Dataset Information

Revealing the Disturbed Vaginal Micobiota Caused by Cervical Cancer Using High-Throughput Sequencing Technology.

ABSTRACT: Cervical cancer is the fourth most prevalent cancer type among all malignancies, so it is of great significance to find its actual pathogenesis mechanisms. In the present study, 90 women were enrolled, and high-throughput sequencing technology was firstly used to analyze the vaginal microbiota of healthy women (C group), cervical intraepithelial neoplasia patients (CIN group) and cervical cancer patients (CER group). Our results indicates that compared with C group, a higher HPV infection rate as well as increased Neutrophil ratio and tumor marker squamous cell carcinoma antigen (SCCA) were obtained, and a decrease in Lymphocyte ratio and Hemoglobin were also present. In addition, the cervical cancer showed a strong association with reduced probiotics Lactobacillus, increased pathogens Prevotella spp., Sneathia spp. and Pseudomonas spp. These results prove that the immunological changes generated by the cervical cancer and the vaginal microbiota can interact with each other. However, further study investigating the key bacteria for cervical cancer is still needed, which can be a clue for the diagnosis or treatment of cervical cancer.

SUBMITTER: Xie Y

PROVIDER: S-EPMC7750457 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Revealing the Disturbed Vaginal Micobiota Caused by Cervical Cancer Using High-Throughput Sequencing Technology.

Xie Yupei Y Feng Ying Y Li Wenyu W Zhan Fuliang F Huang Genhua G Hu Hui H Xiong Yifei Y Tan Buzhen B Chen Tingtao T

Frontiers in cellular and infection microbiology 20201207

Cervical cancer is the fourth most prevalent cancer type among all malignancies, so it is of great significance to find its actual pathogenesis mechanisms. In the present study, 90 women were enrolled, and high-throughput sequencing technology was firstly used to analyze the vaginal microbiota of healthy women (C group), cervical intraepithelial neoplasia patients (CIN group) and cervical cancer patients (CER group). Our results indicates that compared with C group, a higher HPV infection rate a ...[more]

PMID: 33365275

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Project description:BackgroundGenetic markers are pivotal to modern genomics research; however, discovery and genotyping of molecular markers in oat has been hindered by the size and complexity of the genome, and by a scarcity of sequence data. The purpose of this study was to generate oat expressed sequence tag (EST) information, develop a bioinformatics pipeline for SNP discovery, and establish a method for rapid, cost-effective, and straightforward genotyping of SNP markers in complex polyploid genomes such as oat.ResultsBased on cDNA libraries of four cultivated oat genotypes, approximately 127,000 contigs were assembled from approximately one million Roche 454 sequence reads. Contigs were filtered through a novel bioinformatics pipeline to eliminate ambiguous polymorphism caused by subgenome homology, and 96 in silico SNPs were selected from 9,448 candidate loci for validation using high-resolution melting (HRM) analysis. Of these, 52 (54%) were polymorphic between parents of the Ogle1040 × TAM O-301 (OT) mapping population, with 48 segregating as single Mendelian loci, and 44 being placed on the existing OT linkage map. Ogle and TAM amplicons from 12 primers were sequenced for SNP validation, revealing complex polymorphism in seven amplicons but general sequence conservation within SNP loci. Whole-amplicon interrogation with HRM revealed insertions, deletions, and heterozygotes in secondary oat germplasm pools, generating multiple alleles at some primer targets. To validate marker utility, 36 SNP assays were used to evaluate the genetic diversity of 34 diverse oat genotypes. Dendrogram clusters corresponded generally to known genome composition and genetic ancestry.ConclusionsThe high-throughput SNP discovery pipeline presented here is a rapid and effective method for identification of polymorphic SNP alleles in the oat genome. The current-generation HRM system is a simple and highly-informative platform for SNP genotyping. These techniques provide a model for SNP discovery and genotyping in other species with complex and poorly-characterized genomes.

Dataset Information

Revealing the Disturbed Vaginal Micobiota Caused by Cervical Cancer Using High-Throughput Sequencing Technology.

Publications

Revealing the Disturbed Vaginal Micobiota Caused by Cervical Cancer Using High-Throughput Sequencing Technology.

Similar Datasets

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