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Human papillomavirus (HPV) is a clear etiology of cervical cancer (CC). However, the associations between HPV infection and DNA methylation have not been thoroughly investigated. Additionally, it remains unknown whether HPV‐related methylation signatures can identify subtypes of CC and stratify the prognosis of CC patients. DNA methylation profiles were obtained from The Cancer Genome Atlas to identify HPV‐related methylation sites. Unsupervised clustering analysis of HPV‐related methylation sites was performed to determine the different CC subtypes. CC patients were categorized into cluster 1 (Methylation‐H), cluster 2 (Methylation‐M), and cluster 3 (Methylation‐L). Compared to Methylation‐M and Methylation‐L, Methylation‐H exhibited a significantly improved overall survival (OS). Gene set enrichment analysis (GSEA) was conducted to investigate the functions that correlated with different CC subtypes. GSEA indicated that the hallmarks of tumors, including KRAS signaling, TNFα signaling via NF‐κB, inflammatory response, epithelial–mesenchymal transition, and interferon‐gamma response, were enriched in Methylation‐M and Methylation‐L. Based on mutation and copy number variation analyses, we found that aberrant mutations, amplifications, and deletions among the MYC, Notch, PI3K‐AKT, and RTK‐RAS pathways were most frequently detected in Methylation‐H. Additionally, mutations, amplifications, and deletions within the Hippo, PI3K‐AKT, and TGF‐β pathways were presented in Methylation‐M. Genes within the cell cycle, Notch, and Hippo pathways possessed aberrant mutations, amplifications, and deletions in Methylation‐L. Moreover, the analysis of tumor microenvironments revealed that Methylation‐H was characterized by a relatively low degree of immune cell infiltration. Finally, a prognostic signature based on six HPV‐related methylation sites was developed and validated. Our study revealed that CC patients could be classified into three heterogeneous clusters based on HPV‐related methylation signatures. Additionally, we derived a prognostic signature using six HPV‐related methylation sites that stratified the OS of patients with CC into high‐ and low‐risk groups. Cervical cancer (CC) patients could be classified into three clusters with distinct survival based on the human papillomavirus (HPV)‐related methylation sites. Specific biological processes, pathways, and genomic alterations could be correlated with the different outcomes in the three clusters. Additionally, we derived a prognostic signature using six HPV‐related methylation sites that stratified the patients with CC into high‐ and low‐risk groups.

In women with locally advanced cancer of the cervix (LACC), staging defines disease extent and guides therapy. Currently, undetected disease outside the radiation field can result in undertreatment or, if disease is disseminated, overtreatment. To determine whether adding fludeoxyglucose F 18 positron emission tomography-computed tomography (PET-CT) to conventional staging with CT of the abdomen and pelvis affects therapy received in women with LACC. A randomized clinical trial was conducted. Women with newly diagnosed histologically confirmed International Federation of Gynecology and Obstetrics stage IB to IVA carcinoma of the cervix who were candidates for chemotherapy and radiation therapy (CRT) were allocated 2:1 to PET-CT plus CT of the abdomen and pelvis or CT alone. Enrollment occurred between April 2010 and June 2014 at 6 regional cancer centers in Ontario, Canada. The PET-CT scanners were at 6 associated academic institutions. The median follow-up at the time of the analysis was 3 years. The analysis was conducted on March 30, 2017. Patients received either PET-CT plus CT of the abdomen and pelvis or CT of the abdomen and pelvis. Treatment delivered, defined as standard pelvic CRT vs more extensive CRT, ie, extended field radiotherapy or therapy with palliative intent. One hundred seventy-one patients were allocated to PET-CT (n = 113) or CT (n = 58). The trial stopped early before the planned target of 288 was reached because of low recruitment. Mean (SD) age was 48.1 (11.2) years in the PET-CT group vs 48.9 (12.7) years in the CT group. In the 112 patients who received PET-CT, 68 (60.7%) received standard pelvic CRT, 38 (33.9%) more extensive CRT, and 6 (5.4%) palliative treatment. The corresponding data for the 56 patients who received CT alone were 42 (75.0%), 11 (19.6%), and 3 (5.4%). Overall, 44 patients (39.3%) in the PET-CT group received more extensive CRT or palliative treatment compared with 14 patients (25.0%) in the CT group (odds ratio, 2.05; 95% CI, 0.96-4.37; P = .06). Twenty-four patients in the PET-CT group (21.4%) received extended field radiotherapy to para-aortic nodes and 14 (12.5%) to common iliac nodes compared with 8 (14.3%) and 3 (5.4%), respectively, in the CT group (odds ratio, 1.64; 95% CI, 0.68-3.92; P = .27). There was a trend for more extensive CRT with PET-CT, but the difference was not significant because the trial was underpowered. This trial provides information on the utility of PET-CT for staging in LACC. ClinicalTrials.gov Identifier: NCT00895349.

BACKGROUNDCervical cancer (CC) remains the fourth leading cause of cancer-related deaths in women globally, with poor prognosis for metastatic and recurrent cases. Although genomic alterations have been extensively characterized, global proteogenomic landscape of the disease is largely under explored.METHODSHere, we present the first genome-wide proteogenomic characterization of CC, analyzing 139 tumor-normal tissue pairs using whole-genome sequencing, transcriptomics, proteomics, and phosphoproteomics.RESULTSWe identified 4 distinct molecular subtypes with unique clinical outcomes: epithelial-mesenchymal transition (EMT, C1), proliferation (C2), immune response (C3), and epithelial differentiation (C4). A 4-protein classifier (CDH13, TP53BP1, NNMT, HSPB1) was developed with strong prognostic and predictive value, particularly for immunotherapy response in subtype C3. Phosphoproteomic profiling uncovered subtype-specific kinase activity, identifying actionable therapeutic targets.CONCLUSIONOur findings further revealed previously uncharacterized somatic copy number alterations, extrachromosomal DNA landscape, and human-HPV fusion peptides, with implications for genetic heterogeneity and therapeutic targets. This study enhances the understanding of cervical cancer through deeper proteogenomic insights and facilitates the development of personalized therapeutic strategies to improve patient outcomes.FUNDINGNoncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0544102);The National Natural Science Foundation of China (82172584); Key Technology R&D Program of Hubei (2024BCB057 and 2025BCB053); National Natural Science Foundation of China (82373260); the \"4+X\" clinical trial programs of Women's Hospital, School of Medicine, Zhejiang University (LY2022004); and the programs of Zhejiang Traditional Chinese Medicine Innovation Team (CZ2024009); and Guangxi Natural Science Foundation (2024GXNSFBA010045).

Cervical cancer remains one of the leading causes of cancer-related deaths worldwide. Here we report the extensive molecular characterization of 228 primary cervical cancers, one of the largest comprehensive genomic studies of cervical cancer to date. We observed notable APOBEC mutagenesis patterns and identified SHKBP1 , ERBB3 , CASP8 , HLA-A and TGFBR2 as novel significantly mutated genes in cervical cancer. We also discovered amplifications in immune targets CD274 (also known as PD-L1 ) and PDCD1LG2 (also known as PD-L2 ), and the BCAR4 long non-coding RNA, which has been associated with response to lapatinib. Integration of human papilloma virus (HPV) was observed in all HPV18-related samples and 76% of HPV16-related samples, and was associated with structural aberrations and increased target-gene expression. We identified a unique set of endometrial-like cervical cancers, comprised predominantly of HPV-negative tumours with relatively high frequencies of KRAS , ARID1A and PTEN mutations. Integrative clustering of 178 samples identified keratin-low squamous, keratin-high squamous and adenocarcinoma-rich subgroups. These molecular analyses reveal new potential therapeutic targets for cervical cancers. This paper describes molecular subtypes of cervical cancers, including squamous cell carcinoma and adenocarcinoma clusters defined by HPV status and molecular features, and distinct molecular pathways that are activated in cervical carcinomas caused by different somatic alterations and HPV types. Genomic and molecular basis of cervical cancer Cervical cancer is one of the main causes of cancer-related deaths worldwide, and 95% of cases result from human papilloma virus (HPV) infection. The Cancer Genome Atlas Research Network now reports the genomic and molecular characterization of 228 primary cervical cancers. The authors identify significantly mutated genes and pathways that differ by cervical cancer subtype, and find that keratin-low squamous, keratin-high squamous and adenocarcinoma-rich clusters are marked by different HPV types and molecular features.

Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. Traditional classification algorithms often require segmentation and feature extraction techniques to detect cervical cancer. In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. Based on limited datasets, transfer learning was applied directly to pap smear images to perform a classification task. A comprehensive comparison of 16 pre-trained models (VGG16, VGG19, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, DenseNet121, DenseNet169, DenseNet201, MobileNet, XceptionNet, InceptionV3, and InceptionResNetV2) were carried out for cervical cancer classification by relying on the Herlev dataset and Sipakmed dataset. A comparison of the results revealed that ResNet50 achieved 95% accuracy both for 2-class classification and for 7-class classification using the Herlev dataset. Based on the Sipakmed dataset, VGG16 obtained an accuracy of 99.95% for 2-class and 5-class classification, DenseNet121 achieved an accuracy of 97.65% for 3-class classification. Our findings indicate that DTL models are suitable for automating cervical cancer screening, providing more accurate and efficient results than manual screening.

Screening for breast cancer and cervical cancer in the newly independent states of the former Soviet Union is largely opportunistic, and countries in the region have among the highest cervical cancer incidence in the WHO European Region. We aimed to compare the stage-specific distributions and changes over time in breast cancer and cervical cancer incidence in the newly independent states of the former Soviet Union. We collected breast cancer and cervical cancer incidence data from official statistics from Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Ukraine, and Uzbekistan for the years 2008–17 by tumour, node, metastasis (TNM) stage, and by age where population-based cancer registry data were available. We used log-linear regression to quantify the changes over time in age-standardised rates. During the period 2013–17, more than 50% of breast cancer cases across the analysed countries, and more than 75% of breast cancer cases in Belarus, Kazakhstan, and Ukraine, were registered at stages I–II. The proportion of stage I breast cancer cases was highest in the screening age group (50–69 years) compared with other ages in Moldova and the Russian registries, but was highest in those aged 15–49 years in Georgia and Ukraine. Breast cancer stage-specific incidence rates increased over time, most prominently for stage I cancers. For cervical cancer, the proportions of cancers diagnosed at a late stage (stages III and IV) were high, particularly in Moldova and Armenia (>50%). The proportion of stage I cervical cancer cases decreased with age in all countries, whereas the proportions of late stage cancers increased with age. Stage-specific incidence rates of cervical cancer generally increased over the period 2008–17. Our results suggest modest progress in early detection of breast cancer in the newly independent states of the former Soviet Union. The high proportions of early-stage disease in the absence of mammography screening (eg, in Belarus) provide a benchmark for what is achievable with rapid diagnosis. For cervical cancer, there is a need to tackle the high burden and unfavourable stage-specific changes over time in the region. A radical shift in national policies away from opportunistic screening toward organised, population-based, quality-assured human papillomavirus vaccination and screening programmes is urgently needed. Union for International Cancer Control, WHO Regional Office for Europe, and Ministry of Health of Ukraine.

Endocervical adenocarcinomas (ECAs) are currently classified according to the 2014 World Health Organization (WHO) system, which is predominantly based on descriptive morphologic characteristics, considers factors bearing minimal etiological, clinical, or therapeutic relevance, and lacks sufficient reproducibility. The 2017 International Endocervical Adenocarcinoma Criteria and Classification (IECC) system was developed by a group of international collaborators to address these limitations. The IECC system separates ECAs into two major groups—those that are human papillomavirus-associated (HPVA) and those that are non-HPV-associated (NHPVA)—based on morphology (linked to etiology) alone, precluding the need for an expensive panel of immunohistochemical markers for most cases. The major types of HPVA ECA include the usual (with villoglandular and micropapillary architectural variants) and mucinous types (not otherwise specified [NOS], intestinal, signet-ring, and invasive stratified mucin-producing carcinoma). Invasive adenocarcinoma NOS is morphologically uninformative, yet considered part of this group when HPV positive. NHPVA ECAs include gastric, clear cell, endometrioid, and mesonephric types. The IECC system is supported by demographic and clinical features (HPVA ECAs develop in younger patients, are smaller, and are diagnosed at an earlier stage), p16/HPV status (almost all HPVA ECAs are p16 and/or HPV positive), prognostic parameters (NHPVA ECAs more often have lymphovascular invasion, lymph node metastases, and are Silva pattern C), and survival data (NHPVA ECAs are associated with worse survival). A move from the morphology-based WHO system to the IECC system will likely provide clinicians with an improved means to diagnose and classify ECAs, and ultimately, to better personalize treatment for these patients.

AimsThe International Endocervical Adenocarcinoma Criteria and Classification (IECC) was recently proposed as an improved method for categorising endocervical adenocarcinoma (EA) into human papillomavirus (HPV)–associated adenocarcinomas (HPVAs) and non-HPV-associated adenocarcinomas (NHPVAs). Such categorisation correlates with patient age and tumour size; however, its association with patient outcome remains to be established.MethodsInstitutional cases of EA with histological material available were selected. Three gynaecological pathologists independently classified all tumours according to the IECC with consensus review used when necessary. Clinicopathologic variables were recorded for each case.ResultsOf a total of 87 EAs, 71 (82%) were classified as HPVA and 16 (18%) as NHPVA. Among HPVA, most were usual type (51/71, 72%) followed by mucinous not otherwise specified (10/71, 14%) and invasive stratified mucin-producing carcinoma (ISMC, 8/71, 11%). Most NHPVAs were of gastric type (12/16, 71%) followed by clear cell and mesonephric (two each, 12%). Compared with HPVAs, NHPVAs were significantly associated with older age (p<0.001), larger horizontal extent (p=0.013), greater depth of invasion (p=0.003), lymphovascular space invasion (p<0.001), advanced stage (p<0.001) and invasive pattern C (p<0.001). On univariate analysis, worse disease-free survival (DFS) and disease-specific survival (DSS) correlated with NHPVA group. Among the HPVA subtypes, ISMC showed worse DFS and DSS compared with other HPVA types.ConclusionsThe simple morphological approach of the IECC appears to be prognostically valuable. NHPVA (in particular gastric type) and ISMC (a recently recognised subset of HPVA) have an adverse outcome and their recognition following the IECC is important. We provide further evidence to replace the current WHO classification with the IECC.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network. Firstly, the SE module is embedded in DenseNet121 to increase the model’s focus on the nucleus region, which contains important diagnostic information, and reduce the focus on redundant information. Secondly, the sizes of the convolutional kernel and pooling window of the Stem layer are adjusted to adapt to the characteristics of the cervical cell images, so that the model can extract the local detailed information more effectively. Finally, the Atrous Dense Block (ADB) is constructed, and four ADB modules are integrated into DenseNet121 to enable the model to acquire global and local salient feature information. The accuracy of A2SDNet121 for two and seven-classification tasks on the Herlev dataset is 99.75% and 99.14%, respectively. The accuracy for two, three, and five-classification tasks on the SIPaKMeD dataset reaches 99.55%, 99.75% and 99.22%, respectively. Compared with other state-of-the-art algorithms, the A2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening.

Cervical cancer is caused in the vast majority of cases by the human papilloma virus (HPV) through sexual contact and requires a specific molecular-based analysis to be detected. As an HPV vaccine is available, the incidence of cervical cancer is up to ten times higher in areas without adequate healthcare resources. In recent years, liquid cytology has been used to overcome these shortcomings and perform mass screening. In addition, classifiers based on convolutional neural networks can be developed to help pathologists diagnose the disease. However, these systems always require the final verification of a pathologist to make a final diagnosis. For this reason, explainable AI techniques are required to highlight the most significant data to the healthcare professional, as it can be used to determine the confidence in the results and the areas of the image used for classification (allowing the professional to point out the areas he/she thinks are most important and cross-check them against those detected by the system in order to create incremental learning systems). In this work, a 4-phase optimization process is used to obtain a custom deep-learning classifier for distinguishing between 4 severity classes of cervical cancer with liquid-cytology images. The final classifier obtains an accuracy over 97% for 4 classes and 100% for 2 classes with execution times under 1 s (including the final report generation). Compared to previous works, the proposed classifier obtains better accuracy results with a lower computational cost. Graphical abstract