Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics.

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

The microbiota and renal cell carcinoma.

Renal cell carcinoma (RCC) accounts for about 2% of cancer diagnoses and deaths worldwide. Recent studies emphasized the critical involvement of microbial populations in RCC from oncogenesis, tumor growth, and response to anticancer therapy. Microorganisms have been shown to be involved in various renal physiological and pathological processes by influencing the immune system function, metabolism of the host and pharmaceutical reactions. These findings have extended our understanding and provided more possibilities for the diagnostic or therapeutic development of microbiota, which could function as screening, prognostic, and predictive biomarkers, or be manipulated to prevent RCC progression, boost anticancer drug efficacy and lessen the side effects of therapy. This review aims to present an overview of the roles of microbiota in RCC, including pertinent mechanisms in microbiota-related carcinogenesis, the potential use of the microbiota as RCC biomarkers, and the possibility of modifying the microbiota for RCC prevention or treatment. According to these scientific findings, the clinical translation of microbiota is expected to improve the diagnosis and treatment of RCC.

A Fluid Multivalent Magnetic Interface for High-Performance Isolation and Proteomic Profiling of Tumor-Derived Extracellular Vesicles.

Isolation and analysis of tumor-derived extracellular vesicles (T-EVs) are important for clinical cancer management. Here, we develop a fluid multivalent magnetic interface (FluidmagFace) in a microfluidic chip for high-performance isolation, release, and protein profiling of T-EVs. The FluidmagFace increases affinity by 105-fold with fluidity-enhanced multivalent binding to improve isolation efficiency by 13.9 % compared with a non-fluid interface. Its anti-adsorption property and microfluidic hydrodynamic shear minimize contamination, increasing detection sensitivity by two orders of magnitude. Moreover, its reversibility and expandability allow high-throughput recovery of T-EVs for mass spectrometric protein analysis. With the chip, T-EVs were detected in all tested cancer samples with identification of differentially expressed proteins compared with healthy controls. The FluidmagFace opens a new avenue to isolation and release of targets for cancer diagnosis and biomarker discovery.

Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome.

The spatial transcriptome has enabled researchers to resolve transcriptome expression profiles while preserving information about cell location to better understand the complex biological processes that occur in organisms. Due to technical limitations, the current high-throughput spatial transcriptome sequencing methods (known as next-generation sequencing with spatial barcoding methods or spot-based methods) cannot achieve single-cell resolution. A single measurement site, called a spot, in these technologies frequently contains multiple cells of various types. Computational tools for determining the cellular composition of a spot have emerged as a way to break through these limitations. These tools are known as deconvolution tools. Recently, a couple of deconvolution tools based on different strategies have been developed and have shown promise in different aspects. The resulting single-cell resolution expression profiles and/or single-cell composition of spots will significantly affect downstream data mining; thus, it is crucial to choose a suitable deconvolution tool. In this review, we present a list of currently available tools for spatial transcriptome deconvolution, categorize them based on the strategies they employ, and explain their advantages and limitations in detail in order to guide the selection of these tools in future studies.

Comprehensive Characterization of Metabolism-Associated Subtypes of Renal Cell Carcinoma to Aid Clinical Therapy.

Renal cell carcinoma (RCC) is a disease characterized by excessive administration complexity because it exhibits extraordinary nonuniformity among distinct molecular subtypes. We herein intended to delineate the metabolic aspects of clear cell RCC (ccRCC) in terms of the gene expression profile. Recent studies have revealed that metabolic variations within tumors are related to the responsiveness to immune checkpoint inhibitor (ICI) therapy and patient prognosis. We used 100 previously reported metabolic (MTB) pathways to quantify the metabolic landscape of the 729 ccRCC patients. Three MTB subtypes were established, and the MTB scores were calculated using principal component analysis (PCA). The high MTB score group had better overall survival (OS) and was associated with higher expression of immune-checkpoint and immune-activity signatures. The opposite was true of the low MTB score group, which may explain the poor prognosis of these patients. Three ICI-treated cohorts or tyrosine kinase inhibitor (TKI) treated cohort proved that patients with higher MTB scores exhibited notable therapeutic benefits and clinical gains. This research explained that the MTB score could be applied as a powerful prognostic indicator and predictive of ICI or TKI therapy. Assessing the MTB scores in a more extended group will facilitate our perception of tumor metabolism and provide guidance for studies on targeted approaches for ccRCC patients.

Ferroptosis-Related Gene Signature Predicts the Prognosis of Skin Cutaneous Melanoma and Response to Immunotherapy.

Ferroptosis is a non-apoptotic regulated cell death process, and much research has indicated that ferroptosis can induce the non-apoptotic death of tumor cells. Ferroptosis-related genes are expected to become a biological target for cancer treatment. However, the regulation of ferroptosis-related genes in skin cutaneous melanoma (SKCM) has not been well studied. In the present study, we conducted a systematic analysis of SKCM based on RNA sequencing data and clinical data obtained from The Cancer Genome Atlas (TCGA) database and the FerrD database. SKCM patients from the GSE78220 and MSKCC cohorts were used for external validation. Applying consensus clustering on RNA sequencing data from TCGA the generated ferroptosis subclasses of SKCM, which were analyzed based on the set of differentially expressed ferroptosis-related genes. Then, a least absolute shrinkage and selection operator (LASSO)-Cox regression was used to construct an eight gene survival-related linear signature. The median cut-off risk score was used to divide patients into high- and low-risk groups. The time-dependent receiver operating characteristic curve was used to examine the predictive power of the model. The areas under the curve of the signature at 1, 3, and 5 years were 0.673, 0.716, and 0.746, respectively. Kaplan-Meier survival analysis showed that the prognosis of high-risk patients was worse than that of low-risk patients. Univariate and multivariate Cox regression analyses showed that the risk signature was a robust independent prognostic indicator. By incorporating risk scores with tumor staging, a nomogram was constructed to predict prognostic outcomes for SKCM patients. In addition, the immunological analysis showed different immune cell infiltration patterns. Programmed-death-1 (PD-1) immunotherapy showed more significant benefits in the low-risk group than in the high-risk group. In summary, a model based on ferroptosis-related genes can predict the prognosis of SKCM and could have a potential role in guiding targeted therapy of SKCM.

Construction and Validation of a Novel Immunosignature for Overall Survival in Uveal Melanoma.

OBJECTIVES: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, and immune infiltration plays a crucial role in the prognosis of UM. This study aimed to generate an immunological marker-based predictive signature for the overall survival (OS) of UM patients. METHODS: Single-sample gene-set enrichment analysis (ssGSEA) was used to profile immune cell infiltration in 79 patients with UM from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate least absolute shrinkage and selection operator (LASSO) Cox regressions were used to determine the prognostic factors for UM and construct the predictive immunosignature. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the clinical ability and accuracy of the model. In addition, the predictive accuracy was compared between the immunosignature and the Tumor, Node, Metastasis (TNM) staging system of American Joint Committee on Cancer (AJCC). We further analyzed the differences in clinical characteristics, immune infiltrates, immune checkpoints, and therapy sensitivity between high- and low-risk groups characterized by the prognostic model. RESULTS: Higher levels of immune cell infiltration in UM were related to a lower survival rate. Matrix metallopeptidase 12 (MMP12), TCDD inducible poly (ADP-ribose) polymerase (TIPARP), and leucine rich repeat neuronal 3 (LRRN3) were identified as prognostic signatures, and an immunological marker-based prognostic signature was constructed with good clinical ability and accuracy. The immunosignature was developed with a concordance index (C-index) of 0.881, which is significantly better than that of the TNM staging system (p < 0.001). We further identified 1,762 genes with upregulated expression and 798 genes with downregulated expression in the high-risk group, and the differences between the high- and low-risk groups were mainly in immune-related processes. In addition, the expression of most of the immune checkpoint-relevant and immune activity-relevant genes was significantly higher in the high-risk group, which was more sensitive to therapy. CONCLUSION: We developed a novel immunosignature constructed by MMP12, TIPARP, and LRRN3 that could effectively predict the OS of UM.

The ceRNA PVT1 inhibits proliferation of ccRCC cells by sponging miR-328-3p to elevate FAM193B expression.

Clear cell renal cell carcinoma (ccRCC) is a common and fatal malignancy. Long noncoding RNAs (lncRNAs) have emerged as crucial biomarkers and regulators in many cancers, warranting the detailed investigation of their biological functions and molecular mechanisms. In this study, we explored the role and mechanism of plasmacytoma variant translocation 1 (PVT1), a competitive endogenous RNA (ceRNA) in ccRCC tissues in vitro and in vivo. We found that PVT1 is upregulated in ccRCC cells and promoted cell proliferation. Bioinformatic analysis, dual-luciferase reporter assays, argonaute 2-RNA immunoprecipitation (AGO2-RIP), quantitative PCR arrays, western blot assay, and rescue experiments were conducted to explore the underlying mechanisms of PVT1. Our analyses revealed that miR-328-3p was a direct target of PVT1 and that FAM193B was a direct target of miR-328-3p. FAM193B is upregulated in ccRCC tissues and promotes cell proliferation by activating the MAPK/ERK and PI3K/AKT pathways. Our results indicated that PVT1 promotes ccRCC cells proliferation by sponging miR-328-3p to upregulate FAM193B and activate the MAPK/ERK and PI3K/AKT pathways. Collectively, these results suggest that PVT1- miR-328-3p-FAM193B loop could serve as a potential biomarker and therapeutic target for ccRCC.

Intra-arterial infusion chemotherapy utilizing cisplatin inhibits bladder cancer by decreasing the fibrocytic myeloid-derived suppressor cells in an m6A-dependent manner.

Intra-arterial infusion chemotherapy (IAIC), using immunomodulatory cisplatin, is a novel treatment for bladder cancer (BC) that allows the delivery of specific drugs to the local malignant lesion. To explore the immunomodulatory effect of cisplatin during IAIC, we detected the proportion of immunosuppressed cells in BC tissue from eight BC patients, with the reduction of myeloid-derived suppressor cells (MDSCs), more specifically fibrocytic-MDSCs (f-MDSCs). Further, we demonstrated that cisplatin inhibits their proliferation and immunosuppressive activity. f-MDSCs promote tumor proliferation and metastasis in the BC immune environment. Then, we analyzed the genetic differences detected in samples before and after chemotherapy and found that granulocyte colony-stimulating factors (G-CSF) decreased after IAIC. Furthermore, G-CSF methylation decreased following treatment with cisplatin. Specifically, treatment with cisplatin decreased the methylase (METTL3) levels in BC cells, which is important for G-CSF production. Collectively, cisplatin decreased the number of f-MDSCs during IAIC, by blocking G-CSF methylation via targeting METTL3.

IL-6 Promotes the Proliferation and Immunosuppressive Function of Myeloid-Derived Suppressor Cells via the MAPK Signaling Pathway in Bladder Cancer.

Muscle-invasive bladder cancer (MIBC) is characterized by a highly complex immune environment, which is not well understood. Interleukin-6 (IL-6) is generated and secreted by multifarious types of cells, including tumor cells. This study was aimed at demonstrating that the levels of IL-6 and the number of myeloid-derived suppressor cells (MDSCs), with a positive correlation between them, increased in MIBC tissues, promoting MIBC cell proliferation, especially in patients with recurrence. In coculture analysis, MDSCs, with the stimulation of IL-6, could significantly lower the proliferation ability of CD4+ or CD8+ T lymphocytes. Further, this study demonstrated that IL-6 could upregulate the mitogen-activated protein kinase (MAPK) signaling pathway in MDSCs. The MAPK signaling inhibitor, aloesin, partially reversed the effects of IL-6 on MDSCs. These data suggested that IL-6 promoted MIBC progression by not only accelerating proliferation but also improving the immune suppression ability of MDSCs through activating the MAPK signaling pathway.

Accumulation of CD45RO+CD8+ T cells is a diagnostic and prognostic biomarker for clear cell renal cell carcinoma.

Renal cell carcinoma is characterized by high immunogenicity and infiltration of immune cells. CD45RO+CD8+ T cells are well known as a critical role in host defense of the immune environment. However, their role in clear cell renal carcinoma (ccRCC) remains unknown. To elucidate the clinical importance of CD45RO+CD8+ T cells in ccRCC as well as its underlying mechanism, we analyzed several types of peripheral immune cells from 274 patients with ccRCC who have received radical or partial nephrectomy and 350 healthy people. Flow cytomety assays showed there was no significant difference in the proportions of CD8+ T cells and its subtypes other than CD45RO+/CD45RA+CD8+ cells. Both gene and protein expression levels of CD45RO in ccRCC tissues were decreased. CD45RO+CD8+ T cells showed increased proliferative abilities but decreased apoptotic abilities through MAPK signaling activation in ccRCC. High expression level of CD45RO+CD8+ T cells inhibited ccRCC progression, including proliferation, invasion, as well as autophagy of ccRCC through many signaling pathways. Bioinformatics and immunohistochemical chip analysis measured gene and protein levels of CD45RO and other related proteins. The combination of UCHL1, HMGB3, and CD36 has diagnostic value in ccRCC and is able to predict prognosis. Collectively, CD45RO+CD8+ T cells play a critical role in ccRCC progression and may be regarded as clinical indicators.

Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).

BACKGROUND: Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources. METHODS: In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. RESULTS: From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765-0.875) and the internal validation cohort was 0.762 (95% CI, 0.768-0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627-0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit. CONCLUSION: In this study, our predicting model based on five clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

NEAT1/miR-200b-3p/SMAD2 axis promotes progression of melanoma.

Melanoma is a skin malignancy with a high mutation frequency of genetic alterations. MicroRNA (miR)-200b-3p is involved in various cancers, while in melanoma its bio-function remains unknown. In this study, we found that miR-200b-3p was down-regulated in melanoma tissues and cell lines compared to benign nevus cells. Overexpression of miR-200b-3p significantly inhibited the proliferation and invasion of melanoma cells. According to bioinformatics analysis and sequencing data, we supposed that SMAD family member 2 (SMAD2) was the target gene and nuclear enriched abundant transcript 1 (NEAT1) was the upstream long non-coding RNA (lncRNA) of miR-200b-3p. These predictions were verified by western blotting and quantitative real-time reverse transcription PCR (RT-qPCR). Luciferase reporter assays revealed that NEAT1 up-regulated SMAD2 by directly sponging miR-200b-3p. In vitro and in vivo, we demonstrated that both NEAT1 and SMAD2 could promote the proliferation and invasion of melanoma cells, and these effects were reversed by up-regulating miR-200b-3p. In addition, NEAT1/miR-200b-3p/SMAD2 axis promoted melanoma progression by activating EMT signaling pathway and immune responses. Taken together, the NEAT1/miR-200b-3p/SMAD2 signaling pathway promotes melanoma via activation of EMT, cell invasion and is related with immune responses, which provides new insights into the molecular mechanisms and therapeutic targets for melanoma.

G-MDSCs-Derived Exosomal miRNA-143-3p Promotes Proliferation via Targeting of ITM2B in Lung Cancer.

BACKGROUND: The immune environment of lung cancer is complex, and the critical immune factors that promote lung cancer progression need to be explored. Granulocytic myeloid-derived suppressor cells (G-MDSCs) are regarded as immune suppressing cells. However, they also promote tumor progression through other ways, which needs to be explored further. Therefore, we sought to study the regulatory mechanisms underlying the cancer promoting function of G-MDSCs in lung cancer. METHODS: G-MDSCs were isolated from lung cancer tissues using flow cytometry. Exosomes were separated from the G-MDSCs supernatant by ultracentrifugation and verified using flow cytometry, Western blot, and transmission electron microscopy (TEM). RNA sequencing was used to identify the differential miRNAs and genes. Real-time quantitative real-time PCR (RT-qPCR) confirmed these results. The proliferation rate was assessed using the CCK-8 assay. Lentiviral vectors were used to alter the expression of the miRNAs and genes to analyze their effects on lung cancer progression. RESULTS: G-MDSCs secreted more exosomes in the lung cancer tissues, which promoted cancer progression by accelerating proliferation. Micro RNA-143-3p (miR-143-3p) increased in G-MDSCs derived exosomes and downregulated integral membrane protein 2B (ITM2B) by targeting the 3'-untranslated region (UTR) region. Overexpression of miR-143-3p enhanced proliferation by inhibiting transcription of ITM2B to activate the PI3K/Akt signaling pathway, which can be blocked by deguelin. This phenomenon was further confirmed by accelerated tumor growth and worse prognosis in mice. CONCLUSION: The key findings of this study highlight the potential of the G-MDSC-derived exosomes and the miR-143-3p/ITM2B axis as therapeutic targets and clinical indicators of lung cancer.

The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.

BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

Long Noncoding RNA PVT1 Promotes Prostate Cancer Metastasis by Increasing NOP2 Expression via Targeting Tumor Suppressor MicroRNAs.

BACKGROUND: Metastatic disease caused by prostate cancer (PCa) is the principal cause of PCa-related mortality. Long non-protein-coding RNAs may possess significant cellular functions. Plasmacytoma variant translocation 1 (PVT1), a long non-coding RNA encoded by the human PVT1 gene, is an oncogene, which can regulate several tumor-related genes. In PCa, the function and mechanism of PVT1 are unclear. NOP2 is being pursued as a prognostic marker for cancer aggressiveness, which promotes mouse fibroblast growth and tumor formation. Essentially, nothing is known about the specific interactions between the PVT1 and NOP2. METHODS:  190 pairs of PCa tissues and adjacent normal tissues were collected and RNA sequencing was used to identify the differential lncRNAs. Real-time quantitative real-time PCR (RT-qPCR) confirmed these results and gene regulatory relationship. Lentiviral vectors were used to alter PVT1 and genes to analyze their effects on PCa progression. Transwell migration and invasion assays were performed to test the metastasis ability. Biofunction of PVT1 and NOP2 were confirmed in vitro and in vivo. RESULTS: In this study, we reported that the long noncoding RNA-PVT1 was upregulated in PCa metastasis tissues and promoted migration of PCa cells in vitro and their metastasis in vivo. High levels of PVT1 significantly downregulated tumor suppressor microRNAs (miRNAs), such as miR-15b-5p, miR-27a-3p, miR-143-3p, and miR-627-5p, whose levels in metastasis tissues were low compared to those in non-metastasis tissues. In vitro and in vivo, PVT1 promotes PCa metastasis via targeting miRNAs. Furthermore, the expression level of PVT1 was positively associated with the expression of NOP2, a cancer metastasis-related protein. We demonstrated that NOP2 promoted invasion and migration of PCa. For specific mechanism, correlation analysis showed that PVT1 promoted metastasis by up-regulating NOP2. CONCLUSION: Taken together, our results show that PVT1 acts as an inducer of PCa metastasis via targeting miRNAs, thereby promoting NOP2. This axis may have diagnostic and therapeutic potential for advanced PCa.

Hyperprogressive disease in patients with advanced renal cell carcinoma: a new pattern of post-treatment cancer behavior.

Renal cell carcinoma (RCC) is among the most common cancers of the genitourinary system. Once RCC has progressed to a high tumor stage, surgery is no longer the optimal option, and treatment with drugs is more suitable. However, a proportion of patients with advanced RCC (aRCC) experience accelerated progression following targeted therapy or immunotherapy, a condition known as hyperprogressive disease (HPD). There is a growing body of literature that recognizes the importance of HPD. In the present review, thousands of studies that describe a variety of treatments for aRCC were identified in PubMed, Web of Science, and Cochrane Library and analyzed to establish the severity of clinical outcomes. Therefore, we managed to perform a review related to HPD of aRCC in these databases. It was found that 7~74% of patients advanced into progressive disease, 0~45% of patients died during post-treatment assessment, possibly due to fatal HPD. However, risk factors, mechanisms, and predictive factors are still not entirely clear. It is suggested that combination therapies might play a pivotal role in preventing HPD. Additional light needs to be shed on customization of therapies for aRCC after more data is collected and analyzed for HPD.

Long noncoding RNA LINC00963 induces NOP2 expression by sponging tumor suppressor miR-542-3p to promote metastasis in prostate cancer.

Metastatic disease caused by castration-resistant prostate cancer (CRPC) is the principal cause of prostate cancer (PCa)-related mortality. CRPC occurs within 2-3 years of initiation of androgen deprivation therapy (ADT), which is an important factor of influencing PCa metastasis. Recent studies have revealed that non-coding RNAs in PCa can enhance metastasis and progression, while the mechanisms are still unclear. In this study, we reported that the long noncoding RNA-LINC00963 was increased in CRPC tissues and promoted migration of PCa cells in vitro and their metastasis in vivo. High levels of LINC00963 significantly decreased tumor suppressor miR-542-3p, whose levels in metastasis tissues were low compared to those in non-metastasis tissues. LINC00963 promotes and miR-542-3p inhibits metastasis. Furthermore, the expression levels of LINC00963 and miR-542-3p were positively and negatively associated with the expression of NOP2. We demonstrated that NOP2 promoted PCa by activating the epithelial-mesenchymal transition (EMT) pathway. For specific mechanism, dual luciferase reporter assays showed that miR-542-3p directly binds to both 3'-untranslated region (UTR) of LINC00963 and NOP2 mRNA. Taken together, our results show that LINC00963 acts as an inducer of PCa metastasis by binding miR-542-3p, thereby promoting NOP2. This axis may have diagnostic and therapeutic potential for advanced PCa.

Immune environment modulation in pneumonia patients caused by coronavirus: SARS-CoV, MERS-CoV and SARS-CoV-2.

Currently, we are on a global pandemic of Coronavirus disease-2019 (COVID-19) which causes fever, dry cough, fatigue and acute respiratory distress syndrome (ARDS) that may ultimately lead to the death of the infected. Current researches on COVID-19 continue to highlight the necessity for further understanding the virus-host synergies. In this study, we have highlighted the key cytokines induced by coronavirus infections. We have demonstrated that genes coding interleukins (Il-1α, Il-1ß, Il-6, Il-10), chemokine (Ccl2, Ccl3, Ccl5, Ccl10), and interferon (Ifn-α2, Ifn-ß1, Ifn2) upsurge significantly which in line with the elevated infiltration of T cells, NK cells and monocytes in SARS-Cov treated group at 24 hours. Also, interleukins (IL-6, IL-23α, IL-10, IL-7, IL-1α, IL-1ß) and interferon (IFN-α2, IFN2, IFN-γ) have increased dramatically in MERS-Cov at 24 hours. A similar cytokine profile showed the cytokine storm served a critical role in the infection process. Subsequent investigation of 463 patients with COVID-19 disease revealed the decreased amount of total lymphocytes, CD3+, CD4+, and CD8+ T lymphocytes in the severe type patients which indicated COVID-19 can impose hard blows on human lymphocyte resulting in lethal pneumonia. Thus, taking control of changes in immune factors could be critical in the treatment of COVID-19.

The diagnosis of pandemic coronavirus pneumonia: A review of radiology examination and laboratory test.

Since the outbreak of novel coronavirus disease 2019 (COVID-19), epidemic prevention strategies have been implemented worldwide. For the sake of controlling the infectious coronavirus pneumonia, early diagnosis and quarantine play an imperative role. Currently, the mainstream diagnostic methods are imaging and laboratory diagnosis, which differ in their efficacy of diagnosis. To compare the detection rate, we reviewed numerous literature on pneumonia caused by coronaviruses (SARS, MERS, and SARS-CoV-2) and analyzed two different ways of diagnosis. The results showed that the detection rate of computed tomography (CT) diagnosis was significantly higher than that of real-time quantitative polymerase chain reaction (qPCR) (P = 0.00697). Still, clinicians should combine radiology and laboratory methods to achieve a higher detection rate, so that instant isolation and treatment could be effectively conducted to curb the rampant spread of the epidemic.