Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated. METHODS: Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan-Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment. RESULTS: We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group. CONCLUSIONS: The CRRS model has reliable predictive value for the prognosis of HCC patients.

Comparison of ischemic stroke diagnosis models based on machine learning.

Background: The incidence, prevalence, and mortality of ischemic stroke (IS) continue to rise, resulting in a serious global disease burden. The prediction models have a great value in the early prediction and diagnosis of IS. Methods: The R software was used to screen the differentially expressed genes (DEGs) of IS and control samples in the datasets GSE16561, GSE58294, and GSE37587 and analyze DEGs for enrichment analysis. The feature genes of IS were obtained by several machine learning algorithms, including the least absolute shrinkage and selector operation (LASSO) logistic regression, the support vector machine-recursive feature elimination (SVM-RFE), and the Random Forest (RF). The IS diagnostic models were constructed based on transcriptomics by machine learning and artificial neural network (ANN). Results: A total of 69 DEGs, mainly involved in immune and inflammatory responses, were identified. The pathways enriched in the IS group were complement and coagulation cascades, lysosome, PPAR signaling pathway, regulation of autophagy, and toll-like receptor signaling pathway. The feature genes selected by LASSO, SVM-RFE, and RF were 17, 10, and 12, respectively. The area under the curve (AUC) of the LASSO model in the training dataset, GSE22255, and GSE195442 was 0.969, 0.890, and 1.000. The AUC of the SVM-RFE model was 0.957, 0.805, and 1.000, respectively. The AUC of the RF model was 0.947, 0.935, and 1.000, respectively. The models have good sensitivity, specificity, and accuracy. The AUC of the LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 1.000, 0.995, and 0.997, respectively, in the training dataset. However, the AUC of LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 0.688, 0.605, and 0.619, respectively, in the GSE22255 dataset. The AUC of the LASSO+ANN and RF+ANN models was 0.740 and 0.630, respectively, in the GSE195442 dataset. In the training dataset, the sensitivity, specificity, and accuracy of the LASSO+ANN model were 1.000, 1.000, and 1.000, respectively; of the SVM-RFE+ANN model were 0.946, 0.982, and 0.964, respectively; and of the RF+ANN model were 0.964, 1.000, and 0.982, respectively. In the test datasets, the sensitivity was very satisfactory; however, the specificity and accuracy were not good. Conclusion: The LASSO, SVM-RFE, and RF models have good prediction abilities. However, the ANN model is efficient at classifying positive samples and is unsuitable at classifying negative samples.

Immunological analysis and differential genes screening of venous thromboembolism.

PURPOSE: To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. METHODS: The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. RESULTS: Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. CONCLUSIONS: Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

Exploring the Pathological Mechanism of Bladder Cancer Based on Tumor Mutational Burden Analysis.

Although immunotherapy has progressed in the treatment of bladder cancer, some patients still have poor prognosis. New therapeutic targets are eager to be discovered to improve the outcomes of bladder cancer. With the development of high-throughput sequencing and tumor profiling, potential tumor biomarkers were identified. Through the interpretation of related data from the Cancer Genome Atlas database (TCGA), some key genes have been discovered to drive the development and prognosis of urinary bladder neoplasm. On account of the success of immunotherapy in many cancer types, we established the relationship between tumor mutation burden and immune microenvironment of bladder cancer and found the changes of several immune cells in this disease. Based on the understanding of the bladder tumor genome and immune environment, this study is supposed to provide new therapies for the treatment of bladder neoplasm.

Prevalence of Hyperuricemia and its Correlation with Serum Lipids and Blood Glucose in Physical Examination Population in 2015 - 2018: a Retrospective Study.

BACKGROUND: Hyperuricemia (HUA) is a metabolic disease caused by a disorder of purine metabolism, which increases the risk of cardio-cerebrovascular disease. Serum lipids and blood glucose are risk factors for metabolic syndrome. Thus, this study aimed to assess the prevalence of HUA and its relationship with serum lipids and blood glucose. METHODS: A total of 59,074 cases (32,623 males and 26,451 females) from three hospitals in Lanzhou city from January 2015 to December 2018 were grouped according to serum uric acid (SUA) level to analyze the differences in age, total cholesterol (TC), triacylglycerol (TG), low density lipoprotein (LDL), high density lipoprotein (HDL), fasting blood glucose (FBG), creatinine (Cr), and blood urea nitrogen (BUN). The changes of prevalence of HUA among different age and gender groups was analyzed. Pearson's correlation analysis was used to analyze the correlation of SUA level with clinical indicators. The risk factors of HUA were analyzed by using binary logistic regression analysis. ROC curve was used to analyze the independent risk factors of elevated SUA. RESULTS: The overall prevalence rate of HUA was 19.87% and the prevalence rate of males was significantly higher than that of females (28.35% vs. 9.41%, χ2 = 3,289.143, p < 0.01). The prevalence rates of HUA from 2015 - 2018 were 19.54%, 19.31%, 18.64%, and 21.81%, respectively. Compared with the normal SUA group, TC, TG, and LDL significantly increased in the HUA group. The correlation analysis showed that SUA was negatively correlated with gender and HDL, and positively correlated with age, FBG, TC, TG, and LDL. The logistic regression analysis revealed that TG, TC, and LDL were risk factors for HUA. The ROC curve analysis showed that the risk of HUA significantly increased when TG was above 1.645 mmol/L. CONCLUSIONS: The overall prevalence of HUA in physical examination population has generally been at a high level in the past 4 years. Serum lipids and blood glucose may be independent risk factors for predicting HUA.

Chromanopyrones and a flavone from Hypericum monogynum.

Six new compounds, including three pyrone derivatives (2-4), one new flavone (5), and two new naturally-occurring compounds (1 and 6), together with 16 known compounds were isolated from the leaves and twigs of Hypericum monogynum. In addition, compounds 2-4 are racemates. Their structures and absolute configurations were determined on the basis of extensive analysis of spectroscopic data and ECD calculation. All compounds were evaluated for the inhibitory effects on α-glucosidase, compounds 1, 5, and 7 showed moderate inhibitory activities with IC50 values of 161.46, 257.78, and 11.54µg/ml, respectively. Compound 8 exhibited weak anti-oxidant activity with IC50 value of 12.55µg/ml.

Monoterpenoid indole alkaloids from Gardneria multiflora.

Six new monoterpenoid indole alkaloids, 19(E)-9-demethoxy-16-dehydroxylchitosenine-17-O- ß-d-glucopyranoside (1), 19(E)-9,10-didemethoxy-16-dehydroxylchitosenine-17-O-ß-d-gluco-pyranoside (2), 19(E)-9,10-didemethoxy-16-dehydroxyl-11-methoxychitosenine (3), 19(E)-9,10-didemethoxy-16-dehydroxyl-11-methoxychitosenine-17-O-ß-d-glucopyranoside (4), 19(Z)-18-carboxylgardneramine (5), and 19(E)-18-demethoxygardneramine-N (4)-oxide (6), along with four known alkaloids, were isolated from Gardneria multiflora, and their structures were elucidated by spectroscopic analysis. Compounds 1, 2 and 4 are the first example of Gardneria alkaloids whose glucose units were attached to C-17. None of the compounds were cytotoxic to any of five human cancer cell lines.

[Phenotypic diversity of natural Cyclocarya paliurus populations seed traits].

With the seeds from nine natural Cyclocarya paliurus populations as test materials, their phenotypic traits, including 1000-seed weight, seed size, seed diameter, seed thickness, and seed diameter/thickness ratio, were investigated, and the phenotypic diversity among and within the populations were studied by the methods of ANOVA analysis, hierarchical cluster analysis, and correlation analysis. Significant differences were observed in the 1000-seed weight, seed size, seed diameter, and seed thickness among and within the populations, and in the seed diameter/thickness ratio within the populations, illustrating that there existed high phenotypic diversity of seed traits at these two levels. The mean phenotypic differentiation coefficient (V) of test seed traits was 20.54%, i. e., the variation among the populations was far smaller than that (79.46%) within the populations. The seed traits had different correlation degrees with geographical factors, and most affected by mean annual air temperature. According to the hierarchical cluster analysis based on the Euclidean distance, the nine natural Cyclocarya paliurus populations could be classified into three groups.