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Herranz JM, López-Pascual A, Clavería-Cabello A, Uriarte I, Latasa MU, Irigaray-Miramon A, Adán-Villaescusa E, Castelló-Uribe B, Sangro B, Arechederra M, Berasain C, Avila MA, Fernández-Barrena MG. Comprehensive analysis of epigenetic and epitranscriptomic genes' expression in human NAFLD. J Physiol Biochem 2023; 79:901-924. [PMID: 37620598 PMCID: PMC10636027 DOI: 10.1007/s13105-023-00976-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: 06/15/2023] [Accepted: 07/19/2023] [Indexed: 08/26/2023]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a multifactorial condition with a complex etiology. Its incidence is increasing globally in parallel with the obesity epidemic, and it is now considered the most common liver disease in Western countries. The precise mechanisms underlying the development and progression of NAFLD are complex and still poorly understood. The dysregulation of epigenetic and epitranscriptomic mechanisms is increasingly recognized to play pathogenic roles in multiple conditions, including chronic liver diseases. Here, we have performed a comprehensive analysis of the expression of epigenetic and epitranscriptomic genes in a total of 903 liver tissue samples corresponding to patients with normal liver, obese patients, and patients with non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH), advancing stages in NAFLD progression. We integrated ten transcriptomic datasets in an unbiased manner, enabling their robust analysis and comparison. We describe the complete landscape of epigenetic and epitranscriptomic genes' expression along the course of the disease. We identify signatures of genes significantly dysregulated in association with disease progression, particularly with liver fibrosis development. Most of these epigenetic and epitranscriptomic effectors have not been previously described in human NAFLD, and their altered expression may have pathogenic implications. We also performed a comprehensive analysis of the expression of enzymes involved in the metabolism of the substrates and cofactors of epigenetic and epitranscriptomic effectors. This study provides novel information on NAFLD pathogenesis and may also guide the identification of drug targets to treat this condition and its progression towards hepatocellular carcinoma.
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Affiliation(s)
- Jose M Herranz
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
- CIBERehd, Instituto de Salud Carlos III, Madrid, Spain
| | - Amaya López-Pascual
- Hepatology Unit, CCUN, Navarra University Clinic, Pamplona, Spain
- Instituto de Investigaciones Sanitarias de Navarra IdiSNA, Pamplona, Spain
| | - Alex Clavería-Cabello
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
| | - Iker Uriarte
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
- CIBERehd, Instituto de Salud Carlos III, Madrid, Spain
| | - M Ujúe Latasa
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
| | - Ainara Irigaray-Miramon
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
| | - Elena Adán-Villaescusa
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
| | - Borja Castelló-Uribe
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
| | - Bruno Sangro
- CIBERehd, Instituto de Salud Carlos III, Madrid, Spain
- Hepatology Unit, CCUN, Navarra University Clinic, Pamplona, Spain
- Instituto de Investigaciones Sanitarias de Navarra IdiSNA, Pamplona, Spain
| | - María Arechederra
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
- CIBERehd, Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Investigaciones Sanitarias de Navarra IdiSNA, Pamplona, Spain
| | - Carmen Berasain
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
- CIBERehd, Instituto de Salud Carlos III, Madrid, Spain
| | - Matías A Avila
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain
- CIBERehd, Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Investigaciones Sanitarias de Navarra IdiSNA, Pamplona, Spain
| | - Maite G Fernández-Barrena
- Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, Pamplona, Spain.
- CIBERehd, Instituto de Salud Carlos III, Madrid, Spain.
- Instituto de Investigaciones Sanitarias de Navarra IdiSNA, Pamplona, Spain.
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Morabito F, Adornetto C, Monti P, Amaro A, Reggiani F, Colombo M, Rodriguez-Aldana Y, Tripepi G, D’Arrigo G, Vener C, Torricelli F, Rossi T, Neri A, Ferrarini M, Cutrona G, Gentile M, Greco G. Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy. Front Oncol 2023; 13:1198992. [PMID: 37719021 PMCID: PMC10501728 DOI: 10.3389/fonc.2023.1198992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 07/31/2023] [Indexed: 09/19/2023] Open
Abstract
Analyzing gene expression profiles (GEP) through artificial intelligence provides meaningful insight into cancer disease. This study introduces DeepSHAP Autoencoder Filter for Genes Selection (DSAF-GS), a novel deep learning and explainable artificial intelligence-based approach for feature selection in genomics-scale data. DSAF-GS exploits the autoencoder's reconstruction capabilities without changing the original feature space, enhancing the interpretation of the results. Explainable artificial intelligence is then used to select the informative genes for chronic lymphocytic leukemia prognosis of 217 cases from a GEP database comprising roughly 20,000 genes. The model for prognosis prediction achieved an accuracy of 86.4%, a sensitivity of 85.0%, and a specificity of 87.5%. According to the proposed approach, predictions were strongly influenced by CEACAM19 and PIGP, moderately influenced by MKL1 and GNE, and poorly influenced by other genes. The 10 most influential genes were selected for further analysis. Among them, FADD, FIBP, FIBP, GNE, IGF1R, MKL1, PIGP, and SLC39A6 were identified in the Reactome pathway database as involved in signal transduction, transcription, protein metabolism, immune system, cell cycle, and apoptosis. Moreover, according to the network model of the 3D protein-protein interaction (PPI) explored using the NetworkAnalyst tool, FADD, FIBP, IGF1R, QTRT1, GNE, SLC39A6, and MKL1 appear coupled into a complex network. Finally, all 10 selected genes showed a predictive power on time to first treatment (TTFT) in univariate analyses on a basic prognostic model including IGHV mutational status, del(11q) and del(17p), NOTCH1 mutations, β2-microglobulin, Rai stage, and B-lymphocytosis known to predict TTFT in CLL. However, only IGF1R [hazard ratio (HR) 1.41, 95% CI 1.08-1.84, P=0.013), COL28A1 (HR 0.32, 95% CI 0.10-0.97, P=0.045), and QTRT1 (HR 7.73, 95% CI 2.48-24.04, P<0.001) genes were significantly associated with TTFT in multivariable analyses when combined with the prognostic factors of the basic model, ultimately increasing the Harrell's c-index and the explained variation to 78.6% (versus 76.5% of the basic prognostic model) and 52.6% (versus 42.2% of the basic prognostic model), respectively. Also, the goodness of model fit was enhanced (χ2 = 20.1, P=0.002), indicating its improved performance above the basic prognostic model. In conclusion, DSAF-GS identified a group of significant genes for CLL prognosis, suggesting future directions for bio-molecular research.
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Affiliation(s)
| | - Carlo Adornetto
- Department of Mathematics and Computer Science, University of Calabria, Cosenza, Italy
| | - Paola Monti
- Mutagenesis and Cancer Prevention Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico San Martino, Genoa, Italy
| | - Adriana Amaro
- Tumor Epigenetics Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico San Martino, Genoa, Italy
| | - Francesco Reggiani
- Tumor Epigenetics Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico San Martino, Genoa, Italy
| | - Monica Colombo
- Molecular Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Giovanni Tripepi
- Consiglio Nazionale delle Ricerche, Istituto di Fisiologia Clinica del Consiglio Nazionale delle Ricerche (CNR), Reggio Calabria, Italy
| | - Graziella D’Arrigo
- Consiglio Nazionale delle Ricerche, Istituto di Fisiologia Clinica del Consiglio Nazionale delle Ricerche (CNR), Reggio Calabria, Italy
| | - Claudia Vener
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Federica Torricelli
- Laboratory of Translational Research, Azienda Unità Sanitaria Locale - Istituto di Ricovero e Cura a Crabtree Scientifico (USL-IRCCS) of Reggio Emilia, Reggio Emilia, Italy
| | - Teresa Rossi
- Laboratory of Translational Research, Azienda Unità Sanitaria Locale - Istituto di Ricovero e Cura a Crabtree Scientifico (USL-IRCCS) of Reggio Emilia, Reggio Emilia, Italy
| | - Antonino Neri
- Scientific Directorate, Azienda Unità Sanitaria Locale - Istituto di Ricovero e Cura a Carattere Scientifico (USL-IRCCS) of Reggio Emilia, Reggio Emilia, Italy
| | - Manlio Ferrarini
- Unità Operariva (UO) Molecular Pathology, Ospedale Policlinico San Martino Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Genoa, Italy
| | - Giovanna Cutrona
- Molecular Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico San Martino, Genoa, Italy
| | - Massimo Gentile
- Hematology Unit, Department of Onco-Hematology, Azienda Ospedaliera (A.O.) of Cosenza, Cosenza, Italy
- Department of Pharmacy and Health and Nutritional Sciences, University of Calabria, Cosenza, Italy
| | - Gianluigi Greco
- Department of Mathematics and Computer Science, University of Calabria, Cosenza, Italy
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Pushkarev SV, Vinnik VA, Shapovalova IV, Švedas VK, Nilov DK. Modeling the Structure of Human tRNA-Guanine Transglycosylase in Complex with 7-Methylguanine and Revealing the Factors that Determine the Enzyme Interaction with Inhibitors. BIOCHEMISTRY. BIOKHIMIIA 2022; 87:443-449. [PMID: 35790378 DOI: 10.1134/s0006297922050054] [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: 02/21/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 06/15/2023]
Abstract
tRNA-guanine transglycosylase, an enzyme catalyzing replacement of guanine with queuine in human tRNA and participating in the translation mechanism, is involved in the development of cancer. However, information on the small-molecule inhibitors that can suppress activity of this enzyme is very limited. Molecular dynamics simulations were used to determine the amino acid residues that provide efficient binding of inhibitors in the active site of tRNA-guanine transglycosylase. It was demonstrated using 7-methylguanine molecule as a probe that the ability of the inhibitor to adopt a charged state in the environment of hydrogen bond acceptors Asp105 and Asp159 plays a key role in complex formation. Formation of the hydrogen bonds and hydrophobic contacts with Gln202, Gly229, Phe109, and Met259 residues are also important. It has been predicted that introduction of the substituents would have a different effect on the ability to inhibit tRNA-guanine transglycosylase, as well as the DNA repair protein poly(ADP-ribose) polymerase 1, which can contribute to the development of more efficient and selective compounds.
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Affiliation(s)
- Sergey V Pushkarev
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Valeriia A Vinnik
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Irina V Shapovalova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Vytas K Švedas
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Dmitry K Nilov
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia.
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Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage. Cancers (Basel) 2021; 13:cancers13246253. [PMID: 34944874 PMCID: PMC8699523 DOI: 10.3390/cancers13246253] [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: 11/11/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 11/17/2022] Open
Abstract
Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.
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Bian M, Huang S, Yu D, Zhou Z. tRNA Metabolism and Lung Cancer: Beyond Translation. Front Mol Biosci 2021; 8:659388. [PMID: 34660690 PMCID: PMC8516113 DOI: 10.3389/fmolb.2021.659388] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 08/25/2021] [Indexed: 12/15/2022] Open
Abstract
Lung cancer, one of the most malignant tumors, has extremely high morbidity and mortality, posing a serious threat to global health. It is an urgent need to fully understand the pathogenesis of lung cancer and provide new ideas for its treatment. Interestingly, accumulating evidence has identified that transfer RNAs (tRNAs) and tRNA metabolism–associated enzymes not only participate in the protein translation but also play an important role in the occurrence and development of lung cancer. In this review, we summarize the different aspects of tRNA metabolism in lung cancer, such as tRNA transcription and mutation, tRNA molecules and derivatives, tRNA-modifying enzymes, and aminoacyl-tRNA synthetases (ARSs), aiming at a better understanding of the pathogenesis of lung cancer and providing new therapeutic strategies for it.
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Affiliation(s)
- Meng Bian
- Department of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shiqiong Huang
- Department of Pharmacy, The First Hospital of Changsha, Changsha, China
| | - Dongsheng Yu
- Department of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zheng Zhou
- Department of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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