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

Risk factors and actionable molecular signatures in COVID-19-associated lung adenocarcinoma and lung squamous cell carcinoma patients

The molecular mechanism of COVID-19's pathogenic effect on lung cancer patients is yet unknown. In this study, we used differential gene expression pattern analysis to try to figure out the possible disease mechanism of COVID-19 and its associated risk factors in patients with the two most common types of non-small-cell lung cancer, lung adenocarcinoma and lung squamous cell carcinoma. We also used network-based approaches to identify potential diagnostic and molecular targets for COVID-19-infected lung cancer patients. Our study showed that lung cancer and COVID-19 patients share 36 genes that are expressed differently and in common. Most of these genes are expressed in lung tissues and are mostly involved in the pathogenesis of different respiratory tract diseases. Additionally, we also found that COVID-19 may affect the expression of several cancer-associated genes in lung cancer patients, such as the oncogenes JUN, TNC, and POU2AF1. Moreover, we also reported that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Also, our findings in concert with published literature suggest that molecular signatures like hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Overall, the scientific results of this research will aid in the formulation of suitable management strategies as well as the development of diagnostic and therapeutic methods for COVID-19-infected lung cancer patients.

Transcriptional profiles and common genes link lung cancer with the development and severity of COVID-19

Lung cancer patients with COVID-19 present an increased risk of developing severe disease and, consequently, have poor outcomes. Determining SARS-CoV-2-host interactome in lung cancer cells and tissues, infected or uninfected with SARS-CoV-2, may reveal molecular mechanisms associated with COVID-19 development and severity in lung cancer patients. Here, we integrated transcriptome data of lung tumors from patients with small- or non-small cell lung cancer (SCLC and NSCLC) and normal lung and lung cancer cells infected with SARS-CoV-2. We aimed to characterize molecular mechanisms potentially associated with COVID-19 development and severity in lung cancer patients and to predict the SARS-CoV-2-host cell interactome. We found that the gene expression profiles of lung cell lines infected with SARS-CoV-2 resemble more primary lung tumors than non-malignant lung tissues. In addition, the transcriptomic-based interactome analysis of SCLC and NSCLC revealed increased expression of cancer genes BRCA1 and CENPF, whose proteins are known or predicted to interact with the SARS-CoV-2 spike glycoprotein and helicase, respectively. We also found that TRIB3, a gene coding a putative host-SARS-CoV-2 interacting protein associated with COVID-19 infection, is co-expressed with the up-regulated genes MTHFD2, ADM2, and GPT2 in all tested conditions. Our analysis identified biological processes such as amino acid metabolism and angiogenesis and 22 host mediators of SARS-CoV-2 infection and replication that may contribute to the development and severity of COVID-19 in lung cancers.

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

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.

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

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.