Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins
Abstract Understanding shared pathways and mechanisms involved in the pathogenesis of diseases like oral squamous cell carcinoma (OSCC) and COVID-19 could lead to the development of novel therapeutic strategies and diagnostic biomarkers. This study aims to predict the interactome of OSCC and COVID-1...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-88819-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862465397522432 |
|---|---|
| author | Pradeep Kumar Yadalam Deepavalli Arumuganainar Prabhu Manickam Natarajan Carlos M. Ardila |
| author_facet | Pradeep Kumar Yadalam Deepavalli Arumuganainar Prabhu Manickam Natarajan Carlos M. Ardila |
| author_sort | Pradeep Kumar Yadalam |
| collection | DOAJ |
| description | Abstract Understanding shared pathways and mechanisms involved in the pathogenesis of diseases like oral squamous cell carcinoma (OSCC) and COVID-19 could lead to the development of novel therapeutic strategies and diagnostic biomarkers. This study aims to predict the interactome of OSCC and COVID-19 based on salivary inflammatory proteins. Datasets for OSCC and COVID-19 were obtained from https://www.salivaryproteome.org/differential-expression and selected for differential gene expression analysis. Differential gene expression analysis was performed using log transformation and a fold change of two. Hub proteins were identified using Cytoscape and Cytohubba, and machine learning algorithms including naïve Bayes, neural networks, gradient boosting, and random forest were used to predict hub genes. Top hub genes identified included ALDH1A1, MT-CO2, SERPINC1, FGB, and TF. The random forest model achieved the highest accuracy (93%) and class accuracy (84%). The naive Bayes model had lower accuracy (63%) and class accuracy (66%), while the neural network model showed 55% accuracy and class accuracy, possibly due to data pre-processing issues. The gradient boosting model outperformed all models with an accuracy of 95% and class accuracy of 95%. Salivary proteomic interactome analysis revealed novel hub proteins as potential common biomarkers. |
| format | Article |
| id | doaj-art-28a0bfd72e584e8ea0d4cb48b6fb3e3c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-28a0bfd72e584e8ea0d4cb48b6fb3e3c2025-02-09T12:30:16ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-025-88819-2Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteinsPradeep Kumar Yadalam0Deepavalli Arumuganainar1Prabhu Manickam Natarajan2Carlos M. Ardila3Department of Periodontics, Saveetha Institute of Medical and Technology sciences, Saveetha Dental College, SIMATS, Saveetha UniversityDepartment of Periodontics, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospital, Saveetha UniversityDepartment of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman UniversityBasic Sciences Department, Faculty of Dentistry, University of Antioquia, U de AAbstract Understanding shared pathways and mechanisms involved in the pathogenesis of diseases like oral squamous cell carcinoma (OSCC) and COVID-19 could lead to the development of novel therapeutic strategies and diagnostic biomarkers. This study aims to predict the interactome of OSCC and COVID-19 based on salivary inflammatory proteins. Datasets for OSCC and COVID-19 were obtained from https://www.salivaryproteome.org/differential-expression and selected for differential gene expression analysis. Differential gene expression analysis was performed using log transformation and a fold change of two. Hub proteins were identified using Cytoscape and Cytohubba, and machine learning algorithms including naïve Bayes, neural networks, gradient boosting, and random forest were used to predict hub genes. Top hub genes identified included ALDH1A1, MT-CO2, SERPINC1, FGB, and TF. The random forest model achieved the highest accuracy (93%) and class accuracy (84%). The naive Bayes model had lower accuracy (63%) and class accuracy (66%), while the neural network model showed 55% accuracy and class accuracy, possibly due to data pre-processing issues. The gradient boosting model outperformed all models with an accuracy of 95% and class accuracy of 95%. Salivary proteomic interactome analysis revealed novel hub proteins as potential common biomarkers.https://doi.org/10.1038/s41598-025-88819-2Oral cancerCOVID-19Hub proteinsInteractomeMachine learning |
| spellingShingle | Pradeep Kumar Yadalam Deepavalli Arumuganainar Prabhu Manickam Natarajan Carlos M. Ardila Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins Scientific Reports Oral cancer COVID-19 Hub proteins Interactome Machine learning |
| title | Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins |
| title_full | Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins |
| title_fullStr | Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins |
| title_full_unstemmed | Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins |
| title_short | Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins |
| title_sort | predicting the hub interactome of covid 19 and oral squamous cell carcinoma uncovering aldh mediated wnt β catenin pathway activation via salivary inflammatory proteins |
| topic | Oral cancer COVID-19 Hub proteins Interactome Machine learning |
| url | https://doi.org/10.1038/s41598-025-88819-2 |
| work_keys_str_mv | AT pradeepkumaryadalam predictingthehubinteractomeofcovid19andoralsquamouscellcarcinomauncoveringaldhmediatedwntbcateninpathwayactivationviasalivaryinflammatoryproteins AT deepavalliarumuganainar predictingthehubinteractomeofcovid19andoralsquamouscellcarcinomauncoveringaldhmediatedwntbcateninpathwayactivationviasalivaryinflammatoryproteins AT prabhumanickamnatarajan predictingthehubinteractomeofcovid19andoralsquamouscellcarcinomauncoveringaldhmediatedwntbcateninpathwayactivationviasalivaryinflammatoryproteins AT carlosmardila predictingthehubinteractomeofcovid19andoralsquamouscellcarcinomauncoveringaldhmediatedwntbcateninpathwayactivationviasalivaryinflammatoryproteins |