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Dolfini D, Gnesutta N, Mantovani R. Expression and function of NF-Y subunits in cancer. Biochim Biophys Acta Rev Cancer 2024; 1879:189082. [PMID: 38309445 DOI: 10.1016/j.bbcan.2024.189082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/05/2024]
Abstract
NF-Y is a Transcription Factor (TF) targeting the CCAAT box regulatory element. It consists of the NF-YB/NF-YC heterodimer, each containing an Histone Fold Domain (HFD), and the sequence-specific subunit NF-YA. NF-YA expression is associated with cell proliferation and absent in some post-mitotic cells. The review summarizes recent findings impacting on cancer development. The logic of the NF-Y regulome points to pro-growth, oncogenic genes in the cell-cycle, metabolism and transcriptional regulation routes. NF-YA is involved in growth/differentiation decisions upon cell-cycle re-entry after mitosis and it is widely overexpressed in tumors, the HFD subunits in some tumor types or subtypes. Overexpression of NF-Y -mostly NF-YA- is oncogenic and decreases sensitivity to anti-neoplastic drugs. The specific roles of NF-YA and NF-YC isoforms generated by alternative splicing -AS- are discussed, including the prognostic value of their levels, although the specific molecular mechanisms of activity are still to be deciphered.
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Affiliation(s)
- Diletta Dolfini
- Dipartimento di Bioscienze, Università degli Studi di Milano, Via Celoria 26, Milano 20133, Italy
| | - Nerina Gnesutta
- Dipartimento di Bioscienze, Università degli Studi di Milano, Via Celoria 26, Milano 20133, Italy
| | - Roberto Mantovani
- Dipartimento di Bioscienze, Università degli Studi di Milano, Via Celoria 26, Milano 20133, Italy.
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Guo J, Zhang Y, Gao Y, Li S, Xu G, Tian Z, Xu Q, Li X, Li Y, Zhang Y. Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:453-465. [PMID: 37651591 DOI: 10.1080/21691401.2023.2252477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 09/02/2023]
Abstract
Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Ya Zhang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Yueying Gao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Si Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Gang Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Zhanyu Tian
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Qi Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Xia Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Aghakhani A, Shoshtarian Malak J, Karimi Z, Vosoughi F, Zeraati H, Yekaninejad MS. Predicting the COVID-19 mortality among Iranian patients using tree-based models: A cross-sectional study. Health Sci Rep 2023; 6:e1279. [PMID: 37223657 PMCID: PMC10200963 DOI: 10.1002/hsr2.1279] [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: 02/07/2023] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
Background and Aims To explore the use of different machine learning models in prediction of COVID-19 mortality in hospitalized patients. Materials and Methods A total of 44,112 patients from six academic hospitals who were admitted for COVID-19 between March 2020 and August 2021 were included in this study. Variables were obtained from their electronic medical records. Random forest-recursive feature elimination was used to select key features. Decision tree, random forest, LightGBM, and XGBoost model were developed. Sensitivity, specificity, accuracy, F-1 score, and receiver operating characteristic (ROC)-AUC were used to compare the prediction performance of different models. Results Random forest-recursive feature elimination selected following features to include in the prediction model: Age, sex, hypertension, malignancy, pneumonia, cardiac problem, cough, dyspnea, and respiratory system disease. XGBoost and LightGBM showed the best performance with an ROC-AUC of 0.83 [0.822-0.842] and 0.83 [0.816-0.837] and sensitivity of 0.77. Conclusion XGBoost, LightGBM, and random forest have a relatively high predictive performance in prediction of mortality in COVID-19 patients and can be applied in hospital settings, however, future research are needed to externally confirm the validation of these models.
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Affiliation(s)
- Amirhossein Aghakhani
- Department of Epidemiology and Biostatistics, School of Public HealthTehran University of Medical SciencesTehranIran
| | - Jaleh Shoshtarian Malak
- Department of Digital Health, School of MedicineTehran University of Medical SciencesTehranIran
| | - Zahra Karimi
- Department of Epidemiology and Biostatistics, School of Public HealthTehran University of Medical SciencesTehranIran
| | - Fardis Vosoughi
- Department of Orthopedics and Trauma Surgery, Shariati Hospital and School of MedicineTehran University of Medical SciencesTehranIran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public HealthTehran University of Medical SciencesTehranIran
| | - Mir Saeed Yekaninejad
- Department of Epidemiology and Biostatistics, School of Public HealthTehran University of Medical SciencesTehranIran
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Infrared Spectroscopy as a Potential Diagnostic Tool for Medulloblastoma. Molecules 2023; 28:molecules28052390. [PMID: 36903631 PMCID: PMC10005236 DOI: 10.3390/molecules28052390] [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: 02/08/2023] [Revised: 02/28/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
INTRODUCTION Medulloblastoma (MB) is the most common malignant tumor of the central nervous system in childhood. FTIR spectroscopy provides a holistic view of the chemical composition of biological samples, including the detection of molecules such as nucleic acids, proteins, and lipids. This study evaluated the applicability of FTIR spectroscopy as a potential diagnostic tool for MB. MATERIALS AND METHODS FTIR spectra of MB samples from 40 children (boys/girls: 31/9; age: median 7.8 years, range 1.5-21.5 years) treated in the Oncology Department of the Children's Memorial Health Institute in Warsaw between 2010 and 2019 were analyzed. The control group consisted of normal brain tissue taken from four children diagnosed with causes other than cancer. Formalin-fixed and paraffin-embedded tissues were sectioned and used for FTIR spectroscopic analysis. The sections were examined in the mid-infrared range (800-3500 cm-1) by ATR-FTIR. Spectra were analysed using a combination of principal component analysis, hierarchical cluster analysis, and absorbance dynamics. RESULTS FTIR spectra in MB were significantly different from those of normal brain tissue. The most significant differences related to the range of nucleic acids and proteins in the region 800-1800 cm-1. Some major differences were also revealed in the quantification of protein conformations (α-helices, β-sheets, and others) in the amide I band, as well as in the absorbance dynamics in the 1714-1716 cm-1 range (nucleic acids). It was not, however, possible to clearly distinguish between the various histological subtypes of MB using FTIR spectroscopy. CONCLUSIONS MB and normal brain tissue can be distinguished from one another to some extent using FTIR spectroscopy. As a result, it may be used as a further tool to hasten and enhance histological diagnosis.
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