Machine learning algorithms identify target genes and the molecular mechanism of matrine against diffuse large B-cell lymphoma
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for refractory or relapsed DLBCL.
OBJECTIVE: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL.
METHODS: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available databases. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms.
RESULTS: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms, respectively. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway.
CONCLUSION: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
Current computer-aided drug design - (2023) vom: 21. Aug. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhu, Yidong [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 22.08.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.2174/1573409920666230821102806 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM361039778 |
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| 100 | 1 | |a Zhu, Yidong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine learning algorithms identify target genes and the molecular mechanism of matrine against diffuse large B-cell lymphoma |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Revised 22.08.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for refractory or relapsed DLBCL | ||
| 520 | |a OBJECTIVE: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL | ||
| 520 | |a METHODS: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available databases. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms | ||
| 520 | |a RESULTS: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms, respectively. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway | ||
| 520 | |a CONCLUSION: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Machine learning algorithms | |
| 650 | 4 | |a PI3K-Akt signaling pathwayMachine learning algorithms | |
| 650 | 4 | |a diffuse large B-cell lymphoma | |
| 650 | 4 | |a matrine | |
| 650 | 4 | |a network pharmacology | |
| 650 | 4 | |a network pharmacologyMachine learning algorithms | |
| 650 | 4 | |a the least absolute shrinkage and selection operator | |
| 650 | 4 | |a weighted gene co-expression network analysis | |
| 700 | 1 | |a Ning, Zhongping |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ximing |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Zhikang |e verfasserin |4 aut | |
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