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Pourbagheri-Sigaroodi A, Fallah F, Bashash D, Karimi A. Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC). Cell Biochem Funct 2024; 42:e3913. [PMID: 38269520 DOI: 10.1002/cbf.3913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/14/2023] [Accepted: 12/17/2023] [Indexed: 01/26/2024]
Abstract
Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.
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Affiliation(s)
- Atieh Pourbagheri-Sigaroodi
- Pediatric Infections Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Fallah
- Pediatric Infections Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Davood Bashash
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abdollah Karimi
- Pediatric Infections Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Alberghina L. The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation. Int J Mol Sci 2023; 24:15787. [PMID: 37958775 PMCID: PMC10648413 DOI: 10.3390/ijms242115787] [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: 09/29/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
The Warburg effect is the long-standing riddle of cancer biology. How does aerobic glycolysis, inefficient in producing ATP, confer a growth advantage to cancer cells? A new evaluation of a large set of literature findings covering the Warburg effect and its yeast counterpart, the Crabtree effect, led to an innovative working hypothesis presented here. It holds that enhanced glycolysis partially inactivates oxidative phosphorylation to induce functional rewiring of a set of TCA cycle enzymes to generate new non-canonical metabolic pathways that sustain faster growth rates. The hypothesis has been structured by constructing two metabolic maps, one for cancer metabolism and the other for the yeast Crabtree effect. New lines of investigation, suggested by these maps, are discussed as instrumental in leading toward a better understanding of cancer biology in order to allow the development of more efficient metabolism-targeted anticancer drugs.
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Affiliation(s)
- Lilia Alberghina
- Centre of Systems Biology, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy
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Schmidt SV, Drysch M, Reinkemeier F, Wagner JM, Sogorski A, Macedo Santos E, Zahn P, Lehnhardt M, Behr B, Registry GB, Puscz F, Wallner C. Improvement of Predictive Scores in Burn Medicine through Different Machine Learning Approaches. Healthcare (Basel) 2023; 11:2437. [PMID: 37685472 PMCID: PMC10487036 DOI: 10.3390/healthcare11172437] [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: 06/27/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
The mortality of severely burned patients can be predicted by multiple scores which have been created over the last decades. As the treatment of burn injuries and intensive care management have improved immensely over the last years, former prediction scores seem to be losing accuracy in predicting survival. Therefore, various modifications of existing scores have been established and innovative scores have been introduced. In this study, we used data from the German Burn Registry and analyzed them regarding patient mortality using different methods of machine learning. We used Classification and Regression Trees (CARTs), random forests, XGBoost, and logistic regression regarding predictive features for patient mortality. Analyzing the data of 1401 patients via machine learning, the factors of full-thickness burns, patient's age, and total burned surface area could be identified as the most important features regarding the prediction of patient mortality following burn trauma. Although the different methods identified similar aspects, application of machine learning shows that more data are necessary for a valid analysis. In the future, the usage of machine learning can contribute to the development of an innovative and precise predictive score in burn medicine and even to further interpretations of relevant data regarding different forms of outcome from the German Burn registry.
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Affiliation(s)
- Sonja Verena Schmidt
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Marius Drysch
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Felix Reinkemeier
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Johannes Maximilian Wagner
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Alexander Sogorski
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Elisabete Macedo Santos
- Department of Anesthesiology, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Peter Zahn
- Department of Anesthesiology, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Marcus Lehnhardt
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Björn Behr
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - German Burn Registry
- German Society for Burn Treatment (DGV), Committee of the German Burn Registry, Luisenstrasse 58-59, 10117 Berlin, Germany
| | - Flemming Puscz
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
| | - Christoph Wallner
- Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
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