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Das D, Banerjee R, Bandyopadhyay M, Nag A. Exploring the potential of Andrographis paniculata for developing novel HDAC inhibitors: an in silico approach. J Biomol Struct Dyn 2023:1-13. [PMID: 37969010 DOI: 10.1080/07391102.2023.2281635] [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: 05/17/2023] [Accepted: 11/04/2023] [Indexed: 11/17/2023]
Abstract
Cancer is one of the dreaded diseases of the twentieth century, emerging the major global causes of human morbidity. Cancer research in the last 15 years has provided unprecedented information on the role of epigenetics in cancer initiation and progression. Histone deacetylases (HDACs) are recognized as important epigenetic markers in cancer, whose overexpression leads to increased metastasis and angiogenesis. In the current study, thirty-four (34) compounds from Andrographis paniculata were screened for the identification of potential candidate drugs, targeting three Class I HDACs (Histone deacetylases), namely HDAC1 (PDB id 5ICN), HDAC3 (PDB id 4A69) and HDAC8 (PDB id 5FCW) through computer-assisted drug discovery study. Results showed that some of the phytochemicals chosen for this study exhibited significant drug-like properties. In silico molecular docking study further revealed that out of 34 compounds, the flavonoid Andrographidine E had the highest binding affinities towards HDAC1 (-9.261 Kcal mol-1) and 3 (-9.554 Kcal mol-1) when compared with the control drug Givinostat (-8.789 and -9.448 Kcal mol-1). The diterpenoid Andrographiside displayed the highest binding affinity (-9.588 Kcal mol-1) to HDAC8 compared to Givinostat (-8.947 Kcal mol-1). Statistical analysis using Principal Component Analysis tool revealed that all 34 phytocompounds could be clustered in four statistical groups. Most of them showed high or comparable inhibitory potentials towards HDAC target protein. Finally, the stability of top-ranked complexes (Andrographidine E-HDAC1 and HDAC3; Andrographiside-HDAC8) at the physiological condition was validated by Molecular Dynamic Simulation and MM-PBSA study.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Debalina Das
- Plant Molecular Cytogenetics and Plant Biotechnology Laboratory, Department of Botany, Centre of Advanced Studies, University of Calcutta, Kolkata, West Bengal, India
| | - Ritesh Banerjee
- School of Biological and Environmental Sciences, Shoolini University, Solan, Himachal Pradesh, India
| | - Maumita Bandyopadhyay
- Plant Molecular Cytogenetics and Plant Biotechnology Laboratory, Department of Botany, Centre of Advanced Studies, University of Calcutta, Kolkata, West Bengal, India
| | - Anish Nag
- Department of Life Sciences, CHRIST (Deemed to be University), Bangalore Central Campus, Bangalore, India
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Shi T, Li M, Yu Y. Machine learning-enhanced insights into sphingolipid-based prognostication: revealing the immunological landscape and predictive proficiency for immunomotherapy and chemotherapy responses in pancreatic carcinoma. Front Mol Biosci 2023; 10:1284623. [PMID: 38028544 PMCID: PMC10643633 DOI: 10.3389/fmolb.2023.1284623] [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: 08/28/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background: With a poor prognosis for affected individuals, pancreatic adenocarcinoma (PAAD) is known as a complicated and diverse illness. Immunocytes have become essential elements in the development of PAAD. Notably, sphingolipid metabolism has a dual function in the development of tumors and the invasion of the immune system. Despite these implications, research on the predictive ability of sphingolipid variables for PAAD prognosis is strikingly lacking, and it is yet unclear how they can affect PAAD immunotherapy and targeted pharmacotherapy. Methods: The investigation process included SPG detection while also being pertinent to the prognosis for PAAD. Both the analytical capability of CIBERSORT and the prognostic capability of the pRRophetic R package were used to evaluate the immunological environments of the various HCC subtypes. In addition, CCK-8 experiments on PAAD cell lines were carried out to confirm the accuracy of drug sensitivity estimates. The results of these trials, which also evaluated cell survival and migratory patterns, confirmed the usefulness of sphingolipid-associated genes (SPGs). Results: As a result of this thorough investigation, 32 SPGs were identified, each of which had a measurable influence on the dynamics of overall survival. This collection of genes served as the conceptual framework for the development of a prognostic model, which was carefully assembled from 10 chosen genes. It should be noted that this grouping of patients into cohorts with high and low risk was a sign of different immune profiles and therapy responses. The increased abundance of SPGs was identified as a possible sign of inadequate responses to immune-based treatment approaches. The careful CCK-8 testing carried out on PAAD cell lines was of the highest importance for providing clear confirmation of drug sensitivity estimates. Conclusion: The significance of Sphingolipid metabolism in the complex web of PAAD development is brought home by this study. The novel risk model, built on the complexity of sphingolipid-associated genes, advances our understanding of PAAD and offers doctors a powerful tool for developing personalised treatment plans that are specifically suited to the unique characteristics of each patient.
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Affiliation(s)
| | | | - Yabin Yu
- Department of Hepatobiliary Surgery, The Affiliated Huaian No 1 People’s Hospital of Nanjing Medical University, Huaian, China
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Zhang L, Zhou L, Wang Y, Li C, Liao P, Zhong L, Geng S, Lai P, Du X, Weng J. Deep learning-based transcriptome model predicts survival of T-cell acute lymphoblastic leukemia. Front Oncol 2022; 12:1057153. [DOI: 10.3389/fonc.2022.1057153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Identifying subgroups of T-cell acute lymphoblastic leukemia (T-ALL) with poor survival will significantly influence patient treatment options and improve patient survival expectations. Current efforts to predict T-ALL survival expectations in multiple patient cohorts are lacking. A deep learning (DL)-based model was developed to determine the prognostic staging of T-ALL patients. We used transcriptome sequencing data from TARGET to build a DL-based survival model using 265 T-ALL patients. We found that patients could be divided into two subgroups (K0 and K1) with significant difference (P< 0.0001) in survival rate. The more malignant subgroup was significantly associated with some tumor-related signaling pathways, such as PI3K-Akt, cGMP-PKG and TGF-beta signaling pathway. DL-based model showed good performance in a cohort of patients from our clinical center (P = 0.0248). T-ALL patients survival was successfully predicted using a DL-based model, and we hope to apply it to clinical practice in the future.
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Ye W, Wu Z, Gao P, Kang J, Xu Y, Wei C, Zhang M, Zhu X. Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer. Front Oncol 2022; 12:939021. [PMID: 35978819 PMCID: PMC9376789 DOI: 10.3389/fonc.2022.939021] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/06/2022] [Indexed: 12/15/2022] Open
Abstract
Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.
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Affiliation(s)
- Weilong Ye
- School of Laboratory Medicine and Biological Engineering, Hangzhou Medical College, Hangzhou, China
- Computational Oncology Laboratory, Guangdong Medical University, Zhanjiang, China
| | - Zhengguo Wu
- Department of Thoracic Surgery, Yantian District People’s Hospital, Shenzhen, China
| | - Pengbo Gao
- Computational Oncology Laboratory, Guangdong Medical University, Zhanjiang, China
| | - Jianhao Kang
- Computational Oncology Laboratory, Guangdong Medical University, Zhanjiang, China
| | - Yue Xu
- Computational Oncology Laboratory, Guangdong Medical University, Zhanjiang, China
| | - Chuzhong Wei
- Computational Oncology Laboratory, Guangdong Medical University, Zhanjiang, China
| | - Ming Zhang
- Department of Physical Medicine and Rehabilitation, Zibo Central Hospital, Zibo, China
- *Correspondence: Ming Zhang, ; Xiao Zhu,
| | - Xiao Zhu
- School of Laboratory Medicine and Biological Engineering, Hangzhou Medical College, Hangzhou, China
- Computational Oncology Laboratory, Guangdong Medical University, Zhanjiang, China
- *Correspondence: Ming Zhang, ; Xiao Zhu,
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Yuan H, Huang Y, Tao S, Li B, Xu Z, Qi Y, Wu B, Luo H, Zhu X. Editorial: Epigenetics in Cancer: Mechanisms and Drug Development. Front Genet 2022; 13:831704. [PMID: 35836578 PMCID: PMC9274269 DOI: 10.3389/fgene.2022.831704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Huiqing Yuan
- School of Laboratory Medicine and Biomedical Engineering, Hangzhou Medical College, Hangzhou, China
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China
| | - Yongmei Huang
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China
- The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, China
| | - Susu Tao
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China
- The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, China
| | - Biaoru Li
- Cancer Center, Medical College of Georgia, Augusta University, Augusta, GA, United States
| | - Zhenhua Xu
- Center for Cancer and Immunology, Children’s National Health System, Washington, DC, DC, United States
| | - Yi Qi
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China
- The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, China
- *Correspondence: Yi Qi, ; Binhua Wu, ; Hui Luo, ; Xiao Zhu,
| | - Binhua Wu
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China
- The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, China
- *Correspondence: Yi Qi, ; Binhua Wu, ; Hui Luo, ; Xiao Zhu,
| | - Hui Luo
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, China
- The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, China
- *Correspondence: Yi Qi, ; Binhua Wu, ; Hui Luo, ; Xiao Zhu,
| | - Xiao Zhu
- School of Laboratory Medicine and Biomedical Engineering, Hangzhou Medical College, Hangzhou, China
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China
- *Correspondence: Yi Qi, ; Binhua Wu, ; Hui Luo, ; Xiao Zhu,
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