Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms.

BACKGROUND/AIMS: Hepatocellular carcinoma, a highly malignant tumor, is difficult to diagnose, treat, and predict the prognosis. Notch signaling pathway can affect hepatocellular carcinoma. We aimed to predict the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms. MATERIALS AND METHODS: We downloaded hepatocellular carcinoma data from the Cancer Genome Atlas and Gene Expression Omnibus databases and used machine learning methods to screen the hub Notch signal-related genes. Machine learning classification was used to construct a prediction model for the classification and diagnosis of hepatocellular carcinoma cancer. Bioinformatics methods were applied to explore the expression of these hub genes in the hepatocellular carcinoma tumor immune microenvironment. RESULTS: We identified 4 hub genes, namely, LAMA4, POLA2, RAD51, and TYMS, which were used as the final variables, and found that AdaBoostClassifie was the best algorithm for the classification and diagnosis model of hepatocellular carcinoma. The area under curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of this model in the training set were 0.976, 0.881, 0.877, 0.977, 0.996, 0.500, and 0.932; respectively. The area under curves were 0.934, 0.863, 0.881, 0.886, 0.981, 0.489, and 0.926. The area under curve in the external validation set was 0.934. Immune cell infiltration was related to the expression of 4 hub genes. Patients in the low-risk group of hepatocellular carcinoma were more likely to have an immune escape. CONCLUSION: The Notch signaling pathway was closely related to the occurrence and development of hepatocellular carcinoma. The hepatocellular carcinoma classification and diagnosis model established based on this had a high degree of reliability and stability.

Development and Validation of an Inflammatory Response-Related Gene Signature for Predicting the Prognosis of Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a highly dangerous malignant tumor of the digestive tract, and difficult to diagnose, treat, and predict the prognosis. As we all know, tumor and inflammation can affect each other, and thus the inflammatory response in the microenvironment can be used to affect the prognosis. So far, the prognostic value of inflammatory response-related genes in PAAD is still unclear. Therefore, this study aimed to explore the inflammatory response-related genes for predicting the prognosis of PAAD. In this study, the mRNA expression profiles of PAAD patients and the corresponding clinical characteristics data of PAAD patients were downloaded from the public database. The least absolute shrinkage and selection operator (LASSO) Cox analysis model was used to identify and construct the prognostic gene signature in The Cancer Genome Atlas (TCGA) cohort. The PAAD patients used for verification are from the International Cancer Genome Consortium (ICGC) cohort. The Kaplan-Meier method was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were performed to identify the independent predictors of OS. Gene set enrichment analysis (GSEA) was performed to obtain gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the correlation between gene expression and immune infiltrates was investigated via single sample gene set enrichment analysis (ssGSEA). The GEPIA database was performed to examine prognostic genes in PAAD. LASSO Cox regression analysis was used to construct a model of inflammatory response-related gene signature. Compared with the low-risk group, patients in the high-risk group had significantly lower OS. The receiver operating characteristic curve (ROC) analysis confirmed the signature's predictive capacity. Multivariate Cox analysis showed that risk score is an independent predictor of OS. Functional analysis shows that the immune status between the two risk groups is significantly different, and the cancer-related pathways were abundant in the high-risk group. Moreover, the risk score is significantly related to tumor grade, stage, and immune infiltration types. It was also obtained that the expression level of prognostic genes was significantly correlated with the sensitivity of cancer cells to anti-tumor drugs. In addition, there are significant differences in the expression of PAAD tissues and adjacent non-tumor tissues. The novel signature constructed from five inflammatory response-related genes can be used to predict prognosis and affect the immune status of PAAD. In addition, suppressing these genes may be a treatment option.

Screening, Expression, Purification and Functional Characterization of Novel Antimicrobial Peptide Genes from Hermetia illucens (L.).

Antimicrobial peptides from a wide spectrum of insects possess potent microbicidal properties against microbial-related diseases. In this study, seven new gene fragments of three types of antimicrobial peptides were obtained from Hermetia illucens (L), and were named cecropinZ1, sarcotoxin1, sarcotoxin (2a), sarcotoxin (2b), sarcotoxin3, stomoxynZH1, and stomoxynZH1(a). Among these genes, a 189-basepair gene (stomoxynZH1) was cloned into the pET32a expression vector and expressed in the Escherichia coli as a fusion protein with thioredoxin. Results show that Trx-stomoxynZH1 exhibits diverse inhibitory activity on various pathogens, including Gram-positive bacterium Staphylococcus aureus, Gram-negative bacterium Escherichia coli, fungus Rhizoctonia solani Khün (rice)-10, and fungus Sclerotinia sclerotiorum (Lib.) de Bary-14. The minimum inhibitory concentration of Trx-stomoxynZH1 is higher against Gram-positive bacteria than against Gram-negative bacteria but similar between the fungal strains. These results indicate that H. illucens (L.) could provide a rich source for the discovery of novel antimicrobial peptides. Importantly, stomoxynZH1 displays a potential benefit in controlling antibiotic-resistant pathogens.