High Levels of Within-Host Variations of Human Papillomavirus 16 E1/E2 Genes in Invasive Cervical Cancer

Effects of neoadjuvant chemotherapy combined with radical laparoscopic surgery with quality nursing care on clinical efficacy and sex hormones in patients with cervical cancer

OBJECTIVE

To observe the effects of neoadjuvant chemotherapy combined with radical laparoscopic surgery with quality nursing care on the clinical efficacy and sex hormones of cervical cancer patients.

METHODS

The clinical data of 107 patients with cervical cancer admitted to Yanan University Affiliated Hospital between January 2017 and January 2020 were retrospectively analyzed in this study. Among them, 50 patients received only laparoscopic radical surgery (surgical group), and the other 57 received neoadjuvant chemotherapy combined with laparoscopic radical surgery (Joint group); patients in both groups received quality nursing care. The baseline and surgical data of the two groups were compared, and the changes in tumor markers and sex hormones before and after treatment were analyzed. Cox regression was used to analyze the independent prognostic factors affecting patients' 2-year survival.

RESULTS

The patients in the two groups did not show statistical differences in baseline and surgical data (all P > 0.05). After treatment, the levels of squamous cell carcinoma antigen (SCC-Ag), carcinoembryonic antigen (CEA), and serum glycan antigen 125 (CA125) were significantly reduced in both groups. However, the reduction was more pronounced in the joint group than that in the surgical group (P < 0.0001). Meanwhile, estrogen (E2) levels decreased more significantly in the Joint group, while follicle-stimulating hormone (FSH) and luteinizing hormone (LH) increased more significantly (all P < 0.0001). Multifactorial Cox regression analysis revealed that E2, LH and SCC-Ag were independent prognostic factors affecting 2-year survival (all P < 0.05).

CONCLUSION

Neoadjuvant chemotherapy combined with laparoscopic radical surgery is more effective in reducing the levels of tumor markers and significantly affects the levels of sex hormones. E2, LH, and SCC-Ag are the independent prognostic factors for 2-year survival in patients with cervical cancer. This study provides evidence to support the comprehensive treatment of cervical cancer.

Intra-Patient Genomic Variations of Human Papillomavirus Type 31 in Cervical Cancer and Precancer

Human papillomavirus type 31 (HPV31) is detected less frequently in cervical cancer than two major causative types, HPV16 and HPV18. Here, we report a comprehensive analysis of HPV31 genome sequences in cervical lesions collected from Japanese women. Of 52 HPV31-positive cervical specimens analyzed by deep sequencing, 43 samples yielded complete genome sequences of around 7900 base pairs and 9 samples yielded partially deleted genome sequences. Phylogenetic analysis showed that HPV31 variant distribution was lineage A in 19 samples (36.5%), lineage B in 28 samples (53.8%), and lineage C in 5 samples (9.6%), indicating that lineage B variants are dominant among HPV31 infections in Japan. Deletions in the viral genome were found in the region from the E1 to L1 genes, but all the deleted genomes retained the E6/E7 genes. Among intra-patient nucleotide variations relative to a consensus genome sequence in each sample, C-to-T substitutions were most frequently detected, followed by T-to-C and C-to-A substitutions. High-frequency, intra-patient mutations (>10%) in cervical cancer samples were found in the E1, E2, and E7 genes, and all of them were nonsynonymous substitutions. The enrichment of high-frequency nonsynonymous substitutions strongly suggests that these intra-patient mutations are positively selected during the development of cervical cancer/precancer.

Characterization of the Human Papillomavirus 16 Oncogenes in K14HPV16 Mice: Sublineage A1 Drives Multi-Organ Carcinogenesis

The study of human papillomavirus (HPV)-induced carcinogenesis uses multiple in vivo mouse models, one of which relies on the cytokeratin 14 gene promoter to drive the expression of all HPV early oncogenes. This study aimed to determine the HPV16 variant and sublineage present in the K14HPV16 mouse model. This information can be considered of great importance to further enhance this K14HPV16 model as an essential research tool and optimize its use for basic and translational studies. Our study evaluated HPV DNA from 17 samples isolated from 4 animals, both wild-type (n = 2) and HPV16-transgenic mice (n = 2). Total DNA was extracted from tissues and the detection of HPV16 was performed using a qPCR multiplex. HPV16-positive samples were subsequently whole-genome sequenced by next-generation sequencing techniques. The phylogenetic positioning clearly shows K14HPV16 samples clustering together in the sub-lineage A1 (NC001526.4). A comparative genome analysis of K14HPV16 samples revealed three mutations to the human papillomaviruses type 16 sublineage A1 representative strain. Knowledge of the HPV 16 variant is fundamental, and these findings will allow the rational use of this animal model to explore the role of the A1 sublineage in HPV-driven cancer.

Bacterial DNA involvement in carcinogenesis

The incidence of cancer is high worldwide, and biological factors such as viruses and bacteria play an important role in the occurrence of cancer. Helicobacter pylori, human papillomavirus, hepatitis B viruses and other organisms have been identified as carcinogens. Cancer is a disease driven by the accumulation of genome changes. Viruses can directly cause cancer by changing the genetic composition of the human body, such as cervical cancer caused by human papillomavirus DNA integration and liver cancer caused by hepatitis B virus DNA integration. Recently, bacterial DNA has been found around cancers such as pancreatic cancer, breast cancer and colorectal cancer, and the idea that bacterial genes can also be integrated into the human genome has become a hot topic. In the present paper, we reviewed the latest phenomenon and specific integration mechanism of bacterial DNA into the human genome. Based on these findings, we also suggest three sources of bacterial DNA in cancers: bacterial DNA around human tissues, free bacterial DNA in bacteremia or sepsis, and endogenous bacterial DNA in the human genome. Clarifying the theory that bacterial DNA integrates into the human genome can provide a new perspective for cancer prevention and treatment.

Role of HPV16 E1 in cervical carcinogenesis

Cervical cancer is the fourth most common cancer in women worldwide. More than 90% of cases are caused by the human papillomavirus (HPV). Vaccines developed only guard against a few HPV types and do not protect people who have already been infected. HPV is a small DNA virus that infects the basal layer of the stratified epithelium of the skin and mucosa through small breaks and replicates as the cells differentiate. The mucosal types of HPV can be classified into low-risk and high-risk groups, based on their association with cancer. Among HPV types in high-risk group, HPV type 16 (HPV-16) is the most common, causing 50% of all cancer cases. HPV infection can occur as transient or persistent infections, based on the ability of immune system to clear the virus. Persistent infection is characterized by the integration of HPV genome. HPV-16 exhibits a different integration pattern, with only 50% reported to be integrated at the carcinoma stage. Replication of the HPV genome depends on protein E1, an ATP-dependent helicase. E1 is essential for the amplification of the viral episome in infected cells. Previous studies have shown that E1 does not only act as a helicase protein but is also involved in recruiting and interacting with other host proteins. E1 has also been deemed to drive host cell proliferation. Recent studies have emphasized the emerging role of HPV E1 in cervical carcinogenesis. In this review, a possible mechanism by which E1 drives cell proliferation and oncogenesis will be discussed.

In-silico discovery of inhibitors against human papillomavirus E1 protein

High-risk (HR) Human papillomavirus (e.g. HPV16 and HPV18) causes approximately two-thirds of all cervical cancers in women. Although the first and second-generation vaccines confer some protection against individuals, there are no approved drugs to treat HR-HPV infections to-date. The HPV E1 protein is an attractive drug target because the protein is highly conserved across all HPV types and is crucial for the regulation of viral DNA replication. Hence, we used the Random Forest algorithm to construct a Quantitative-Structure Activity Relationship (QSAR) model to predict the potential inhibitors against the HPV E1 protein. Our QSAR classification model achieved an accuracy of 87.5%, area under the receiver operating characteristic curve of 1.00, and F-measure of 0.87 when evaluated using an external test set. We conducted a drug repurposing campaign by deploying the model to screen the Drugbank database. The top three compounds, namely Cinalukast, Lobeglitazone, and Efatutazone were analyzed for their cell membrane permeability, toxicity, and carcinogenicity. Finally, these three compounds were subjected to molecular docking and 200 ns-long Molecular Dynamics (MD) simulations. The predicted binding free energies for the candidates were calculated using the MM-GBSA method. The binding free energies for Cinalukast, Lobeglitazone, and Efatutazone were -37.84 kcal/mol, -25.30 kcal/mol, and -29.89 kcal/mol respectively. Therefore, we propose their chemical scaffolds for future rational design of E1 inhibitors.Communicated by Ramaswamy H. Sarma.

FastViFi: Fast and accurate detection of (Hybrid) Viral DNA and RNA

DNA viruses are important infectious agents known to mediate a large number of human diseases, including cancer. Viral integration into the host genome and the formation of hybrid transcripts are also associated with increased pathogenicity. The high variability of viral genomes, however requires the use of sensitive ensemble hidden Markov models that add to the computational complexity, often requiring > 40 CPU-hours per sample. Here, we describe FastViFi, a fast 2-stage filtering method that reduces the computational burden. On simulated and cancer genomic data, FastViFi improved the running time by 2 orders of magnitude with comparable accuracy on challenging data sets. Recently published methods have focused on identification of location of viral integration into the human host genome using local assembly, but do not extend to RNA. To identify human viral hybrid transcripts, we additionally developed ensemble Hidden Markov Models for the Epstein Barr virus (EBV) to add to the models for Hepatitis B (HBV), Hepatitis C (HCV) viruses and the Human Papillomavirus (HPV), and used FastViFi to query RNA-seq data from Gastric cancer (EBV) and liver cancer (HBV/HCV). FastViFi ran in <10 minutes per sample and identified multiple hybrids that fuse viral and human genes suggesting new mechanisms for oncoviral pathogenicity. FastViFi is available at https://github.com/sara-javadzadeh/FastViFi.