Cervical cancer is a public health problem and has devastating effects in low-to-middle-income countries (LTMICs) such as the sub-Saharan African (SSA) countries. Infection by the human papillomavirus (HPV) is the main cause of cervical cancer. HIV positive women have higher HPV prevalence and cervical cancer incidence than their HIV negative counterparts do. Concurrent HPV/HIV infection is catastrophic, particularly to African women due to the high prevalence of HIV infections. Although various studies show a relationship between HPV, HIV and cervical cancer, there is still a gap in the knowledge concerning the precise nature of this tripartite association. Firstly, most studies show the relationship between HPV and cervical cancer at genomic and epigenetic levels, while the transcriptomic landscape of this relationship remains to be elucidated. Even though many studies have shown HPV/HIV dual viral pathogenesis, the dual molecular oncoviral effects on the development of cervical cancer remains largely uncertain. Furthermore, the effect of highly active antiretroviral therapy (HAART) on the cellular splicing machinery is unclear. Emerging evidence indicates the vital role played by host splicing events in both HPV and HIV infection in the development and progression to cervical cancer. Therefore, decoding the transcriptome landscape of this tripartite relationship holds promising therapeutic potential. This review will focus on the link between cellular splicing machinery, HPV, HIV infection and the aberrant alternative splicing events that take place in HIV/HPV-associated cervical cancer. Finally, we will investigate how these aberrant splicing events can be targeted for the development of new therapeutic strategies against HPV/HIV-associated cervical cancer. Histologically, cervical cancer can be divided into two main types, squamous cell carcinoma (SCC) and adenocarcinoma (AC). SCC is the most common type accounting for over 70% of all cases ,2. CerviIn sub-Saharan Africa (SSA), HIV positive women have increased HPV prevalence and cervical cancer incidence compared to HIV negative women. This may be partly attributed to HIV’s altering effect on HPV pathogenesis. Furthermore, HIV positive women have increased risk of HPV infection . GloballSeveral extrinsic dynamics pose a threat in the fight against cervical cancer in LTMICs. Such factors include gender inequality, inequitable laws and inappropriate traditional practices and violence between intimate partners. These factors are unfavourable to women, limit women’s access to information, education, employment, opportunities and access to social and health services. Increased HPV and HIV co-infection has been frequently documented. Unfortunately, African women are mostly affected by this catastrophic dual viral infection. Globally, young women <35 years in the sub-Saharan African (SSA) region make up ~60% of HIV infection prevalence and almost 70% of newly acquired infections in Africa. With no proper interventions in place, the World Health Organization (WHO) estimates that cervical cancer deaths will double to over 400,000 by 2030, and the SSA region will make up 90% of the cases resulting in mortality ,19.Cancers associated with viral infections are on the rise globally, and in SSA ,21 Figu. ApproxiWith a worldwide age standardised incidence rate (ASR) of 15.6 per 100,000 per year, cervical cancer is the fourth most diagnosed cancer in women worldwide. The three cancers most common in women are breast, colorectal, and lung cancer. Based on the 2020 GLOBOCAN stats, the Southern African region has the highest incidence rates with an ASR of 36 per 100,000 A. This rThe region with the next highest incidence rate is South America with an ASR of 19.1 per 100,000, followed by Middle Africa (ASR 17.4), Eastern Asia and the Caribbean (both with an ASR of 15.8), Central America (ASR 15.1), Western Africa (ASR 13.9), Northern Europe (ASR 12.1), Southern Europe (ASR 11.5) and Western Europe (ASR 10.1). The Western Europe region contains countries such as the UK with an incidence rate of 9.9 and a mortality rate of 1.9 C. It alsMore than 200 genotypes of HPV based on their sequence can be divided into α, β, γ, δ, µ genera ,6. The HHPV infects the squamous epithelial cells, which have the ability to proliferate and gain access to basal cells during abrasion or trauma. HPV then induces the viral genes’ expression, which is critical for viral replication in the basal cells. The HPV interaction with the host cells occurs via heparin sulfate proteoglycans and host cell surface receptors such as α-6 integrins. The E2 protein acts as a transcriptional repressor of E6 and E7. HPV replicates by the rolling circle mechanism, during which it may integrate into the human genome. This integration perturbs the expression of E2, thereby favouring the upregulation of the expression of the E6 and E7 oncoproteins, leading ultimately to cell transformation. Following viral replication, L1 and L2 proteins form the virus capsid and the subsequent mature virus production. The mature virus is then released with the help of the E4 protein and desquamation of cells ,6,7.Cancers caused by HPV arise in tissues and organs where HPV infects epithelial cells. These tissues contain squamous cells that line the inside of these organs and are present within these tissues. It is these cells that are the target of HPV infection. The sites of HPV infection include the oropharynx, anus, penis, vagina, vulva and most importantly for this review, the cervix . With soThe association between HPV, HAART components and cervical cancer in HIV positive women remains poorly understood. It has been reported that initiating HAART early and attaining sustained adherence is more likely to reduce SIL  and cervical intraepithelial neoplasia (CIN), and thus reduce the incidence of invasive cervical cancer . FurtherHPV has been classified as a direct carcinogen while HIV has been classified as an indirect carcinogen through immunosuppression. The carcinogenic classification of these viruses was done by the International Agency for Research on Cancer ,34.Alternative splicing (AS) is an important physiological process that contributes to proteome diversity. The protein isoforms formed from various mRNA transcripts of the same gene may have similar or opposing functions. The AS process is tightly regulated by various small nuclear ribonucleoproteins (snRNP) and heterogeneous nuclear ribonucleoproteins (hnRNPs). The two protein families are trans-regulatory to the AS process and their regulation is crucial in cellular homeostasis. In various cancers, including cervical cancer, these regulatory elements have been reported to be altered ,36,37. UThe HPV genome is a circular double stranded (ds) DNA genome of about 8 kb and has three regions, namely, (a) the long control region (LCR), (b) the early region and (c) the late region. The LCR is responsible for the regulation of transcription and replication, the early region is responsible for viral infection and establishing the virus in the host cell, while the late region is responsible for viral encapsulation by coding for the L1 and L2 proteins ,38. E6 aThe LCR of HPV is the regulatory region that controls transcription of the virus. The early promoter responsible for early gene expression is located on the E6 ORF, while the E7 ORF is responsible for late gene expression. Differential transcription strategies are employed by E6 and E7 in high-risk and low-risk HPVs, indicating the significance of alternative splicing in HPV infection. In low-risk HPV, the E6 and E7 genes are transcribed by two different promoters, while in high-risk HPV E6 and E7 are transcribed by a single promoter from a polycistronic pre-mRNA . In highThe E2 and E6 proteins of HPV 16 interrupt RNA splicing using multiple ways. Firstly, through the preferential binding of E2 and E6 C-terminus to the pre-mRNA introns. Secondly, these two proteins (E2 and E6) bind to splicing factors such as SRSF4, 5, 6, 9. It is also interesting to note that this interaction between E2 and E6 and these SR proteins occurs at the same site as RNA binding ,46. SR pThe aberrant activity of both SR proteins and hnRNPs in cervical cancer has been demonstrated to elicit the production of oncoproteins by processing pre-mRNA transcripts that are either derived from the human genome or from the HPV genome. Both SR proteins and hnRNPs have been shown to control the production of viral oncoprotein isoforms that are key in the HPV life cycle and cell transformation. Protein isoforms generated through alternative splicing are expressed selectively in a time-dependent and tissue-specific manner. This allows for the regulation of various metabolic pathways involved in cell proliferation, differentiation, cell cycle control, and cell death ,49.HIV also regulates the host splicing machinery, thereby advancing the progression of the viral infection. The altered splicing leads to the expression of viral genes responsible for invasion, replication and assembly of the virus. Contrary to HPV, HIV-related malignancies evolve as a result of immunosuppression that may lead to co-infection by other oncogenic viruses such as HPV. As a retrovirus, HIV depends completely on the cellular machinery of the host for amplification.The HIV genome codes for 10 proteins with various functions, all of which are important for the HIV lifecycle, The splicing process comprises of sequential reactions that involve the recruitment of spliceosome components and interaction with the cis-acting regulatory intronic sequences such as the 50 splice donor (SD) site and the 30 splice acceptor (SA) site, the intervening branch point and the polypyrimidine tract . The altBoth SRSFs and hnRNPs have been reported to be upregulated in the basal and middle layers of the cervical epithelium, while being downregulated in the terminal differentiated layers . For exaIn addition, high levels of hnRNP H and I have been reported in cervical cancer and high-grade CIN compared to normal tissues. Knockdown of hnRNP I was shown to reduce the proliferation of HeLa cells. Furthermore, the lncRNA ARAP1-AS1 binds with PTB-associated splicing factor (PSF) and causes the release of hnRNP I, which in turn, enhances c-Myc expression and subsequent cell proliferation while inhibiting apoptosis of CaSki and SiHa cervical cancer cells ,60.Both SRSF9 and SRSF 10 have also been shown to be overexpressed in cervical cancer compared to normal epithelium. Interestingly, E6/E7 silencing in CaSKi cervical cancer cells results in the downregulation of SRSF 10 ,25,45,58Many oncogenic viruses such as HPV may affect the DNA damage response pathway (DDR) and cell cycle regulation. Different studies have previously addressed the effect of specific HPV proteins on DNA repair pathways. This begins with the dependency of HPV on the host machinery to amplify its genome. To achieve this, HPV must induce the host to re-enter the DNA replication S-phase of the cell cycle . This isE6 targets the p53 tumour suppressor protein. E6 alters p53 functional capacity by forming a ternary complex with E6-associated protein (E6AP), inducing its degradation by the ubiquitin-mediated proteolysis pathway ,37,38. AThe SSA region has the highest burden of dual HPV/HIV viral infections, exacerbating the cervical cancer pandemic in this region. Poor diagnostic and prognostic tools in LTMIC are obstacles in the efforts to manage cervical cancer. Although preventative measures are common in developed countries, the high burden of the already infected women with both HPV and HIV urgently warrants interventions . There iHPV prophylactic vaccines have been identified as the most potent intervention method against cervical cancer. The licensed HPV vaccines currently showing safety, high efficacy and immunogenicity are 2-valent CervarixTM, 4-valent Gardasil and 9-valent Gardasil ,69. Recu


G-optimality for predicting individual parameters in such models and establishes an equivalence theorem for confirming the G-optimality of an approximate design. Because the criterion is non-differentiable and requires solving multiple nested optimization problems, it is much harder to find and study G-optimal designs analytically. We propose a nature-inspired meta-heuristic algorithm called competitive swarm optimizer (CSO) to generate G-optimal designs for linear mixed models with different means and covariance structures. We further demonstrate that CSO is flexible and generally effective for finding the widely used locally D-optimal designs for nonlinear models with multiple interacting factors and some of the random effects are correlated. Our numerical results for a few examples suggest that G and D-optimal designs may be equivalent and we establish that D and G-optimal designs for hierarchical linear models are equivalent when the models have only a random intercept only. The challenging mathematical question of whether their equivalence applies more generally to other hierarchical models remains elusive.Hierarchical linear models are widely used in many research disciplines and estimation issues for such models are generally well addressed. Design issues are relatively much less discussed for hierarchical linear models but there is an increasing interest as these models grow in popularity. This paper discusses the  Hierarchical models are widely used to analyze data in various disciplines, such as in psychology, medicine, manufacturing industry and education. Such models are especially appealing for analyzing longitudinal analysis because they allow for the presence of missing data, time-varying or invariant covariates, and subjects measured at different time points. In educational research, hierarchical models are commonly used to evaluate the effectiveness of teaching methods using data from students nested within classrooms and classrooms are nested within schools that use different teaching methods. The distinguishing feature of these models is that they account for both individual-level and population-level effects. In the literature across disciplines, hierarchical models are variously referred to as multilevel models, nested data models, mixed models, random coefficient, random-effects models or random parameter models. In our work, we make no distinction among them and refer to them as statistical models with parameters that vary at one or more levels.N observations for the study, what are the optimal number of points, where these points are and how many replicates to take at each of these points. We denote these quantities by k, N.When the experimental settings are under the control of the investigator, design issues arise and they must be carefully addressed for maximal precision in the statistical inference at a minimal cost. The basic questions to answer are given a statistical model defined on a compact design space k, An alternative option is to formulate the design problem to find an optimal approximate design, where we determine n Kiefer  and theyG-optimal designs for hierarchical linear models, with G standing for global. These designs are best for estimating the overall response surface with minimal variance across the design space G-optimality is a minimax-type of design criterion, it is non-differentiable and requires at least solving two nested layers of optimization problems over different search spaces. Consequently, even for fixed-effects models, G-optimal designs are among the most difficult to study mathematically. Compounding the optimization problem is that current algorithms for searching optimal designs cannot find G-optimal designs effectively, let alone one with proof of convergence.A primary interest of our paper is to find G-optimal designs for hierarchical linear models, propose a nature-inspired meta-heuristic algorithm to find them and develop an equivalence theorem to confirm the G-optimality of an approximate design for hierarchical linear models. We demonstrate the flexibility and effectiveness of the algorithm for finding G-optimal designs when the mean response is modeled by fractional polynomials and D-optimal designs for hierarchical nonlinear models with multiple interacting factors with possibly correlated random effects. We also establish that D and G-optimal designs for hierarchical linear models are equivalent when the models have only a random intercept only and pose the challenging mathematical question of whether their equivalence applies more generally to other hierarchical models.The main aims of this paper are to find D-optimal designs for such models. Section G-optimality and Sect. G-optimal designs for various types of hierarchical models for relatively simple models. To find G-optimal designs for more complicated models, Sect. G-optimal designs for more complicated hierarchical linear models, such as, when the mean function is a fractional polynomial and the random effects may be correlated. We further show CSO can find locally D-optimal designs for estimating model parameters in a Poisson hierarchical model with multiple factors defined on a user-specified design space D and G-optimal designs for linear mixed models.The rest of the paper is organized as follows. Section This section gives the background, a brief literature review on constructing optimal designs for hierarchical linear models before we describe our statistical models.Constructing optimal designs for a given model under a specified criterion is challenging if the model is complex, and more so if the criterion is complicated. An analytical approach for finding optimal designs is limiting and is possible only for simple models with a couple of parameters, see examples in design monographs, such as, Fedorov  and BergD-optimality and a D-optimal design satisfiesD-optimal design D-optimality is measured by its D-efficiency defined byD-optimal design D-efficiency of Specifically, suppose the given design space D-optimality is as follows: D-optimal if and only if for all D-optimal design The function  Fedorov , it can mentclass2pt{minimG-optimal designs for fixed-effects linear models and assess the quality of the generated design using an equivalence theorem.Each concave functional has a directional derivative which is used to derive its own unique equivalence theorem; see details in design monographs, such as Fedorov  and Bergand Cook , where tD-optimal designs for linear models; see references in Berger and Wong  to find G-optimal designs and note that some of the results in this section are found by CSO.We now provide examples of G-optimal design for model  is popular because many simulation results in the literature show that it either outperforms or is competitive with several state-of-the-art evolutionary algorithms. This conclusion was arrived at after comparing CSO’s performance with several state-of-the-art evolutionary algorithms using a variety of benchmark functions with dimensions up to 5000 ; operation t; social factor CSO was initially proposed by Cheng and Jin  to tacklThe tuning parameters we used in the CSO algorithm for finding the optimal designs are similar to those suggested by Cheng and Jin . For exaG-optimal design, we set up a two-layer optimization structure for min-max the objective function. The inner optimization step is a low-dimensional maximization problem represented in formula (To search for a  formula  and the  formula , which, x’s that maximize x’s that maximize After finding a design k points. There are 2k variables in the pairs x that maximizes To find the probability measure G-optimality via the equivalence theorem. On average, the CPU time required to solve (G-optimal designs is to first solve the simpler optimization problem in (The CSO-generated designs for the next few examples were found by optimizing  and we cto solve  was abouto solve . This suoblem in  before soblem in .G-optimality criterion values. We display the sensitivity plot of each CSO-generated design across the design space and it confirms the G-optimality of the CSO-generated design. All parameters for the design problems, such as the design space, the elements in the covariance matrix n and m are chosen randomly for illustrative purposes. The error variance The next few tables list CSO-generated designs for various linear mixed models with different assumptions on the covariance structure of the random effects and their D-optimal designs for estimating parameters in a Poisson regression model with possibly interacting factors and some factors are random.This subsection demonstrates the utility and flexibility of CSO to find other types of optimal designs, such as locally Poisson models are commonly used to study count data in a regression setting even though they have restrictive assumptions, such as, requiring the mean and variance of the response to be equal. Negative binomial regression models extend Poisson models when the variance is larger or smaller than the mean response. These models are frequently used in clinical trials when the primary outcome is a count variable, such as the number of falls by an elderly patient, or number of CT scans received in the last three months or modeling the numbers of insurance claims and results. Walters , 2007b uD-optimal designs for two-factor mixed Poisson models with an intercept term have been reported for two models, one with and the other without an interaction term. By using the quasi-information matrix defined in Niaparast and Schwabe (D-optimal designs, including an equivalence theorem to confirm the D-optimality of an approximate design. We recall that minimally supported designs have the number of support points equal to the number of parameters in the mean function and so cannot be used to perform a lack of fit test to check model adequacy. Thus their approach can be restrictive in practice and it is also not clear whether their numerical procedure works for finding D-optimal designs when the model has more interacting factors with random effects or under another design criterion.Locally  Schwabe  and assu Schwabe  providedD-optimal designs in Tables 1, 2 and 3 of Naderi et al. (D-optimal. Interestingly, the results show different design spaces can produce D-optimal designs that are not minimally supported.D-optimal designs for mixed models when some random coefficients are correlated and able to find D-optimal designs that may not be minimally supported. We demonstrate using two Poisson models,one with four and the other with five mixed factors and both have some interaction terms. This is helpful because the bulk of theoretical optimal designs in the literature for nonlinear models have only a couple of additive factors and numerical results for models with interaction terms are also very limited.We applied CSO and found the same locally i et al.  for diffFor the model with four factors, we assume thatThe second example concerns a Poisson regression model with five factors (D-efficiency, the lower bound depends on the maximum value of the sensitivity function of the design. Since CSO is a stochastic algorithm and depends on the initial design and choice of tuning parameters, we ran the algorithm multiple times and reported the minimum of the D-efficiency of the generated design. For this example, the CSO generated design is D-efficiency is A common strategy to try to overcome the above problem is to hybridize the algorithm with another algorithm, such that the hybridized version performs better than either of the algorithms. Another option is to use the equivalence theorem to derive an efficiency lower bound for the generated approximate design without knowing the optimum Pazman . For D-eD-optimal or highly D-efficient. Unlike previous examples, the sensitivity functions are now high dimensional and so it is difficult to plot them and visually appreciate their features. One can discretize the design space using a fine grid evaluate the values of their sensitivity function at every grid point but this can be time-consuming. Another way is to apply CSO to optimize the multi-dimensional sensitivity function and confirm all peaks occur at the design points of the CSO-generated design. We chose the latter option and have verified that the CSO-generated design For models with three or more factors, like in the last two examples, the CSO-generated designs appear to be D and G-optimal approximate designs are equivalent (Kiefer and Wolfowitz It is well known that for regression models with homoscedastic errors, the n-dimensional square matrix whose elements are all ones, let D-optimal design for model (D and G-optimality criteria on different settings for the mixed quadratic model with random components having various covariance matrices. Tables D and G-optimal designs for these models are almost equivalent since their D and G-efficiencies relative to the other are all very close to 1. Additional numerical results not shown here for space consideration, for other models we have investigated with more polynomial or fractional polynomial terms and different types of covariance structures also show that the two types of optimal designs have relative efficiencies very close to 1.Let entclass1pt{minimaD-optimality discussed in Prus and Schwabe (q, the D-criterion for prediction is the logarithm of the product of the q largest eigenvalues of D-optimality, and showed that the D-optimal design in the fixed-effects model is also D-optimal for prediction in the random intercept model.Our numerical results in the tables suggest that the celebrated theorem of Kiefer and Wolfowitz may also apply to hierarchical linear models, but a general proof is elusive at this time. However, we are able to show the equivalence of the two types of designs holds for hierarchical linear models when they contain only a random intercept. To see this, we recall  Schwabe  for the  Schwabe . Assumin Schwabe  used thiG-optimal design for prediction in a hierarchical linear model with a random intercept. Assume We next consider using the entclass1pt{minimafined in .D-optimal for the fixed-effects model, we haveG-optimal for prediction of individual parameters. Therefore, the D-optimal and G-optimal designs are equivalent for prediction in the random-intercept model.It follows that the lower bound in  is\documG-optimal designs are challenging to determine and study because the criterion is not differentiable and they require solving two or more layers of nested optimization problems over different spaces. However, the criterion is compelling and should appeal to researchers interested to design an experiment to estimate the overall response surface. To facilitate greater use of such designs for linear models with random effects, we proposed an effective meta-heuristic algorithm call CSO to find G-optimal designs and developed an equivalence theorem to confirm whether a design is G-optimal.D-optimal designs for Poisson mixed regression models with several interacting factors, where some random effects may be correlated. We also provide R-codes freely and the interested reader can request them from the third author.Additionally, we showed CSO is flexible and can also search for locally


Although cervical cancer is mainly caused by infection with human papillomavirus (HPV), some cervical cancers test negative for HPV. As these HPV-negative cervical cancers are often diagnosed at an advanced stage and have a poor prognosis, it is important to understand their molecular pathology. Here, we provide an overview of the potential causes of false-negative HPV tests, as well as the histology, genetic profile, and pathogenesis of HPV-negative cancers.Cervical cancer is the fourth most common cancer in women worldwide and is predominantly caused by infection with human papillomavirus (HPV). However, a small subset of cervical cancers tests negative for HPV, including true HPV-independent cancers and false-negative cases. True HPV-negative cancers appear to be more prevalent in certain pathological adenocarcinoma subtypes, such as gastric- and clear-cell-type adenocarcinomas. Moreover, HPV-negative cervical cancers have proven to be a biologically distinct tumor subset that follows a different pathogenetic pathway to HPV-associated cervical cancers. HPV-negative cervical cancers are often diagnosed at an advanced stage with a poor prognosis and are expected to persist in the post-HPV vaccination era; therefore, it is important to understand HPV-negative cancers. In this review, we provide a concise overview of the molecular pathology of HPV-negative cervical cancers, with a focus on their definitions, the potential causes of false-negative HPV tests, and the histology, genetic profiles, and pathogenesis of HPV-negative cancers. Cervical cancer is the fourth most common cancer in women worldwide, accounting for approximately 6.5% of all female cancer cases . The leaHPVs are classified by genotype according to differences in their L1 DNA sequence of >10% compared to other established genotypes, numbered by their date of discovery . AlthougNearly all squamous cell carcinomas (SqCCs) of the cervix and most pre-invasive lesions, including high-grade squamous intraepithelial lesions (HSILs)  contain HPV. Although the increased global availability of HPV vaccines has decreased the number of deaths from HPV-positive cervical cancer ,10 and tTM HPV and BD’s OnclarityTM HPV) and transcription-mediated amplification , and non-nucleic acid amplification methods such as hybridization capture  and invader chemistry   and and91] aTP53 (50%), MSH6 (43%), CDKN2A/B (36%), POLE (36%), SLX4 (36%), ARID1A (29%), STK11 (29%), BRCA2 (21%), and MSH2 (21%) ,,85], andKRAS, HRAS, WT1, ER, or TP53. Recently, Lee et al. reported a case of a woman exposed to DES in utero who developed cervical CCC with a somatic mutation in POLE . AR. ARKRAS t tumors % and somKRAS or NRAS mutations were found in a follow-up study of ten mesonephric hyperplasia cases [KRAS mutations, one of which had chromosome 1q gain. Thus, atypical mesonephric hyperplasia could be a pre-invasive lesion in which KRAS mutation and chromosome 1q gain may contribute to the tumorigenesis process of mesonephric carcinoma [FGFR2 mutation was reported [Although mesonephric carcinoma precursor lesions have not yet been fully characterized, no activating ia cases . Notablyarcinoma . Recentlreported . PIK3CA (50%), PTEN (50%), CTNNB1 (3/8%), FBXW7 (25%), KRAS (1/8%), AKT1 (1/8%), and MSI-H (1/8 %) [Primary cervical endometrioid carcinoma arises from the cervix and exhibits endometrioid morphologic characteristics, such as tumor cells lacking mucin with a sparse, eosinophilic cytoplasm resembling endometrial epithelium. These tumors are rare, constituting less than 5% of all cervical adenocarcinomas , and do  (1/8 %) . Therefo (1/8 %) ,116. MolTP53 mutations shared with cervical tumors [Primary cervical serous carcinoma is exceptionally rare and often has a papillary architecture with prominent tufted papillae lined by tumor cells with high-grade nucleus atypia . Howeverl tumors . Collectn = 6, HPV-negative) harbors mutations in TP53 (50%), KRAS (33%), PIK3CA (17%), and PTEN (17%) [To date, no studies have reported the genetic features of HPV-negative serous carcinoma of the cervix, thereby ruling out these two possible differential diagnoses. However, Jenkins et al. recently reported that cervical serous carcinoma (EN (17%) . The posEN (17%) . TherefoEN (17%) , whereasEN (17%)  and can EN (17%) .PIK3CA (2/14%), TP53 (1/14%), and KRAS (1/14%), HER2 amplification, and PTEN copy number loss [Cervical adenosquamous carcinoma (ASC) is defined as a malignant epithelial tumor that exhibits both squamous and glandular differentiation  and repober loss , which pber loss . Both han = 49) had HPV DNA, while 55% harbored HPV16, 41% had HPV18, and 4% were positive for other HPV types [PIK3CA (17% vs. 36%), TP53 (11% vs. 43%), PTEN (8% vs. 50%), ARID1A (5% vs. 36%), and RB1 (4% vs. 36%). Although there was no difference in the frequency of TMB-H between HPV-positive and HPV-negative NECs, only HPV-16 and 18 were tested in this study. Notably, a case of trans-differentiation from cervical mesonephric carcinoma to NEC was recently reported [U2AF1 and GATA3 mutations and MYCN amplification. Furthermore, the NEC component harbored TP53 and MST1R mutations that were not present in the mesonephric carcinoma. Together, these data suggest a clonal origin for the two components of this rare entity, rather than a collision tumor [Neuroendocrine carcinomas (NECs) of the cervix are invasive carcinomas that display neuroendocrine differentiation and account for less than 2% of all cervical cancers. However, these tumors are aggressive and have a high tendency for lymph node metastases and spread to distant organs , with HPPV types . PreviouPV types , with HPPV types  and revereported , which ion tumor . There hon tumor , pancreaon tumor , and endon tumor . When HPPreclinical models of cervical cancer have greatly contributed to the understanding of cervical cancer carcinogenesis and tumor progression. To date, preclinical animal models of HPV-associated cervical squamous cell carcinoma have been developed and used for various research. These studies have revealed the process of tumorigenesis, interactions with the immune system, virus clearance, or establishment of latency ,136. FurPatient-derived xenograft (PDX) models is another valuable animal model which closely resemble the tumor features of patients and retain the molecular and histological features of diseases. A recent systematic review of PDX of cervical cancer revealed among 98 donor patients, 61 CC-PDX were established, and the overall success rate was 62.2% (0% to 75%) . No corrAlthough treatment for cervical cancer is not currently stratified based on HPV positivity ,147, HPVp = 0.01). Interestingly, the association between HPV status and DFS persisted even when adjusting for multiple covariates. No differences were observed in terms of DFS or OS after grouping patients according to histological type (SCCs vs. ADCs) [p < 0.01). HPV-negative cases were more frequently diagnosed at advanced stage  and more frequently had lymph node metastases . Patients with HPV-negative cervical cancer had a significantly shorter DFS  and OS  than patients with HPV-positive cancers [Several studies have reported that women with HPV-negative tumors are more frequently diagnosed at advanced stages, with a higher rate of lymph node metastasis and impaired disease-free survival (DFS) and overall survival (OS) ,148,149.s. ADCs) . In anot cancers . HoweverIt is also becoming clear that HPV-negative cervical cancers have distinct clinicopathological and genetic features, and that their treatment could be tailored to different molecular therapeutic targets. Currently, cervical cancer treatment is not stratified according to HPV status or histology ,152. ForHowever, false-negative HPV tests should also be considered, particularly those that occur because of improper specimen processing . Further


Excessive osteoclast activation is an important cause of imbalanced bone remodeling that leads to pathological bone destruction. This is a clear feature of many osteolytic diseases such as rheumatoid arthritis, osteoporosis, and osteolysis around prostheses. Because many natural compounds have therapeutic potential for treating these diseases by suppressing osteoclast formation and function, we hypothesized that α-mangostin, a natural compound isolated from mangosteen, might be a promising treatment as it exhibits anti‐inflammatory, anticancer, and cardioprotective effects.We evaluated the therapeutic effect of α-mangostin on the processes of osteoclast formation and bone resorption. The receptor activator of nuclear factor-κB (NF-κB) ligand (RANKL) induces osteoclast formation in vitro, and potential pathways of α-mangostin to inhibit osteoclast differentiation and function were explored. A mouse model of lipopolysaccharide‐induced calvarial osteolysis was established. Subsequently, micro-computed tomography and histological assays were used to evaluate the effect of α-mangostin in preventing inflammatory osteolysis.We found that α-mangostin could inhibit RANKL-induced osteoclastogenesis and reduced osteoclast‐related gene expression in vitro. F-actin ring immunofluorescence and resorption pit assays indicated that α-mangostin also inhibited osteoclast functions. It achieved these effects by disrupting the activation of NF-κB/mitogen-activated protein kinase signaling pathways. Our in vivo data revealed that α-mangostin could protect mouse calvarial bone from osteolysis.Our findings demonstrate that α-mangostin can inhibit osteoclastogenesis both in vitro and in vivo and may be a potential option for treating osteoclast-related diseases.The online version contains supplementary material available at 10.1186/s13020-022-00589-5. Bone remodeling is achieved by an appropriate balance between osteoclastic bone resorption and osteoblastic bone formation, and these important metabolic processes regulate bone structure and function . OsteoclReceptor activators of nuclear factor-κB (NF-κB) ligand (RANKL) and macrophage colony-stimulating factor have been shown to mediate osteoclast differentiation by activating different signaling cascades, such as NF-κB and mitogen-activated protein kinase (MAPK) pathways –8. Theseα-mangostin is the most representative xanthone isolated from the pericarp of mangosteen and was reported to have a variety of pharmacological effects . SpecifiHere we studied the effects of α-mangostin on the induction and outcomes of RANKL-induced osteoclastogenesis, then explored its mechanism and discovered that its affects two key signaling pathways: NF-κB and MAPK. Finally, the bone protective effect of α-mangostin was verified in a lipopolysaccharide (LPS)-induced osteolysis calvarial mouse model.α-mangostin  and treated with different concentrations of α-mangostin  in the presence of 25 ng/mL M-CSF and 50 ng/mL RANKL. The culture medium was replaced every 2 days. After culturing for 5 days, the cells were fixed with 4% paraformaldehyde (PFA) and then stained for TRAP based on the instructions. TRAP‐positive multinucleated cells with ≥ 3 nuclei were counted using a light microscope .PBS and DMSO (Sigma-Aldrich) were used as vehicle and negative control for all treatments. Bone marrow cells were acquired from the long bones of 6-week-old male C57BL/6 mice, as described previously , and the3 cells per well) and cultured in complete α-MEM medium for 48 or 96 h with different concentrations of α-mangostin (0–4 µM). Next, CCK8 reagent (10 µL) was added to each well, and the plate was incubated for another 2–4 h. The optical density (OD) was measured using a MR7000 microplate reader  at 450 nm. The viabilities of BMMs exposed to α-mangostin are expressed as a percentage of untreated cells.To test the effects of α-mangostin on BMM viability, Cell Counting Kit 8 (CCK8) assays were performed. Based on the manufacturer’s instructions , cells were seeded in 96-well plates  plasmid were purchased from Genepharma Corporation . All the procedures were followed with the manufacture protocols. The transfected cells were treated with 2 μg/mL puromycin until all the cells in the control group died (untransfected cells). The transfection efficiency of the cells was further confirmed by western blot before they were used in experiments.5 cells per well) were placed in six-well plates and treated with different concentrations of α-mangostin for 5 days with the medium changed every 2 days, then cells were incubated with Hoechst 33342 for 20 min. A fluorescence microscope (Olympus) was utilized to visualize morphological changes of apoptotic cells at 365 nm.Cells (4 × 105 per well) were seeded in six-well plates and cultured with different concentrations of α-mangostin  for 24 h. After washing the cells with phosphate-buffered saline (PBS), they were collected and incubated with the reagents for 20 min in the dark. All steps were carried out according to the manufacturer’s protocols. V-FITC (+)/PI (−) and Annexin V- FITC (+)/PI (+) staining signified cells that were in the early and later stages of apoptosis, respectively. All the samples were analyzed by a flow cytometer .Apoptosis was measured using Annexin V-fluorescein isothiocyanate/propidium iodide (FITC/PI) apoptosis kits . BMMs . The culture medium was replaced every 2 days. After 5 days, the cells were washed twice with PBS, fixed with 4% PFA, and stained for TRAP. TRAP-positive cells with ≥ 3 nuclei were counted as mature osteoclasts under a light microscope.BMMs were planted into a 96-well plate at a density of 8 × 10Total RNA from cultured cells was extracted using the TRIzol reagent  according to the manufacturer’s protocols. Complementary DNA was synthesized from 1 µg of total RNA using PrimeScript RT Master Mix . Real-time PCR was performed using the TB Green Premix Ex Taq kit (TaKaRa Biotechnology) on a StepOnePlus Real-Time PCR System . Each reaction was run at the following conditions: 95 °C for 60 s and then 40 cycles of 95 °C for 10 s, 60 °C for 20 s and 72 °C for 20 s. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as the endogenous control. The mouse primer sequences are shown in Table 3 cells/cm2) onto bovine bone slices and allowed them to adhere overnight. Then cells were treated with different concentrations of α-mangostin  for another 2 days. The cells were then fixed with 4% PFA for 15 min, permeabilized with 0.4% Triton X-100 for 10 min, then stained with rhodamine-conjugated phalloidin  diluted in 0.5% bovine serum albumin (BSA)–PBS for 30 min. Fluorescent images were captured utilizing a fluorescence microscope  and analyzed by ImageJ software . To observe resorption pits, the bone slices were washed twice with PBS, and adhered cells were removed by mechanical brushing. Bone slice images were acquired with a scanning electron microscope  and analyzed by ImageJ software.In order to visualize F-actin rings, BMMs were treated with 25 ng/mL M-CSF and 50 ng/mL RANKL for 4 days. We seeded differentiated osteoclasts (2 × 105 cells per well) and cultured with 25 ng mL × 1 M-CSF and 50 ng mL × 1 RANKL in the presence or absence of 2 µM α-mangostin for 0, 2, 4, or 6 days. Then the cells were collected and lysed with radioimmunoprecipitation assay buffer (Sigma-Aldrich) containing a protease inhibitor and phosphatase inhibitor cocktail (Sigma-Aldrich). According to the steps in the manufacturer’s protocols, the supernatant was collected. Bicinchoninic acid protein assay kits  were used to quantify the total amounts of protein. Equal amounts of protein samples were separated by 8–15% sodium dodecyl sulfate–polyacrylamide gel electrophoresis at 75 V for 1.5 h and subsequently transferred onto 2.2-µm polyvinylidene fluoride membranes  at 250 mA for 2 h in a humid atmosphere. The membranes were blocked with 10% milk or 5% BSA (Sigma-Aldrich) and incubated with the primary antibody overnight at 4 °C. After three washes in Tris-buffered saline with Tween 20 (10 min each), the membranes were incubated with horseradish peroxidase-conjugated secondary antibodies  for 1 h at room temperature. The target bands were developed with enhanced chemiluminescence kits (Millipore).The main signaling pathways affected by α-mangostin were detected by western blotting. Cells were treated with or without 2 µM α-mangostin for 4 h, then stimulated with 50 ng mL × 1 RANKL for 0, 5, 10, 20, 30, or 60 min. To explore the effects of α-mangostin on c-Fos and NFATc1 expression, BMMs were seeded in six-well plates : sham, LPS (vehicle), and LPS + α-mangostin groups (both LPS and α-mangostin were first dissolved in dimethyl sulfoxide and then diluted with PBS). After anesthetization with intraperitoneal sodium pentobarbital (50 mg/kg), the cranial periosteum of mice was separated, and 5 mg/kg body weight (50 μl 10 mM) LPS (Sigma‐Aldrich) in PBS was embedded under the periosteum at the middle suture of the calvaria in the LPS (vehicle) and LPS + (100 μl 5 mM) α-mangostin groups on days 1 and 4, while PBS was injected in the sham group. Mice in the LPS + α-mangostin group also received daily subcutaneous injections of 10 mg/kg α-mangostin for 7 days. Mice in the sham and LPS groups were administered PBS as a control. Α-mangostin doses were determined according to previous studies , 27. AllCalvaria were measured (n = 5 per group) by a high-resolution micro-CT . The scanning protocol was set at an isometric resolution of 9 µm and X-ray energy settings of 80 kV and 80 µA. Then, three-dimensional reconstruction was performed, and a 3 mm × 3 mm region of interest surrounding the midline suture was selected for further qualitative and quantitative analyses. Bone volume/tissue volume (BV/TV), number of porosities, and porosity percentage for each specimen were measured as reported previously .Fixed tissue samples (n = 5 per group) were decalcified in 10% ethylenediaminetetraacetic acid (pH = 7.4) for 2 weeks and then embedded in paraffin. Next, the calvaria were cut into 4-μm-thick histological sections for H&E and TRAP staining. The sections were photographed under a light microscope . Histomorphometric parameters of BV/TV, erosion area, the number of TRAP-positive osteoclasts, and surface area of osteoclasts per bone surface (OcS/BS) were assessed for each sample.All data are expressed as mean ± standard deviation (SD). Each experiment was repeated at least three times, and the results were analyzed with Prism 6.01 . Two-tailed, unpaired Student’s t-tests were used for the comparisons between two groups. One-way analyses of variance with post hoc Newman–Keuls test were performed to analyze differences in multiple comparisons. Differences were considered significant at P < 0.05.Cell viability assays were performed to evaluate a potential cytotoxic effect of mangostin on BMMs. The cells were treated with different concentrations of mangostin  for 48 or 96 h. The CCK-8 results revealed that mangostin caused no obvious BMM cytotoxicity at concentrations ≤ 2 μmol/L  in the presence of M-CSF (25 ng/mL) and RANKL (50 ng/mL). After 5 days of incubation, the osteoclasts had differentiated into BMMs as confirmed by TRAP staining. Notably, α-mangostin reduced osteoclast differentiation in a dose-dependent manner  for 48 h. The RT-PCR results confirmed that α-mangostin suppressed the expression levels of osteoclast-related genes including Ctsk, Dc-stamp, TRAP, CTR, V-ATPase-d2, and NFATc1 in time-dependent Fig. A and dos4 cells/well) were seeded on Osteo Assay Plates and then incubated with RANKL and M-CSF and different concentrations of α-mangostin for 5–6 days. The tight F-actin ring is necessary for osteoclasts to perform bone resorption and can be used to evaluate whether mature osteoclasts are functioning. The immunofluorescence results showed that the shape and size of the F-actin ring were destroyed by α-mangostin in a dose-dependent fashion  in the LPS (vehicle) and LPS + α-mangostin groups. Mice in the LPS + α-mangostin group were intragastrically administered 10 mg/kg α-mangostin every day for 7 days. Mice in the sham and LPS (vehicle) groups were administered PBS intragastrically as a control. After 7 days, the calvaria were collected and fixed in 4% PFA, then analyzed by micro-CT and histology Fig. A. Micro‐Histological assessments further confirmed the results from Micro‐CT showing that α-mangostin had a therapeutic effect against osteolysis. H&E staining showed that LPS induced severe osteolytic changes in the LPS (vehicle) group, but a significantly reduced extent of bone erosion was found in the α-mangostin group Fig. C. MoreovIncreased osteoclast-induced bone resorption is an important factor leading to periprosthetic osteolysis and osteoporosis , 24. Theα-mangostin has extensive biological activities and pharmacological properties; it is antioxidant, antineoplastic, antiproliferation, and induces apoptosis , 25, 26.We examined whether α-mangostin had a toxic effect on BMMs and if it could inhibit the osteoclast differentiation. The CCK-8 results showed that α-mangostin had no obvious inhibitory effect on BMMs at concentrations < 2 μmol/L. To clarify whether the effect of α-mangostin on osteoclast formation is indeed achieved by inhibiting their differentiation rather than promoting apoptosis, we performed flow cytometry and western blotting, and the results were consistent with our previous conclusions. Additional file Under RANKL stimulation, the upregulation of the expression of several specific genes is closely related to osteoclast differentiation . Real‐tiNF‐κB signaling is a classic pathway investigated in osteoclastogenesis studies . When RAPrevious studies reported that JNK1 regulates RANKL-induced osteoclastogenesis by activating the Bcl-2-Beclin1-autophagy pathway, and our findings suggest that the JNK pathway is regulated by α-mangostin . For thiBecause previous results indicated that α-mangostin obstructed RANKL-induced osteoclast formation and decreased the expression of osteoclastic-related genes by blocking the NF-κB and MAPK signaling cascades in vitro, we investigated whether α-mangostin could inhibit pathological osteolysis in an LPS-induced murine calvarial osteolysis model . Micro-CNevertheless, there are several limitations of the current study. First, because there is a balance between osteoclastic bone resorption and osteoblastic bone formation, further research on the effect of α-mangostin on osteoblasts is needed. The present study proved that α-mangostin markedly suppressed RANKL-induced osteoclast formation by inhibiting the pathway in vitro. We still need to study whether α-mangostin also exerts its anti-osteoclastogenic effect through this pathway in vivo. Unfortunately, we did not explore the exact binding target of α-mangostin. Identifying this target should explain why α-mangostin blocks NF-κB and MAPK signaling induced by RANKL. In the early stage of the RANKL pathway, after RANKL binds to RANK, tumor necrosis factor receptor-related factor 6 (TRAF6) is recruited to form a complex that further activates the MAPK and NF-κB pathways , 39. BecOur results demonstrate that α-mangostin inhibited RANKL-induced osteoclastogenesis and bone resorption in vitro. These inhibitory effects were mediated by suppressing RANKL-induced NF-κB and MAPK signaling. In addition, α-mangostin exerted a protective effect against LPS-induced inflammatory bone loss in vivo, indicating that it can be used as a potential drug to prevent or treat osteoclast-related diseases.Additional file 1: Figure S1. (A), When the concentration of α-mangostin is 4 μM, the proportion of apoptotic cells in the flow cytometry result. (B), The results of Hoechst 33342 staining indicated that there was no obvious cell apoptosis when the concentration of mangotin was 2 μM, but after reaching 4 μM, the proportion of apoptotic cells increased significantly. The red arrow means apoptotic cells. scale bar = 500 μm. (C), Expression of apoptosis-related proteins in BMMs treated with different concentrations of α-mangostin, N = 3. (D), The number and area of TRAP-positive cells were analyzed. 1: BMMs were treated with control medium. 2 and 3: BMMs were treated with α-mangostin and berberine. 4: BMMs (ACP5) were treated with α-mangostin. 5: BMMs were pretreated with anisomycin(5 ng/ml) and α-mangostin. scale bar = 200 μm. (E), The number and area of TRAP-positive cells were analyzed, N = 3. Data are presented as mean ± SD. **P < 0.01, compared with the controls; ***P < 0.001, compared with the treated groups.Additional file 2: Figure S2. (A), Verify the effect of BMMs cell transfection at the protein level. (B), Representative images of bone resorption pits were acquired by scanning electron microscopy (SEM). 1: osteoclasts were treated with control medium. 2: osteoclasts were treated with α-mangostin. 3: osteoclasts differentiated from BMMs (ACP5) were treated with α-mangostin. 5: osteoclasts were pretreated with anisomycin(5 ng/ml) and α-mangostin. scale bar = 100 μm. (C), The expression level of p-JNK protein in the presence or absence of anisomycin and α-mangostin. (D), BMMs were pretreated with or without 2 µM α-mangostin for 4 h and then cultured with RANKL for the indicated periods. (E), Pathway-related proteins of each group were measured by western blot. Tissue protein is obtained from the skull by a tissue homogenizer.


The data with regards to the regional variants of distinct HPV types is of great value. Accordance with this, this study aimed to investigate the sequence variations of E6 gene and long control region of HPV 39 among normal, premalignant and malignant cervical samples in order to characterize the frequent HPV 39 variants circulating in Tehran, Iran.In total, 70 cervical samples  infected with HPV 39 were analyzed by nested-PCR and sequencing.Our results revealed that all samples belonged to A lineage. Almost all sequences (98.6%) were classified in A1 sublineage and only one sample (1.4%) was A2 sub lineage.Our findings showed that lineages A, sublineage A1, is dominant in Tehran, Iran. However, the small sample size was the most important limitations of this study. Further studies with larger sample size from different geographical regions of Iran are necessary to estimate the pathogenicity risk of HPV 39 variants in this population.The online version contains supplementary material available at 10.1186/s12985-021-01619-8. Cervical cancer is reported as the fourth most frequent cancer among women worldwide . Human pA distinct HPV type is considered when the DNA sequence of the L1 gene was differ from that of any other characterized type by at least 10%. Isolates of the same HPV type are designated to as lineage and sublineage when the nucleotide sequences of the L1 gene differ by less than 1–10% and 0.5–1%, respectively. Up to date, HPV 39 has two distinct lineages A and B; which lineage A include 2 different sublineages A1 and A2 .While the co-evolution of HPV 16 and HPV 18 variants and human populations is well characterized , 16–19, The data with regards to the regional variants of distinct HPV types is of great value as it would provide a rational for future studies on their evolution, epidemiology, pathogenicity, and biology. Whereas the spreading of HPV types is identified in Iran, there is much less known about HPV variants. Previous studies were investigated the common HPV 16, 18, 31, and 45 variants in Iran –21. RegaTo characterize lineages and sublineages of HPV 39 a cross-sectional study was designed from 2018 to 2020. One-hundred and fifty-six formalin-fixed paraffin-embedded (FFPE) samples  were obtained from Immam-Khomeini hospital in Tehran. Moreover, 55 ThinPrep Pap Test specimens  that were HPV 39 positive were collected from three referral laboratories in Tehran from 2018 to 2020. These samples were previously genotyped by Cobas assay or INNO-LiPA® HPV Genotyping assay. All of study subjects sign the informed consent and all information regard to their medical records were stored on secure system.DNA extraction from ThinPrep Pap Test specimens was carried out by the High Pure Viral Nucleic Acid Kit  according to the manufacturer’s instruction. Genomic DNA from formalin-fixed paraffin-embedded (FFPE) samples was isolated using phenol–chloroform assay according to previously published procedure .http://www.ncbi.nlm.nih.gov/blast/) to find HPV genotypes. All of HPV positive samples that were genotyped by sequencing also screened by nested-PCR with sequence-specific primers of HPV 39 to amplify of E6 gene . Nucleotide sequences were edited using Bioedit software and converted to FASTA format. Finally, edited sequences were blasted using the Blast server  was investigated using nested-PCR with the primer pairs were shown in Table 2, 10 pmol of each primer, and 2 U of Taq DNA polymerase. PCR amplification cycles were as follow for both round: 35 cycles of 95 °C for 20 s, 56 °C for 50 s and 72 °C for 50 s. A reaction mixture without template DNA, as a negative control, was included in every set of PCR run  and the p-value less than 0.05 was considered statistically significant.HPV 39 was detected in 15 FFPE samples . In total, 70 HPV 39-positive cervical samples of normal (n = 45), premalignant (n = 16), and malignant (n = 9) specimens were investigated in this study. The mean age of studied groups was 51.5, 37.5, and 30 years old in malignant, premalignant, and normal samples, respectively.The complete E6 gene nt 107-583) of 70 HPV 39-positive samples were sequenced and compared with the HPV 39 E6 prototype sequence (GeneBank accession number M62849). As indicated in Table 07-583 ofTo distinguish sublineages A1 and A2 from each other, the partial sequence of LCR was investigated. Regard to LCR analysis, five nucleotide substitutions were found in only one of our studied samples at positions of C7203A, G7398T, T7438G, A7571C, and A7702G. Phylogenetic tree analysis of HPV LCR was conducted in MEGA6 by using the Maximum Likelihood method based on the Kimura 2-parameter model . PhylogeP > 0.05).As shown in Table As indicated in Table It is well recognized that the distribution of HPV 16 and 18 variants can be different geographically due to the evolution associated to the population ethnicity –27. HoweAlmost uniform distribution of HPV 39 variants was found in normal and premalignant/malignant groups, as lineage A was dominant. Our result is consistent with few studies that conducted in some geographical regions of the world. One study in the United States of America was investigated the HPV 39 lineages in 479 samples as the most of samples belonged to lineage A (99%) and B lineage was only detected in 1% . AnotherIt is suggested that like HPV 16-related alpha-9 species variants, isolates of alpha-7 species have shown co-divergence of archaic Hominid and HPV variants. It seems that rapid expansion of the host population causing viral variant lineages and sublineages that had occurred almost 0.2–1.0 million years ago . Indeed,Viral variants of each high-risk HPV types show differences in infectivity, long-term persistence, development of precancer lesions, and invasion of HPV containing cell . Indeed,An association between population-based oncogenicity of HPV 39 variants and host genetic variations, particularly HLA class I and II alleles, could be assumed. Although in this regard no data was found for HPV 39, it is indicated that the E-G350 variant of HPV 16 had 4–fivefold higher risk for cancer development among Swedish women with HLA-B*44, HLA-B*51, or HLA-B*57 alleles . A signiThe most important limitations of this study were found to be the small sample size and multiple infections.Our findings showed that lineages A, sublineage A1, is dominant in Tehran, Iran. HPV variant studies have been made important data that could help better understanding the carcinogenicity of different HPV types at molecular level. However, the small sample size and multiple infections were the most important limitations of this study. Further studies with larger sample size of single infection with HPV 39 from different geographical regions of Iran are necessary to estimate the pathogenicity risk of HPV 39 variants in this population. The characterization of HLA in Iranian women with cervical cancer is highly recommended as it may shed a new light to understand a link between HPVs variants and genetic background.Additional file 1. Figure S1: Gel electrophoresis to detect a 616 bp of E6 gene of HPV39. M: 100 bp+3K marker and N: negative control.Additional file 2. Figure S2: Gel electrophoresis to detect a 704 bp of long control region of HPV39. M: 100 bp+3K marker and N: negative control.


Risk factors for the different human papillomavirus (HPV) genotypes are not well understood, although the risk of cancer is known to vary among them. Our aim was to evaluate the association of diverse behavioral and reproductive factors with genotype-specific HPV prevalence among 879 unvaccinated women aged 18–75 years referred to the colposcopy clinic at Helsinki University Hospital in Finland. Cervical swabs for HPV genotyping were collected in the first visit and assessed for 34 high-risk (hr) and low-risk (lr) HPV genotypes. Participants completed a questionnaire on behavioral, reproductive, and lifestyle factors. Differences in genotype-specific HPV prevalence were analyzed overall and in age groups using binary logistic regression. Smoking was associated with higher prevalence in HPV16 compared with other hrHPV genotypes together with decreasing age, being highest among younger women <30 years old, odds ratio (OR) 3.74 (95% CI 1.42–9.88). The later the sexual debut, the more it seemed to protect from HPV16 infection. The best protection was achieved when the sexual debut took place at >20 years of age, with an OR of 0.43 (95% CI 0.23–0.83). This association was not seen with other hrHPV genotypes. Methods of contraception seemed not to have an effect on hrHPV positivity, regardless of the HPV genotype. The genotype specific hrHPV prevalence differs, depending on behavioral factors, especially among younger women referred to colposcopy. Persistent infection with high-risk human papillomavirus (hrHPV) is necessary for the development of cervical precancerous lesions and cancer . Among wIt is not well known, however, whether all risk factors associate with or contribute to the presence or persistence of all HPV genotypes equally. The carcinogenic potential between the different HPV genotypes varies significantly, with HPV16 being the most carcinogenic genotype , having Among women referred to colposcopy, the prevalence of HPV infection is much higher than in the overall screening population, and the HPV infection has likely persisted for a longer period, enough to cause the cytopathological changes. Our objective was to investigate the association between reproductive, behavioral, and other lifestyle-related risk factors and their association with the prevalence of the different HPV genotypes in this unvaccinated, well-screened population referred to colposcopy in Finland.This study was part of a prospective cohort study (ISRCTN10933736) conducted at Helsinki University Hospital in Finland between January 2014 and December 2017, recruiting 1383 women referred to colposcopy according to the Finnish Current Care Guidelines . All parCervical samples were collected in a sample transport medium  for HPV DNA detection at baseline and stored at −20 °C. They were later divided into three aliquots and stored further at −80 °C. One frozen aliquot controlled for the sufficiency of each sample was sent to an HPV reference laboratory at the Karolinska Institute in Stockholm, Sweden for HPV genotyping, which was performed with the Maldi-Tof Luminex assay, as previously described in . The samThe referral cytology results were classified according to the Bethesda system, including atypical squamous cells of undetermined significance (ASCUS), low-grade squamous intraepithelial lesions (LSILs), high-grade squamous intraepithelial lesions (HSILs), atypical squamous cells that cannot exclude HSIL (ASC-H), atypical glandular cells not otherwise specified (AGC-NOS), and atypical glandular cells favoring neoplasia (AG-FN).All participants had a colposcopic examination with punch biopsies according to the discretion of the colposcopist. Concurrent with the colposcopic examination, the women completed a questionnaire concerning parity, recent contraception, age at first sexual intercourse, number of lifetime sex partners, practice of anal or oral sex, having had more than one sex partner (>1) in the last 12 months, termination of pregnancy, miscarriages, alcohol consumption, tobacco use, drug abuse, atopic skin, and use of vitamin D. Those who did not complete the questionnaire at the clinic were invited to participate by text messaging to fill in the same questionnaire through a web-based data collection tool Webropol  designed to support data capture for research studies .Because of the variation of HPV prevalence and background factors by age, we computed the prevalence percentages of all background characteristics from the questionnaire and the corresponding frequencies of all 34 HPV genotypes overall and in three age groups . The HPV genotyping results were categorized as follows: HPV16 only (HPV16), hrHPV without HPV16 (non16-hrHPV), and lrHPV. Non16-hrHPV included all high-risk HPVs except HPV16 . LrHPV included all low-risk HPVs, either presented as a single or multiple infections, not involving an hrHPV. Multiple HPV was defined as infection with two or more (≥2) different HPV genotypes simultaneously.p < 0.05), we stratified the analysis according to the two relevant risk factor groups. Differences in HPV genotype prevalence among age groups were calculated using a chi-square test or Fisher’s exact test when appropriate. All statistical tests were two-sided, with p-values <0.05 considered statistically significant. All analyses were performed using STATA/SE 15 .We used binary logistic regression to calculate the odds ratios (ORs) with 95% confidence intervals (95% CIs) to evaluate the association between the different risk factors and HPV group positivity  overall, stratified for each age group. The reference group for HPV 16 and non16-hrHPV consisted of HPV-negative women and women who were positive for lrHPV (single or multiple), and for lrHPV, the reference group consisted of all HPV-negative women. We further formally tested for interaction between groups of risk factors for different HPV group positivity. Whenever statistically significant interaction was present  completed the questionnaire using a web-based data collection tool and 396 (45.0%) completed the paper version. The age at sampling varied between 19–75 years with a mean age of 36.9 years (SD ± 10.5). Of all 879 women, 234 (26.6%) were under 30 years, 444 (50.5%) were 30–44 years old, and 201 (22.9%) were 45 years or older. Most of the women were referred due to abnormal cytology (94.0%); other reasons included a history of postcoital bleeding (1.0%), persistent HPV infection (1.4%), and a loop electrosurgical excision procedure (LEEP) or follow-up (3.6%).LSIL was the most common referral cytology in the two oldest age groups, whereas HSIL and ASC-H had the highest proportion in the youngest age group . In womeThe baseline prevalence of the different HPV genotypes by age group is presented in Approximately a quarter of the women under 45 years of age were current smokers , but only 18.4% smoked in the group of ≥45-year-old women . SmokersThe majority of the women in this study (62.2%) had experienced their sexual debuts at 16–19 years of age. The later in life the first intercourse was, the lower the prevalence of HPV16 infections was . A later). The increase in risk was highest for HPV16, with ORs ranging from 2.01 to 2.94 in the different age groups among women with 4–20 partners or more. Higher lrHPV prevalence was also significantly associated with multiple partners; however, this risk was not significantly increased statistically after 10 partners or more (The lifetime number of partners was higher and the age at first intercourse was lower in younger women (<30 years) compared with older women (≥45 years). An increasing number of lifetime partners significantly increased the risk of overall hrHPV positivity . The inc or more . YoungerAlmost half of the participants were nulliparous (45.5%), and only 13.8% had had three or more births. An increasing number of births was associated with lower prevalence of HPV16 compared with nulliparity overall . Furthern = 24), with an OR of 3.09 (95%CI 1.03–9.25) (data not shown). The use of vitamin D and atopia protected from HPV16 among women between ages 30–44, and drug abuse increased the risk of non16-hrHPV among all women. Termination of pregnancy, miscarriages, more than one partner within the last 12 months, and the use of alcohol were not associated with the prevalence of different HPV genotypes. Compared to ASCUS or being negative for intraepithelial lesion or malignancy (NILM), HSIL referral cytology among women under 45 years was strongly associated with the increased prevalence of HPV16 and non-16-hrHPV infections  in the whole study population and in all age groups, followed by HPV31 and HPV52. This genotype distribution is in line with previously published data from Finland showing a slightly different HPV genotype profile in the screening population compared with other countries in Europe , where H9.1% in tThe rate of smokers in our study population (24.1%) was higher than in the general population (13%) in Finland . SmokingSurprisingly, among older women, no significant association between smoking and HPV16 positivity was found. This result was likely related to the fact that in the oldest age group, there were less smokers, and HPV16 was less prevalent. While the increasing number of smoking years significantly increased the risk of HPV16 in the two youngest age groups, the risk of HPV16 was not increased in the oldest age group with increased smoking years. Smoking affects the immune system and increases the risk of acquiring new HPV infections, as well as the risk of HPV persistence ,19,20. OAn early age at first intercourse is considered a risk factor of cervical HSIL . InteresThe relationship between the number of lifetime sex partners and HPV infections has been reported consistently in the literature ,5,8, andA clear difference was observed between the youngest and oldest age groups in the number of sex partners and in the age of sexual debut. Younger women had more partners and earlier initiation, which could refer to behavioral changes between generations. This result is consistent with data obtained in earlier population-based studies from Finland and Britain, where HPV16 seroprevalence was found to have increased, the number of sex partners was higher, and the recorded age at first intercourse was lower among younger women ,26.Previous meta-analyses have demonstrated that the use of an IUD was protective for cervical cancer compared with never using an IUD ,28. FurtA high number of births (≥3) had a protective effect against all hrHPV genotypes (and an increasing number of births against HPV16). In contrast to our work, a previous study found an increased risk for HSIL in women with higher numbers of (full-term) pregnancies . HrHPV pThis study was carried out among the patient population of the colposcopy clinic in Helsinki, which is large, with over 4000 colposcopies performed per year. We were not able to take into account the incident character of HPV infections. However, we assume that the infections identified here represent persistent rather than transient infections, as our data consisted of women with a high frequency of high-grade cytology. Questionnaire-based data are susceptible to recall bias, and answers to some points in the questionnaire were missing. However, we conducted a substantial questionnaire for a well-defined group of women who eagerly and on a voluntary basis accepted to participate in this study to firmly expose the background information. Thus, possible differences in recall bias according to HPV genotype are unlikely.The carcinogenic potentials of the different hrHPV genotypes, as well as their prevalence in different ages, vary. HPV infections are affected by various background factors, and these appear to differ between the different HPV genotypes. Younger smokers and women with sexual debuts at early ages were more likely to be positive for HPV16 than for other hrHPV genotypes. The methods of contraception, however, did not seem to have a clear effect on high-risk HPV-positivity regardless of the genotype. Further studies on the modifiable risk factors for the different HPV genotypes are still warranted for better targeting of possible interventions.


This study aims to evaluate the usefulness of liquid-based brush cytology for malignancy diagnosis and HPV detection in patients with suspected oropharyngeal and oral carcinomas, as well as for the diagnosis of tumoral persistence after treatment.Seventy-five patients with suspicion of squamous cell carcinoma of the oropharynx or oral cavity were included. Two different study groups were analyzed according to the date of the sample collection: (1) during the first endoscopy exploration and (2) in the first control endoscopy after treatment for squamous cell carcinoma. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for malignancy diagnosis as well as for HPV-DNA detection on brush cytologies were assessed.Before treatment, the brush cytology showed a sensitivity of 88%, specificity of 100%, and accuracy of 88%. After treatment, it showed a sensitivity of 71%, specificity of 77%, and accuracy of 75%. HPV-DNA detection in cytology samples showed a sensitivity of 85%, specificity of 100%, and accuracy of 91% before treatment and an accuracy of 100% after treatment.Liquid-based brush cytology showed good accuracy for diagnosis of oropharyngeal and oral squamous cell carcinoma before treatment, but its value decreases after treatment. Nevertheless, it is useful for HPV-DNA detection, as well as to monitor the patients after treatment.Brush cytology samples are reliable for the detection of HPV-DNA before and after treatment and may be a useful method to incorporate in the HPV testing guidelines. High-risk human papillomavirus (HPV) infection is a well-established risk factor for a proportion of head and neck squamous cell carcinomas, mostly located in the oropharynx , 2, withThe HPV-related oropharyngeal squamous cell carcinoma (OPSCC) has shown distinctive biology with different epidemiological, clinical, and prognostic features from HPV negative OPSCC. Particularly important are the improved response to treatment and survival of HPV-related tumors over the HPV negative tumors , 6 that Thus, the determination of the HPV status is crucial to discriminate between HPV-related tumors from those which are not. Indeed, the HPV status is required in the American Joint Committee on Cancer (AJCC) 8th edition for the TNM of OPSCC staging classification –12.The detection of HPV is based on immunohistochemical p16 expression as a surrogate marker for transcriptionally active high-risk HPV, HPV-DNA detection by in situ hybridization (ISH), HPV E6/E7 mRNA by ISH, type-specific polymerase chain reaction (PCR) techniques, and real-time PCR assays to quantify viral load , 14. AllPatients’ management might benefit from cytology procedures not only for early diagnosis purposes but also as a follow-up diagnostic tool. This study aims to evaluate the usefulness of liquid-based brush cytology diagnosis and HPV detection for (1) the diagnosis of clinically suspected oropharyngeal and oral carcinomas and (2) the diagnosis of tumoral persistence after treatment for oropharyngeal or oral carcinomas.This cohort study was conducted at the Pathology Department of the Hospital Clínic of Barcelona, Spain. Seventy-five patients, first diagnosed or suspected of a squamous cell carcinoma of the oropharynx or oral cavity, were included in the study between 2015 and 2018 . An aliquot of each sample was processed with liquid-based technology , stained using the Papanicolaou method, and assessed under microscopy. All cases were revised by two pathologists  and one cytotechnologist  blinded to the clinical condition of the patient. Positive cytology was defined as the presence of atypical cells consistent with squamous cell carcinoma.From another aliquot of each sample, HPV detection was performed using the Cobas HPV test (Roche Diagnostics), following published procedures . This teBiopsy of suspected lesions was performed in 60 patients. The biopsies were fixed in neutral-buffered formalin (4%) and processed for histological analysis using standard methods. Histologic diagnosis was rendered in each case from the hematoxylin and eosin–stained tissue section, according to the World Health Organization classification for head and neck tumors  and revip16 staining was performed from formalin-fixed and paraffin-embedded tissue sections measuring 3 µm in thickness which were deparaffinized following antigen retrieval (ethylenediaminetetraacetic acid [pH 9.0] at 958C for 30 min). p16 staining  was performed on a Leica BOND MAX instrument  using the Bond Polymer Refine Detection Kit (Leica). p16 positivity was defined as ≥ 70% tumor cells with strong nuclear and cytoplasmic staining .Specifications on DNA extraction and HPV detection and typing are described elsewhere . BrieflyIn all lesions, the HPV status was assessed following the established guidelines . p16 wasp-value of 0.05 was defined as statistically significant. To evaluate sensitivity, specificity, positive/negative predictive value, and accuracy of cytology test results after treatment, the disease was considered present (SCC persistence) when resulting in a confirmed positive biopsy, whereas no malignancy (absence of SCC persistence) was assessed by the follow-up of patients who did not show tumoral persistence or recurrence in the next 12 months.Data were analyzed using IBM SPSS for windows version 24.0. A From the 75 patients with a suspected carcinoma, a squamous cell carcinoma (SCC) was diagnosed in 73 cases (97%), 72 of which were infiltrative SCC, one with in situ SCC. The remaining 2 lesions (3%) were diagnosed with oropharynx lymphoid hyperplasia.Figure The demographic characteristics and tumor stage of all patients diagnosed with squamous cell carcinoma included in the study are presented in Table n = 40) and oral cavity (n = 20). The test showed high sensitivity and specificity  and high accuracy (88%) for the diagnosis of squamous cell carcinoma  and oral cavity (n = 20). The samples from the oropharynx included the 2 lymphoid hyperplasia that were negative for HPV-DNA and 34 SCC. On these SCCs, HPV-DNA positivity was detected in 11 cytobrush samples , identifying an HPV16 genotype in all but one sample. A discrepancy was observed in two non-keratinizing SCCs , in which the p16 immunostain in the tissue sample was positive, whereas the HPV-DNA detection in the cytobrush sample was negative. In both cases, HPV-DNA testing was re-conducted in the tissue samples showing positivity for HPV16 in the further analysis. Thus, the HPV detection using cytobrush samples from oropharyngeal squamous cell carcinoma showed 85% sensitivity, 100% specificity, and 91% accuracy  and oral cavity (n = 4) tumor site. Patients had been treated with surgery (n = 3), radiotherapy alone (n = 4), and/or a combination with chemotherapy (n = 13). The samples were collected from 36 to 249 (median 136) days after finishing treatment.During the follow-up after treatment, brush cytology was assessed in a total of 20 samples from the oropharynx  showed a sensitivity of 71.4%, specificity of 76.9%, and accuracy of 75% Table .Table 4VHPV-DNA detection was available in 22 cytobrushes performed after treatment, demonstrating 19 HPV-DNA negative cases, and one case HPV16-DNA positive. The HPV-positive case corresponded to an HPV-related SCC treated with chemotherapy and radiotherapy with tumoral persistence 3 months after treatment, confirmed by biopsy and cytology.The 19 HPV negative results were consistent with the HPV expected status after treatment: 8 oropharynx HPV-associated SCC with no tumoral persistence after treatment and 11 SCC that were HPV-non associated SCC before treatment . Thus, the HPV detection using cytobrush samples showed 100% sensitivity, specificity, and accuracy Table .Table 5VIn two cases , the cytobrush sample had insufficient DNA quality or quantity for testing, leading to invalid results.To the best of our knowledge, this is the first study to assess the usefulness of both liquid-based cytology and HPV detection in patients with oropharyngeal and oral cancer before and after treatment. Only Kofler et al. have also evaluated post-treatment samples but only from the oropharynx and for HPV determination .According to our results, the study of morphology on liquid-based cytology obtained by cytobrush before and after treatment may give equivocal results for squamous cell carcinoma diagnosis, although it can be of great utility for the detection of HPV-DNA.The use of cytobrush for further liquid-based cytology is a well-tolerated and minimally invasive technique for collecting samples , 21; howIn addition, we evaluated the value of the cytomorphology for the diagnosis after treatment. To that purpose, the cytology results were evaluated against the persistence or not of SCC, assessed by biopsy or by the follow-up of the patients. To our understanding, to date, no data is rendered on the effectiveness of this technique in samples taken post-treatment since most authors center on the cytology utility as a screening test or for initial diagnosis and not for follow-up , 25. OurConcerning HPV detection, our results correlate with several studies confirming that cytobrush samples are useful in determining HPV status with the Roche Cobas HPV test , 27, 28.In our study, erroneous stratification would be performed if HPV status was based in one method alone. In 2 HPV-associated oropharyngeal carcinomas (6%), HPV-DNA was not detected in the cytology sample. In addition, the HPV status based only on the p16 immunostain could falsely classify the patients as HPV positive or negative, according to the results in the literature, ranging from 4.9 to 26.2% , 33The validation of methods for the correct diagnosis of HPV status in post-treated patients is crucial for prognosis and surveillance. Our results showed that HPV determination in samples after treatment successfully correlates with the HPV expected status post-treatment with an accuracy of 100%. In our series, only one patient with HPV16-related tumor showed tumoral persistence, which correlates with the HPV-DNA positivity in the post-treatment sample. As shown in the data published by Kofler et al., the same genotype as that identified in the pre-treatment analysis was identified in the HPV-positive post-treatment sample. In our study, HPV-associated OPSCC with an HPV negative result after treatment correlated with no tumoral persistence, in agreement with Kofler et al. results . NeverthImportantly, the selection of cytobrush procedure instead of using oral rinses for the extraction of the samples might play a part in the good results observed in our study. Some previous studies have compared the performance of cytobrush samples vs oral rinses samples for HPV determination and found poor agreement between the two methods  and statDue to ethical issues, patients with low suspicion of carcinoma persistence were not biopsied. However, a long follow-up of the patients confirmed the absence of tumoral persistence after treatment in this group of patients. In addition, in our series, only squamous cell carcinomas were evaluated, so the render of cytology in dysplastic lesions was not assessed in our study. To properly address cytology accuracy for diagnosis, the tested samples should comprise more negative samples.All results considered, this study contributes to raising the evidence on the effectiveness of cytobrush for establishing the HPV status in oral and oropharyngeal carcinomas, which could improve the diagnostic approaches and algorithms. This study also confirms the usefulness of these samples to rule out HPV-associated oropharyngeal carcinoma persistence after treatment, as a potential method to monitor the patients.Brush cytology samples are reliable for the detection of HPV DNA before and after treatment and may be a useful method to incorporate in the HPV testing guidelines. Brush cytology morphology showed fair results for the diagnosis of SCC before and after treatment. Further analysis on a larger patient cohort is necessary to confirm our results.


The COVID-19 pandemic and associated public health measures to prevent its spread have important implications for the health and wellbeing of older Veterans. Prior to the pandemic, social isolation was already recognized as a risk for older adults, contributing to increased risk of depression, physical inactivity, and mortality. Stay-at-home orders, social distancing, and transitions to new ways of delivering care have meant that many of the ways in which older Veterans connect with VA and others have changed. Older Veterans and Veterans with serious mental illness (SMI) are especially vulnerable to experience negative impacts from social isolation and loneliness. This symposium will present on four novel and adapted strategies for engaging with older Veterans during the COVID-19 pandemic and beyond: 1) VA Connection Plans, a whole health intervention to promote social connections for older Veterans with and without SMI (Peeples); 2) telehealth adaptations to PEER, an in-person, peer-delivered exercise intervention for older Veterans with SMI ; 3) VA Compassionate Contact Corps, a VA Voluntary Service program to connect older Veterans with friendly volunteers via telephone (Sullivan); and 4) group telehealth interventions to foster social connection among older Veterans and their families (Weiskittle). Kim Van Orden, geropsychologist and director of the Hope Lab (Helping Older People Engage) at the University of Rochester Medical Center, will serve as discussant.