COVID-19 is caused by the coronavirus SARS-CoV-2 that has an affinity for neural tissue. There are reports of encephalitis, encephalopathy, cranial neuropathy, Guillain-Barre syndrome, and myositis/rhabdomyolysis in patients with COVID-19. In this review, we focused on the neuromuscular manifestations of SARS-CoV-2 infection. We analyzed all published reports on SARS-CoV-2-related peripheral nerve, neuromuscular junction, muscle, and cranial nerve disorders. Olfactory and gustatory dysfunction is now accepted as an early manifestation of COVID-19 infection. Inflammation, edema, and axonal damage of olfactory bulb have been shown in autopsy of patients who died of COVID-19. Olfactory pathway is suggested as a portal of entry of SARS-CoV-2 in the brain. Similar to involvement of olfactory bulb, isolated oculomotor, trochlear and facial nerve has been described. Increasing reports Guillain-Barre syndrome secondary to COVID-19 are being published. Unlike typical GBS, most of COVID-19-related GBS were elderly, had concomitant pneumonia or ARDS, more prevalent demyelinating neuropathy, and relatively poor outcome. Myalgia is described among the common symptoms of COVID-19 after fever, cough, and sore throat. Duration of myalgia may be related to the severity of COVID-19 disease. Few patients had muscle weakness and elevated creatine kinase along with elevated levels of acute-phase reactants. All these patients with myositis/rhabdomyolysis had severe respiratory complications related to COVID-19. A handful of patients with myasthenia gravis showed exacerbation of their disease after acquiring COVID-19 disease. Most of these patients recovered with either intravenous immunoglobulins or steroids.

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Neuromuscular presentations in patients with COVID-19.

Glioblastoma multiforme (GBM), the most aggressive brain cancer, recurs because glioblastoma stem cells (GSCs) are resistant to all standard therapies. We showed that GSCs, but not normal astrocytes, are sensitive to lysis by healthy allogeneic natural killer (NK) cells in vitro. Mass cytometry and single-cell RNA sequencing of primary tumor samples revealed that GBM tumor-infiltrating NK cells acquired an altered phenotype associated with impaired lytic function relative to matched peripheral blood NK cells from patients with GBM or healthy donors. We attributed this immune evasion tactic to direct cell-to-cell contact between GSCs and NK cells via αv integrin-mediated TGF-β activation. Treatment of GSC-engrafted mice with allogeneic NK cells in combination with inhibitors of integrin or TGF-β signaling or with TGFBR2 gene-edited allogeneic NK cells prevented GSC-induced NK cell dysfunction and tumor growth. These findings reveal an important mechanism of NK cell immune evasion by GSCs and suggest the αv integrin/TGF-β axis as a potentially useful therapeutic target in GBM.

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Targeting the αv integrin/TGF-β axis improves natural killer cell function against glioblastoma stem cells.

Extracellular vesicles (EVs) are released by most cell types as part of an intracellular communication system in crucial processes such as inflammation, cell proliferation, and immune response. However, EVs have also been implicated in the pathogenesis of several diseases, such as cancer and numerous infectious diseases. An important feature of EVs is their ability to deliver a wide range of molecules to nearby targets or over long distances, which allows the mediation of different biological functions. This delivery mechanism can be utilized for the development of therapeutic strategies, such as vaccination. Here, we have highlighted several studies from a historical perspective, with respect to current investigations on EV-based vaccines. For example, vaccines based on exosomes derived from dendritic cells proved to be simpler in terms of management and cost-effectiveness than dendritic cell vaccines. Recent evidence suggests that EVs derived from cancer cells can be leveraged for therapeutics to induce strong anti-tumor immune responses. Moreover, EV-based vaccines have shown exciting and promising results against different types of infectious diseases. We have also summarized the results obtained from completed clinical trials conducted on the usage of exosome-based vaccines in the treatment of cancer, and more recently, coronavirus disease.

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Exosome-Based Vaccines: History, Current State, and Clinical Trials.

Background Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth. Methods Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore. Results Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate. Conclusion Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.

The molecular feature of macrophages in tumor immune microenvironment of glioma patients.

Given that abnormal autophagy is involved in the pathogenesis of cancers, we sought to explore the potential value of autophagy-associated genes in lung adenocarcinoma (LUAD). RNA sequencing and clinical data on tumour and normal samples were acquired from The Cancer Genome Atlas (TCGA) database and randomly assigned to training and testing groups. Differentially expressed autophagy-associated genes (AAGs) were screened. Within the training group, Cox regression and Lasso regression analyses were conducted to screen five prognostic AAGs, which were used to develop a model. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were plotted to determine the performance of the model in both groups. Immunohistochemistry was used to demonstrate the differential expression of AAGs in tumour and normal tissues at the protein level. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to further elucidate the roles of AAGs in LUAD. The data from the TCGA database included 497 tumour and 54 normal samples, within which 30 differentially expressed AAGs were screened. Using Cox regression and Lasso regression analyses for the training group, 5 prognostic AAGs were identified and the prognostic model was constructed. Patients with low risk had better overall survival (OS) in the training group (3-year OS, 73.0% vs 48.0%; 5-year OS, 45.0% vs 33.8%; P = 1.305E−04) and in the testing group (3-year OS, 66.8% vs 41.2%; 5-year OS, 31.7% vs 25.8%; P = 1.027E−03). The areas under the ROC curves (AUC) were significant for both the training and testing groups (3-year AUC, 0.810 vs 0.894; 5-year AUC, 0.792 vs 0.749). We developed a survival model for LUAD and validated the performance of the model, which may provide superior outcomes for the patients.

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Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes

This study aimed to describe the use of a novel 4-lncRNA signature to predict prognosis in patients with laryngeal cancer and to explore its possible mechanisms. We identified lncRNAs that were differentially expressed between 111 tumor tissue samples and 12 matched normal tissue samples from The Cancer Genome Atlas Database (TCGA). We used Cox regression analysis to identify lncRNAs that were correlated with prognosis. A 4-lncRNA signature was developed to predict the prognosis of patients with laryngeal cancer. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to verify the validity of this Cox regression model, and an independent prognosis analysis was used to confirm that the 4-lncRNA signature was an independent prognostic factor. Furthermore, the function of these lncRNAs was inferred using related gene prediction and Gene ontology (GO) enrichment analysis in order to clarify the possible mechanisms underlying their predictive ability. In total, 214 differentially expressed lncRNAs were identified, and a 4-lncRNA signature was constructed using Cox survival analysis. The risk coefficients in the multivariate Cox analysis revealed that LINC02154 and MNX1-AS1 are risk factors for laryngeal cancer, whereas MYHAS and LINC01281 appear to be protective factors. The results of a functional annotation analysis suggested that the mechanisms by which these lncRNAs influence prognosis in laryngeal cancer may involve the extracellular exosome, the Notch signaling pathway, voltage-gated calcium channels, and the Wnt signaling pathway. We identified a novel 4-lncRNA signature that can predict the prognosis of patients with laryngeal cancer and that may influence the prognosis of laryngeal cancer by regulating immunity, tumor apoptosis, metastasis, invasion, and other characteristics through the Notch signaling pathway, voltage-gated calcium channels, and the Wnt signaling pathway.

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Identification and potential mechanisms of a 4-lncRNA signature that predicts prognosis in patients with laryngeal cancer.

Glioma is a common malignant tumours in the central nervous system (CNS), that exhibits high morbidity, a low cure rate, and a high recurrence rate. Currently, immune cells are increasingly known to play roles in the suppression of tumourigenesis, progression and tumour growth in many tumours. Therefore, given this increasing evidence, we explored the levels of some immune cell genes for predicting the prognosis of patients with glioma. We extracted glioma data from The Cancer Genome Atlas (TCGA). Using the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm, the relative proportions of 22 types of infiltrating immune cells were determined. In addition, the relationships between the scales of some immune cells and sex/age were also calculated by a series of analyses. A P-value was derived for the deconvolution of each sample, providing credibility for the data analysis (P < 0.05). All analyses were conducted using R version 3.5.2. Five-year overall survival (OS) also showed the effectiveness and prognostic value of each proportion of immune cells in glioma; a bar plot, correlation-based heatmap (corheatmap), and heatmap were used to represent the proportions of immune cells in each glioma sample. In total, 703 transcriptomes from a clinical dataset of glioma patients were drawn from the TCGA database. The relative proportions of 22 types of infiltrating immune cells are presented in a bar plot and heatmap. In addition, we identified the levels of immune cells related to prognosis in patients with glioma. Activated dendritic cells (DCs), eosinophils, activated mast cells, monocytes and activated natural killer (NK) cells were positively related to prognosis in the patients with glioma; however, resting NK cells, CD8+ T cells, T follicular helper cells, gamma delta T cells and M0 macrophages were negatively related to prognosis in the patients with glioma. Specifically, the proportions of several immune cells were significantly related to patient age and sex. Furthermore, the level of M0 macrophages was significant in regard to interactions with other immune cells, including monocytes and gamma delta T cells, in glioma tissues through sample data analysis. We performed a novel gene expression-based study of the levels of immune cell subtypes and prognosis in glioma, which has potential clinical prognostic value for patients with glioma.

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A gene expression-based study on immune cell subtypes and glioma prognosis

Glioma is a common malignant tumor of the central nervous system, which is characterized by a low cure rate, high morbidity, and high recurrence rate. Consequently, it is imperative to explore some indicators for prognostic prediction in glioma. We obtained glioma data from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were obtained by R software from TCGA data sets. Through Cox regression analysis, risk scores were obtained to assess the weighted gene-expression levels, which could predict the prognosis of patients with glioma. The validity and the prognostic value of this model in glioma were confirmed by the manifestation of receiver-operating characteristic (ROC) curves, area under the curve (AUC), and 5-year overall survival (OS). In total, 920 DEGs of transcriptome genes in glioma were extracted from the TCGA database. We identified a novel seven-gene signature associated with glioma. Among them, AL118505.1 and SMOC1 were positively related to the 5-year OS of patients with glioma, showing a better prognosis for glioma; however, RAB42, SHOX2, IGFBP2, HIST1H3G, and IGF2BP3 were negatively related to 5-year OS, displaying a worse prognosis. In addition, according to risk scores, AL118505.1 was also a protective factor, while others were risk factors. Furthermore, the expression levels of SHOX2, IGFBP2, and IGF2BP3 were significantly positively correlated with glioma grades. Receiver-operating characteristic (ROC) curve assessed the accuracy and sensitivity of the gene signature. Each of the seven genes for patients with the distribution of the risk score was presented in the heat map. We identified a novel seven-gene signature in patients with glioma, which could be used as a predictor for the prognosis of patients with glioma in the future.

Seven genes for the prognostic prediction in patients with glioma.

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer.

Human cell division cycle associated 8 (CDCA8) a key regulator of mitosis, has been described as a potential prognostic biomarker for a variety of cancers, such as breast, colon and lung cancers. We aimed to evaluate the potential role of CDCA8 expression in the prognosis of liver cancer by analysing data from The Cancer Genome Atlas (TCGA). The Wilcoxon rank-sum test was used to compare the difference in CDCA8 expression between liver cancer tissues and matched normal tissues. Then, we applied logistic regression and the Wilcoxon rank-sum test to identify the association between CDCA8 expression and clinicopathologic characteristics. Cox regression and the Kaplan–Meier method were used to examine the clinicopathologic features correlated with overall survival (OS) in patients from the TCGA. Gene set enrichment analysis (GSEA) was performed to explore possible mechanisms of CDCA8 according to the TCGA dataset. CDCA8 expression was higher in liver cancer tissues than in matched normal tissues. Logistic regression and the Wilcoxon rank-sum test revealed that the increased level of CDCA8 expression in liver cancer tissues was notably related to T stage (OR = 1.64 for T1/2 vs. T3/4), clinical stage (OR = 1.66 for I/II vs. III/IV), histologic grade (OR = 6.71 for G1 vs. G4) and histological type (OR = 0.24 for cholangiocarcinoma [CHOL] vs. hepatocellular carcinoma [LIHC]) (all P-values < 0.05). Kaplan–Meier survival analysis indicated that high CDCA8 expression was related to a poor prognosis in liver cancer (P = 2.456 × 10−6). Univariate analysis showed that high CDCA8 expression was associated with poor OS in liver cancer patients, with a hazard ratio (HR) of 1.85 (95% confidence interval [CI]: 1.47–2.32; P = 1.16 × 10–7). Multivariate analysis showed that CDCA8 expression was independently correlated with OS (HR = 1.74; CI: 1.25–12.64; P = 1.27 × 10–5). GSEA revealed that the apoptosis, cell cycle, ErbB, MAPK, mTOR, Notch, p53 and TGF-β signaling pathways were differentially enriched in the CDCA8 high expression phenotype. High CDCA8 expression is a potential molecular predictor of a poor prognosis in liver cancer.

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Erxi Fan

Papers

Identification and potential mechanisms of a 4-lncRNA signature that predicts prognosis in patients with laryngeal cancer.

A gene expression-based study on immune cell subtypes and glioma prognosis

Seven genes for the prognostic prediction in patients with glioma.

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer.

CDCA8 as an independent predictor for a poor prognosis in liver cancer