37349/etat.2024.00211] [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: 08/29/2023] [Accepted: 12/18/2023] [Indexed: 03/12/2024] Open
Abstract
Long noncoding RNAs (lncRNAs) derived from noncoding regions in the human genome were once regarded as junks with no biological significance, but recent studies have shown that these molecules are highly functional, prompting an explosion of studies on their biology. However, these recent efforts have only begun to recognize the biological significance of a small fraction (< 1%) of the lncRNAs. The basic concept of these lncRNA functions remains controversial. This controversy arises primarily from conventional biased observations based on limited datasets. Fortunately, emerging big data provides a promising path to circumvent conventional bias to understand an unbiased big picture of lncRNA biology and advance the fundamental principles of lncRNA biology. This review focuses on big data studies that break through the critical concepts of the lncRNA functional system and its endogenous regulatory roles in all cancers. lncRNAs have unique functional systems distinct from proteins, such as transcriptional initiation and regulation, and they abundantly interact with mitochondria and consume less energy. lncRNAs, rather than proteins as traditionally thought, function as the most critical endogenous regulators of all cancers. lncRNAs regulate the cancer regulatory regime by governing the endogenous regulatory network of all cancers. This is accomplished by dominating the regulatory network module and serving as a key hub and top inducer. These critical conceptual breakthroughs lay a blueprint for a comprehensive functional picture of the human genome. They also lay a blueprint for combating human diseases that are regulated by lncRNAs.
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Affiliation(s)
- Anyou Wang
- Feinstone Center for Genomic Research, University of Memphis, Memphis, TN 38152, USA
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25
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Zhang N, Wang W, Gao X, Gao F. Editorial: Clinical risk assessment and intervention of gastrointestinal tumors driven by big-data. Front Med (Lausanne) 2024; 11:1379762. [PMID: 38476446 PMCID: PMC10927941 DOI: 10.3389/fmed.2024.1379762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 02/15/2024] [Indexed: 03/14/2024] Open
Affiliation(s)
- Nan Zhang
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
| | - Wei Wang
- Department of Pathology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin Gao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer Science Program, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Feng Gao
- Department of General Surgery, Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
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26
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Mehrotra S, Sharma S, Pandey RK. A journey from omics to clinicomics in solid cancers: Success stories and challenges. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:89-139. [PMID: 38448145 DOI: 10.1016/bs.apcsb.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.
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27
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Yan X, Qi Y, Yao X, Zhou N, Ye X, Chen X. DNMT3L inhibits hepatocellular carcinoma progression through DNA methylation of CDO1: insights from big data to basic research. J Transl Med 2024; 22:128. [PMID: 38308276 PMCID: PMC10837993 DOI: 10.1186/s12967-024-04939-9] [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: 11/17/2023] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND DNMT3L is a crucial DNA methylation regulatory factor, yet its function and mechanism in hepatocellular carcinoma (HCC) remain poorly understood. Bioinformatics-based big data analysis has increasingly gained significance in cancer research. Therefore, this study aims to elucidate the role of DNMT3L in HCC by integrating big data analysis with experimental validation. METHODS Dozens of HCC datasets were collected to analyze the expression of DNMT3L and its relationship with prognostic indicators, and were used for molecular regulatory relationship evaluation. The effects of DNMT3L on the malignant phenotypes of hepatoma cells were confirmed in vitro and in vivo. The regulatory mechanisms of DNMT3L were explored through MSP, western blot, and dual-luciferase assays. RESULTS DNMT3L was found to be downregulated in HCC tissues and associated with better prognosis. Overexpression of DNMT3L inhibits cell proliferation and metastasis. Additionally, CDO1 was identified as a target gene of DNMT3L and also exhibits anti-cancer effects. DNMT3L upregulates CDO1 expression by competitively inhibiting DNMT3A-mediated methylation of CDO1 promoter. CONCLUSIONS Our study revealed the role and epi-transcriptomic regulatory mechanism of DNMT3L in HCC, and underscored the essential role and applicability of big data analysis in elucidating complex biological processes.
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Affiliation(s)
- Xiaokai Yan
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
| | - Yao Qi
- Shanghai Molecular Medicine Engineering Technology Research Center, Shanghai, 201203, China
- Shanghai National Engineering Research Center of Biochip, Shanghai, 201203, China
| | - Xinyue Yao
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Nanjing Zhou
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xinxin Ye
- Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xing Chen
- Department of Hepatopancreatobiliary Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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28
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Wu X, Li W, Tu H. Big data and artificial intelligence in cancer research. Trends Cancer 2024; 10:147-160. [PMID: 37977902 DOI: 10.1016/j.trecan.2023.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/19/2023]
Abstract
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.
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Affiliation(s)
- Xifeng Wu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wenyuan Li
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Huakang Tu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
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29
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Zheng L, Shi S, Sun X, Lu M, Liao Y, Zhu S, Zhang H, Pan Z, Fang P, Zeng Z, Li H, Li Z, Xue W, Zhu F. MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics. Brief Bioinform 2024; 25:bbae006. [PMID: 38305456 PMCID: PMC10835750 DOI: 10.1093/bib/bbae006] [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: 11/22/2023] [Revised: 12/26/2023] [Accepted: 01/01/2024] [Indexed: 02/03/2024] Open
Abstract
Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.
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Affiliation(s)
- Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou 330110, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou 330110, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou 330110, China
| | - Yang Liao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Sisi Zhu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Pan Fang
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhenyu Zeng
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Honglin Li
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zhaorong Li
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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30
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Anoshkin SS, Shishkin II, Markina DI, Logunov LS, Demir HV, Rogach AL, Pushkarev AP, Makarov SV. Photoinduced Transition from Quasi-Two-Dimensional Ruddlesden-Popper to Three-Dimensional Halide Perovskites for the Optical Writing of Multicolor and Light-Erasable Images. J Phys Chem Lett 2024; 15:540-548. [PMID: 38197909 DOI: 10.1021/acs.jpclett.3c03151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Optical data storage, information encryption, and security labeling technologies require materials that exhibit local, pronounced, and diverse modifications of their structure-dependent optical properties under external excitation. Herein, we propose and develop a novel platform relying on lead halide Ruddlesden-Popper phases that undergo a light-induced transition toward bulk perovskite and employ this phenomenon for the direct optical writing of multicolor patterns. This transition causes the weakening of quantum confinement and hence a reduction in the band gap. To extend the color gamut of photoluminescence, we use mixed-halide compositions that exhibit photoinduced halide segregation. The emission of the films can be tuned across the range of 450-600 nm. Laser irradiation provides high-resolution direct writing, whereas continuous-wave ultraviolet exposure is suitable for recording on larger scales. The luminescent images created on such films can be erased during the visualization process. This makes the proposed writing/erasing platform suitable for the manufacturing of optical data storage devices and light-erasable security labels.
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Affiliation(s)
| | - Ivan I Shishkin
- ITMO University, Kronverkskiy pr. 49, 197101 St. Petersburg, Russia
| | - Daria I Markina
- ITMO University, Kronverkskiy pr. 49, 197101 St. Petersburg, Russia
| | - Lev S Logunov
- ITMO University, Kronverkskiy pr. 49, 197101 St. Petersburg, Russia
| | - Hilmi Volkan Demir
- UNAM-Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center, Department of Electrical and Electronics Engineering, Department of Physics, Bilkent University, Ankara 06800, Turkey
- LUMINOUS! Center of Excellence for Semiconductor Lighting and Displays, School of Electrical and Electronic Engineering, School of Physical and Materials Sciences, School of Materials Science and Nanotechnology, Nanyang Technological University, Singapore 639798
| | - Andrey L Rogach
- Department of Materials Science and Engineering and Centre for Functional Photonics (CFP), City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR 999077, P. R. China
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, Shandong, P. R. China
| | | | - Sergey V Makarov
- ITMO University, Kronverkskiy pr. 49, 197101 St. Petersburg, Russia
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, Shandong, P. R. China
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31
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Derbal Y. Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence. Cancer Control 2024; 31:10732748241264704. [PMID: 38897721 PMCID: PMC11189021 DOI: 10.1177/10732748241264704] [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: 04/04/2024] [Revised: 05/17/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024] Open
Abstract
Therapeutic resistance is a major challenge facing the design of effective cancer treatments. Adaptive cancer therapy is in principle the most viable approach to manage cancer's adaptive dynamics through drug combinations with dose timing and modulation. However, there are numerous open issues facing the clinical success of adaptive therapy. Chief among these issues is the feasibility of real-time predictions of treatment response which represent a bedrock requirement of adaptive therapy. Generative artificial intelligence has the potential to learn prediction models of treatment response from clinical, molecular, and radiomics data about patients and their treatments. The article explores this potential through a proposed integration model of Generative Pre-Trained Transformers (GPTs) in a closed loop with adaptive treatments to predict the trajectories of disease progression. The conceptual model and the challenges facing its realization are discussed in the broader context of artificial intelligence integration in oncology.
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Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada
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32
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Singh B, Jevnikar AM, Desjardins E. Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities. Arch Immunol Ther Exp (Warsz) 2024; 72:aite-2024-0006. [PMID: 38421272 DOI: 10.2478/aite-2024-0006] [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: 12/14/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.
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Affiliation(s)
- Bhagirath Singh
- Department of Microbiology and Immunology, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Rotman Institute of Philosophy, University of Western Ontario, London, ON, Canada
| | - Anthony M Jevnikar
- Department of Microbiology and Immunology, University of Western Ontario, London, ON, Canada
- Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Eric Desjardins
- Rotman Institute of Philosophy, University of Western Ontario, London, ON, Canada
- Department of Philosophy, University of Western Ontario, London, ON, Canada
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Jeong E, Yoon S. Current advances in comprehensive omics data mining for oncology and cancer research. Biochim Biophys Acta Rev Cancer 2024; 1879:189030. [PMID: 38008264 DOI: 10.1016/j.bbcan.2023.189030] [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: 02/08/2023] [Revised: 09/05/2023] [Accepted: 11/19/2023] [Indexed: 11/28/2023]
Abstract
The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.
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Affiliation(s)
- Euna Jeong
- Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Sukjoon Yoon
- Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea; Department of Biological Sciences, Sookmyung Women's University, Seoul 04310, Republic of Korea.
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Liu D, He C, Liu Z, Xu L, Li J, Zhao Z, Hu X, Chen H, Sun B, Wang Y. The Prognostic and Immune Significance of CILP2 in Pan-Cancer and Its Relationship with the Progression of Pancreatic Cancer. Cancers (Basel) 2023; 15:5842. [PMID: 38136386 PMCID: PMC10741840 DOI: 10.3390/cancers15245842] [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: 09/09/2023] [Revised: 11/18/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Cartilage intermediate layer protein 2 (CILP2) facilitates interactions between matrix components in cartilage and has emerged as a potential prognostic biomarker for cancer. This study aimed to investigate the function and mechanisms of CILP2 in pan-cancer. We evaluated the pan-cancer expression, methylation, and mutation data of CILP2 for its clinical prognostic value. Additionally, we explored the immunological characteristics of CILP2 in pan-cancer and then focused specifically on pancreatic ductal adenocarcinoma (PAAD). The subtype analysis of PAAD identified subtype-specific expression and immunological characteristics. Finally, in vitro and in vivo experiments assessed the impact of CILP2 on pancreatic cancer progression. CILP2 exhibited high expression in most malignancies, with significant heterogeneity in epigenetic modifications across multiple cancer types. The abnormal methylation and copy number variations in CILP2 were correlated with poor prognoses. Upregulated CILP2 was associated with TGFB/TGFBR1 and more malignant subtypes. CILP2 exhibited a negative correlation with immune checkpoints in PAAD, suggesting potential for immunotherapy. CILP2 activated the AKT pathway, and it increased proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) in pancreatic cancer. We demonstrated that CILP2 significantly contributes to pancreatic cancer progression. It serves as a prognostic biomarker and a potential target for immunotherapy.
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Affiliation(s)
- Danxi Liu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Cong He
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Zonglin Liu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Licheng Xu
- Department of Otorhinolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China
- The Key Laboratory of Myocardial Ischemia, Ministry of Education, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Jiacheng Li
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Zhongjie Zhao
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Xuewei Hu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
| | - Hua Chen
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Bei Sun
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Yongwei Wang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; (D.L.); (Z.Z.)
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
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Biswas A, Kumari A, Gaikwad DS, Pandey DK. Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:550-569. [PMID: 38100404 DOI: 10.1089/omi.2023.0197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.
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Affiliation(s)
- Aakanksha Biswas
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - Aditi Kumari
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - D S Gaikwad
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Dhananjay K Pandey
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
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Li L, Li Y, Lu M, Wang Y, Li Z, Hu X, He X, Gong T, Luo Y, Zhou Y, Min L, Tu C. The combination of baseline neutrophil to lymphocyte ratio and dynamic changes during treatment can better predict the survival of osteosarcoma patients. Front Oncol 2023; 13:1235158. [PMID: 38033504 PMCID: PMC10682781 DOI: 10.3389/fonc.2023.1235158] [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: 06/05/2023] [Accepted: 10/23/2023] [Indexed: 12/02/2023] Open
Abstract
Background Osteosarcoma is a primary malignant bone tumor with a high metastatic potential that accounts for a significant proportion of all bone tumors. The prognosis for patients with metastatic or recurrence disease remains poor. The neutrophil-to-lymphocyte ratio (NLR) has become a potential prognostic biomarker for cancer. Recent evidence suggests that the dynamic changes in neutrophil-to-lymphocyte ratio (NLR) during treatment may be more informative in predicting patient prognosis, but the value of dynamic NLR in osteosarcoma has not yet been determined. Methods This retrospective study retrospectively analyzed the clinical information of 251 osteosarcoma patients diagnosed and treated in West China Hospital of Sichuan University, explored the impact of baseline NLR and changes in NLR during treatment on the prognosis of osteosarcoma patients, and further combined baseline NLR with Delta NLR to build an NLR staging system. Results The results showed that both baseline NLR and delta NLR had some predictive ability for the prognosis of osteosarcoma patients (P = 6.90e-4, P = 0.022). Patients with high baseline NLR were more likely to have a decrease in delta NLR (P = 1.24e-10). The NLR stage had a better predictive ability than baseline NLR and delta NLR, and was an independent prognostic factor for overall survival in osteosarcoma patients HR: 2.456 (1.625-3.710) (P = 1.97e-05). Conclusion NLR has value in continuous monitoring, and continuous monitoring of NLR can better predict the survival of osteosarcoma patients compared to baseline NLR.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Li Min
- Department of Orthopedics, Orthopedics Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Chongqi Tu
- Department of Orthopedics, Orthopedics Research Institute, West China Hospital, Sichuan University, Chengdu, China
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Greten TF, Villanueva A, Korangy F, Ruf B, Yarchoan M, Ma L, Ruppin E, Wang XW. Biomarkers for immunotherapy of hepatocellular carcinoma. Nat Rev Clin Oncol 2023; 20:780-798. [PMID: 37726418 DOI: 10.1038/s41571-023-00816-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2023] [Indexed: 09/21/2023]
Abstract
Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches.
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Affiliation(s)
- Tim F Greten
- Gastrointestinal Malignancies Section, Thoracic and Gastrointestinal Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
| | - Augusto Villanueva
- Divisions of Liver Disease and Hematology/Medical Oncology, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Firouzeh Korangy
- Gastrointestinal Malignancies Section, Thoracic and Gastrointestinal Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Benjamin Ruf
- Gastrointestinal Malignancies Section, Thoracic and Gastrointestinal Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Mark Yarchoan
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lichun Ma
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Xin W Wang
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
- Liver Carcinogenesis Section, Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
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Li B, Kong Z, Liu Y, Xu B, Liu X, Li S, Zhang Z. A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer. Transl Cancer Res 2023; 12:2477-2492. [PMID: 37969387 PMCID: PMC10643944 DOI: 10.21037/tcr-23-344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 09/01/2023] [Indexed: 11/17/2023]
Abstract
Background Polyamine metabolism is critically involved in the proliferation and metastasis of tumor cells, including in kidney renal clear cell (KIRC) cancer. However, the molecular mechanisms underlying the effect of polyamines in KIRC cancer remain largely unknown. Methods The messenger RNA (mRNA) expression profile of KIRC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress database. Differential expression analysis was performed with the "limma" package in R. Univariate Cox regression and multivariable Cox regression were used to estimate correlation between variables and prognosis. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to screen variables and construct a risk signature. A nomogram model was established using the risk signature and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. Results We identified nine differentially expressed polyamine metabolism-related genes (PMRGs) in TCGA-KIRC. Of these, six were closely associated with patients' outcomes. These six genes participated in different pathways and originated from different cell types within the tumor microenvironment (TME). Using the mRNA expression values of these genes, we constructed a 4-gene PMRG risk signature. Patients with high PMRG risk exhibited worse outcomes, and our analysis showed that the PMRG risk signature was an independent prognostic factor when clinical information was used as a covariate. We also found that multiple immune- or metabolism-related pathways were differentially enriched in high or low PMRG risk groups, suggesting that altering these pathways could lead to different clinical outcomes. Finally, in two external datasets, we found that the PMRG risk signature could predict the response of patients to immune therapy. Conclusions In summary, our study identified several potentially important PMRGs in KIRC and constructed a practical risk signature, which could serve as a foundation for further development of polyamine metabolism-based targeted therapies for KIRC.
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Affiliation(s)
- Bo Li
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
- Reproductive Medicine Department, Yuncheng Central Hospital of Shanxi Province, Yuncheng, China
| | - Zheng Kong
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Yang Liu
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Bifeng Xu
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xun Liu
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Shuai Li
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zhihong Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
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Živalj M, Van Ginderachter JA, Stijlemans B. Lipocalin-2: A Nurturer of Tumor Progression and a Novel Candidate for Targeted Cancer Therapy. Cancers (Basel) 2023; 15:5159. [PMID: 37958332 PMCID: PMC10648573 DOI: 10.3390/cancers15215159] [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: 09/15/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Within the tumor microenvironment (TME) exists a complex signaling network between cancer cells and stromal cells, which determines the fate of tumor progression. Hence, interfering with this signaling network forms the basis for cancer therapy. Yet, many types of cancer, in particular, solid tumors, are refractory to the currently used treatments, so there is an urgent need for novel molecular targets that could improve current anti-cancer therapeutic strategies. Lipocalin-2 (Lcn-2), a secreted siderophore-binding glycoprotein that regulates iron homeostasis, is highly upregulated in various cancer types. Due to its pleiotropic role in the crosstalk between cancer cells and stromal cells, favoring tumor progression, it could be considered as a novel biomarker for prognostic and therapeutic purposes. However, the exact signaling route by which Lcn-2 promotes tumorigenesis remains unknown, and Lcn-2-targeting moieties are largely uninvestigated. This review will (i) provide an overview on the role of Lcn-2 in orchestrating the TME at the level of iron homeostasis, macrophage polarization, extracellular matrix remodeling, and cell migration and survival, and (ii) discuss the potential of Lcn-2 as a promising novel drug target that should be pursued in future translational research.
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Affiliation(s)
- Maida Živalj
- Brussels Center for Immunology, Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Myeloid Cell Immunology Laboratory, VIB Center for Inflammation Research, 1050 Brussels, Belgium
| | - Jo A. Van Ginderachter
- Brussels Center for Immunology, Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Myeloid Cell Immunology Laboratory, VIB Center for Inflammation Research, 1050 Brussels, Belgium
| | - Benoit Stijlemans
- Brussels Center for Immunology, Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Myeloid Cell Immunology Laboratory, VIB Center for Inflammation Research, 1050 Brussels, Belgium
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Rassy E, Andre F. A forgotten dimension of big data in drug repositioning. Eur J Cancer 2023; 192:113277. [PMID: 37647850 DOI: 10.1016/j.ejca.2023.113277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Affiliation(s)
- Elie Rassy
- Department of Medical Oncology, Gustave Roussy, University Paris-Saclay, Villejuif, France; CESP, INSERM U1018, Université Paris-Saclay, Villejuif, France.
| | - Fabrice Andre
- Department of Medical Oncology, Gustave Roussy, University Paris-Saclay, Villejuif, France; Gustave Roussy, INSERM U981, Université Paris-Saclay, Villejuif, France
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Zhang Z, Zhou P, Liu M, Pei B. Expression And Prognostic Role of PRDX1 In Gastrointestinal Cancers. J Cancer 2023; 14:2895-2907. [PMID: 37781072 PMCID: PMC10539570 DOI: 10.7150/jca.86568] [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: 05/27/2023] [Accepted: 07/23/2023] [Indexed: 10/03/2023] Open
Abstract
Esophageal, gastric, liver, and colorectal cancers represent four prevalent gastrointestinal cancers that pose substantial threats to global health due to their high morbidity and mortality rates. Peroxiredoxin 1 (PRDX1), a significant component of the PRDXs family, primarily functions to counteract the peroxides produced by metabolic activities in the body, thereby maintaining the dynamic equilibrium of peroxides in vivo. Intriguingly, PRDX1 expression correlates strongly with cancer's onset, progression, and prognosis. This study mainly applied bioinformatics methods to analyze PRDX1's expression, diagnosis, and prognosis in gastrointestinal cancers and to summarize current research advancements. Evidence from the bioinformatics database suggested that the high expression of PRDX1 was a prominent characteristic of these four gastrointestinal cancers, with this observation reaching statistical significance. The high expression of PRDX1 in gastrointestinal cancer cells also confirms this result. Notably, the primary alteration in PRDX1 within these cancers is the presence of genetic mutations. PRDX1 demonstrated the highest diagnostic efficacy for colorectal cancer. Nevertheless, elevated PRDX1 levels only significantly diminished the survival time of liver cancer patients, exerting no statistically significant impact on the survival duration of patients afflicted by the other three types of gastrointestinal cancers. Recent research has indicated variability in PRDX1 expression across different cancer types, with high expression being predominantly observed in these four gastrointestinal cancers and, in most instances, unfavorable prognosis. These findings broadly align with the results derived from bioinformatics. This research underscores the high expression of PRDX1 in gastrointestinal cancers, its relevance to the diagnosis and prognosis monitoring of these cancers, and its potential to guide clinical treatment for these cancers.
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Affiliation(s)
- Zhou Zhang
- Department of Clinical Laboratory, Wuxi Huishan District People's Hospital, Wuxi, Jiangsu Province, 214000, China
| | - Pengli Zhou
- College of Basic Medicine, China Medical University, Shenyang, Liaoning province 110000, China
| | - Mingyue Liu
- Department of Ultrasound, Wuxi No.2 People's Hospital; Jiangnan University Medical Center, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu province 214002, China
| | - Bing Pei
- Department of Clinical Laboratory, The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu, 223800, China
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Wang M, An K, Huang J, Mprah R, Ding H. A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs. Front Endocrinol (Lausanne) 2023; 14:1193992. [PMID: 37745699 PMCID: PMC10517861 DOI: 10.3389/fendo.2023.1193992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Background Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs. Methods Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples. Results Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets. Conclusion In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.
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Affiliation(s)
- Mingming Wang
- Department of Physiology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ke An
- Department of Physiology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jing Huang
- Department of Medical Informatics Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Richard Mprah
- Department of Physiology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Huanhuan Ding
- Department of Physiology, Xuzhou Medical University, Xuzhou, Jiangsu, China
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Cheng Y, Qu Z, Jiang Q, Xu T, Zheng H, Ye P, He M, Tong Y, Ma Y, Bao A. Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2305095. [PMID: 37665594 DOI: 10.1002/adma.202305095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/26/2023] [Indexed: 09/05/2023]
Abstract
Neoadjuvant and adjuvant therapies have made significant progress in cancer treatment. However, tumor adjuvant therapy still faces challenges due to the intrinsic heterogeneity of cancer, genomic instability, and the formation of an immunosuppressive tumor microenvironment. Functional materials possess unique biological properties such as long circulation times, tumor-specific targeting, and immunomodulation. The combination of functional materials with natural substances and nanotechnology has led to the development of smart biomaterials with multiple functions, high biocompatibilities, and negligible immunogenicities, which can be used for precise cancer treatment. Recently, subcellular structure-targeting functional materials have received particular attention in various biomedical applications including the diagnosis, sensing, and imaging of tumors and drug delivery. Subcellular organelle-targeting materials can precisely accumulate therapeutic agents in organelles, considerably reduce the threshold dosages of therapeutic agents, and minimize drug-related side effects. This review provides a systematic and comprehensive overview of the research progress in subcellular organelle-targeted cancer therapy based on functional nanomaterials. Moreover, it explains the challenges and prospects of subcellular organelle-targeting functional materials in precision oncology. The review will serve as an excellent cutting-edge guide for researchers in the field of subcellular organelle-targeted cancer therapy.
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Affiliation(s)
- Yanxiang Cheng
- Department of Gynecology, Renmin Hospital, Wuhan University, No.238 Jiefang Road, Wuchang, Wuhan, 430060, P. R. China
| | - Zhen Qu
- Department of Blood Transfusion Research, Wuhan Blood Center (WHBC), HUST-WHBC United Hematology Optical Imaging Center, No.8 Baofeng 1st Road, Wuhan, Hubei, 430030, P. R. China
| | - Qian Jiang
- Department of Blood Transfusion Research, Wuhan Blood Center (WHBC), HUST-WHBC United Hematology Optical Imaging Center, No.8 Baofeng 1st Road, Wuhan, Hubei, 430030, P. R. China
| | - Tingting Xu
- Department of Clinical Laboratory, Wuhan Blood Center (WHBC), No.8 Baofeng 1st Road, Wuhan, Hubei, 430030, P. R. China
| | - Hongyun Zheng
- Department of Clinical Laboratory, Renmin Hospital, Wuhan University, No.238 Jiefang Road, Wuchang, Wuhan, 430060, P. R. China
| | - Peng Ye
- Department of Pharmacy, Renmin Hospital, Wuhan University, No.238 Jiefang Road, Wuchang, Wuhan, 430060, P. R. China
| | - Mingdi He
- Department of Blood Transfusion Research, Wuhan Blood Center (WHBC), HUST-WHBC United Hematology Optical Imaging Center, No.8 Baofeng 1st Road, Wuhan, Hubei, 430030, P. R. China
| | - Yongqing Tong
- Department of Clinical Laboratory, Renmin Hospital, Wuhan University, No.238 Jiefang Road, Wuchang, Wuhan, 430060, P. R. China
| | - Yan Ma
- Department of Blood Transfusion Research, Wuhan Blood Center (WHBC), HUST-WHBC United Hematology Optical Imaging Center, No.8 Baofeng 1st Road, Wuhan, Hubei, 430030, P. R. China
| | - Anyu Bao
- Department of Clinical Laboratory, Renmin Hospital, Wuhan University, No.238 Jiefang Road, Wuchang, Wuhan, 430060, P. R. China
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Baaz M, Cardilin T, Jirstrand M. Model-based prediction of progression-free survival for combination therapies in oncology. CPT Pharmacometrics Syst Pharmacol 2023; 12:1227-1237. [PMID: 37300376 PMCID: PMC10508530 DOI: 10.1002/psp4.13003] [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: 02/10/2023] [Revised: 05/12/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Progression-free survival (PFS) is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. After the completion of a clinical trial, a descriptive analysis of the patients' PFS is often performed post hoc using the Kaplan-Meier estimator. However, to perform predictions, more sophisticated quantitative methods are needed. Tumor growth inhibition models are commonly used to describe and predict the dynamics of preclinical and clinical tumor size data. Moreover, frameworks also exist for describing the probability of different types of events, such as tumor metastasis or patient dropout. Combining these two types of models into a so-called joint model enables model-based prediction of PFS. In this paper, we have constructed a joint model from clinical data comparing the efficacy of FOLFOX against FOLFOX + panitumumab in patients with metastatic colorectal cancer. The nonlinear mixed effects framework was used to quantify interindividual variability (IIV). The model describes tumor size and PFS data well, and showed good predictive capabilities using truncated as well as external data. A machine-learning guided analysis was performed to reduce unexplained IIV by incorporating patient covariates. The model-based approach illustrated in this paper could be useful to help design clinical trials or to determine new promising drug candidates for combination therapy trials.
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Affiliation(s)
- Marcus Baaz
- Fraunhofer‐Chalmers Research Centre for Industrial MathematicsGothenburgSweden
- Department of Mathematical SciencesChalmers University of Technology and University of GothenburgGothenburgSweden
| | - Tim Cardilin
- Fraunhofer‐Chalmers Research Centre for Industrial MathematicsGothenburgSweden
| | - Mats Jirstrand
- Fraunhofer‐Chalmers Research Centre for Industrial MathematicsGothenburgSweden
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Zheng R, Zhang L, Parvin R, Su L, Chi J, Shi K, Ye F, Huang X. Progress and Perspective of CRISPR-Cas9 Technology in Translational Medicine. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300195. [PMID: 37356052 PMCID: PMC10477906 DOI: 10.1002/advs.202300195] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/29/2023] [Indexed: 06/27/2023]
Abstract
Translational medicine aims to improve human health by exploring potential treatment methods developed during basic scientific research and applying them to the treatment of patients in clinical settings. The advanced perceptions of gene functions have remarkably revolutionized clinical treatment strategies for target agents. However, the progress in gene editing therapy has been hindered due to the severe off-target effects and limited editing sites. Fortunately, the development in the clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR-Cas9) system has renewed hope for gene therapy field. The CRISPR-Cas9 system can fulfill various simple or complex purposes, including gene knockout, knock-in, activation, interference, base editing, and sequence detection. Accordingly, the CRISPR-Cas9 system is adaptable to translational medicine, which calls for the alteration of genomic sequences. This review aims to present the latest CRISPR-Cas9 technology achievements and prospect to translational medicine advances. The principle and characterization of the CRISPR-Cas9 system are firstly introduced. The authors then focus on recent pre-clinical and clinical research directions, including the construction of disease models, disease-related gene screening and regulation, and disease treatment and diagnosis for multiple refractory diseases. Finally, some clinical challenges including off-target effects, in vivo vectors, and ethical problems, and future perspective are also discussed.
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Affiliation(s)
- Ruixuan Zheng
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Division of Pulmonary MedicineThe First Affiliated HospitalWenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Lexiang Zhang
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Oujiang Laboratory (Zhejiang Lab for Regenerative MedicineVision and Brain Health); Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang325000P. R. China
| | - Rokshana Parvin
- Oujiang Laboratory (Zhejiang Lab for Regenerative MedicineVision and Brain Health); Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang325000P. R. China
| | - Lihuang Su
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Division of Pulmonary MedicineThe First Affiliated HospitalWenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Junjie Chi
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Keqing Shi
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Fangfu Ye
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Oujiang Laboratory (Zhejiang Lab for Regenerative MedicineVision and Brain Health); Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang325000P. R. China
- Beijing National Laboratory for Condensed Matter PhysicsInstitute of PhysicsChinese Academy of SciencesBeijing100190P. R. China
| | - Xiaoying Huang
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Division of Pulmonary MedicineThe First Affiliated HospitalWenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
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Zhang ZG, Shi ZD, Dong JJ, Chen YA, Cao MY, Li YT, Ma WM, Hao L, Pang K, Zhou JH, Zhang WD, Dong Y, Han CH. Novel potential urinary biomarkers for effective diagnosis and prognostic evaluation of high-grade bladder cancer. Transl Cancer Res 2023; 12:1992-2007. [PMID: 37701108 PMCID: PMC10493797 DOI: 10.21037/tcr-23-98] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 07/21/2023] [Indexed: 09/14/2023]
Abstract
Background High-grade bladder cancer (HGBC) has a higher malignant potential, recurrence and progression rate compared to low-grade phenotype. Its early symptoms are often vague, making non-invasive diagnosis using urinary biomarkers a promising approach. Methods The gene expression data from urine samples of patients with HGBC was extracted from the GSE68020 dataset. The clinical information and gene expression data in tumor tissues of HGBC patients were obtained from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis was used to predict the optimal risk model. The protein-protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. Overall survival (OS) was evaluated in the Gene Expression Profiling Interactive Analysis (GEPIA) online platform. Competing endogenous RNA (ceRNA) network was also visualized using Cytoscape. The expression levels of specific genes were assessed through quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Moreover, co-expressed genes and potential biological functions related to specific genes were explored based on the Cancer Cell Line Encyclopedia (CCLE) database. Results A total of 560 differentially expressed genes (DEGs) were identified when comparing the urine sediment samples from HGBC patients with the benign ones. Using these urinary DEGs and the clinical information of HGBC patients, we developed an optimal risk model consisting of eight genes to predict the patient outcome. By integrating the node degree values in the PPI network with the expression changes in both urine and tissue samples, eighteen hub genes were selected out. Among them, DKC1 and SNRPG had the most prominent comprehensive values, and EFTUD2, LOR and EBNA1BP2 were relevant to a worse OS in bladder cancer patients. The ceRNA network of hub genes indicated that DKC1 may be directly regulated by miR-150 in HGBC. The upregulation of both SNRPG and DKC1 were detected in HGBC cells, which were also observed in various tumor tissues and malignant cell lines, displaying high correlations with other hub genes. Conclusions Our study may provide theoretical basis for the development of effective non-invasive detection and treatment strategies, and further research is necessary to explore the clinical applications of these findings.
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Affiliation(s)
- Zhi-Guo Zhang
- Medical College of Soochow University, Suzhou, China
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
| | - Zhen-Duo Shi
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
- College of Life Sciences, Jiangsu Normal University, Xuzhou, China
| | - Jia-Jun Dong
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Yu-Ang Chen
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Ming-Yang Cao
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Yun-Tian Li
- Graduate School of Bengbu Medical College, Bengbu, China
| | - Wei-Ming Ma
- Medical College of Soochow University, Suzhou, China
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
| | - Lin Hao
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Kun Pang
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Jia-He Zhou
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Wen-Da Zhang
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Yang Dong
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
| | - Cong-Hui Han
- Medical College of Soochow University, Suzhou, China
- Department of Urology, Xuzhou Central Hospital, Xuzhou, China
- Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China
- College of Life Sciences, Jiangsu Normal University, Xuzhou, China
- School of Medicine, Jiangsu University, Zhenjiang, China
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Shi Y, Zhang Q, Mei J, Liu J. Editorial: Multi-omics analysis in tumor microenvironment and tumor heterogeneity. Front Genet 2023; 14:1271295. [PMID: 37680200 PMCID: PMC10482244 DOI: 10.3389/fgene.2023.1271295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023] Open
Affiliation(s)
- Yuxin Shi
- Department of Oncology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Qinglin Zhang
- Department of Gastroenterology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jie Mei
- Department of Oncology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jinhui Liu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Huang HH, Li J, Cho WC. Editorial: Integrative analysis for complex disease biomarker discovery. Front Bioeng Biotechnol 2023; 11:1273084. [PMID: 37671188 PMCID: PMC10476627 DOI: 10.3389/fbioe.2023.1273084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 08/14/2023] [Indexed: 09/07/2023] Open
Affiliation(s)
- Hai-Hui Huang
- Provincial Demonstration Software Institute, Shaoguan University, Shaoguan, China
- Faculty of Information Technology, Macau University of Science and Technology, Macau, China
| | - Jie Li
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - William C. Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong SAR, China
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Chilaca-Rosas MF, Contreras-Aguilar MT, Garcia-Lezama M, Salazar-Calderon DR, Vargas-Del-Angel RG, Moreno-Jimenez S, Piña-Sanchez P, Trejo-Rosales RR, Delgado-Martinez FA, Roldan-Valadez E. Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation. Diagnostics (Basel) 2023; 13:2669. [PMID: 37627927 PMCID: PMC10453217 DOI: 10.3390/diagnostics13162669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Radiomics refers to the acquisition of traces of quantitative features that are usually non-perceptible to human vision and are obtained from different imaging techniques and subsequently transformed into high-dimensional data. Diffuse midline gliomas (DMG) represent approximately 20% of pediatric CNS tumors, with a median survival of less than one year after diagnosis. We aimed to identify which radiomics can discriminate DMG tumor regions (viable tumor and peritumoral edema) from equivalent midline normal tissue (EMNT) in patients with the positive H3.F3K27M mutation, which is associated with a worse prognosis. PATIENTS AND METHODS This was a retrospective study. From a database of 126 DMG patients (children, adolescents, and young adults), only 12 had H3.3K27M mutation and available brain magnetic resonance DICOM file. The MRI T1 post-gadolinium and T2 sequences were uploaded to LIFEx software to post-process and extract radiomic features. Statistical analysis included normal distribution tests and the Mann-Whitney U test performed using IBM SPSS® (Version 27.0.0.1, International Business Machines Corp., Armonk, NY, USA), considering a significant statistical p-value ≤ 0.05. RESULTS EMNT vs. Tumor: From the T1 sequence 10 radiomics were identified, and 14 radiomics from the T2 sequence, but only one radiomic identified viable tumors in both sequences (p < 0.05) (DISCRETIZED_Q1). Peritumoral edema vs. EMNT: From the T1 sequence, five radiomics were identified, and four radiomics from the T2 sequence. However, four radiomics could discriminate peritumoral edema in both sequences (p < 0.05) (CONVENTIONAL_Kurtosis, CONVENTIONAL_ExcessKurtosis, DISCRETIZED_Kurtosis, and DISCRETIZED_ExcessKurtosis). There were no radiomics useful for distinguishing tumor tissue from peritumoral edema in both sequences. CONCLUSIONS Less than 5% of the radiomic characteristics identified tumor regions of medical-clinical interest in T1 and T2 sequences of conventional magnetic resonance imaging. The first-order and second-order radiomic features suggest support to investigators and clinicians for careful evaluation for diagnosis, patient classification, and multimodality cancer treatment planning.
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Affiliation(s)
- Maria-Fatima Chilaca-Rosas
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | - Manuel-Tadeo Contreras-Aguilar
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | - Melissa Garcia-Lezama
- Directorate of Research, Hospital General de Mexico Dr Eduardo Liceaga, Mexico City 06720, Mexico;
| | - David-Rafael Salazar-Calderon
- Radiotherapy Department, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico; (M.-F.C.-R.); (D.-R.S.-C.)
| | | | - Sergio Moreno-Jimenez
- Neurological Center, Neurosurgery Department of National Institute of Neurology and Neurosurgery, Mexico City 14269, Mexico;
- Neurological Center, Neurosurgery Department of American British Cowdray Medical Center, Mexico City 01120, Mexico
| | - Patricia Piña-Sanchez
- Oncology Diagnostic, Unidad de Investigacion Medica en Enfermedades Oncologicas U.I.M.E.O, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico;
| | - Raul-Rogelio Trejo-Rosales
- Medical Oncology, Hospital de Oncología, Centro Medico Nacional Siglo XXI, Instituto Mexicano Del Seguro Social, Mexico City 06720, Mexico;
| | - Felipe-Alfredo Delgado-Martinez
- Magnetic Resonance Service, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico;
| | - Ernesto Roldan-Valadez
- Directorate of Research, Hospital General de Mexico Dr Eduardo Liceaga, Mexico City 06720, Mexico;
- Department of Radiology, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119992 Moscow, Russia
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Vázquez-Blomquist D, Ramón AC, Rosales M, Pérez GV, Rosales A, Palenzuela D, Perera Y, Perea SE. Gene expression profiling unveils the temporal dynamics of CIGB-300-regulated transcriptome in AML cell lines. BMC Genomics 2023; 24:373. [PMID: 37400761 DOI: 10.1186/s12864-023-09472-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Protein kinase CK2 activity is implicated in the pathogenesis of various hematological malignancies like Acute Myeloid Leukemia (AML) that remains challenging concerning treatment. This kinase has emerged as an attractive molecular target in therapeutic. Antitumoral peptide CIGB-300 blocks CK2 phospho-acceptor sites on their substrates but it also binds to CK2α catalytic subunit. Previous proteomic and phosphoproteomic experiments showed molecular and cellular processes with relevance for the peptide action in diverse AML backgrounds but earlier transcriptional level events might also support the CIGB-300 anti-leukemic effect. Here we used a Clariom S HT assay for gene expression profiling to study the molecular events supporting the anti-leukemic effect of CIGB-300 peptide on HL-60 and OCI-AML3 cell lines. RESULTS We found 183 and 802 genes appeared significantly modulated in HL-60 cells at 30 min and 3 h of incubation with CIGB-300 for p < 0.01 and FC > = │1.5│, respectively; while 221 and 332 genes appeared modulated in OCI-AML3 cells. Importantly, functional enrichment analysis evidenced that genes and transcription factors related to apoptosis, cell cycle, leukocyte differentiation, signaling by cytokines/interleukins, and NF-kB, TNF signaling pathways were significantly represented in AML cells transcriptomic profiles. The influence of CIGB-300 on these biological processes and pathways is dependent on the cellular background, in the first place, and treatment duration. Of note, the impact of the peptide on NF-kB signaling was corroborated by the quantification of selected NF-kB target genes, as well as the measurement of p50 binding activity and soluble TNF-α induction. Quantification of CSF1/M-CSF and CDKN1A/P21 by qPCR supports peptide effects on differentiation and cell cycle. CONCLUSIONS We explored for the first time the temporal dynamics of the gene expression profile regulated by CIGB-300 which, along with the antiproliferative mechanism, can stimulate immune responses by increasing immunomodulatory cytokines. We provided fresh molecular clues concerning the antiproliferative effect of CIGB-300 in two relevant AML backgrounds.
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Affiliation(s)
- Dania Vázquez-Blomquist
- Pharmacogenomic Group, Department of System Biology, Biomedical Research Division, Center for Genetic Engineering & Biotechnology (CIGB), 10600, Havana, Cuba.
| | - Ailyn C Ramón
- Molecular Oncology Group, Department of Pharmaceuticals, Biomedical Research Division, CIGB, 10600, Havana, Cuba
| | - Mauro Rosales
- Molecular Oncology Group, Department of Pharmaceuticals, Biomedical Research Division, CIGB, 10600, Havana, Cuba
- Department of Animal and Human Biology, Faculty of Biology, University of Havana (UH), 10400, Havana, Cuba
| | - George V Pérez
- Molecular Oncology Group, Department of Pharmaceuticals, Biomedical Research Division, CIGB, 10600, Havana, Cuba
| | - Ailenis Rosales
- Department of Animal and Human Biology, Faculty of Biology, University of Havana (UH), 10400, Havana, Cuba
| | - Daniel Palenzuela
- Pharmacogenomic Group, Department of System Biology, Biomedical Research Division, Center for Genetic Engineering & Biotechnology (CIGB), 10600, Havana, Cuba
| | - Yasser Perera
- Molecular Oncology Group, Department of Pharmaceuticals, Biomedical Research Division, CIGB, 10600, Havana, Cuba.
- China-Cuba Biotechnology Joint Innovation Center (CCBJIC), Hunan Province, Yongzhou Zhong Gu Biotechnology Co., Ltd, Lengshuitan District, Yongzhou City, 425000, China.
| | - Silvio E Perea
- Molecular Oncology Group, Department of Pharmaceuticals, Biomedical Research Division, CIGB, 10600, Havana, Cuba.
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