microRNA-34a and Long Non-Coding RNA MALAT1 Is Associated With HPV Status and Viral Load In Premalignant Cervical Lesions

Background: Cervical cancer (CC) is one of the most common gynecological malignancies in the world, and human papillomavirus (HPV) infection is the most important risk factor for their development. Although there are methods for the early detection of CC and HPV infection, but there are not highly sensitive and specific, for it´s necessary to investigate alternatives such as miR-34a and MALAT1, implicated in the pathogenesis of CC. The objective was to evaluate the association of HPV status, viral load, the presence of coinfections, and the grade of CC precursor lesions with miR-34a and MALAT1 expression in patients with high and low-grade cervical lesions (CL) and patients without CL but HPV+. Methods: Liquid-based cervical cytology (LBCC) specimens were obtained from 67 women diagnosed with low and high-grade CL, as well as LBCC HPV+, from which DNA and RNA were extracted. From DNA we genotyped and quantified the viral load for HPV 16, 18, and 51. From RNA, we performed a retrotranscription and evaluated the expression of MALAT1 (n=67) and miR-34a (n=29), all using a droplet-digital PCR assay. Statistical analysis was performed with SPSS 27.0 software using U Mann-Whitney and Kruskal-Wallis tests. Results: We identified a statistically significant association between the under-expression of miR-34a, HPV+ status (p=0.010), coinfections (p=0.030), low (p =0.042), and high viral load (p=0.014), but not with the lesion grade. Also, MALAT1 overexpression was associated with HPV+ status (p=0.008) and high viral load (p =0.027), but not with co-infections or the grade of CC precursor lesions. Conclusions: The expression of MALAT1 and miR-34a are associated with HPV status and viral load, but not with the grade of CC precursor lesions

Generating human papillomavirus (HPV) reference databases to maximize genomic mapping

Genomic experiments analyzing human papillomaviruses (HPVs) require a carefully selected list of sequences as a reference database to map millions of reads. The available sources, such as the Papillomavirus Episteme (PaVE), are organized based on variations in the L1 gene rather than the whole HPV sequence. Moreover, the PaVE process uses complex multiple sequence alignments containing hundreds or thousands of sequences. These issues complicate the generation of a reference database for genomics, leading to the generation of per-analysis-defined databases. Here, we propose a de novo strategy considering all HPV sequences reported in the NCBI database to define a subset of highly representative HPV sequences. The strategy is based on oligonucleotide frequency profiling of the whole sequence followed by hierarchical clustering. Using data from HPV capture experiments, we demonstrate that this strategy selects suitable sequences as a reference database to map most mappable reads unambiguously. We provide some recommendations to improve HPV mapping. The generated .fasta files can be accessed at https://github.com/vtrevino/HPV-Ref-Genomes