Analysis of the Differential Expression and Antiviral Activity of Porcine Interferon-α In Vitro.

Porcine interferon α (poIFN-α) is a crucial cytokine that can prevent and treat viral infections. Seventeen functional porcine IFN-α subtypes were found in the porcine genome. In this study, multiple sequence alignment was performed to analyze IFN-α protein structure and function. Phylogenetic tree analysis of the poIFN gene family defined the evolutionary relationship of various subtypes. PoIFN-αs, including poIFN-α1-17, were expressed in an Escherichia coli expression system. The antiviral activities of these IFN-α proteins against vesicular stomatitis virus (VSV) and pseudorabies virus (PRV) were examined in PK-15 cells. We found that the antiviral activity of different poIFN-α molecules greatly differed as follows: the poIFN-α14 and 17 subtypes had the greatest antiviral activities against VSV and PRV in PK-15 cells, poIFN-α1, 2, 3, and 8 exhibited lower biological activities, and poIFN-α4, 5, 6, 7, 9, 10, 11, 12, 13, and 16 had minimal or no effect in the tested target cell‒virus systems. Moreover, our studies demonstrated that the antiviral activity of IFN-α was positively correlated with the induction of IFN-stimulated genes, such as 2'-5' oligoadenylate synthetase 1 (OSA1), interferon-stimulated gene 15 (ISG15), myxoma resistance protein 1 (Mx1), and protein kinase R (PKR). Thus, our experimental results provide important information about the antiviral functions and mechanism of poIFN-α.

A Risk Model Developed Based on Necroptosis Predicts Overall Survival for Hepatocellular Carcinoma and Identification of Possible Therapeutic Drugs.

Background: Necroptosis is a form of regulatory cell death (RCD) that attracts and activates immune cells, resulting in pro-tumor or anti-tumor effects. The purpose of this study was to investigate genes associated with necroptosis, to construct a risk score for predicting overall survival in patients with hepatocellular carcinoma, and to find potentially effective drugs. Methods: The three algorithms ssGSEA, EPIC, and ESTIMATE were used to quantify the immune cell infiltration of the samples, differentially expressed genes (DEGs) analysis, and weighted gene co-expression network analysis were used to screen necroptosis related genes. Variables were screened according to random survival forest analysis, and combinations with significant p-values and a low number of genes were defined as prognostic signatures by using log-rank test after gene combination. Based on the sensitivity data of PRISM and CTRP2.0 datasets, we predicted the potential therapeutic agents for high-NRS patients. Results: Seven genes such as TOP2A were used to define necroptosis-related risk score (NRS). The prognostic value of risk score was further validated, where high NRS was identified as a poor prognostic factor and tended to have higher grades of histologic grade, pathologic stage, T stage, BCLC, CLIP, and higher AFP. Higher NRS was also negatively correlated with the abundance of DCs, Neutrophils, Th17 cells, Macrophages, Endothelial, and positively correlated with Th2 cells. Necroptosis is often accompanied by the release of multiple cytokines, and we found that some cytokines were significantly correlated with both NRS and immune cells, suggesting that necroptosis may affect the infiltration of immune cells through cytokines. In addition, we found that TP53 mutations were more common in samples with high NRS, and these mutations may be associated with changes in NRS. Patients with high NRS may be more sensitive to gemcitabine, and gemcitabine may be an effective drug to improve the prognosis of patients with high NRS, which may play a role by inhibiting the expression of TOP2A. Conclusions: We constructed a necroptosis-related scoring model to predict OS in HCC patients, and NRS was associated with immune response, TP53 mutation, and poor clinical classification in HCC patients. In addition, gemcitabine may be an effective drug for high-NRS patients.

Identification of immune cells and mRNA associated with prognosis of gastric cancer.

BACKGROUND: The clinical success demonstrates the enormous potential of immunotherapy in cancer treatment. METHODS: This article presented research linking gastric cancer to immune cells, based on RNA-seq data of Stomach adenocarcinoma (STAD) and gene expression profile of GSE84437, 24 kinds of tumor-infiltrating immune cells were quantified by single-sample gene set enrichment analysis. RESULTS: Th2 cells, T helper cells, and Mast cells were identified as prognostic immune cells in both TCGA and GEO groups. Then SUPV3L1 and SLC22A17 were identified as hub genes which may affect immune cell infiltration by correlation analysis. Survival analysis further proved that hub genes and prognostic immune cells are associated with the prognosis of gastric cancer. In gastrointestinal tumors, hub genes and prognostic immune cells also found differences in non-tumor and tumor tissues. CONCLUSIONS: We found that three immune cells infiltration are associated with the prognosis of gastric cancer and further identify two hub genes. These two key genes may affect immune cell infiltration, result in the different prognosis of patients.

Identification of three m6A-related mRNAs signature and risk score for the prognostication of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer and is extremely harmful to human health. In recent years, N6-methyladenosine (m6A) RNA methylation in eukaryotic mRNA has been increasingly implicated in cancer pathogenesis and prognosis. In this study, we downloaded the expression profile and clinical information of 307 patients from The Cancer Genome Atlas database and 64 patients from the Gene Expression Omnibus (GEO) database, and univariate Cox analysis revealed that METTL14 was a prognostic m6A RNA methylation regulator. For further study on the related genes of METTL14, weighted gene co-expression network analysis was used to find the relationship between METTL14 and gene expression, and univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) methods were used to identify hub genes that may be associated with HCC prognosis. The results indicated that cysteine sulfinic acid decarboxylase, glutamic-oxaloacetic transaminase 2, and suppressor of cytokine signaling 2 were key genes affecting the prognosis of HCC patients, and m6A methylation of these mRNAs may be regulated by METTL14. Finally, a nomogram was constructed based on the hub gene expression levels, and its prediction accuracy and discriminative ability were measured by the C-index and a calibration curve. In conclusion, METTL14, an m6A RNA methylation regulator, may participate in the malignant progression of HCC by adjusting the m6A of cysteine sulfinic acid decarboxylase, glutamic-oxaloacetic transaminase 2, and suppressor of cytokine signaling 2, and these genes are useful for prognostic stratification and treatment strategy development.