Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma

Wang, Mengmeng; Zhong, B.; Li, Man; Wang, Yanjuan; Yang, Huanjie; Du, Ke

doi:10.1042/bsr20204148

Cited by 16 publications

(7 citation statements)

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“…Compared with the RF algorithm results, the selected genes from mRNA expression showed an important role in those pathways and revealed that the RF algorithm results made a good performance. In summary, the result of enrichment analysis was roughly similar to mentioned in the literature [ 36 , 37 ], which demonstrated a partial overlap with the feature importance results obtained by the previous RF.…”

Section: Discussionsupporting

confidence: 85%

Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Yuan

et al. 2022

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Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Method: The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. Applied a total of 12 supervised machine learning models to forecast the survival outcome of HNSC and compare the results. And a vitro qPCR was performed to verify core genes predicted by the Random forest algorithm. Results: For omics of HNSC, the results of the six models all showed that the performance of multi-omics was better than each single-omic alone. Results were presented which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of a vitro qPCR were consistent with the RF algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better

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Section: Discussionsupporting

confidence: 85%

Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Yuan

et al. 2022

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“…Next, we screened the GeneCards database ( https://auth.lifemapsc.com/ ) to obtain CI-related proteins, and transcriptomic information ( Wang Y. et al, 2020 ; Chen et al, 2021 ). Based on the abovementioned screening results, we drew a Venn diagram to display the interaction networks of TMGT-associated components and CI-related targets using Venny (2.1.0) ( http://bioinformatics.psb.ugent.be/webtools/venn/ ) ( Wang et al, 2021 ). Finally, we constructed the interaction network of chemical composition-targets of action, and the potential targets of TMGT against CI using Cytoscape (3.6.1) ( Xu et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Underlying Mechanism and Active Ingredients of Tianma Gouteng Acting on Cerebral Infarction as Determined via Network Pharmacology Analysis Combined With Experimental Validation

et al. 2021

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Cerebral infarction (CI), a common cerebrovascular disease worldwide, is caused by unknown factors common to many diseases, including hypokalemia, respiratory diseases, and lower extremity venous thrombosis. Tianma Gouteng (TMGT), a traditional Chinese Medicine (TCM) prescription, has been used for the clinical treatment of CI. In this study, high-performance liquid chromatography (HPLC) fingerprint analysis was used to detect and identify major chemical constituents of TMGT. TCMSP and BATMAN-TCM databases were used to screen for active TMGT constituent compounds, while the GeneCards database was used to screen for protein targets associated with CI. Next, GO and KEGG enrichment analysis of these core nodes were performed to determine the identities of key associated biological processes and signal pathways. Meanwhile, a total of six possible gene targets of TMGT, including NFKBIA, PPARG, IL6, IL1B, CXCL8, and HIF1A, were selected for further study using two cellular models of CI. For one model, PC12 cells were treated under oxygen and glucose deprivation (OGD) conditions to generate an OGD cellular model of CI, while for the other model, BV2 cells were stimulated with lipopolysaccharide (LPS) to generate a cellular model of CI-associated inflammation. Ultimately TMGT treatment increased PPARγ expression and downregulated the expression of p-P65, p-IκBα, and HIF-1α in both OGD-induced and LPS-induced cell models of CI. In addition, molecular docking analysis showed that one TMGT chemical constituent, quercetin, may be a bioactive TMGT compound with activity that may be associated with the alleviation of neuronal damage and neuroinflammation triggered by CI. Moreover, additional data obtained in this work revealed that TMGT could inhibit neuroinflammation and protect brain cells from OGD-induced and LPS-induced damage by altering HIF-1α/PPARγ/NF-κB pathway functions. Thus, targeting this pathway through TMGT administration to CI patients may be a strategy for alleviating nerve injury and neuroinflammation triggered by CI.

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“…Duplicates were removed with the aid of Venn Diagram tool (https://bioinformatics.psb. ugent.be/webtools/Venn/); common intersecting genes of leptin resistance and obesity were obtained (37). We found 212 common targets and used them for further analysis.…”

Section: Targets Of Leptin Resistance and Obesitymentioning

confidence: 99%

Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology

Suriagandhi

Nachiappan

2022

Front. Endocrinol.

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BackgroundObesity is a medical problem with an increased risk for other metabolic disorders like diabetes, heart problem, arthritis, etc. Leptin is an adipose tissue-derived hormone responsible for food intake, energy expenditure, etc., and leptin resistance is one of the significant causes of obesity. Excess leptin secretion by poor diet habits and impaired hypothalamic leptin signaling leads to LR. Melatonin a sleep hormone; also possess antioxidant and anti-inflammatory properties. The melatonin can attenuate the complications of obesity by regulating its targets towards LR induced obesity.AimThe aim of this study includes molecular pathway and network analysis by using a systems pharmacology approach to identify a potential therapeutic mechanism of melatonin on leptin resistance-induced obesity.MethodsThe bioinformatic methods are used to find therapeutic targets of melatonin in the treatment of leptin resistance-induced obesity. It includes target gene identification using public databases, Gene ontology, and KEGG pathway enrichment by ‘ClusterProfiler’ using the R language, network analysis by Cytoscape, and molecular Docking by Autodock.ResultsWe obtained the common top 33 potential therapeutic targets of melatonin and LR-induced obesity from the total melatonin targets 254 and common LR obesity targets 212 using the data screening method. They are involved in biological processes related to sleep and obesity, including the cellular response to external stimulus, chemical stress, and autophagy. From a total of 180 enriched pathways, we took the top ten pathways for further analysis, including lipid and atherosclerosis, endocrine, and AGE-RAGE signaling pathway in diabetic complications. The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. From a wide range of pathway classes, melatonin can reduce LR-induced obesity risks by regulating the major six classes. It includes signal transduction, endocrine system, endocrine and metabolic disease, environmental adaptation, drug resistance antineoplastic, and cardiovascular disease.ConclusionThe pharmacological mechanism of action in this study shows the ten therapeutic targets of melatonin in LR-induced obesity.

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Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma

Cited by 16 publications

References 50 publications

Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Underlying Mechanism and Active Ingredients of Tianma Gouteng Acting on Cerebral Infarction as Determined via Network Pharmacology Analysis Combined With Experimental Validation

Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology

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