Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma

Liang, Xiao; Chen, Yali; Fan, Yuchao

doi:10.1007/s11356-021-17321-9

Cited by 6 publications

(4 citation statements)

References 106 publications

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“…Toshifumi M. et al proposed that SARS-CoV-2 via two of its products, namely NSP1 and ORF6, impairs STAT1 function and subsequently hyperactivation of STAT3, which results in chains of events ending up with overexpression of pro-in ammatory cytokines and also hypercoagulopathy 26 . Xiao Li et al identi ed 112 co-expressed genes among COVID-19 and lung adenocarcinoma and reported IL-17 signaling and MAPK signaling as top pathways altered by hub co-expressed genes 27 . Another study on the subject, highlighted ALB, CXCL8, FGF2, IL6, INS, MMP2, MMP9, PTGS2, STAT3, and VEGFA as hub differentially expressed genes among COVID-19 and lung cancer and reported EGFR tyrosine kinase inhibitor resistance, IL-17, AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathways and pathways in cancer to be common in both conditions 28 .…”

Section: Discussionmentioning

confidence: 99%

Identification of genes and signaling pathways associated with severe COVID-19: high-throughput data analysis with a system virology approach

Mostafavi

Forghani-Ramandi

Yaslianifard

et al. 2023

Preprint

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Corona Virus Disease 2019 (COVID-19) has caused over six million deaths worldwide so far. COVID-19 has presented a variety of severities and outcomes which is able to damage many different organs. In this study, we aimed to identify factors responsible for severe illness and also alterations caused by the virus in various organs at the molecular level. First, after preprocessing steps, we chose one mRNA expression profile (GSE164805) for further analysis. Differentially Expressed Genes (DEGs) were screened with the Limma R package and considered for the PPI network construction. By maximizing co-expression value, we constructed subnetworks and subjected them to the Gene Sets Net Correlation Analysis (GSNCA). Successfully passed clusters were subjected to enrichment analysis. From 60k genes, 7106, 3151, and 1809 genes were considered as DEGs in normal vs. mild, normal vs. severe, and mild vs. severe comparisons, respectively, with p < 0.05 and |LogFC| > 2 as thresholds. PPI network analysis resulted in 17 modules, and 11 of them successfully passed GSNCA analysis with a P value < 0.05. Enrichment analysis culminated in identifying genes and signaling pathways with possible roles in the establishment of severe disease. We noticed considerable similarities between altered signaling pathways in COVID-19 and various malignancies. In addition, we detected alterations of pathways that can help to explain neurological involvement.

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Section: Discussionmentioning

confidence: 99%

Identification of genes and signaling pathways associated with severe COVID-19: high-throughput data analysis with a system virology approach

Mostafavi

Forghani-Ramandi

Yaslianifard

et al. 2023

Preprint

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“…The downregulated expression of TMPRSS2 caused by SARS-CoV-2 infection may aggravate a variety of symptoms. Lung cancer patients should take adequate preventive measures to avoid COVID-19 infection and continuously monitor cell metabolism- and immune-related indicators [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Decreased TMPRSS2 expression by SARS-CoV-2 predicts the poor prognosis of lung cancer patients through metabolic pathways and immune infiltration

Liu

Shang

et al. 2022

Aging

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread around the world and became a global pandemic in 2020. One promising drug target for SARS-CoV-2 is the transmembrane protease serine 2 (TMPRSS2). This study was designed to explore the expression status, prognostic significance and molecular functions of TMPRSS2 in lung cancer. TMPRSS2 expression was investigated using the TIMER, Oncomine, UALCAN, GEO, HPA and TCGA databases. The prognostic value of TMPRSS2 was examined using Cox regression and a nomogram. KEGG, GO and GSEA were performed to investigate the cellular function of TMPRSS2 in lung cancer. The relationship between TMPRSS2 and immune infiltration was determined using the TIMER and CIBERSORT algorithms. TMPRSS2 mRNA and protein expression was significantly reduced in lung cancer. Decreased TMPRSS2 expression and increased DNA methylation of TMPRSS2 were associated with various clinicopathological parameters in patients with lung cancer. Low TMPRSS2 mRNA expression also correlated with poor outcome in lung cancer patients. Moreover, a nomogram was constructed and exhibited good predictive power for the overall survival of lung cancer patients. KEGG and GO analyses and GSEA implied that multiple immune- and metabolism-related pathways were significantly linked with TMPRSS2 expression. Intriguingly, TMPRSS2 expression associated with immune cell infiltration in lung cancer. More importantly, TMPRSS2 expression was markedly decreased in SARS-CoV-infected cells. These findings indicate that TMPRSS2 may be a promising prognostic biomarker and therapeutic target for lung cancer through metabolic pathways and immune cell infiltration.

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“…In addition, we did not retrieve reports using public datasets to explore the role of COVID-19-related gene sets in AML patients, although there have been many reports in other tumors. For example, Huang et al identified a novel prognostic signature and nomogram based on SARS-CoV-2-related genes as reliable prognostic predictors for KIRC patients and provided potential therapeutic targets for KIRC with COVID-19 infection, and Liang et al revealed commonality in specific gene expression by patients with COVID-19 and LUAD ( Huang et al, 2021 ; Liang et al, 2022 ). Both COVID-19 and cancers provide complicated challenges that require ongoing research and development in medicine.…”

Section: Discussionmentioning

confidence: 99%

Systematic analysis of prognostic and immunologic characteristics associated with coronavirus disease 2019 regulators in acute myeloid leukemia

et al. 2022

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The coronavirus disease 2019 (COVID-19) pandemic has so far damaged the health of millions and has made the treatment of cancer patients more complicated, and so did acute myeloid leukemia (AML). The current problem is the lack of understanding of their interactions and suggestions of evidence-based guidelines or historical experience for the treatment of such patients. Here, we first identified the COVID-19-related differentially expressed genes (C-DEGs) in AML patients by analyzing RNA-seq from public databases and explored their enrichment pathways and candidate drugs. A total of 76 C-DEGs associated with the progress of AML and COVID-19 infection were ultimately identified, and the functional analysis suggested that there are some shared links between them. Their protein–protein interactions (PPIs) and protein–drug interactions were then recognized by multiple bioinformatics algorithms. Moreover, a COVID-19 gene-associated prognostic model (C-GPM) with riskScore was constructed, patients with a high riskScore had poor survival and apparently immune-activated phenotypes, such as stronger monocyte and neutrophil cell infiltrations and higher immunosuppressants targeting expressions, meaning which may be one of the common denominators between COVID-19 and AML and the reason what complicates the treatment of the latter. Among the study’s drawbacks is that these results relied heavily on publicly available datasets rather than being clinically confirmed. Yet, these findings visualized those C-DEGs’ enrichment pathways and inner associations, and the C-GPM based on them could accurately predict survival outcomes in AML patients, which will be helpful for further optimizing therapies for AML patients with COVID-19 infections.

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Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma

Cited by 6 publications

References 106 publications

Identification of genes and signaling pathways associated with severe COVID-19: high-throughput data analysis with a system virology approach

Identification of genes and signaling pathways associated with severe COVID-19: high-throughput data analysis with a system virology approach

Decreased TMPRSS2 expression by SARS-CoV-2 predicts the poor prognosis of lung cancer patients through metabolic pathways and immune infiltration

Systematic analysis of prognostic and immunologic characteristics associated with coronavirus disease 2019 regulators in acute myeloid leukemia

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