The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection / Hassan, Karami; Afshin, Derakhshani; Mohammad, Ghasemigol; Mohammad, Fereidouni; Ebrahim, Miri-Moghaddam; Behzad, Baradaran; Neda, Jalili Tabrizi; Souzan, Najafi; Antonio Giovanni, Solimando; Leigh M., Marsh; Nicola, Silvestris; Simona, De Summa; Angelo Virgilio, Paradiso; Racanelli, Vito; Hossein, Safarpour. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 10:16(2021), pp. 356701-356726. [10.3390/jcm10163567]

Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection

Vito, Racanelli;
2021-01-01

Abstract

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.
2021
16
Hassan, Karami; Afshin, Derakhshani; Mohammad, Ghasemigol; Mohammad, Fereidouni; Ebrahim, Miri-Moghaddam; Behzad, Baradaran; Neda, Jalili Tabrizi; Souzan, Najafi; Antonio Giovanni, Solimando; Leigh M., Marsh; Nicola, Silvestris; Simona, De Summa; Angelo Virgilio, Paradiso; Racanelli, Vito; Hossein, Safarpour
Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection / Hassan, Karami; Afshin, Derakhshani; Mohammad, Ghasemigol; Mohammad, Fereidouni; Ebrahim, Miri-Moghaddam; Behzad, Baradaran; Neda, Jalili Tabrizi; Souzan, Najafi; Antonio Giovanni, Solimando; Leigh M., Marsh; Nicola, Silvestris; Simona, De Summa; Angelo Virgilio, Paradiso; Racanelli, Vito; Hossein, Safarpour. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 10:16(2021), pp. 356701-356726. [10.3390/jcm10163567]
File in questo prodotto:
File Dimensione Formato  
jcm-10-03567-v4.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 7.15 MB
Formato Adobe PDF
7.15 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/386979
Citazioni
  • ???jsp.display-item.citation.pmc??? 22
  • Scopus 28
  • ???jsp.display-item.citation.isi??? 26
social impact