Polymorphism of E6 and E7 Genes in Human Papillomavirus Types 31 and 33 in Northeast China.

Persistent infection with human papillomavirus (HPV) types 31 and 33 is an important causative factor for cervical cancer. The E6/E7 genes are key oncogenes involved in the immortalization and transformation of human epithelial cells. Genetic polymorphism may lead to differences in the virus' carcinogenic potential, the immune reaction of the host, and the potencies of vaccines. Few studies on HPV31/33 E6/E7 genetic polymorphism have been carried out. To study the genetic polymorphism of HPV31 and HPV33 E6/E7 genes in northeast China, these genes (HPV31 E6/E7, n = 151; HPV33 E6/E7, n = 136) were sequenced and compared to reference sequences (J04353.1, M12732.1) using BioEdit. Phylogenetic trees were constructed by the neighbor-joining method using MegaX. The diversity of the secondary structure was estimated using the PSIPred server. The positively selected sites were analyzed using PAML4.9. The major histocompatibility complex (MHC) class I and MHCII epitopes were predicted using the ProPred-I server and ProPredserver. B-cell epitopes were predicted using the ABCpred server. In the 151 HPV31E6 sequences, 25 (25/450) single-nucleotide mutations were found, 14 of which were synonymous mutations and 11 were nonsynonymous. In the 151 HPV31E7 sequences, 8 (8/297) nucleotide mutations were found, 3 of which were synonymous mutations and 5 were nonsynonymous. In the 136 HPV33E6 sequences, 17 (17/450) nucleotide mutations were observed, 7 of which were synonymous mutations and 10 were nonsynonymous. C14T/G (T5I/S) was a triallelic mutation. Finally, in the 136 HPV33E7 sequences, 9 (9/294) nucleotide mutations were observed, 3 of which were synonymous mutations and 6 were nonsynonymous. C134T/A (A45V/E) and C278G/A (T93S/N) were triallelic mutations. Lineage A was the most common lineage in both HPV31 and HPV33. In all of the sequences, we only identified one positively selected site, HPV33 E6 (K93N). Most nonsynonymous mutations were localized at sites belonging to MHC and/or B-cell predicted epitopes. Data obtained in this study should contribute to the development and application of detection probes, targeted drugs, and vaccines.

Identification of key genes involved in JAK/STAT pathway in colorectal cancer.

JAK/STAT pathway has been well confirmed in the development of colorectal cancer (CRC), however, the exact mechanism is unclear. Therefore, we aimed to identify key genes involved in JAK/STAT pathway in CRC, as well as the potential mechanism. RT² profiler PCR arrays were performed to identify key genes of the JAK/STAT pathway. GO, KEGG pathway and PPI analyses were performed to screen the main functions of differentially expressed genes (DEGs). Moreover, the expression of DEGs was detected by GEPIA based on TCGA database and verified by qPCR and/or Western blot. Subsequently, the association between the two DEGs (CXCL9 and IL6ST) and clinicopathological features were determined by immunohistochemistry, and survival analysis was also conducted. Finally, the effects of IL6ST overexpression on STAT3 activation and HT29 cell functions were analyzed. A total of 14 DEGs were identified. Among the DEGs, GHR, NR3C1, IL6ST and A2M were confirmed to be statistically decreased, while CXCL9 was significantly increased in the CRC tissues. Furthermore, CXCL9 was significantly associated with differentiation, lymph node metastasis, distant metastasis and invasion, while IL6ST was related with tumor size, differentiation, stage and invasion. Patients with high expression of IL6ST presented significantly lower lifetime, however, CXCL9 showed the opposite results without significance. Additionally, we found that overexpression of IL6ST statistically elevated p-STAT3 level, cell viability, adhesion rate and migration, and decreased apoptosis, but had no effects on cell cycle. Our results suggest that IL6ST is a critical key gene involved in JAK/STAT signaling pathway in CRC.

Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD.

PURPOSE: This study aims to systematically analyze multi-omics data to explore new prognosis biomarkers in colon adenocarcinoma (COAD). MATERIALS AND METHODS: Multi-omics data of COAD and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox analysis was used to select genes which significantly related to the overall survival. GISTIC 2.0 software was used to identify significant amplification or deletion. Mutsig 2.0 software was used to identify significant mutation genes. The 9-gene signature was screened by random forest algorithm and Cox regression analysis. GSE17538 dataset was used as an external dataset to verify the predictive ability of 9-gene signature. qPCR was used to detect the expression of 9 genes in clinical specimens. RESULTS: A total of 71 candidate genes are obtained by integrating genomic variation, mutation and prognostic data. Then, 9-gene signature was established, which includes HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, PYGO2, CTNNA1, PTPRK, and NAT1. The 9-gene signature is an independent prognostic risk factor for COAD patients. In addition, the signature shows good predicting performance and clinical practicality in training set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, and PYGO2 was increased in colon cancer tissues and the expression of CTNNA1, PTPRK, NAT1 was decreased in colon cancer tissues. CONCLUSION: In this study, 9-gene signature is constructed as a new prognostic marker to predict the survival of COAD patients.

STAT1 expression and HPV16 viral load predict cervical lesion progression.

Cervical cancer is the fourth leading cause of cancer-associated mortality worldwide. However, its underlying molecular mechanisms are unclear. It is important to explore these mechanisms in order to identify novel diagnostic and prognostic biomarkers. The present study determined the association between STAT1 and human papillomavirus (HPV)16 in cervical lesions. STAT1 expression was detected by immunohistochemistry. Quantitative PCR was used to detect HPV16 viral load and STAT1 expression in cervical lesions. The potential associations among STAT1 expression, HPV16 viral load and the severity of cervical lesions in patients were analyzed using receiver operating characteristic (ROC) curves. The Cancer Genome Atlas database was used to analyze STAT1 expression and survival. High STAT1 expression was observed in 10.71 (3/28), 41.18 (14/34), 53.06 (26/49) and 90.00% (27/30) of normal tissue, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL) and cervical squamous cell carcinoma samples, respectively. The HPV16 copy number gradually increased with the progression of cervical lesions, with the highest copy number observed in cervical cancer samples. In addition, STAT1 expression was positively correlated with HPV16 viral load. Furthermore, ROC curve analysis demonstrated that the combination of STAT1 expression and HPV16 viral load was able to differentiate between LSIL/HSIL and cervical cancer samples. Bioinformatics analysis revealed that STAT1 expression was associated with improved survival in cervical cancer. Additionally, STAT1 expression was positively associated with the progression of cervical lesions, and HPV16 viral load may affect STAT1 expression. Overall, these findings indicate that STAT1 may be an indicator of the status of cervical lesions.

Bibliometric analysis of subject trends and knowledge structures of gut microbiota.

BACKGROUND: Gut microbiota is an emerging field of research, with related research having breakthrough development in the past 15 years. Bibliometric analysis can be applied to analyze the evolutionary trends and emerging hotspots in this field. AIM: To study the subject trends and knowledge structures of gut microbiota related research fields from 2004 to 2018. METHODS: The literature data on gut microbiota were identified and downloaded from the PubMed database. Through biclustering analysis, strategic diagrams, and social network analysis diagrams, the main trend and knowledge structure of research fields concerning gut microbiota were analyzed to obtain and compare the research hotspots in each period. RESULTS: According to the strategic coordinates and social relationship network map, Clostridium Infections/microbiology, Clostridium Infections/therapy, RNA, Ribosomal, 16S/genetics, Microbiota/genetics, Microbiota/immunology, Dysbiosis/immunology, Infla-mmation/immunology, Fecal Microbiota Transplantation/methods, Fecal Microbiota Transplantation can be used as an emerging research hotspot in the past 5 years (2014-2018). CONCLUSION: Some subjects were not yet fully studied according to the strategic coordinates; and the emerging hotspots in the social network map can be considered as directions of future research.