Machine Learning-Based Identification of B4GALNT1 as a Key Player in Hepatocellular Carcinoma: A Comprehensive Bioinformatics and Structural Analysis
Abstract Liver hepatocellular carcinoma (LIHC) is one of the most frequent types of malignant cancer in the globe. The identification of new biomarkers for the LIHC is critical. We used TCGA-LIHC gene expression datasets for this study. Several feature selection methods were used to find the top gene signatures that distinguish LIHC cancer from normal samples. Eleven machine learning algorithms were used on these selected characteristics, and model performance evaluation revealed that Naive Bayes Classifiers (AUC = 0.965) performs the best for a selection of 55 protein coding genes. Among 55 protein coding genes we found B4GALNT1 (Beta-1,4-N-acetyl-galactosaminyltransferase 1) which is differentially regulated in LIHC. With several evidence B4GALNT1 plays crucial role in tumorigenesis in many cancers, therefore we conducted systematic bioinformatics approach with mutational and structural analysis of B4GALNT1 in LIHC. Moreover, survival analysis, immune cell infiltration, most significant associated methylated CpG probe and access the accuracy of B4GALNT1 conducted to find the potential role of B4GALNT1. The results suggested that B4GALNT1 was significantly expressed in most cancers including LIHC. Finally, 16 missense mutations identified through cBioportal, Cosmic Database, and Human Variant Database, among which 6 mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) found to be deleterious when analysed byin-silicoprediction algorithms such as SIFT, PolyPhen2, I Mutent2 and CADD in LIHC. Molecular Dynamics simulation analysis was performed to understand the atomic details of the structure and functional changes. Results from this study suggest the impact of these missense variants on the structure of the B4GALNT1 protein and its pathogenic relevance. Our study demonstrated that B4GALNT1 may be evaluated as a novel target for liver cancer therapy because it has been found to be overexpressed in Liver and correlates with a poor prognosis..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 02. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Verma, Rohit Kumar [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2024.01.29.577885 |
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| PPN (Katalog-ID): |
XBI042350018 |
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| 520 | |a Abstract Liver hepatocellular carcinoma (LIHC) is one of the most frequent types of malignant cancer in the globe. The identification of new biomarkers for the LIHC is critical. We used TCGA-LIHC gene expression datasets for this study. Several feature selection methods were used to find the top gene signatures that distinguish LIHC cancer from normal samples. Eleven machine learning algorithms were used on these selected characteristics, and model performance evaluation revealed that Naive Bayes Classifiers (AUC = 0.965) performs the best for a selection of 55 protein coding genes. Among 55 protein coding genes we found B4GALNT1 (Beta-1,4-N-acetyl-galactosaminyltransferase 1) which is differentially regulated in LIHC. With several evidence B4GALNT1 plays crucial role in tumorigenesis in many cancers, therefore we conducted systematic bioinformatics approach with mutational and structural analysis of B4GALNT1 in LIHC. Moreover, survival analysis, immune cell infiltration, most significant associated methylated CpG probe and access the accuracy of B4GALNT1 conducted to find the potential role of B4GALNT1. The results suggested that B4GALNT1 was significantly expressed in most cancers including LIHC. Finally, 16 missense mutations identified through cBioportal, Cosmic Database, and Human Variant Database, among which 6 mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) found to be deleterious when analysed byin-silicoprediction algorithms such as SIFT, PolyPhen2, I Mutent2 and CADD in LIHC. Molecular Dynamics simulation analysis was performed to understand the atomic details of the structure and functional changes. Results from this study suggest the impact of these missense variants on the structure of the B4GALNT1 protein and its pathogenic relevance. Our study demonstrated that B4GALNT1 may be evaluated as a novel target for liver cancer therapy because it has been found to be overexpressed in Liver and correlates with a poor prognosis. | ||
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| 700 | 1 | |a Singh, Ashutosh |4 aut | |
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