Ovarian Cancer Biomarkers in the COVID-19 Era.

Ovarian Cancer (OC) diagnosis is entrusted to CA125 and HE4. Since the latter has been found increased in COVID-19 patients, in this study, we aimed to evaluate the influence of SARS-CoV-2 infection on OC biomarkers. HE4 values above the cut-off were observed in 65% of OC patients and in 48% of SARS-CoV-2-positive patients (not oncologic patients), whereas CA125 values above the cut-off were observed in 71% of OC patients and in 11% of SARS-CoV-2 patients. Hence, by dividing the HE4 levels into quartiles, we can state that altered levels of HE4 in COVID-19 patients were mostly detectable in quartile I (151-300 pmol/L), while altered levels in OC patients were mostly clustered in quartile III (>600, pmol/L). In light of these observations, in order to better discriminate women with ovarian cancer versus those with COVID-19, we established a possible HE4 cut-off of 328 pmol/L by means of a ROC curve. These results demonstrate that the reliability of HE4 as a biomarker in ovarian cancer remains unchanged, despite COVID-19 interference; moreover, it is important for a proper diagnosis that whether the patient has a recent history of SARS-CoV-2 infection is determined.

In the SARS-CoV-2 Pandora Pandemic: Can the Stance of Premorbid Intestinal Innate Immune System as Measured by Fecal Adnab-9 Binding of p87:Blood Ferritin, Yielding the FERAD Ratio, Predict COVID-19 Susceptibility and Survival in a Prospective Population Database?

SARS-CoV-2 severity predictions are feasible, though individual susceptibility is not. The latter prediction allows for planning vaccination strategies and the quarantine of vulnerable targets. Ironically, the innate immune response (InImS) is both an antiviral defense and the potential cause of adverse immune outcomes. The competition for iron has been recognized between both the immune system and invading pathogens and expressed in a ratio of ferritin divided by p87 (as defined by the Adnab-9 ELISA stool-binding optical density, minus the background), known as the FERAD ratio. Associations with the FERAD ratio may allow predictive modeling for the susceptibility and severity of disease. We evaluated other potential COVID-19 biomarkers prospectively. Patients with PCR+ COVID-19 tests (Group 1; n = 28) were compared to three other groups. In Group 2 (n = 36), and 13 patients displayed COVID-19-like symptoms but had negative PCR or negative antibody tests. Group 3 (n = 90) had no symptoms and were negative when routinely PCR-tested before medical procedures. Group 4 (n = 2129) comprised a pool of patients who had stool tests and symptoms, but their COVID-19 diagnoses were unknown; therefore, they were chosen to represent the general population. Twenty percent of the Group 4 patients (n = 432) had sufficient data to calculate their FERAD ratios, which were inversely correlated with the risk of COVID-19 in the future. In a case report of a neonate, we studied three biomarkers implicated in COVID-19, including p87, Src (cellular-p60-sarcoma antigen), and Abl (ABL-proto-oncogene 2). The InImS of the first two were positively correlated. An inverse correlation was found between ferritin and lysozyme in serum (p < 0.05), suggesting that iron could have impaired an important innate immune system anti-viral effector and could partially explain future COVID-19 susceptibility.

Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases.

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China. Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes' protein expression levels. Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC. Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

Seven bacterial response-related genes are biomarkers for colon cancer.

BACKGROUND: Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis. METHODS: The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues. RESULTS: A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480). CONCLUSIONS: We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Anti-cancer effect of COVID-19 vaccines in mice models.

AIMS: Without any doubt, vaccination was the best choice for Coronavirus disease 2019 (COVID-19) pandemic control. According to the American Society of Clinical Oncology (ASCO) and European Society for Medical Oncology (ESMO), people with cancer or a history of cancer have a higher risk of dying from Covid-19 than ordinary people; hence, they should be considered a high-priority group for vaccination. On the other hand, the effect of the Covid-19 vaccination on cancer is not transparent enough. This study is one of the first in vivo studies that try to show the impact of Sinopharm (S) and AstraZeneca (A) vaccines on breast cancer, the most common cancer among women worldwide. MATERIALS AND METHODS: Vaccination was performed with one and two doses of Sinopharm (S1/S2) or AstraZeneca (A1/A2) on the 4T1 triple-negative breast cancer (TNBC) mice model. The tumor size and body weight of mice were monitored every two days. After one month, mice were euthanized, and the existence of Tumor-infiltrating lymphocytes (TILs) and expression of the important markers in the tumor site was assessed. Metastasis in the vital organs was also investigated. KEY FINDINGS: Strikingly, all of the vaccinated mice showed a decrease in tumor size and this decrease was highest after two vaccinations. Moreover, we observed more TILs in the tumor after vaccination. Vaccinated mice demonstrated a decrease in the expression of tumor markers (VEGF, Ki-67, MMP-2/9), CD4/CD8 ratio, and metastasis to the vital organs. SIGNIFICANCE: Our results strongly suggest that COVID-19 vaccinations decrease tumor growth and metastasis.

Characteristics of vasculogenic mimicry and tumour to endothelial transdifferentiation in human glioblastoma: a systematic review.

BACKGROUND: Glioblastoma, the most common primary malignant brain tumour in adults, is a highly vascular tumour characterised by abnormal angiogenesis. Additional mechanisms of tumour vascularisation have also been reported in glioblastoma, including the formation of tumour cell-derived vessels by vasculogenic mimicry (VM) or the transdifferentiation of tumour cells to endothelial cells. VM and endothelial transdifferentiation have frequently been reported as distinct processes, however, the use of both terms to describe a single process of vascularisation also occurs. Some overlapping characteristics have also been reported when identifying each process. We therefore aimed to determine the markers consistently attributed to VM and endothelial transdifferentiation in the glioblastoma literature. METHODS: Ovid MEDLINE and Ovid Embase were searched for studies published between January 1999 and July 2021 that assessed VM or tumour to endothelial transdifferentiation in human glioblastoma. The online systematic review tool Covidence was used for screening and data extraction. Extracted data included type of tumour-derived vasculature reported, methods and techniques used, and markers investigated. Studies were grouped based on type of vasculature reported for further assessment. RESULTS: One hundred and thirteen of the 419 unique records identified were included for analysis. VM was reported in 64/113 studies, while tumour to endothelial transdifferentiation was reported in 16/113 studies. The remaining studies used both terms to describe a single process, did not define the process that occurred, or concluded that neither VM nor endothelial transdifferentiation occurred. Absence of CD34 and/or CD31 in vascular structures was the most common indicator of VM, while expression of CD34 and/or CD31, in addition to various other endothelial, stem cell or tumour cell markers, indicated tumour to endothelial transdifferentiation. CONCLUSION: Cells derived from tumour to endothelial transdifferentiation express typical endothelial markers including CD34 and CD31, while tumour cells contributing to VM lack CD34 and CD31 expression. Additional tumour markers are required to identify transdifferentiation in glioblastoma tissue, and this process requires further characterisation.

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 differentially expressed genes and pathways in kidney of ANCA-associated vasculitis by integrated bioinformatics analysis.

The antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systematic of relatively rare autoimmune diseases with unknown cause. Kidney involvement is one of the most common clinical manifestations, and the degree of renal damage is closely associated with the development and prognosis of AAV. In this study, we utilized the Robust Rank Aggreg (RRA) method in R to integrate GSE104948, GSE104954, GSE108109, GSE108112, and GSE108113 profile datasets loaded from Gene Expression Omnibus (GEO) database and identified a set of differentially expressed genes (DEGs) in kidney between AAV patients and living donors. Then, the results of gene ontology (GO) functional annotation showed that immunity and metabolism involved process of AAV both in glomerulus and tubulointerstitial. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that following pathways, such as complement and coagulation cascades pathway; Staphylococcus aureus infection; disease-COVID-19; and systemic lupus erythematosus (SLE) pathway play a crucial role in AAV. Next, the results analyzed by protein-protein interaction (PPI) network and Cytoscape software exhibited the hub genes ALB, TYROBP, and CYBB existed in both glomerular and tubulointerstitial compartments datasets. Finally, KEGG analysis using genes of two most important modules also further validated complement and coagulation cascades pathway and S. aureus infection existed both in glomerulus and tubulointerstitial compartments datasets. In conclusion, this study identified key genes and pathways involved in kidney of AAV, which was benefit to further uncover the mechanisms underlying the development and progress of AAV, biomarkers, and potential therapeutic targets as well.

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.

Crystal-reconstructed BiVO4 semiconductor photoelectrochemical sensor for ultra-sensitive tumor biomarker detection.

In this study, we developed a crystal-reconstructed-BiVO4 aptamer photoelectrochemical (PEC) biosensor by a high-energy laser treatment technique. This biosensor achieves a limit of detection (LOD) (0.82 ag mL-1), linear detection range (1 ag mL-1 to 2 ng mL-1), and resolution ratio (∼18 molecules per mL) for prostate-specific antigen (PSA) tumor biomarker detection. Furthermore, reconstructed surface microstructure and oxygen vacancy doping energy formation after crystal reconstruction induce the stereo-hindrance effect and photogenerated hole energy is reduced during PSA target detection. In this case, a photocurrent inhibition phenomenon for PSA detection is noticed. Based on this photocurrent inversion phenomenon, some dysoxidizable nucleonic acid tumor (miRNA-21) and virus biomarkers (RdRp-COVID) can be detected with a LOD level of ∼10-16 M by linking the corresponding base paring probe on the surface of the crystal-reconstructed photoanode. In addition to high sensitivity, this PEC biosensor presents high detection specificity, stability, and accuracy in clinical verification. Thus, this crystal-reconstructed PEC biosensor shows application potential in the fields of multi-tumor or viral biomarker detection.

Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma.

Background: The coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma. Methods: Two sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury. Results: We discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein-protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor-gene interactions, DEG-microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes. Conclusion: We identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.

Sudden unexpected death in a 17-year-old boy due to unacknowledged adamantinoma-like Ewing sarcoma.

A 17-year-old male with no previous medical history was admitted 2 days before his death to a local hospital after mild dyspnea. Electrocardiography, chest radiography, and blood analysis revealed no abnormalities. Blood oxygen saturation was 99%, and SARS-CoV-2 nasopharyngeal swabs tested negative; thus, he was discharged without prescriptions. After 2 days, the subject died suddenly during a pool party. Forensic autopsy was performed analyzing all anatomical districts. Cardiac causes were fully excluded after deep macroscopic and microscopic evaluation; lung and brain analyses showed no macroscopic pathology. Finally, a large subglottic solid mass was detected. The whitish neoplasm showed an aggressive invasion pattern to the thyroid and adjacent deep soft tissues and occluded the trachea. High-power microscopy showed sheets of small, uniform cells with scant cytoplasm; round nuclei; and small, punctate nucleoli, with immunohistochemical expression of CK8-18, AE1/AE3, and CD99. Using FISH analysis, the break-apart molecular probes (EWSR1 (22q12) Break - XL, Leica Biosystem, Nussloch, Germany) showed distinct broken red and green fluorochromes, diagnostic of Ewing sarcoma. The neoplasm was characterized as adamantinoma-like Ewing sarcoma, and the mechanism of death was identified as airway obstruction. The rarity of the case resides in the circumstances of death, which pointed to the possibility of sudden unexpected death due to heart disease, but an oncological cause and the underlying mechanism were finally diagnosed. The best method to perform autopsies is still complete, extensive, and systematic macroscopic sampling of organs and districts followed by histopathological analysis, in addition to immunohistochemical and molecular investigations in those cases in which they are necessary. In fact, when neoplasms are detected, the application of advanced techniques such as immunohistochemistry and molecular diagnostics is fundamental to accurately certify death.

Lateral Flow Immunoassay Based on Time-Resolved Fluorescence Microspheres for Rapid and Quantitative Screening CA199 in Human Serum.

Carbohydrate antigen 199 (CA199) is a serum biomarker which has certain value and significance in the diagnosis, prognosis, treatment, and postoperative monitoring of cancer. In this study, a lateral flow immunoassay based on europium (III) polystyrene time-resolved fluorescence microspheres (TRFM-based LFIA), integrated with a portable fluorescence reader, has been successfully establish for rapid and quantitative analysis of CA199 in human serum. Briefly, time-resolved fluorescence microspheres (TRFMs) were conjugated with antibody I (Ab1) against CA199 as detection probes, and antibody II (Ab2) was coated as capture element, and a "TRFMs-Ab1-CA199-Ab2" sandwich format would form when CA199 was detected by the TRFM-based LFIA. Under the optimal parameters, the detection limit of the TRFM-based LFIA for visible quantitation with the help of an ultraviolet light was 4.125 U/mL, which was four times lower than that of LFIA based on gold nanoparticles. Additionally, the fluorescence ratio is well linearly correlated with the CA199 concentration (0.00-66.0 U/mL) and logarithmic concentration (66.0-264.0 U/mL) for quantitative detection. Serum samples from 10 healthy people and 10 liver cancer patients were tested to confirm the performances of the point-of-care application of the TRFM-based LFIA, 20.0 U/mL of CA199 in human serum was defined as the threshold for distinguishing healthy people from liver cancer patients with an accuracy of about 60%. The establishment of TRFM-based LFIA will provide a sensitive, convenient, and efficient technical support for rapid screening of CA199 in cancer diagnosis and prognosis.

Bioinformatics analysis of potential pathogenesis and risk genes of immunoinflammation-promoted renal injury in severe COVID-19.

Renal injury secondary to COVID-19 is an important factor for the poor prognosis of COVID-19 patients. The pathogenesis of renal injury caused by aberrant immune inflammatory of COVID-19 remains unclear. In this study, a total of 166 samples from 4 peripheral blood transcriptomic datasets of COVID-19 patients were integrated. By using the weighted gene co-expression network (WGCNA) algorithm, we identified key genes for mild, moderate, and severe COVID-19. Subsequently, taking these genes as input genes, we performed Short Time-series Expression Miner (STEM) analysis in a time consecutive ischemia-reperfusion injury (IRI) -kidney dataset to identify genes associated with renal injury in COVID-19. The results showed that only in severe COVID-19 there exist a small group of genes associated with the progression of renal injury. Gene enrichment analysis revealed that these genes are involved in extensive immune inflammation and cell death-related pathways. A further protein-protein interaction (PPI) network analysis screened 15 PPI-hub genes: ALOX5, CD38, GSF3R, LGR, RPR1, HCK, ITGAX, LYN, MAPK3, NCF4, SELP, SPI1, WAS, TLR2 and TLR4. Single-cell sequencing analysis indicated that PPI-hub genes were mainly distributed in neutrophils, macrophages, and dendritic cells. Intercellular ligand-receptor analysis characterized the activated ligand-receptors between these immune cells and parenchyma cells in depth. And KEGG enrichment analysis revealed that viral protein interaction with cytokine and cytokine receptor, necroptosis, and Toll-like receptor signaling pathway may be potentially essential for immune cell infiltration leading to COVID-19 renal injury. Finally, we validated the expression pattern of PPI-hub genes in an independent data set by random forest. In addition, we found that the high expression of these genes was correlated with a low glomerular filtration rate. Including them as risk genes in lasso regression, we constructed a Nomogram model for predicting severe COVID-19. In conclusion, our study explores the pathogenesis of renal injury promoted by immunoinflammatory in severe COVID-19 and extends the clinical utility of its key genes.

How I Use It: The Exosome Diagnostics (EPI) prostate cancer biomarker utility in urology and primary care.

Prostate-specific antigen (PSA) screening remains the mainstay for early detection of prostate cancer. Although PSA is a nonspecific prostate cancer biomarker, its specificity for high grade prostate cancer can be enhanced by pre-biopsy liquid biomarkers including the Exosome Dx Prostate IntelliScore (EPI) test. EPI is a stand-alone urine genomic test that measures 3 exosome-derived gene expression signatures without the need for digital rectal examination (DRE) or inclusion of standard of care parameters in the test algorithm. EPI has broad clinical utility as a risk stratification tool for clinically significant high grade prostate cancer in men considering diagnostic prostate biopsy (MRI-targeted and systematic biopsy). During the COVID-19 pandemic, the EPI At-Home Collection Kit was introduced and quickly became an important component of tele-urology. The EPI test has emerged as a prioritization tool for primary care referral to urologists and for prostate biopsy scheduling. EPI provides an objective and actionable genomic risk assessment tool for high grade prostate cancer and is a critical part of the informed decision-making regarding biopsy (targeted, systematic or both) in both urology and primary care practices.

The role of microvesicles as biomarkers in the screening of colorectal neoplasm.

BACKGROUND: Colorectal cancer (CRC) is the second cause of cancer death worldwide. The role of circulating microvesicles as a screening tool is a novel, yet effective approach that warrants prioritised research. METHODS: In a two-gate diagnostic accuracy study, 35 patients with benign colorectal polyps (BCRP) (n = 16) and colorectal cancer (CRC) (n = 19) were compared to 17 age-matched healthy controls. Total annexin-V positive microvesicles and sub-populations positive for selected biomarkers relevant to bowel neoplasm were evaluated in patients' plasma using flow cytometry. Statistical methods including factor analysis utilising two component factors were performed to obtain optimal diagnostic accuracy of microvesicles in identifying patients with colorectal neoplasms. RESULTS: Total plasma microvesicles, and sub-populations positive for CD31, CD42a, CD31+/CD42a-, EPHB2, ICAM and LGR5 (component factor-1) were able to identify patients with BCRP and CRC with a receiver operator curve (AUC) accuracy of a 100% (95% CI: 100%-100%) and 95% (95% CI: 88%-100%), respectively. To identify patients with BCRP, a cut-off point value of component factor-1761 microvesicles/µl demonstrated a 100% sensitivity, specificity and negative predictive value (NPV) and a 93% positive predictive value (PPV). To identify patients with CRC, a cut-off value of component factor-1 3 439 microvesicles/µl demonstrated a 100% sensitivity, specificity and NPV and a 65% PPV. CEA+ microvesicles sub-population were significantly (p < 0.02) higher in CRC in comparison to BCRP. CONCLUSIONS: Microvesicles as biomarkers for the early and accurate detection of CRC is a simple and effective tool that yields a potential breakthrough in clinical management.

Effects of Bevacizumab Combined with Chemotherapy on CT, CyFRA21-1, and ProGRP and Prognosis of Lung Cancer Patients under Nursing Intervention.

Objective: Molecular targeted drug therapy and chemotherapy are the main treatments for advanced non-small-cell lung cancer, and the combination of both has advantages in prolonging patients' progression-free survival and overall survival. This study investigated the effects of bevacizumab combined with chemotherapy under nursing intervention on CT, cytokeratin 19 fragment antigen 21-1 (CYFRA21-1), and gastrin-releasing peptide precursor (ProGRP) and prognosis of lung cancer patients. Methods: 102 patients with non-small-cell lung cancer admitted to our hospital from January 2018 to May 2019 were divided into observation group and control group, with 51 cases each. The control group was treated with basic chemotherapy, and the observation group was treated with bevacizumab in combination with the control group, and both groups used nursing interventions. The clinical effects, CYFRA21-1 and ProGRP levels, baseline data, CT parameters, 24-month cumulative survival, and the effects of CYFRA21-1 and ProGRP on long-term survival and lung function were compared. Results: The disease control rate of the observation group was 94.12%, which was significantly higher than that of the control group (76.47%); after 7 d, 30 d, 60 d, and 90 d of treatment, the levels of CYFRA21-1 and ProGRP were statistically downregulated. The difference in lymph node metastasis, lesion diameter, plain Eff-Z, venous stage, and arterial stage normalized iodine concentrations (NIC) was statistically significant; the survival rate at 24 months in the observation group was 74.51% (38/51); the cumulative survival rate at 24 months in the control group was 52.94% (27/51), and the difference was statistically significant (X 2 = 4.980, P = 0.026). The cumulative survival rate at 24 months was significantly lower in patients with high expression of CYFRA21-1 and ProGRP compared with those with low expression of CYFRA21-1 and ProGRP. After treatment, in the observation group, the forceful spirometry (FVC), forceful expiratory volume in one second (FEV1), and FEV1/FVC levels were significantly different from those before treatment and were significantly different from those in the control group. Conclusion: Bevacizumab in combination with standard chemotherapy regimens with nursing interventions could benefit patients with advanced non-small-cell lung cancer and had a good prospect of application.

Identification of tumor antigens and immune subtypes of glioma for mRNA vaccine development.

Recent evidence suggested that the mRNA vaccine has been effective for many tumors, but its progress in gliomas was slow. In this study, we screened potential tumor antigens and suitable populations for mRNA vaccine to develop mRNA vaccine for glioma. We integrated the normalized RNA sequencing expression data and somatic mutation data from TCGA-GBM, TCGA-LGG, and CGGA datasets. Putative antigens in glioma were identified by selecting highly mutated genes with intimate correlation with clinical survival and immune infiltration. An unsupervised partition around medoids algorithm was utilized to stably cluster the patients into five different immune subtypes. Among them, IS1/2 was cold tumor with low tumor mutation burden (TMB), immunogenic cell death (ICDs), and immune checkpoints (ICPs), and IS4/5 was hot tumor with high TMB, ICDs, and ICPs. Monocle3 package was used to evaluate the immune status similarity and evolution in glioma, which identified cluster IS2A/2B within IS2 subtype to be more suitable vaccination receivers. Weighted gene co-expression network analysis identified five hub immune genes as the biomarkers of patients' immune status in glioma. In conclusion, NAT1, FRRS1, GTF2H2C, BRCA2, GRAP, NR5A2, ABCB4, ZNF90, ERCC6L, and ZNF813 are potential antigens suitable for glioma mRNA vaccine. IS1/2A/2B are suitable for mRNA vaccination.

Genetic alterations, RNA expression profiling and DNA methylation of HMGB1 in malignancies.

The high mobility group box 1 (HMGB1) is a potential biomarker and therapeutic target in various human diseases. However, a systematic, comprehensive pan-cancer analysis of HMGB1 in human cancers remains to be reported. This study analysed the genetic alteration, RNA expression profiling and DNA methylation of HMGB1 in more than 30 types of tumours. It is worth noting that HMGB1 is overexpressed in malignant tissues, including lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), pancreatic adenocarcinoma (PAAD) and thymoma (THYM). Interestingly, there is a positive correlation between the high expression of HMGB1 and the high survival prognosis of THYM. Finally, this study comprehensively evaluates the genetic variation of HMGB1 in human malignant tumours. As a prospective biomarker of COVID-19, the role that HMGB1 plays in THYM is highlighted.