Expression of SARS-COV-2 cell receptor gene ACE2 is associated with immunosuppression and metabolic reprogramming in lung adenocarcinoma based on bioinformatics analyses of gene expression profiles

ACE2: the node connecting the lung cancer and COVID-19

Angiotensin-converting Enzyme 2 (ACE2) collaborates with Angiotensin (Ang) 1-7 and Mas receptors to establish the ACE2-Ang (1-7)-Mas receptor axis. ACE2 impacts lung function and can cause lung injury due to its inflammatory effects. Additionally, ACE2 contributes to pulmonary vasculature dysfunction, resulting in pulmonary hypertension. In addition, ACE2 is a receptor for coronavirus entry into host cells, leading to coronavirus infection. Lung cancer, one of the most common respiratory diseases worldwide, has a high rate of infection. Elevated levels of ACE2 in lung cancer patients, which increase the risk of SARS-CoV-2 infection and severe disease, have been demonstrated in clinical studies and by molecular mechanisms. The association between lung cancer and SARS-CoV-2 is closely linked to ACE2. This review examines the basic pathophysiological role of ACE2 in the lung, the long-term effects of SARS-CoV-2 infection on lung function, the development of pulmonary fibrosis, chronic inflammation in long-term COVID patients, and the clinical research and mechanisms underlying the increased susceptibility of lung cancer patients to the virus. Possible mechanisms of lung cancer in SARS-CoV-2-infected individuals and the potential role of ACE2 in this process are also explored in this review. The role of ACE2 as a therapeutic target in the novel coronavirus infection process is also summarized. This will help to inform prevention and treatment of long-term pulmonary complications in patients.

Single-cell RNA sequencing highlights the roles of C1QB and NKG7 in the pancreatic islet immune microenvironment in type 1 diabetes mellitus

Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for characterizing individual cells and elucidating biological mechanisms at the cellular level. Using this technology, this study focuses on the mechanism of C1QB and NKG7 in pancreatic islet immune microenvironment in type 1 diabetes mellitus (T1DM). T1DM-related scRNA-seq data were downloaded from GEO database, followed by batch effect removal, cluster analysis, cell annotation and enrichment analysis. Thereafter, T1DM-related Bulk RNA-seq data were downloaded from GEO database. The infiltrating immune cell abundance was estimated and its correlation with the expression of immune cell marker genes was determined. Functional assays were performed in a constructed rat model of T1DM and cultured monocytes and lymphocytes for further validation. A large number of highly variable genes were found in pancreatic islet samples in T1DM. T1DM islet-derived cells may consist of 14 cell types. Macrophages and T lymphocytes were the major cells in pancreatic islet immune microenvironment. C1QB and NKG7 may be the key genes affecting macrophages and T lymphocytes, respectively. Silencing C1QB inhibited the differentiation of monocytes into macrophages and reduced the number of macrophages. Silencing NKG7 prevented T lymphocyte activation and proliferation. In vivo data confirmed that silencing C1QB and NKG7 reduced the number of macrophages and T lymphocytes in the pancreatic islet of T1DM rats, respectively, and alleviated pancreatic islet β-cell damage. Overall, C1QB and NKG7 can increase the number of macrophages and T lymphocytes, respectively, causing pancreatic islet β-cell damage and promoting T1DM in rats.

Identifying key transcription factors and miRNAs coregulatory networks associated with immune infiltrations and drug interactions in idiopathic pulmonary arterial hypertension

BACKGROUND

The deregulated genetic factors are critically associated with idiopathic pulmonary arterial hypertension (IPAH) development and progression. However, the identification of hub-transcription factors (TFs) and miRNA-hub-TFs co-regulatory network-mediated pathogenesis in IPAH remains lacking.

METHODS

We used GSE48149, GSE113439, GSE117261, GSE33463, and GSE67597 for identifying key genes and miRNAs in IPAH. We used a series of bioinformatics approaches, including R packages, protein-protein interaction (PPI) network, and gene set enrichment analysis (GSEA) to identify the hub-TFs and miRNA-hub-TFs co-regulatory networks in IPAH. Also, we employed a molecular docking approach to evaluate the potential protein-drug interactions.

RESULTS

We found that 14 TFs encoding genes, including ZNF83, STAT1, NFE2L3, and SMARCA2 are upregulated, and 47 TFs encoding genes, including NCOR2, FOXA2, NFE2, and IRF5 are downregulated in IPAH relative to the control. Then, we identified the differentially expressed 22 hub-TFs encoding genes, including four upregulated (STAT1, OPTN, STAT4, and SMARCA2) and 18 downregulated (such as NCOR2, IRF5, IRF2, MAFB, MAFG, and MAF) TFs encoding genes in IPAH. The deregulated hub-TFs regulate the immune system, cellular transcriptional signaling, and cell cycle regulatory pathways. Moreover, the identified differentially expressed miRNAs (DEmiRs) are involved in the co-regulatory network with hub-TFs. The six hub-TFs encoding genes, including STAT1, MAF, CEBPB, MAFB, NCOR2, and MAFG are consistently differentially expressed in the peripheral blood mononuclear cells of IPAH patients, and these hub-TFs showed significant diagnostic efficacy in distinguishing IPAH cases from the healthy individuals. Moreover, we revealed that the co-regulatory hub-TFs encoding genes are correlated with the infiltrations of various immune signatures, including CD4 regulatory T cells, immature B cells, macrophages, MDSCs, monocytes, Tfh cells, and Th1 cells. Finally, we discovered that the protein product of STAT1 and NCOR2 interacts with several drugs with appropriate binding affinity.

CONCLUSIONS

The identification of hub-TFs and miRNA-hub-TFs co-regulatory networks may provide a new avenue into the mechanism of IPAH development and pathogenesis.

Comprehensive analysis and validation of novel immune and vascular remodeling related genes signature associated with drug interactions in pulmonary arterial hypertension

Background: Previous studies revealed that the gene signatures are associated with the modulation and pathogenesis of pulmonary arterial hypertension (PAH). However, identifying critical transcriptional signatures in the blood of PAH patients remains lacking.

Methods: The differentially expressed transcriptional signatures in the blood of PAH patients were identified by a meta-analysis from four microarray datasets. Then we investigated the enrichment of gene ontology and KEGG pathways and identified top hub genes. Besides, we investigated the correlation of crucial hub genes with immune infiltrations, hallmark gene sets, and blood vessel remodeling genes. Furthermore, we investigated the diagnostic efficacy of essential hub genes and their expression validation in an independent cohort of PAH, and we validate the expression level of hub genes in monocrotaline (MCT) induced PAH rats’ model. Finally, we have identified the FDA-approved drugs that target the hub genes and their molecular docking.

Results: We found 1,216 differentially expressed genes (DEGs), including 521 up-regulated and 695 down-regulated genes, in the blood of the PAH patients. The up-regulated DEGs are significantly associated with the enrichment of KEGG pathways mainly involved with immune regulation, cellular signaling, and metabolisms. We identified 13 master transcriptional regulators targeting the dysregulated genes in PAH. The STRING-based investigation identified the function of hub genes associated with multiple immune-related pathways in PAH. The expression levels of RPS27A, MAPK1, STAT1, RPS6, FBL, RPS3, RPS2, and GART are positively correlated with ssGSEA scores of various immune cells as positively correlated with the hallmark of oxidative stress. Besides, we found that these hub genes also regulate the vascular remodeling in PAH. Furthermore, the expression levels of identified hub genes showed good diagnostic efficacy in the blood of PAH, and we validated most of the hub genes are consistently dysregulated in an independent PAH cohort. Validation of hub genes expression level in the monocrotaline (MCT)-induced lung tissue of rats with PAH revealed that 5 screened hub genes (MAPK1, STAT1, TLR4, TLR2, GART) are significantly highly expressed in PAH rats, and 4 screened hub genes (RPS6, FBL, RPS3, and RPS2) are substantially lowly expressed in rats with PAH. Finally, we analyzed the interaction of hub proteins and FDA-approved drugs and revealed their molecular docking, and the results showed that MAPK1, TLR4, and GART interact with various drugs with appropriate binding affinity.

Conclusion: The identified blood-derived key transcriptional signatures significantly correlate with immune infiltrations, hypoxia, glycolysis, and blood vessel remodeling genes. These findings may provide new insight into the diagnosis and treatment of PAH patients.

Thrombocytopenia in COVID‑19 and vaccine‑induced thrombotic thrombocytopenia

The highly heterogeneous symptomatology and unpredictable progress of COVID-19 triggered unprecedented intensive biomedical research and a number of clinical research projects. Although the pathophysiology of the disease is being progressively clarified, its complexity remains vast. Moreover, some extremely infrequent cases of thrombotic thrombocytopenia following vaccination against SARS-CoV-2 infection have been observed. The present study aimed to map the signaling pathways of thrombocytopenia implicated in COVID-19, as well as in vaccine-induced thrombotic thrombocytopenia (VITT). The biomedical literature database, MEDLINE/PubMed, was thoroughly searched using artificial intelligence techniques for the semantic relations among the top 50 similar words (>0.9) implicated in COVID-19-mediated human infection or VITT. Additionally, STRING, a database of primary and predicted associations among genes and proteins (collected from diverse resources, such as documented pathway knowledge, high-throughput experimental studies, cross-species extrapolated information, automated text mining results, computationally predicted interactions, etc.), was employed, with the confidence threshold set at 0.7. In addition, two interactomes were constructed: i) A network including 119 and 56 nodes relevant to COVID-19 and thrombocytopenia, respectively; and ii) a second network containing 60 nodes relevant to VITT. Although thrombocytopenia is a dominant morbidity in both entities, three nodes were observed that corresponded to genes (AURKA, CD46 and CD19) expressed only in VITT, whilst ADAM10, CDC20, SHC1 and STXBP2 are silenced in VITT, but are commonly expressed in both COVID-19 and thrombocytopenia. The calculated average node degree was immense (11.9 in COVID-19 and 6.43 in VITT), illustrating the complexity of COVID-19 and VITT pathologies and confirming the importance of cytokines, as well as of pathways activated following hypoxic events. In addition, PYCARD, NLP3 and P2RX7 are key potential therapeutic targets for all three morbid entities, meriting further research. This interactome was based on wild-type genes, revealing the predisposition of the body to hypoxia-induced thrombosis, leading to the acute COVID-19 phenotype, the 'long-COVID syndrome', and/or VITT. Thus, common nodes appear to be key players in illness prevention, progression and treatment.

Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods

Introduction

The risk of infection with COVID-19 is high in lung adenocarcinoma (LUAD) patients, and there is a dearth of studies on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape.

Objectives

To fill the research void on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape.

Methods

Herein, we identified genes, specifically the differentially expressed genes (DEGs), correlated with the susceptibility of LUAD patients to COVID-19. These were obtained by calculating standard mean deviation (SMD) values for 49 SARS-CoV-2-infected LUAD samples and 24 non-affected LUAD samples, as well as 3931 LUAD samples and 3027 non-cancer lung samples from 40 pooled RNA-seq and microarray datasets. Hub susceptibility genes significantly related to COVID-19 were further selected by weighted gene co-expression network analysis. Then, the hub genes were further analyzed via an examination of their clinical significance in multiple datasets, a correlation analysis of the immune cell infiltration level, and their interactions with the interactome sets of the A549 cell line.

Results

A total of 257 susceptibility genes were identified, and these genes were associated with RNA splicing, mitochondrial functions, and proteasomes. Ten genes, MEA1, MRPL24, PPIH, EBNA1BP2, MRTO4, RABEPK, TRMT112, PFDN2, PFDN6, and NDUFS3, were confirmed to be the hub susceptibility genes for COVID-19 in LUAD patients, and the hub susceptibility genes were significantly correlated with the infiltration of multiple immune cells.

Conclusion

In conclusion, the susceptibility genes for COVID-19 in LUAD patients discovered in this study may increase our understanding of the high risk of COVID-19 in LUAD patients.

Contribution of endothelial cell-derived transcriptomes to the colon cancer based on bioinformatics analysis

Colon tumor endothelial cells (CTECs) plays substantial roles to induce immune invasion, angiogenesis and metastasis. Thus, identification of the CTECs-derived transcriptomes could be helpful for colon cancer diagnosis and potential therapy.

METHODS

By analysis of CTECs-derived gene expression profiling dataset, we identified differentially expressed genes (DEGs) between CTECs and colon normal endothelial cells (CNECs). In addition, we identified the significant pathways and protein-protein interaction (PPI) network that was significantly associated with the DEGs. Furthermore, we identified hub genes whose expression was significantly associated with prognosis and immune cell infiltrations in colon cancer. Finally, we identified the significant correlations between the prognostic hub genes and immune-inhibitory markers in colon cancer.

RESULTS

We identified 362 DEGs in CTECs relative to the CNECs, including117 up-regulated genes and 245 down-regulated genes in the CTECs. In addition, we identified significantly up-regulated pathways in CTECs that were mainly involved in cancer and immune regulation. Furthermore, we identified hub genes (such as SPARC, COL1A1, COL1A2 and IGFBP3) that are associated with prognosis and immune cells infiltrations in colon cancer. Interestingly, we found that prognosis-associated hub genes (SPARC, COL1A1, COL1A2 and IGFBP3) are positively correlated with immune-inhibitory markers of various immunosuppressive cells, including TAM, M2 macrophage, Tregs and T cell exhaustion. Finally, our findings revealed that prognosis-associated upregulated hub genes are positively correlated with immune checkpoint markers, including PD-L1 and PD-L2 and the immunosuppressive markers including TGFB1 and TGFBR1.

CONCLUSIONS

The identification of CTECs-specific transcriptomes may provide crucial insights into the colon tumor microenvironment that mediates the development of colon cancer.

Meta-analysis identifying epithelial-derived transcriptomes predicts poor clinical outcome and immune infiltrations in ovarian cancer

BACKGROUND

Previous studies revealed that the epithelial component is associated with the modulation of the ovarian tumor microenvironment (TME). However, the identification of key transcriptional signatures of laser capture microdissected human ovarian cancer epithelia remains lacking.

METHODS

We identified the differentially expressed transcriptional signatures of human ovarian cancer epithelia by meta-analysis of GSE14407, GSE2765, GSE38666, GSE40595, and GSE54388. Then we investigated the enrichment of KEGG pathways that are associated with epithelia-derived transcriptomes. Finally, we investigated the correlation of key epithelia-hub genes with the survival prognosis and immune infiltrations. Finally, we investigated the genetic alterations of key prognostic hub genes and their diagnostic efficacy in ovarian cancer epithelia.

RESULTS

We identified 1339 differentially expressed genes (DEGs) in ovarian cancer epithelia including 541upregulated and 798 downregulated genes. We identified 21 (such as E2F4, FOXM1, TFDP1, E2F1, and SIN3A) and 11 (such as JUN, DDX4, FOSL1, NOC2L, and HMGA1) master transcriptional regulators (MTRs) that are interacted with upregulated and the downregulated genes in ovarian tumor epithelium, respectively. The STRING-based analysis identified hub genes (such as CDK1, CCNB1, AURKA, CDC20, and CCNA2) in ovarian cancer epithelia. The significant clusters of identified hub genes are associated with the enrichment of KEGG pathways including cell cycle, DNA replication, cytokine-cytokine receptor interaction, pathways in cancer, and focal adhesion. The upregulation of SCNN1A and CDCA3 and the downregulation of SOX6 are correlated with a shorter survival prognosis in ovarian cancer (OV). The expression level of SOX6 is negatively correlated with immune score and positively correlated with tumor purity in OV. Moreover, SOX6 is negatively correlated with the infiltration of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages in OV. Also, SOX6 is negatively correlated with various immune markers including CD8A, PRF1, GZMA, GZMB, NKG7, CCL3, and CCL4, indicating the immune regulatory efficiency of SOX6 in the TME of OV. Furthermore, SCNN1A, CDCA3, and SOX6 genes are genetically altered in OV and the expression levels of SCNN1A and SOX6 genes showed diagnostic efficacy in ovarian cancer epithelia.

CONCLUSIONS

The identified ovarian cancer epithelial-derived key transcriptional signatures are significantly correlated with survival prognosis and immune infiltrations, and may provide new insight into the diagnosis and treatment of epithelial ovarian cancer.