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

Karami, Hassan; Derakhshani, Afshin; GhasemiGol, Mohammad; Fereidouni, Mohammad; Miri‐Moghaddam, Ebrahim; Baradaran, Behzad; Tabrizi, Neda Jalili; Najafi, Souzan; Solimando, Antonio Giovanni; Marsh, Leigh M.; Silvestris, Nicola; Summa, Simona De; Paradiso, Angelo; Racanelli, Vito; Safarpour, Hossein

doi:10.3390/jcm10163567

Cited by 38 publications

(36 citation statements)

References 107 publications

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“…Understanding the biological pathways involved in this disease will ultimately lead to the identification of key genes and pathways which are linked to senescence-aging-COVID-19, thus paving the way to developing novel drug targets and biomarkers for this disease. Indeed, a recent study which combined gene co-expression network analysis with artificial intelligence approaches has identified a potential novel gene signature which may have clinical value in the pathogenesis of COVID-19 [ 278 ].…”

Section: Conclusion and Future Perspectives Of Senescence As A Promising Biomarker And Target In Covid-19mentioning

confidence: 99%

Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease

Lynch

Guo

Gibson

et al. 2021

Cells

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Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic associated with substantial morbidity and mortality worldwide, with particular risk for severe disease and mortality in the elderly population. SARS-CoV-2 infection is driven by a pathological hyperinflammatory response which results in a dysregulated immune response. Current advancements in aging research indicates that aging pathways have fundamental roles in dictating healthspan in addition to lifespan. Our review discusses the aging immune system and highlights that senescence and aging together, play a central role in COVID-19 pathogenesis. In our review, we primarily focus on the immune system response to SARS-CoV-2 infection, the interconnection between severe COVID-19, immunosenescence, aging, vaccination, and the emerging problem of Long-COVID. We hope to highlight the importance of identifying specific senescent endotypes (or “sendotypes”), which can used as determinants of COVID-19 severity and mortality. Indeed, identified sendotypes could be therapeutically exploited for therapeutic intervention. We highlight that senolytics, which eliminate senescent cells, can target aging-associated pathways and therefore are proving attractive as potential therapeutic options to alleviate symptoms, prevent severe infection, and reduce mortality burden in COVID-19 and thus ultimately enhance healthspan.

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Section: Conclusion and Future Perspectives Of Senescence As A Promising Biomarker And Target In Covid-19mentioning

confidence: 99%

Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease

Lynch

Guo

Gibson

et al. 2021

Cells

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“…DEPs were enriched to a Coronavirus disease-COVID-19 pathway by KEGG enrichment analysis. In addition, we referred to previous research strategies, which identified common key genes through enrichment analysis and modules ( 13 ). We also used the COVID-19 KEGG enrichment pathway and the three modules of MCC, Closeness, and Degree to identify key proteins.…”

Section: Discussionmentioning

confidence: 99%

“…We identified key proteins with reference to previous research methods ( 13 ). Cytoscape software was used to calculate the top 30 hub proteins by Degree, Closeness, and MCC methods from PPI networks, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…With the advancement of scientific research, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was found to be the culprit of the COVID-19 pandemic ( 12 ). Further research found that PGLYRP4 and HEPHL1 were also identified, which were novel transcriptional signature genes associated with SARS-CoV-2 virus ( 13 ). Pulmonary and extrapulmonary TB had been reported in COVID-19 patients with TB, with the former accounting for 73% ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

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Mechanism of COVID-19-Related Proteins in Spinal Tuberculosis: Immune Dysregulation

Chen

Liu²,

Liang³

et al. 2022

Front. Immunol.

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PurposeThe purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).MethodsClinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI).ResultsThe nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes.ConclusionLymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.

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“…Thus, several UM genomic databases are now available for usage by researchers worldwide, which has helped to significantly leverage genomic research in the UM field (Table 3). In 2018, Wan et al used the TCGA genomic data involving 10,975 genes from 80 UM patients [148] and performed weighted gene co-expression network analysis (WGCNA) [156][157][158], a popular method frequently employed to ascertain the potential interactions between genes and phenotypes, which has been successfully utilized in studies in neuroscience [159][160][161], cancer [162][163][164][165] and more recently in COVID-19-applied research [166], among other fields [167]. In a simplistic manner, the WGCNA approach transforms the data of gene expression into modules of co-expression, allowing a better understanding of potential signalling pathways that might be strongly linked with phenotypes of interest [158,167].…”

Section: Gene Signatures As Novel Prognostic Biomarkers In Uveal Mela...mentioning

confidence: 99%

Prognostic Biomarkers in Uveal Melanoma: The Status Quo, Recent Advances and Future Directions

Lamas

Martel

Nahon-Estève

et al. 2021

Cancers

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Uveal melanoma (UM) is the most common malignant intraocular tumour in the adult population. It is a rare cancer with an incidence of nearly five cases per million inhabitants per year, which develops from the uncontrolled proliferation of melanocytes in the choroid (≈90%), ciliary body (≈6%) or iris (≈4%). Patients initially present either with symptoms like blurred vision or photopsia, or without symptoms, with the tumour being detected in routine eye exams. Over the course of the disease, metastases, which are initially dormant, develop in nearly 50% of patients, preferentially in the liver. Despite decades of intensive research, the only approach proven to mildly control disease spread are early treatments directed to ablate liver metastases, such as surgical excision or chemoembolization. However, most patients have a limited life expectancy once metastases are detected, since there are limited therapeutic approaches for the metastatic disease, including immunotherapy, which unlike in cutaneous melanoma, has been mostly ineffective for UM patients. Therefore, in order to offer the best care possible to these patients, there is an urgent need to find robust models that can accurately predict the prognosis of UM, as well as therapeutic strategies that effectively block and/or limit the spread of the metastatic disease. Here, we initially summarized the current knowledge about UM by compiling the most relevant epidemiological, clinical, pathological and molecular data. Then, we revisited the most important prognostic factors currently used for the evaluation and follow-up of primary UM cases. Afterwards, we addressed emerging prognostic biomarkers in UM, by comprehensively reviewing gene signatures, immunohistochemistry-based markers and proteomic markers resulting from research studies conducted over the past three years. Finally, we discussed the current hurdles in the field and anticipated the future challenges and novel avenues of research in UM.

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

Cited by 38 publications

References 107 publications

Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease

Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease

Mechanism of COVID-19-Related Proteins in Spinal Tuberculosis: Immune Dysregulation

Prognostic Biomarkers in Uveal Melanoma: The Status Quo, Recent Advances and Future Directions

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