Potential mechanisms and prognostic model of eRNAs-regulated genes in stomach adenocarcinoma.

Gastric Carcinoma is the fourth leading cause of cancer deaths worldwide, in which stomach adenocarcinoma (STAD) is the most common histological type. A growing amount of evidence has suggested the importance of enhancer RNAs (eRNAs) in the cancer. However, the potential mechanism of eRNAs in STAD remains unclear. The eRNAs-regulated genes (eRRGs) were identified through four different enhancer resources. The differentially expressed eRRGs were obtained by 'DESeq2' R package. The prognosis prediction model was constructed by Cox and Lasso regression analysis. The 'ChAMP' R package and 'maftools' R package were used to investigate the multi-omics characters. In this study, combining the concept of contact domain, a total of 9014 eRRGs including 4926 PCGs and 4088 lncRNAs were identified and these eRRGs showed higher and more stable expression. Besides, the functions of these genes were mainly associated with tumor-related biological processes. Then, a prognostic prediction model was constructed and the AUC values of the 1-, 3- and 5-year survival prediction reached 0.76, 0.84 and 0.84, respectively, indicating that this model has a high accuracy. Finally, the difference between high-risk group and low-risk group were investigated using multi-omics data including gene expression, DNA methylation and somatic mutations. Our study provides significant clues for the elucidation of eRNAs in STAD and may help improve the overall survival for STAD patients.

Prognostic signatures and potential pathogenesis of eRNAs-related genes in colon adenocarcinoma.

Enhancer RNAs (eRNAs) are a subclass of long noncoding RNAs (lncRNAs) that have a wide effect in human tumors. However, the systematic analysis of potential functions of eRNAs-related genes (eRGs) in colon cancer (CC) remains unexplored. In this study, a total of 8231 eRGs including 6236 protein-coding genes and 1995 lncRNAs were identified in CC based on the multiple resources. These eRGs showed higher expression level and stability compared to other genes. What's more, the functions of these eRGs were closely related to cancer. Then a prognostic prediction model with 12 eRGs signatures were obtained for colon adenocarcinoma (COAD) patients. ROC curves showed the AUCs were 0.81, 0.77, and 0.78 for 1-, 3-, and 5-year survival prediction, respectively. And the prognostic model also manifested good performance in the validation datasets. Besides, the expression levels of two prognostic signatures, TMEM220 and LRRN2, were verified to be significantly lower in CC tissues than in adjacent noncancerous tissues (p < .05). Finally, the distinct molecular features were characterized between the high- and low-risk group through multiomics analysis including DNA mutation and methylation. Our results show eRGs signatures based prognostic model has high accuracy and may provide innovative biomarkers in COAD.

Bioinformatics-based prediction of conformational epitopes for human parechovirus.

Human parechoviruses (HPeVs) are human pathogens that usually cause diseases ranging from rash to neonatal sepsis in young children. HPeV1 and HPeV3 are the most frequently reported genotypes and their three-dimensional structures have been determined. However, there is a lack of systematic research on the antigenic epitopes of HPeVs, which are useful for understanding virus-receptor interactions, developing antiviral agents or molecular diagnostic tools, and monitoring antigenic evolution. Thus, we systematically predicted and compared the conformational epitopes of HPeV1 and HPeV3 using bioinformatics methods in the study. The results showed that both epitopes clustered into three sites (sites 1, 2 and 3). Site 1 was located on the "northern rim" near the fivefold vertex; site 2 was on the "puff"; and site 3 was divided into two parts, of which one was located on the "knob" and the other was close to the threefold vertex. The predicted epitopes highly overlapped with the reported antigenic epitopes, which indicated that the prediction results were accurate. Although the distribution positions of the epitopes of HPeV1 and HPeV3 were highly consistent, the residues varied largely and determined the genotypes. Three amino acid residues, VP3-91N, -92H and VP0-257S, were the key residues for monoclonal antibody (mAb) AM28 binding to HPeV1 and were also of great significance in distinguishing HPeV1 and HPeV3. We also found that two residues, VP1-85N and -87D, might affect the capability of mAb AT12-015 to bind to HPeV3.

Bioinformatics-based prediction of conformational epitopes for Enterovirus A71 and Coxsackievirus A16.

Enterovirus A71 (EV-A71), Coxsackievirus A16 (CV-A16) and CV-A10 are the major causative agents of hand, foot and mouth disease (HFMD). The conformational epitopes play a vital role in monitoring the antigenic evolution, predicting dominant strains and preparing vaccines. In this study, we employed a Bioinformatics-based algorithm to predict the conformational epitopes of EV-A71 and CV-A16 and compared with that of CV-A10. Prediction results revealed that the distribution patterns of conformational epitopes of EV-A71 and CV-A16 were similar to that of CV-A10 and their epitopes likewise consisted of three sites: site 1 (on the "north rim" of the canyon around the fivefold vertex), site 2 (on the "puff") and site 3 (one part was in the "knob" and the other was near the threefold vertex). The reported epitopes highly overlapped with our predicted epitopes indicating the predicted results were reliable. These data suggested that three-site distribution pattern may be the basic distribution role of epitopes on the enteroviruses capsids. Our prediction results of EV-A71 and CV-A16 can provide essential information for monitoring the antigenic evolution of enterovirus.