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

The molecular mechanism of the pathological impact of COVID-19 in lung cancer patients remains poorly understood to date. 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, namely, 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, our findings suggest that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Additionally, our findings, in concert with published literature, suggest that molecular signatures, such as hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Altogether, the scientific findings of this study will help formulate appropriate management measures and guide the development of diagnostic and therapeutic measures for COVID-19-infected lung cancer patients.

Identification of a key glioblastoma candidate gene, FUBP3, based on weighted gene co-expression network analysis.

BACKGROUND: Glioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene. METHODS: Two GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3. OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis. RESULTS: GSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4 + T cells, CD8 + T cells, and macrophages) and FUBP3. CONCLUSION: FUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.

Objective: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. Methods: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool. Results: CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion: CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

Novel Dormancy Mechanism of Castration Resistance in Bone Metastatic Prostate Cancer Organoids.

Advanced prostate cancer (PCa) patients with bone metastases are treated with androgen pathway directed therapy (APDT). However, this treatment invariably fails and the cancer becomes castration resistant. To elucidate resistance mechanisms and to provide a more predictive pre-clinical research platform reflecting tumor heterogeneity, we established organoids from a patient-derived xenograft (PDX) model of bone metastatic prostate cancer, PCSD1. APDT-resistant PDX-derived organoids (PDOs) emerged when cultured without androgen or with the anti-androgen, enzalutamide. Transcriptomics revealed up-regulation of neurogenic and steroidogenic genes and down-regulation of DNA repair, cell cycle, circadian pathways and the severe acute respiratory syndrome (SARS)-CoV-2 host viral entry factors, ACE2 and TMPRSS2. Time course analysis of the cell cycle in live cells revealed that enzalutamide induced a gradual transition into a reversible dormant state as shown here for the first time at the single cell level in the context of multi-cellular, 3D living organoids using the Fucci2BL fluorescent live cell cycle tracker system. We show here a new mechanism of castration resistance in which enzalutamide induced dormancy and novel basal-luminal-like cells in bone metastatic prostate cancer organoids. These PDX organoids can be used to develop therapies targeting dormant APDT-resistant cells and host factors required for SARS-CoV-2 viral entry.

Multi-omics of the expression and clinical outcomes of TMPRSS2 in human various cancers: A potential therapeutic target for COVID-19.

Growing evidence has shown that Transmembrane Serine Protease 2 (TMPRSS2) not only contributes to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but is also closely associated with the incidence and progression of tumours. However, the correlation of coronavirus disease (COVID-19) and cancers, and the prognostic value and molecular function of TMPRSS2 in various cancers have not been fully understood. In this study, the expression, genetic variations, correlated genes, immune infiltration and prognostic value of TMPRSS2 were analysed in many cancers using different bioinformatics platforms. The observed findings revealed that the expression of TMPRSS2 was considerably decreased in many tumour tissues. In the prognostic analysis, the expression of TMPRSS2 was considerably linked with the clinical consequences of the brain, blood, colorectal, breast, ovarian, lung and soft tissue cancer. In protein network analysis, we determined 27 proteins as protein partners of TMPRSS2, which can regulate the progression and prognosis of cancer mediated by TMPRSS2. Besides, a high level of TMPRSS2 was linked with immune cell infiltration in various cancers. Furthermore, according to the pathway analysis of differently expressed genes (DEGs) with TMPRSS2 in lung, breast, ovarian and colorectal cancer, 160 DEGs genes were found and were significantly enriched in respiratory system infection and tumour progression pathways. In conclusion, the findings of this study demonstrate that TMPRSS2 may be an effective biomarker and therapeutic target in various cancers in humans, and may also provide new directions for specific tumour patients to prevent SARS-CoV-2 infection during the COVID-19 outbreak.

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

Expression and Clinical Significance of SARS-CoV-2 Human Targets in Neoplastic and Non-Neoplastic Lung Tissues.

BACKGROUND: A higher incidence of COVID-19 infection was demonstrated in cancer patients, including lung cancer patients. This study was conducted to get insights into the enhanced frequency of COVID-19 infection in cancer. METHODS: Using different bioinformatics tools, the expression and methylation patterns of ACE2 and TMPRSS2 were analyzed in healthy and malignant tissues, focusing on lung adenocarcinoma and data were correlated to clinical parameters and smoking history. RESULTS: ACE2 and TMPRSS2 were heterogeneously expressed across 36 healthy tissues with the highest expression levels in digestive, urinary and reproductive organs, while the overall analysis of 72 paired tissues demonstrated significantly lower expression levels of ACE2 in cancer tissues when compared to normal counterparts. In contrast, ACE2, but not TMPRSS2, was overexpressed in LUAD, which inversely correlated to the promoter methylation. This upregulation of ACE2 was age-dependent in LUAD, but not in normal lung tissues. TMPRSS2 expression in non-neoplastic lung tissues was heterogeneous and dependent on sex and smoking history, while it was downregulated in LUAD of smokers. Cancer progression was associated with a decreased TMPRSS2 but unaltered ACE2. In contrast, ACE2 and TMPRSS2 of lung metastases derived from different cancer subtypes was higher than organ metastases of other sites. TMPRSS2, but not ACE2, was associated with LUAD patients' survival. CONCLUSIONS: Comprehensive molecular analyses revealed a heterogeneous and distinct expression and/or methylation profile of ACE2 and TMPRSS2 in healthy lung vs. LUAD tissues across sex, age and smoking history and might have implications for COVID-19 disease.

Systematic profiling of ACE2 expression in diverse physiological and pathological conditions for COVID-19/SARS-CoV-2.

Recent retrospective studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) revealed that the patients with common comorbidities of cancers and chronic diseases face significantly poorer clinical outcomes than those without. Since the expression profile of ACE2, a crucial cell entry receptor for SARS-CoV-2, could indicate the susceptibility to SARS-CoV-2 infection, here we systematically dissected ACE2 expression using large-scale multi-omics data from 30 organs/tissues, 33 cancer types and some common chronic diseases involving >28 000 samples. It was found that sex and age could be correlated with the susceptibility of SARS-CoV-2 infection for certain tissues. Strikingly, ACE2 was up-regulated in cervical squamous cell carcinoma and endocervical adenocarcinoma, colon adenocarcinoma, oesophageal carcinoma, kidney renal papillary cell carcinoma, lung adenocarcinoma and uterine corpus endometrial carcinoma compared to controls. Furthermore, the patients with common chronic diseases regarding angiocardiopathy, type 2 diabetes, liver, pneumonia and hypertension were also with higher ACE2 expression compared to related controls, which were validated using independent data sets. Collectively, our study may reveal a novel important mechanism that the patients with certain cancers and chronic diseases may express higher ACE2 expression compared to the individuals without diseases, which could lead to their higher susceptibility to multi-organ injury of SARS-CoV-2 infection.