Background: Few studies have analyzed the association between human telomerase reverse transcriptase (hTERT) protein expression (nuclear and cytoplasmic localization), hTERT methylation status, and human papillomavirus (HPV) genotype infection in cervical cancer. Patients and Methods: One hundred seventy-three patients with cervical cancer were analyzed. hTERT protein expression was detected by immunohistochemistry. hTERT DNA methylation analysis was performed using a PCR-RLB-hTERT assay, targeting two regions of the hTERT promoter. Type specific HPV infection was detected by using GP5+/GP6+PCR-RLB. Results: hTERT protein expression was found in both cytoplasm and nucleus (78.0% of the samples showed a cytoplasmic localization and 79.8% had a nuclear localization). A statistically significant association was found between alpha 9 and 7 HPV species with a nonmethylation pattern of the hTERT promoter and between these species and high expression of hTERT protein with nuclear localization. Conclusion: hTERT protein is found in both the nucleus and cytoplasm of patients with cervical cancer and confirm the relationship between the nonmethylated status of hTERT promoter and some HPV species as well as the relationship between these species and hTERT protein expression. Epidemiological studies have demonstrated a strong causal relationship between persistent infection with human papillomavirus (HPV), especially high-risk HPV (HRHPV) types and the development of cervical cancer (1). In vitro studies have shown that neoplastic transformation of cells infected with HRHPV is mainly due to telomerase activation by the action of HPV E6 oncoprotein (2). Telomerase is a ribonucleoprotein complex with terminal transferase activity and is made up by an RNA component (hTR), a catalytic protein subunit (hTERT) and the telomerase associated protein TEP1 (3). Some studies have shown a significant telomerase expression and telomerase activity levels in different types of cancer (e.g. lung, pancreas, hepatocellular, prostate, skin and certain gastrointestinal tumors) and malignant cell lines, while their levels are very low in 615 This article is freely accessible online.

Human papillomavirus (HPV) infection is strongly associated with cervical cancer (CC). Genomic alterations caused by viral infection and subsequent dysregulation of cellular metabolism under hypoxic conditions could influence the response to treatment. We studied a possible influence of IGF-1Rβ, hTERT, HIF1α, GLUT1 protein expression, HPV species presence and relevant clinical parameters on the response to treatment. In 21 patients, HPV infection and protein expression were detected using GP5+/GP6+PCR-RLB and immunohistochemistry, respectively. The worse response was associated with radiotherapy alone compared with chemoradiotherapy (CTX-RT), anemia and HIF1α expression. HPV16 type was the most frequent (57.1%) followed by HPV-58 (14.2%) and HPV-56 (9.5%). The HPV alpha 9 species was the most frequent (76.1%) followed by alpha 6 and alpha 7. IGF-1Rβ (85.7%), HIF1α (61.9%), GLUT1 (52.3%), and hTERT expression [cytoplasm and nucleus (90.4%)] were detected. The MCA factorial map showed different relationships, standing out, expression of hTERT and alpha 9 species HPV, expression of hTERT and IGF-1Rβ expression [Fisher's exact test (P = 0.04)]. A slight trend of association was observed between, GLUT1 and HIF1 α expression, hTERT and GLUT1 expression. A noteworthy finding was the subcellular localization of hTERT in the nucleus and cytoplasm of CC cells and its possible interaction with IGF-1R in presence of HPV alpha 9 species. Our findings suggest that the expression of HIF1α, hTERT, IGF-1Rβ and GLUT1 proteins that interact with some HPV species may contribute to cervical cancer development, and the modulation of treatment response.

The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-sequencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNAsequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.

Interactions between genes, such as regulations are best represented by gene regulatory networks (GRN). These are often constructed based on gene expression data. Few methods for the construction of GRN exist for RNA sequencing count data. One of the most used methods for microarray data is based on graphical Gaussian networks. Considering that count data have different distributions, a method assuming RNA sequencing counts distribute Poisson has been proposed recently. Nevertheless, it has been argued that the most likely distribution of RNA sequencing counts is not Poisson due to overdispersion. Therefore, the negative binomial distribution is much more likely. For this distribution, no model-based method for the construction of GRN has been proposed until now. Here, we present a graphical, model-based method for the construction of GRN assuming a negative binomial distribution of the RNA sequencing count data. The R code is available under request. We used the method proposed both on simulated RNA sequencing count data and on real data. The graph is showed, and its descriptive measurements were assessed. They were found some interesting biological conclusions. We confirm that using negative binomial distribution for fitting the model is suitable because RNA sequencing data present overdispersion.