Status Of Cervical Cancer Research Articles

Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer.

To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression. Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses. 89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05). Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.

Homoeopathy in Cervical Cancer

Population growth is related to high fertility rates, thus grading multiparous women in the risk zone of cervical cancer.Cervical cancer is a chronic phenomenon of cervical erosion. Following that the body goes through is the Human Papilloma Virusinfection, which becomes the main reason for cervical cancer. The review article deals with the status of cervical cancer at theglobal level, followed by the situation in India. Further, the article discusses the role and status of HPV vaccines globally andnationally. The role of homeopathy of the Ministry of AYUSH of India in cervical cancer is discussed in the later section of thearticle. Here, it is proposed that a multi-stage treatment protocol based on therapeutics in homoeopathic materia medica will bean effective tool. The article's focus on homeopathy as the therapeutic system which can cover masses through its properties likecost effectiveness, therapeutic effectiveness, and less side effects.

Exploration of glycosyltransferases mutation status in cervical cancer reveals PARP14 as a potential prognostic marker.

This study investigates the potential role of Glycosyltransferases (GTs) in the glycosylation process and their association with malignant tumors. Specifically, the study focuses on PARP14, a member of GTs, and its potential as a target for tumors in the diagnosis and treatment of cervical cancer. To gather data, the study used somatic mutation data, gene expression data and clinical information from TCGA-CESE dataset as well as tissue samples from cervical cancer patients. Further verification was conducted through RT-qPCR and immunohistochemistry staining on cervical cancer tissues to confirm the expression of PARP14. The study utilized Kaplan-Meier for survival analysis of cervical cancer patient and found significant mutational abnormalities in GTs. The high frequency mutated gene was identified as PARP14. RT-qPCR revealed significantly higher mRNA expression of PARP14 compared to precancerous tissue. Using IHC combined with Kaplan-Meier,patients in the PARP14 high expression group had a better prognosis than the low expression group. The study identified PARP14 as a frequently mutated gene in cervical cancer and proposed its potential role in diagnosis and treatment.

Value of non-coding RNAs to assess lymph node status in cervical cancer.

Cervical cancer (CC) is the fourth cancer in women and is the leading cause of cancer death in 42 countries. Lymph node metastasis is a determinant prognostic factor, as underlined in the latest FIGO classification. However, assessment of lymph node status remains difficult, despite the progress of imaging such as PET-CT and MRI. In the specific setting of CC, all data underlined the need for new biomarkers easily available to assess lymph node status. Previous studies have underlined the potential value of ncRNA expression in gynecological cancers. In this review, we aimed to evaluate the contribution of ncRNAs in tissue and biofluid samples to determine lymph node status in CC with potential impact on both surgical and adjuvant therapies. In tissue samples, our analysis found that there are arguments to support the role of ncRNAs in physiopathology, differential diagnosis from normal tissue, preinvasive and invasive tumors. In biofluids, despite small studies especially concerning miRNAs expression, promising data opens up new avenue to establish a non-invasive signature for lymph node status as well as a tool to predict response to neo- and adjuvant therapies, thus improving management algorithm of patients with CC.

Retrospective Analysis of the Predictive Value of 18F-FDG PET/CT Metabolic Parameters for PD-L1 Expression in Cervical Cancer

Immunotherapy targeting PD-1/PD-L1 has been proven to be effective for cervical cancer treatment. To explore non-invasive examinations for assessing the PD-L1 status in cervical cancer, we performed a retrospective study to investigate the predictive value of 18F-FDG PET/CT. The correlations between PD-L1 expression, clinicopathological characteristics and 18F-FDG PET/CT metabolic parameters were evaluated in 74 cervical cancer patients. The clinicopathological characteristics included age, histologic type, tumor differentiation, FIGO stage and tumor size. The metabolic parameters included maximum standard uptake (SUVmax), mean standard uptake (SUVmean), total lesion glycolysis (TLG) and tumor metabolic volume (MTV). In univariate analysis, SUVmax, SUVmean, TLG, tumor size and tumor differentiation were obviously associated with PD-L1 status. SUVmax (rs = 0.42) and SUVmean (rs = 0.40) were moderately positively correlated with the combined positive score (CPS) for PD-L1 in Spearman correlation analysis. The results of multivariable analysis showed that the higher SUVmax (odds ratio = 2.849) and the lower degree of differentiation (Odds Ratio = 0.168), the greater probability of being PD-L1 positive. The ROC curve analysis demonstrated that when the cut-off values of SUVmax, SUVmean and TLG were 10.45, 6.75 and 143.4, respectively, the highest accuracy for predicting PD-L1 expression was 77.0%, 71.6% and 62.2%, respectively. The comprehensive predictive ability of PD-L1 expression, assessed by combining SUVmax with tumor differentiation, showed that the PD-L1-negative rate was 100% in the low probability group, whereas the PD-L1-positive rate was 84.6% in the high probability group. In addition, we also found that the H-score of HIF-1α was moderately positively correlated with PD-L1 CPS (rs = 0.51). The SUVmax and differentiation of the primary lesion were the optimum predictors for PD-L1 expression in cervical cancer. There was a great potential for 18F-FDG PET/CT in predicting PD-L1 status and selecting cervical cancer candidates for PD1/PD-L1 immune checkpoint therapy.

Predictive markers of immunotherapy in cervical cancer

Study of PD-L1 expression in squamous and adenosquamous cell cervical cancer (CC) by immunohistochemical (IHC) method, assessment of the relationship between PD-L1 tumor status and its clinical and morphological characteristics, TILs, MSI/dMMR, and HPV tumor status. Surgical material was obtained from 41 patients with CC, on which the expression of PD-L1, proteins of the MMR system and p16 was studied by the IHC method, the TILs index was determined. Positive PD-L1 status was found in 51.2% of the studied CC samples. In the study sample, the level of PD-L1 expression depended on the severity of lymphoid infiltration of the tumor (p=0.038), it was shown that a positive PD-L1 status of CC can be expected with a TILs value greater than or equal to 50%. The age of the patients, the histological variant of the tumor, the pT and pN stage, the presence of lymphovascular invasion, and the HPV status did not statistically significantly affect the level of PD-L1 expression, however, there was an association between the PD-L1 status and the grade of CC malignancy (p=0.027). The presence of the MSI/dMMR phenomenon was detected in a small percentage of carcinomas (4.9%), the PD-L1 status of these tumors was determined as positive. A positive PD-L1 status is determined in a significant number of cases of CC, regardless of most of the studied clinical and morphological characteristics; there is a statistically significant relationship between PD-L1 expression and the degree of tumor differentiation and TILs. It has been shown that CC with the MSI/dMMR phenomenon is characterized by a positive PD-L1 status. The authors consider it necessary to study the expression of PD-L1 in patients with cervical carcinomas in order to determine the possibility of prescribing personalized therapy with immune checkpoint inhibitors.

Genomic landscape, immune characteristics and prognostic mutation signature of cervical cancer in China

PurposeThis study aimed to analyse the genomic alteration profiles and immune characteristics of a cohort of Chinese cervical cancer patients to understand why certain patients benefited from molecular targeted therapies and immunotherapy as well as their prognostic significance.MethodsPD-L1 expression and clinicopathological information were obtained from 98 cervical cancer patients. Differences in PD-L1 expression and gene mutations between squamous cell carcinoma (SCC) and adenocarcinoma (AC) were analysed by the chi-square test or Fisher's exact test. Differences in gene mutations between our cohort and The Cancer Genome Atlas (TCGA) cohort were tested by Fisher's exact test. Logistic regression was used to analyse factors influencing TMB-high.ResultsPositive PD-L1 expression was significantly higher in cervical SCC than in cervical AC (87% vs. 39%, p < 0.001). Frequently mutated genes in cervical cancer included the PIK3CA, KMT2D, and KMT2C genes, among others. PIK3CA gene mutation rates were significantly higher in SCC than in AC (p = 0.004). The TERT gene mutation rate was significantly higher in our cohort than in the TCGA cohort (12% vs. 1%, p < 0.001). The independent predictors of high TMB were KMT2C and LRP1B gene mutations (p < 0.05). We also found that PTEN mutations were associated with worse survival (median PFS, 12.16 vs. 21.75 months, p = 0.0024).ConclusionCervical SCC and AC have different molecular profiles and immune characteristics, suggesting that targeted treatments for SCC and AC patients may improve clinical outcomes. KMT2C and LRP1B gene mutations are independent predictors of TMB-high status in cervical cancer. We also proposed the prognostic value of PTEN mutations.

Exploration of shared gene signature with development of pre-eclampsia and cervical cancer.

Background: The relationship between pre-eclampsia (PE) and cervical cancer (CC) has drawn more attention recently, while little is known about the shared pathogenesis of CC and PE. In the present research, we aimed to generate the shared gene network as well as the prognostic model to reveal the development of CC and PE. Methods: The transcription data of CC and PE patients were obtained and enrolled into weighted gene co-expression network (WGCNA) analysis. Disease-specific modules in CC and PE were determined to discover the shared genes. The expression patterns of genes at protein level were examined by HPA database. Further, LASSO penalty regression and Cox analysis were applied to create a prognostic signature based on the shared genes, with survival curves and ROC plots employed to confirm the predictive capacity. To uncover the function roles and pathways involved in signature, gene set enrichment analysis (GSEA) was conducted. Finally, the immune infiltration status in CC was depicted using CIBERSORT algorithms. Results: WGCNA determined three hub modules between CC and PE. A total of 117 shared genes were obtained for CC and PE and mainly enriched in cell proliferation, regulation of cell development and neuron differentiation. Then, we created a robust prognostic model based on the 10 shared genes by performing stepwise Cox analyses. Our proposed model presented a favorable ability in prognosis forecast and was correlated with the infiltration of immunocytes including B cells, macrophages and T cells. GSEA disclosed that high-risk group was involved in cancer-related pathways. Conclusion: The present project identified the shared genes to uncover the pathogenesis of CC and PE and further proposed and validated a prognostic signature to accurately forecast the clinical outcomes of CC patients.

Genomic landscape, immune characteristics, and a prognostic mutation signature of advanced cervical cancer in China (162)

Objectives: This study aimed to analyze genomic alteration profiles and immune characteristics of a cohort of Chinese advanced cervical cancer patients to understand why certain patients benefited from molecular targeted therapies, immunotherapy, and their prognostic significance. Methods: PD-L1 expression and clinical-pathologic information from 98 cervical cancer samples were obtained. Cervical cancer data were obtained from The Cancer Genome Atlas (TCGA). Differences in PD-L1 expression and gene mutations between squamous cell carcinoma (SCC) and adenocarcinoma (AC) were analyzed using the Chi-square test or Fisher’s exact test. Differences in gene mutations between our cohort and TCGA cohort were tested by Fisher’s exact test. The relationship between gene mutation and TMB was analyzed by the Mann-Whitney U test. Logistic regression was used to analyze factors influencing TMB-high. Kaplan-Meier survival analyses were applied. Results: Positive PD-L1 expression was significantly higher in cervical SCC than in cervical AC (87% vs 39%, p < 0.001). Frequently mutated genes in cervical cancer included PIK3CA, KMT2D, and KMT2C genes, among others. PIK3CA gene mutation rates were significantly higher in SCC than in AC (p = 0.004). TERT gene mutation rate was significantly higher in our cohort than TCGA cohort (12% vs 1%, p < 0.001). SCC had a significantly higher TMB than AC (p < 0.001). The independent predictors of TMB-high were KMT2C and LRP1B gene mutations (p <0.05). In this study, the median progression-free survival (PFS) was 12.16 months in the PTEN mutant group versus 21.75 months in the wild-type group (p=0.0024). Conclusions: Cervical SCC and AC have different molecular profiling and immune characteristics, suggesting that targeted treatments for SCC and AC patients may improve clinical outcomes. KMT2C and LRP1B gene mutations are independent predictors of TMB-high status in cervical cancer. We have also proposed the prognostic value of PTEN mutation. Objectives: This study aimed to analyze genomic alteration profiles and immune characteristics of a cohort of Chinese advanced cervical cancer patients to understand why certain patients benefited from molecular targeted therapies, immunotherapy, and their prognostic significance. Methods: PD-L1 expression and clinical-pathologic information from 98 cervical cancer samples were obtained. Cervical cancer data were obtained from The Cancer Genome Atlas (TCGA). Differences in PD-L1 expression and gene mutations between squamous cell carcinoma (SCC) and adenocarcinoma (AC) were analyzed using the Chi-square test or Fisher’s exact test. Differences in gene mutations between our cohort and TCGA cohort were tested by Fisher’s exact test. The relationship between gene mutation and TMB was analyzed by the Mann-Whitney U test. Logistic regression was used to analyze factors influencing TMB-high. Kaplan-Meier survival analyses were applied. Results: Positive PD-L1 expression was significantly higher in cervical SCC than in cervical AC (87% vs 39%, p < 0.001). Frequently mutated genes in cervical cancer included PIK3CA, KMT2D, and KMT2C genes, among others. PIK3CA gene mutation rates were significantly higher in SCC than in AC (p = 0.004). TERT gene mutation rate was significantly higher in our cohort than TCGA cohort (12% vs 1%, p < 0.001). SCC had a significantly higher TMB than AC (p < 0.001). The independent predictors of TMB-high were KMT2C and LRP1B gene mutations (p <0.05). In this study, the median progression-free survival (PFS) was 12.16 months in the PTEN mutant group versus 21.75 months in the wild-type group (p=0.0024). Conclusions: Cervical SCC and AC have different molecular profiling and immune characteristics, suggesting that targeted treatments for SCC and AC patients may improve clinical outcomes. KMT2C and LRP1B gene mutations are independent predictors of TMB-high status in cervical cancer. We have also proposed the prognostic value of PTEN mutation.

Diagnostic Value of Multimodal Magnetic Resonance Imaging in Discriminating Between Metastatic and Non-Metastatic Pelvic Lymph Nodes in Cervical Cancer.

BackgroundThe status of pelvic lymph node (PLN) metastasis affects treatment and prognosis plans in patients with cervical cancer. However, it is hard to be diagnosed in clinical practice.PurposeThe present study aimed to evaluate the diagnostic value of multimodal magnetic resonance imaging (MRI) in discriminating between metastatic and non-metastatic pelvic lymph nodes (PLNs) in cervical cancer.MethodsThis retrospective study analyzed MRIs of 209 PLNs in 25 women with pathologically proven cervical cancer. All PLNs had been assessed by pre-treatment multimodal MRIs, and their status was finally confirmed by histopathology. In conventional MRI, lymph node characteristics were compared between metastatic and non-metastatic PLNs. Signal intensity, time–intensity curve (TIC) patterns minimal and mean apparent diffusion coefficients (ADC) were compared between them in DWI. In DCE-MRI, quantitative (Ktrans, Kep and Ve) analyses were performed on DCE-MRI sequences, and their predictive values were analyzed by ROC curves.ResultsOf 209 PLNs, 22 (10.53%) were metastases and 187 (89.47%) were non-metastases at histopathologic examination. Considering a comparison of lymph node characteristics, the short axis size, the long axis size, and the boundary differed significantly between the two groups (P<0.05).The differences in ADCmin, TIC types, Ktrans and Ve between metastatic and non-metastatic PLNs were significant as well (P<0.05). The good diagnostic performance of multimodal MRI was shown in discriminating between metastatic and non-metastatic PLNs, with the sensitivity of 85.0% (17/20), specificity of 97.3% (184/189), and accuracy of 96.2% (201/209). ROC analyses showed that the diagnostic accuracy of ADCmin, Ktrans and Ve for discriminating between metastatic and non-metastatic PLNs in cervical cancer was 83.7%, 91.4%, and 92.4% with the cut-off values of 0.72 × 10−3mm2/s, 0.52 min−1, and 0.53 min−1, respectively.ConclusionMultimodal MRI showed good diagnostic performance in determining PLN status in cervical cancer.

Upregulation of FOXP3+ Regulatory T Lymphocytes and CD8+ Lymphocytes in Patients with High-Grade Squamous Intraepithelial Lesions Correlated with HPV Infection

Abstract Modern therapeutic strategies for precancerous cervical intraepithelial neoplasia (CIN) focus on immune-modulatory and cancer vaccination. The local cervical immune status in cervical cancer and CIN could influence HPV infection persistence, progression and carcinogenesis. We analysed the role of FOXP3+ regulatory T lymphocytes, CD4+ and CD8+ T lymphocytes in CIN I, CIN II and CIN III patients with and without HPV infection. Sixty-two patients were enrolled in the study. Each patient underwent a colposcopy-guided cervical biopsy. FOXP3+ lymphocytes and CD4+,CD8+ lymphocytes were detected by immunostaining in tissue samples obtained from a control group (n = 10), patients with CIN I (n = 20), CIN II (n = 14) and CIN III (n = 18) lesions. HPV was assayed by Aptima. The results showed that the numbers of CD4+ T lymphocytes did not differ between patients with CIN I, CIN II, and CIN III. However, patients with CIN II and CIN III had significantly upregulated CD8+T lymphocytes compared to patients with CIN I. In addition, patients with CIN II and CIN III had increased FOXP3 + T lymphocytes compared with patients with CIN I, which was associated with HPV status. Upregulation of FOXP3+ regulatory T lymphocytes and CD8-positive lymphocytes in patients with CIN II and CIN III suggested a pivotal role of T regulatory lymphocytes and CD8+ lymphocytes for counteracting the host immune response in the progression from CIN I to CIN II and CIN III.

Whole Genome Assembly of Human Papillomavirus by Nanopore Long-Read Sequencing

Human papillomavirus (HPV) is a causal agent for most cervical cancers. The physical status of the HPV genome in these cancers could be episomal, integrated, or both. HPV integration could serve as a biomarker for clinical diagnosis, treatment, and prognosis. Although whole-genome sequencing by next-generation sequencing (NGS) technologies, such as the Illumina sequencing platform, have been used for detecting integrated HPV genome in cervical cancer, it faces challenges of analyzing long repeats and translocated sequences. In contrast, Oxford nanopore sequencing technology can generate ultra-long reads, which could be a very useful tool for determining HPV genome sequence and its physical status in cervical cancer. As a proof of concept, in this study, we completed whole genome sequencing from a cervical cancer tissue and a CaSki cell line with Oxford Nanopore Technologies. From the cervical cancer tissue, a 7,894 bp-long HPV35 genomic sequence was assembled from 678 reads at 97-fold coverage of HPV genome, sharing 99.96% identity with the HPV sequence obtained by Sanger sequencing. A 7904 bp-long HPV16 genomic sequence was assembled from data generated from the CaSki cell line at 3857-fold coverage, sharing 99.99% identity with the reference genome (NCBI: U89348). Intriguingly, long reads generated by nanopore sequencing directly revealed chimeric cellular–viral sequences and concatemeric genomic sequences, leading to the discovery of 448 unique integration breakpoints in the CaSki cell line and 60 breakpoints in the cervical cancer sample. Taken together, nanopore sequencing is a unique tool to identify HPV sequences and would shed light on the physical status of HPV genome in its associated cancers.

Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer.

Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.

Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics.

Simple SummaryTumor budding is a histopathologic characteristic which has led to a growing interest in the prognosis prediction of cancers of various sites. We aimed to evaluate whether imaging biomarkers could predict tumor budding status. Preoperative MRI radiomic features were used as imaging biomarkers. Four machine learning classifiers were applied to build prediction models using a training dataset. Internal validation was performed to validate the built models. As a result, radiomics-based models predicted tumor budding status with a mean area under the receiver operating characteristic value of 0.816 and a mean accuracy of 0.779 in the independent test dataset. Final selected features were mostly from filtered images, implying the importance of filtering methods in radiomics. Preoperative prediction of tumor budding status may help personalize treatment in cervical cancer patients.Background: Our previous study demonstrated that tumor budding (TB) status was associated with inferior overall survival in cervical cancer. The purpose of this study is to evaluate whether radiomic features can predict TB status in cervical cancer patients. Methods: Seventy-four patients with cervical cancer who underwent preoperative MRI and radical hysterectomy from 2011 to 2015 at our institution were enrolled. The patients were randomly allocated to the training dataset (n = 48) and test dataset (n = 26). Tumors were segmented on axial gadolinium-enhanced T1- and T2-weighted images. A total of 2074 radiomic features were extracted. Four machine learning classifiers, including logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (NN), were used. The trained models were validated on the test dataset. Results: Twenty radiomic features were selected; all were features from filtered-images and 85% were texture-related features. The area under the curve values and accuracy of the models by LR, RF, SVM and NN were 0.742 and 0.769, 0.782 and 0.731, 0.849 and 0.885, and 0.891 and 0.731, respectively, in the test dataset. Conclusion: MRI-based radiomic features could predict TB status in patients with cervical cancer.

HPV Infection, HPV Vaccines and Cervical Cancer Awareness: A Multi-Centric Survey Study in Istanbul, Turkey

ABSTRACT Our purpose was to examine the level of knowledge and awareness about HPV (Human papillomavirus) infection, HPV vaccines, and cervical cancer of participants. This study was conducted at students and healthcare workers of Uskudar University as well as the healthcare workers of Umraniye Training and Research Hospital and patients admitted to the gynecology outpatient clinics. A 41-question survey was conducted that investigated the 700 participants’ demographic characteristics, HPV infection and cervical cancer status, and attitudes toward vaccination. 44.1% of the participants stated that they knew the cause of cervical cancer, 42.8% did not. 55.9% of those who went to regular doctor control, 38.5% of those who did not, knew that the HPV vaccine prevented warts and cervical cancer. 63.4% of men and 75.4% of women stated that they did not know whether the vaccine was effective against penile, anal and laryngeal cancer. All these were found to be statistically significant (P < .05). Lack of knowledge on vaccine protection, insufficient knowledge regarding HPV, prevalence of prejudices, and overall opinions about health are the most prevalent public health concerns in Turkey.

Assessment of pelvic lymph node metastasis in FIGO IB and IIA cervical cancer using quantitative dynamic contrast-enhanced MRI parameters.

We prospectively determined whether the quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are useful for predicting pelvic lymph node (LN) status in cervical cancer through node-by-node pathologic validation of images. Overall, 182 LNs harvested from 200 consecutive patients with 2018 FIGO stage IB-IIA cervical cancer (82 metastatic and 100 nonmetastatic) were used for node-by-node assessment. Each LN was quantitatively assessed using Ktrans, Ve, and Kep values. The short-axis diameter, ratio of the long-axis to short-axis diameter, and long-axis diameter were also assessed. Data on metastatic LNs were divided into four groups according to the FIGO staging system. Receiver operating characteristic (ROC) curve analysis was performed to evaluate statistically significant parameters derived from DCE-MRI for the differentiation of metastatic LNs from nonmetastatic LNs. The mean short-axis diameter of metastatic LNs was significantly larger than that of nonmetastatic LNs (all P < 0.05) despite several overlaps. In comparison with nonmetastatic LNs, metastatic LNs showed a significantly lower Ktrans (all P < 0.05); however, Kep and Ve were not significantly different (all P > 0.05). For IB3 and IIA2 cervical cancer, Ktrans had moderate diagnostic ability for differentiating metastatic LNs from nonmetastatic LNs (for IB3: area under the curve [AUC] 0.740, 95% CI 0.657-0.838, 61.7% sensitivity, 80.2% specificity, P = 0.007; for IIA2: AUC 0.786, 95% CI 0.650-0.846, 60.2% sensitivity, 81.8% specificity, P = 0.008). Ktrans appears to be a useful parameter for detecting metastatic LNs, especially for IB3 and IIA2 cervical cancer.

Prognosis prediction signature of seven immune genes based on HPV status in cervical cancer

Cervical cancer (CC) has a high incidence and mortality rate, with a low 5-year survival rate, and human papillomavirus (HPV) is one of its carcinogenic risks. However, little evidence exists on the impact of HPV infection on the survival of patients with CC. In the present study, the CC cohort and immune genes were downloaded from the TCGA database and the ImmPort database, respectively. Subsequently, the Gene Set Enrichment Analysis was performed and found that HPV status was involved in multiple immune signaling pathways, which revealed that HPV infection might play critical roles in the immune response. Then seven prognostic immune genes were identified according to HPV status in CC. Using the seven immune genes, we established an immune risk score (IRS) signature and the Kaplan-Meier curve showed that high IRS was significantly correlated with poor prognosis of CC in both the training sets (HR = 2.32, 95% CI = 1.66–3.33; AUC = 0.712) and the validation sets (HR = 1.38, 95% CI = 1.02–1.85 and AUC = 0.583 in TCGA-HNSCC; HR = 2.58, 95% CI = 1.364–4.893, AUC = 0.676 in GSE44001). A nomogram of IRS combined with clinical features was established, and further analyses demonstrated that the power of the nomogram to predict the prognosis of CC was more reliable than that of a single independent factor. In conclusion, this study provided a more comprehensive understanding of the correlation between HPV and immune mechanisms as well as a novel signature that can effectively predict the prognosis of CC patients.

Aberrant methylation-mediated downregulation of lncRNA CCND2 AS1 promotes cell proliferation in cervical cancer

BackgroundLong non-coding RNA (lncRNA) plays an important role in tumorigenesis. The lncRNA CCND2 AS1 has been shown to be involved in the growth of several tumors; however, its role in cervical cancer has not been elucidated. This study aimed to explore the expression, function, and underlying mechanism of action of CCND2 AS1 in cervical cancer. Expression of CCND2 AS1 was examined in cervical cancer and adjacent normal cervical tissues by quantitative real-time polymerase chain reaction (qRT-PCR) and by bioinformatic analysis of data from the Gene Expression Profiling Interactive Analysis (GEPIA) database. The function of CCND2 AS1 was investigated by overexpressing or silencing CCND2 AS1 in HeLa and SiHa cervical cancer cells followed by in vitro and in vivo analyses. Methylation-specific PCR (MSP) and bisulfite genomic sequencing (BGS) were used to detect CCND2 AS1 promoter methylation status in cervical cancer cells.ResultsCCND2 AS1 expression was lower in cervical cancer compared with normal cervical tissues, and the level was significantly correlated with the patient age and tumor size. CCND2 AS1 overexpression inhibited the proliferation and cell cycle progression of HeLa cells in vitro and/or in vivo, whereas CCND2 AS1 silencing had the opposite effects. CCND2 AS1 expression was elevated after treatment of cervical cancer cells with the DNA methyltransferase inhibitor 5′-azacytidine (5′-Aza), and this was mediated, at least in part, via reduced CpG methylation at the CCND2 AS1 promoter.ConclusionCCND2 AS1 expression is downregulated in cervical cancer, potentially through increased CCND2 AS1 promoter methylation, and the upregulation of CCND2 AS1 expression inhibited tumor growth. These data suggest that CCND2 AS1 could be a diagnostic marker and potential therapeutic target for cervical cancer.

Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer.

Aim: To develop and validate a deep learning radiomics model, which could predict the lymph node metastases preoperatively in cervical cancer patients.Patients and methods: We included a cohort of 226 pathological proven operable cervical cancer patients in two academic medical institutions from December 2014 to November 2017. Then this dataset was split into training set (n = 176) and independent testing set (n = 50) randomly. Five radiomic features were selected and a radiomic signature was established. We then combined these five radiomic features with the preoperative tumor histology and grade of these patients together. Baseline logistic regression model (LRM) and support vector machine model (SVM) were established for the comparison. We then explored the performance of a deep neural network (DNN), which is a popular deep learning model nowadays. Finally, performance of this DNN was validated in another independent test set including 50 cases of operable cervical cancer patients.Results: One thousand forty-five radiomic features were extracted for each patient. Twenty-eight features were found to be significantly correlated with the lymph node status in these patients (P < 0.05). Five radiomic features were further selected for further study due to their higher predictive powers. Baseline LRM incorporating these five radiomic and two clinicopathological features was established, which had an area under receiver operating characteristic curve (ROC) of 0.7372 and an accuracy of 89.20%. The established DNN model had four neural layers, in which layer there were 10 neurons. Adagrad optimizer and 1,500 iterations were used in training. The trained DNN had an area under curve (AUC) of 0.99 and an accuracy of 97.16% in the internal validation. To exclude the overfitting, independent external validation was also performed. AUC and accuracy in test set could still retain 0.90 and 92.00% respectively.Conclusion: This study used deep learning method to provide a comprehensive predictive model using preoperative CT images, tumor histology, and grade in cervical cancer patients. This model showed an acceptable accuracy in the prediction of lymph node status in cervical cancer. Our model may help identifying those patients who could benefit a lot from radiation therapy rather than primary hysterectomy surgery if this model could resist strict testing of future randomized controlled trials (RCTs).

The kinetic profile and clinical implication of SCC-Ag in squamous cervical cancer patients undergoing radical hysterectomy using the Simoa assay: a prospective observational study

BackgroundTo study the kinetic profile and clinicopathological implications of squamous cell carcinoma antigen (SCC-Ag) in cervical cancer patients who underwent surgery by a self-developed SCC-Ag single molecule assay (Simoa) prototype immunoassay.MethodsParticipants were prospectively enrolled between 04/2016 and 06/2017. Consecutive serum samples were collected at five points: day 0 (the day before surgery), postoperative day 4, weeks 2–4, months 2–4 and months 5–7. In total, 92 patients and 352 samples were included. The kinetic change in SCC-Ag levels and their associations with clinicopathological characteristics were studied.ResultsSimoa SCC-Ag was validated by comparison with the Architect assay. SCC-Ag levels measured by the Simoa assay were highly correlated with the Architect assay’s levels (Pearson’s correlation coefficient = 0.979, Passing-Bablok regression slope 0.894 (0.847 to 0.949), intercept − 0.009 (− 0.047 to 0.027)). The median values for each time-point detected by the Simoa assay were 2.49, 0.66, 0.61, 0.72, and 0.71 ng/mL, respectively. The SCC-Ag levels decreased dramatically after surgery and then stabilized and fluctuated to some extent within 6 months. Patients with certain risk factors had significantly higher SCC-Ag values than their negative counterparts before surgery and at earlier time points after surgery, while no difference existed at the end of observation. Furthermore, although patients with positive lymph nodes had sustained higher SCC-Ag levels compared to those with negative lymph nodes, similar kinetic patterns of SCC-Ag levels were observed after surgery. Patients who received postoperative treatment had significantly higher SCC-Ag values than those with surgery only at diagnosis, while no difference existed after treatment.ConclusionsThe Simoa SCC-Ag prototype was established for clinical settings. The SCC-Ag levels were higher in patients with risk factors, whereas the kinetic trend of SCC-Ag might be mainly affected by postoperative adjuvant therapy. These data indicate that the SCC-Ag level might be a good predictor for the status of cervical cancer, including disease aggressiveness and treatment response.