Use of protein-based biomarkers of exfoliated cervical cells for primary screening of cervical cancer

Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer

Objective

To investigate the performance of multiparametric magnetic resonance imaging (MRI)-based radiomics models in differentiating early stage of cervical cancer (Stage I-IIa vs. IIb-IV).

Methods

One hundred patients with cervical cancer who underwent preoperative MRI between June 2020 and March 2022 were retrospectively enrolled. Training (n = 70) and testing cohorts (n = 30) were assigned by stratified random sampling. The clinical and pathological features, including age, histological subtypes, tumor grades, and node status, were compared between the two cohorts by t-test or chi-square test. Radiomics features were extracted from each volume of interest (VOI) on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps. The data balance of the training cohort was resampled by synthesizing minority oversampling techniques. Subsequently, the adiomics signatures were constructed by the least absolute shrinkage and selection operator algorithm and minimum-redundancy maximum-relevance with 10-fold cross-validation. Logistic regression was applied to predict the cervical cancer stages (low [I-IIa]) and (high [IIb-IV] FIGO stages). The receiver operating characteristic curve (area under the curve [AUC]) and decision curve analysis were used to assess the performance of the radiomics model.

Results

The characteristics of age, histological subtypes, tumor grades, and node status were not significantly different between the low [I-IIa] and high [IIb-IV] FIGO stages (p > 0.05 for both the training and test cohorts). Three models based on T2WI, ADC maps, and the combined were developed based on six radiomics features from T2WI and three radiomics features from ADC maps, with AUCs of 0.855 (95% confidence interval [CI], 0.777-0.934) and 0.823 (95% CI, 0.727-0.919), 0.861 (95% CI, 0.785-0.936) and 0.81 (95% CI, 0.701-0.918), 0.934 (95% CI, 0.884-0.984) and 0.902 (95% CI, 0.832-0.972) in the training and test cohorts.

Conclusion

The radiomics models combined T2W and ADC maps had good predictive performance in differentiating the early stage from locally advanced cervical cancer.

For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism

Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.

H-CNN combined with tissue Raman spectroscopy for cervical cancer detection

Cervical cancer is one of the most common cancers with a long latent period and slow onset process. Early and accurate identification of the stage of cervical cancer can significantly improve the cure rate and patient survival time. In this study, we collected 699 Raman spectral data of tissue sections from 233 different patients. We analyzed and compared the characteristics and differences of the mean Raman spectra of the seven tissues and pointed out the main differences in the biochemical composition of the seven tissues. In this study, 1D hierarchical convolutional neural network (H-CNN) is proposed by integrating the prior knowledge of hierarchical classification relations with the research of deep learning in Raman spectroscopy. H-CNN is based on CNN and is added with three network branches. Hierarchical classification is performed from coarse to fine for tissue samples of cervicitis, Low-grade Squamous Cell Carcinoma, High-grade Squamous Cell Carcinoma, Well Differentiated Squamous Cell Carcinoma, Moderately Differentiated Squamous Cell Carcinoma, Poorly Differentiated Squamous Cell Carcinoma and cervical adenocarcinoma. To evaluate the recognition performance of H-CNN, we compared it with traditional methods such as Bayesian classifier (NB), decision tree classifier (DT), support vector machine classifier (SVM) and CNN. The experimental results show that H-CNN can accurately identify different classes of tissue sections and has apparent advantages in several aspects such as recognition accuracy, stability and sensitivity compared with the other four traditional recognition methods. The classification Macro-Accuracy of H-CNN can reach 94.91%, Macro-Recall can reach 95.31%, Macro-F1 can reach 95.23%, and Macro-AUC can reach 97.35%. The hierarchical classification method proposed in this study can diagnose patients more accurately. This could lay the foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Sialic acids in gynecological cancer development and progression: Impact on diagnosis and treatment

Gynecological cancers are in the top 10 of most common cancers in women. Survival and outcome are strongly related to the stage at diagnosis. Therefore, early diagnosis is essential in reducing morbidity and mortality. The high mortality rate of gynecological cancers can mainly be attributed to ovarian cancer (OC). OC is commonly diagnosed at an advanced stage due to a lack of proper screening tools allowing early detection. Endometrial cancer (EC) on the contrary, is mostly diagnosed at an early stage and has, in general, better outcomes. The incidence of nonendometrioid EC has increased in the last decade, displaying a shared tumor biology with OC and consequently significantly worse outcome. New approaches allowing detection of gynecological cancers in an early stage are therefore desired. Recent studies on cancer biology have shown the relevance of altered glycosylation in the occurrence and progression of cancer. The aberrant expression of sialic acid, a specific carbohydrate terminating glycoproteins and glycolipids on the cell‐surface, is frequently correlated with malignancy. We aimed to determine the current understanding of sialic acid function in different gynecological cancers to identify the gaps in knowledge and its potential use for new diagnostic and therapeutic avenues. Therefore we performed a review on current literature focusing on studies where sialylation was linked to gynecological cancers. The identified studies showed elevated levels of sialic acid in serum, tissue and sialylated antigens in most patients with gynecological cancers, underlining its potential for diagnosis.

What's new?

Recent studies have shown the relevance of altered glycosylation in the occurrence and progression of cancer. In this review, the authors found elevated levels of sialic acid in serum and tissue and high levels of sialylated antigens in most patients with gynaecological cancers, underlining the potential of sialic acid for diagnosis. Elevated levels of sialylation were related with tumour growth, poor differentiation, inhibition of apoptosis, and chemoresistance. Taken together, the studies suggest that sialylation levels could be used to discriminate healthy and benign samples from cancer samples and even early and advanced stages in ovarian, cervical, and endometrial cancer.

Squamous cell carcinoma antigen combined with HPV-16 infection in predicting high-grade squamous intraepithelial lesions of the cervix

An early screening of HPV and the Thinprep Cytology Test (TCT) can effectively prevent cervical cancer. However, patients with high-grade cervical intraepithelial neoplasia usually escape current screening methods and commonly develop cervical cancer. Hence, to identify effective and specific screening methods for high-grade cervical intraepithelial neoplasia is of vital necessity. In this study, 541 patients collected in Sun Yat-Sen hospital from January 2007 to December 2016 were selected. HPV genotype detection and SCC-ag detection were done in these patients. It was found that when serum SCC-ag level exceeded over 0.39 ng/ml in HPV-16 positive patients, the sensitivity and specificity of this novel approach to predict high-grade cervical intraepithelial neoplasia could reach to 83.1% and 62.1%, respectively. The result suggested that the combination of serum SCC-ag level and HPV-16 infection could be used as a novel approach for high-grade cervical intraepithelial neoplasia screening.Impact statementWhat is already known on this subject? Patients with a high-grade cervical intraepithelial neoplasia usually escape current screening methods.What do the results of this study add? When serum SCC-ag level exceeded over 0.39 ng/ml in HPV-16 positive patients, the sensitivity and specificity to predict high-grade cervical intraepithelial neoplasia could reach to 83.1 and 62.1%, respectively.What are the implications of these findings for clinical practice and/or further research? Combination of serum SCC-ag level and HPV-16 infection could be used to screen high-grade cervical intraepithelial neoplasia.

Recent advances in heat shock proteins in cancer diagnosis, prognosis, metabolism and treatment

Heat shock proteins (HSPs) are a group of proteins, also known as molecular chaperones, which participate in protein folding and maturation in response to stresses or high temperature. According to their molecular weights, mammalian HSPs are classified into HSP27, HSP40, HSP60, HSP70, HSP90, and large HSPs. Previous studies have revealed that HSPs play important roles in oncogenesis and malignant progression because they can modulate all six hallmark traits of cancer. Because of this, HSPs have been propelled into the spotlight as biomarkers for cancer diagnosis and prognosis, as well as an exciting anticancer drug target. However, the relationship between the expression level of HSPs and their activity and cancer diagnosis, prognosis, metabolism and treatment is not clear and has not been completely established. Herein, this review summarizes and discusses recent advances and perspectives in major HSPs as biomarkers for cancer diagnosis, as regulators for cancer metabolism or as therapeutic targets for cancer therapy, which may provide new directions to improve the accuracy of cancer diagnosis and develop more effective and safer anticancer therapeutics.

Understanding Cervical Cancer through Proteomics

Cancer is one of the leading public health issues worldwide, and the number of cancer patients increases every day. Particularly, cervical cancer (CC) is still the second leading cause of cancer death in women from developing countries. Thus, it is essential to deepen our knowledge about the molecular pathogenesis of CC and propose new therapeutic targets and new methods to diagnose this disease in its early stages. Differential expression analysis using high-throughput techniques applied to biological samples allows determining the physiological state of normal cells and the changes produced by cancer development. The cluster of differential molecular profiles in the genome, the transcriptome, or the proteome is analyzed in the disease, and it is called the molecular signature of cancer. Proteomic analysis of biological samples of patients with different grades of cervical intraepithelial neoplasia (CIN) and CC has served to elucidate the pathways involved in the development and progression of cancer and identify cervical proteins associated with CC. However, several cervical carcinogenesis mechanisms are still unclear. Detecting pathologies in their earliest stages can significantly improve a patient's survival rate, prognosis, and recurrence. The present review is an update on the proteomic study of CC.

Cervical cancer risk profiling: molecular biomarkers predicting the outcome of hrHPV infection

INTRODUCTION

Cervical cancer affects half a million women worldwide annually. Given the association between high-risk human papillomavirus (hrHPV) infection and carcinogenesis, hrHPV DNA testing became an essential diagnostic tool. However, hrHPV alone does not cause the disease, and, most importantly, many cervical lesions regress to normal in a year because of the host immune system. Hence, the low specificity of hrHPV DNA tests and their inability to predict the outcome of infections have triggered a further search for biomarkers.

AREAS COVERED

We evaluated the latest viral and cellular biomarkers validated for clinical use as primary screening or triage for cervical cancer and assessed their promise for prevention as well as potential use in the future. The literature search focused on effective biomarkers for different stages of the disease, aiming to determine their significance in predicting the outcome of hrHPV infections.

EXPERT OPINION

Biomarkers such as p16/Ki-67, hrHPV genotyping, hrHPV transcriptional status, and methylation patterns have demonstrated promising results. Their eventual implementation in the screening programs may support the prompt diagnosis of hrHPV infection and its progression to cancer. These biomarkers will help in making clinical management decisions on time, thus, saving the lives of hrHPV-infected women, particularly in developing countries.

Over Expressed TKTL1, CIP-2A, and B-MYB Proteins in Uterine Cervix Epithelium Scrapings as Potential Risk Predictive Biomarkers in HR-HPV-Infected LSIL/ASCUS Patients

High oncogenic risk human papillomaviruses (HR-HPVs) promote cervical carcinoma development, the fourth most common feminine cancer. A slow oncodevelopmental phase—defined histopathologically as Cervical Intraepithelial Neoplasia (CIN) grades 1–3, or cytologically as Low- or High-grade Squamous Intraepithelial Lesions (LSIL or HSIL)—precedes the malignancy. Cervical carcinoma screenings through HR-HPV genotyping and Pap smears are regularly performed in Western countries. Faulty cytology screening or genotyping or patients' non-compliance with follow-ups can let slip an oncoprogression diagnosis. Novel biomarker tests flanking HR-HPV genotyping and cytology could objectively predict the risk of disease progression thus helping triage LSIL/ASCUS patients. Here, anonymized leftovers of fresh cervical epithelium scrapings from twice (LSIL/ASCUS and HR-HPV DNA)-positive and twice (Pap smear- and HR-HPV DNA)-negative (control) patients in a proteome-preserving solution served to assess the biomarker worth of three cervical carcinoma-related proteins, i.e., B-MYB (or MYBL2), Cancerous Inhibitor of PP2A (CIP-2a), and transketolase-like1 (TKTL1). Leftovers anonymity was strictly kept and storage at −80°C, protein extraction, immunoblotting, and band densitometry were blindly performed. Only after tests completion, the anonymous yet code-corresponding HR-HPV-genotyping and cytology data allowed to assign each sample to the twice-positive or twice-negative group. Descriptive statistics showed that the three proteins levels significantly increased in the twice-positive vs. twice-negative scrapings. Diagnostic ROC curve analysis identified each protein's Optimal Decision Threshold (OTD) showing that TKTL1 and CIP-2a are stronger risk predictive biomarkers (Sensitivity, 0.91–0.93; Specificity, 0.77–0.83) than B-MYB. Logistic Regression coupled with Likelihood-Ratio Tests confirmed that a highly significant relation links increasing TKTL1/CIP-2a/B-MYB protein levels in twice-positive cervical scrapings to the risk of HR-HPV-driven oncoprogression. Finally, a 3 year clinical follow-up showed that 13 patients (50% of total) of the twice-positive group with biomarker values over OTDs compliantly underwent scheduled colposcopy and biopsy. Of these, 11 (i.e., 84.7%) received a positive histological diagnosis, i.e., CIN1 (n = 5; 38.5%) or CIN2/CIN2+ (n = 6; 46,2%). Therefore, TKTL1/CIP-2a/B-MYB protein levels could objectively predict oncoprogression risk in twice (HR-HPV- and Pap smear)-positive women. Further studies will assess the translatability of these findings into clinical settings.