Identification of POLQ as a key gene in cervical cancer progression using integrated bioinformatics analysis and experimental validation.

As the most common gynecologic malignancy worldwide, cervical cancer (CC) is a serious hazard to health. Therefore, the present study aimed to identify the key genes in CC progression using integrated bioinformatics analysis and experimental validation. The mRNA microarray GSE63514 and microRNA (miRNA) microarray GSE86100 were obtained from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in the progression of CC were identified. Thereafter, GO and KEGG functional enrichment analysis, protein­protein interaction (PPI) network and significant subnetworks construction, and miRNA­target regulatory network construction were performed. Based on the results of integrated bioinformatics analysis, the DEGs structural maintenance of chromosomes 4 (SMC4), ATPase family, AAA domain­containing 2 (ATAD2) and DNA polymerase θ (POLQ) were identified as hub genes in the PPI network and were involved in the first significant subnetwork. In addition, these DEGs were predicted to be regulated by miR­106B, miR­17­5P, miR­20A and miR­20B, which were identified as DEMs. Of note, SMC4 and ATAD2 are tumor­promotors in CC. In the present study, small interfering (si)RNAs were used to knock down POLQ expression. Cell Counting Kit­8, Transwell, cell cycle and apoptosis analyses revealed that the downregulation of POLQ restrained cell proliferation, migration and invasion, and promoted apoptosis and the arrest of the cell cycle in the G2 phase. In conclusion, POLQ, which may have a close interaction with SMC4 and ATAD2, may serve a vital role in the progression of CC.

Leveraging 16S rRNA data to uncover vaginal microbial signatures in women with cervical cancer.

Microbiota-relevant signatures have been investigated for human papillomavirus-related cervical cancer (CC), but lack consistency because of study- and methodology-derived heterogeneities. Here, four publicly available 16S rRNA datasets including 171 vaginal samples (51 CC versus 120 healthy controls) were analyzed to characterize reproducible CC-associated microbial signatures. We employed a recently published clustering approach called VAginaL community state typE Nearest CentroId clAssifier to assign the metadata to 13 community state types (CSTs) in our study. Nine subCSTs were identified. A random forest model (RFM) classifier was constructed to identify 33 optimal genus-based and 94 species-based signatures. Confounder analysis revealed confounding effects on both study- and hypervariable region-associated aspects. After adjusting for confounders, multivariate analysis identified 14 significantly changed taxa in CC versus the controls (P < 0.05). Furthermore, predicted functional analysis revealed significantly upregulated pathways relevant to the altered vaginal microbiota in CC. Cofactor, carrier, and vitamin biosynthesis were significantly enriched in CC, followed by fatty acid and lipid biosynthesis, and fermentation of short-chain fatty acids. Genus-based contributors to the differential functional abundances were also displayed. Overall, this integrative study identified reproducible and generalizable signatures in CC, suggesting the causal role of specific taxa in CC pathogenesis.