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. 2022 Mar 15;19(6):3454.
doi: 10.3390/ijerph19063454.

Landscape of Molecular Crosstalk Perturbation between Lung Cancer and COVID-19

Aditi Kuchi  1 , Jiande Wu  1 , Jyotsna Fuloria  2 , Chindo Hicks  1
Affiliations

Landscape of Molecular Crosstalk Perturbation between Lung Cancer and COVID-19

Aditi Kuchi et al. Int J Environ Res Public Health. .

Abstract

Background: Lung cancer patients have the worst outcomes when affected by coronavirus disease 2019 (COVID-19). The molecular mechanisms underlying the association between lung cancer and COVID-19 remain unknown. The objective of this investigation was to determine whether there is crosstalk in molecular perturbation between COVID-19 and lung cancer, and to identify a molecular signature, molecular networks and signaling pathways shared by the two diseases.

Methods: We analyzed publicly available gene expression data from 52 severely affected COVID-19 human lung samples, 594 lung tumor samples and 54 normal disease-free lung samples. We performed network and pathways analysis to identify molecular networks and signaling pathways shared by the two diseases.

Results: The investigation revealed a signature of genes associated with both diseases and signatures of genes uniquely associated with each disease, confirming crosstalk in molecular perturbation between COVID-19 and lung cancer. In addition, the analysis revealed molecular networks and signaling pathways associated with both diseases.

Conclusions: The investigation revealed crosstalk in molecular perturbation between COVID-19 and lung cancer, and molecular networks and signaling pathways associated with the two diseases. Further research on a population impacted by both diseases is recommended to elucidate molecular drivers of the association between the two diseases.

Keywords: COVID-19; SARS-CoV-2; coronavirus; gene expression; lung cancer; networks; signaling pathways.

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Conflict of interest statement

The authors declare no conflict of interest regarding the publication of this paper. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Project design and integrated data analysis workflow of gene expression data from COVID-19, lung cancer and normal lung samples. RNA-Seq data sets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA).
Figure 2
Figure 2
Venn diagram showing signatures of genes uniquely associated with COVID-19 ((A), orange) and a signature of genes associated with lung cancer ((B), blue). A signature of 9026 genes associated with both diseases is shown in the intersection.
Figure 3
Figure 3
Patterns of expression profiles for the top 515 upregulated and downregulated genes in lung cancer and in COVID-19, generated using hierarchical clustering on genes associated with both diseases. Genes are represented in rows, and lung cancer and COVID-19 samples in columns. Red color indicates upregulation and blue represents downregulation.
Figure 4
Figure 4
Highly interconnected gene regulatory networks associated with both COVID-19 and lung cancer. The gene names in red font indicate upregulation and blue font downregulation. The solid lines indicate overlapping functions between the genes in the seven merged networks.
Figure 5
Figure 5
Signaling pathways associated with both COVID-19 and lung cancer represented as bars. The orange solid line indicates the threshold above which a signaling pathway was declared significantly associated with both diseases, as determined by the –log(p-values) shown on the x-axis above. The y-axis shows names of signaling pathways associated with both diseases.
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