Endometrial Cancer

Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer

BACKGROUND

Among gynaecological malignancies, endometrial cancer (EC) is the most prevalent type of uterine cancer affecting women. This study explored the proteomic profiles of plasma samples obtained from EC patients, those with hyperplasia (Hy), and a control group (CO). A combination of techniques, such as 2D-DIGE, mass spectrometry, and bioinformatics, including pathway analysis, was used to identify proteins with modified expression levels, biomarkers and their associated metabolic pathways in these groups.

METHODS

Thirty-four patients, categorized into three groups-10 with EC, 12 with Hy, and 12 CO-between the ages of 46 and 75 years old were included in the study. Untargeted proteomic analysis was carried out using two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

RESULTS

In all three groups, 114 proteins that were significantly (p ≤ 0.05 and fold change ≥ 1.5) altered were successfully identified using peptide mass fingerprints (PMFs). Compared with those in the control group (CO), the EC samples had 85 differentially expressed proteins (39 upregulated and 46 downregulated), and in the Hy group, 81 proteins were dysregulated (40 upregulated and 41 downregulated) compared to those in the CO group, while 33 proteins exhibited differential regulation (12 upregulated and 21 downregulated) in the EC plasma samples compared to those in the Hy group. Vitamin D binding protein and complement C3 distinguished Hy and EC from CO with the greatest changes in expression. Among the differentially expressed proteins identified, enzymes with catalytic activity represented the largest group (42.9%). In terms of biological processes, most of the proteins were involved in cellular processes (28.8%), followed by metabolic processes (16.7%). STRING analysis for protein interactions revealed that the significantly differentially abundant proteins in the three groups are involved in three main biological processes: signalling of complement and coagulation cascades, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), and plasma lipoprotein assembly, remodelling, and clearance.

CONCLUSION

The identified plasma protein markers have the potential to serve as biomarkers for differentiating between EC and Hy, as well as for early diagnosis and monitoring of cancer progression.

Prognostic evaluation of the novel blueprint of DNA methylation sites by integrating bulk RNA-sequencing and methylation modification data in endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is a prevalent malignancy affecting the female population, with an increasing incidence among younger age groups. DNA methylation, a common epigenetic modification, is well-established to play a key role in cancer progression. We suspected whether DNA methylation could be used as biomarkers for EC prognosis.

METHODS

In the present study, we analyzed bulk RNA-sequencing data from 544 EC patients and DNA methylation data from 430 EC patients in the TCGA-UCEC cohort. We applied weighted correlation network analysis to select a key gene set associated with panoptosis. We conducted correlation analysis between transcriptomic data of the selected key genes and DNA methylation data to identify valuable DNA methylation sites. These sites were further screened by Cox regression and least absolute shrinkage and selection operator analysis. Immune microenvironment differences between high-risk and low-risk groups were assessed using single-sample gene set enrichment analysi, xCell and MCPcounter algorithms.

RESULTS

Our results identified five DNA methylation sites (cg03906681, cg04549977, cg06029846, cg10043253 and cg15658376) with significant prognostic value in EC. We constructed a prognostic model using these sites, demonstrating satisfactory predictive performance. The low-risk group showed higher immune cell infiltration. Notably, methylation of site cg03906681 was negatively related to CD8 T cell infiltration, whereas cg04549977 exhibited positive correlations with immune infiltration, particularly in macrophages, activated B cells, dendritic cells and myeloid-derived suppressor cells. PD0325901_1060 was strongly correlated with risk scores, indicating a potential therapeutic response for high-risk EC patients.

CONCLUSION

We have developed a robust DNA methylation-based prognostic model for EC, which holds promise for improving prognosis prediction and personalized treatment approaches. These findings may contribute to better management of EC patients, particularly in identifying those at higher risk who may benefit from tailored interventions.

Protein disulfide isomerase A3 as novel biomarker for endometrial cancer

Objective

This study aims to investigate the potential of PDIA3 as a novel prognostic biomarker and therapeutic target for Endometrial Cancer (EC) with the ultimate goal of improving survival rates in EC patients.

Methods

This study employed a combination of public database analysis and clinical tissue sample assays. The analysis included comparing the gene expression of PDIA3 between EC and adjacent paracancerous tissues, investigating this expression status using qPCR and immunohistochemistry (IHC) assays, studying the correlation of expression with different parameters using Chi-square test, Cox Regression, and log-rank test, as well as exploring the PDIA3-related immune infiltration and metabolic pathway using TIMER and GSEA.

Results

The analysis of public datasets revealed that PDIA3 mRNA and protein expression was significantly higher in EC tissues compared to adjacent tissues (P = 4.1e-03, P = 1.95e-14, and P = 1.6e-27, respectively). The qPCR analysis supported this finding (P = 0.029). IHC analysis revealed a significant increase in PDIA3 expression in endometrial cancer (EC) tissues compared to adjacent normal tissues (P = 0.01). Furthermore, PDIA3 expression showed significant correlations with cancer stage and tumor grade. Multivariate Cox regression analysis suggested that the PDIA3 gene holds promise as a prognostic factor for EC patients (HR = 0.47, 95% CI [0.27, 0.82], P = 0.008). The results from TIMER demonstrated a positive correlation between PDIA3 and tumor-infiltrating CD8 T cells and macrophages, and a negative correlation with tumor-infiltrating CD4 T cells. Additionally, the GSEA results indicated that PDIA3 overexpression was associated with various metabolic processes in EC patients.

Conclusion

PDIA3 has been validated as a potential biomarker for EC, and its expression is further associated with pathological staging and prognosis.

Diagnostic value of serum human epididymis protein 4, carbohydrate antigen 125 and their combination in endometrial cancer: A meta-analysis

BACKGROUND

To systematically analyze the value of human epididymis protein 4 (HE4) and carbohydrate antigen 125 (CA125) in the diagnosis of endometrial cancer, so as to provide evidence-based medical evidence for the selection of serum tumor markers in the early screening of endometrial cancer.

METHODS

We comprehensively searched relevant literature in the Cochrane Library, EMBASE, PubMed, Web of Science, CNKI, VIP, WanFang, and CBM from the date of establishment to November 31, 2021. Quality assessment of diagnostic accuracy studies 2 was applied to evaluate the quality of the included literature. We used Stata 16.0 to calculate the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) and plot summary receiver operating characteristic curve, as well as to assess diagnostic accuracy using the area under the curve (AUC).

RESULTS

A total of 25 studies, including 1980 patients and 2345 controls, were included in this meta-analysis. The pooled SEN, SPE, PLR, NLR, DOR, and AUC of HE4 were 0.58 (95% CI 0.52-0.63), 0.95 (95% CI 0.92-0.97), 11.57 (95% CI 6.88-19.48), 0.45 (95% CI 0.39-0.51), 25.92 (95% CI 14.84-45.26), and 0.80 (95% CI 0.76-0.83), respectively. The pooled SEN, SPE, PLR, NLR, DOR, and AUC of CA125 were 0.41 (95% CI 0.34-0.49), 0.91 (95% CI 0.85-0.95), 4.55 (95% CI 2.73-7.58), 0.65 (95% CI 0.57-0.74), 7.03 (95% CI 3.92-12.62), and 0.68 (95% CI 0.64-0.72), respectively. The pooled SEN, SPE, PLR, NLR, DOR, and AUC of HE4 + CA125 were 0.67 (95% CI 0.60-0.73), 0.92 (95% CI 0.87-0.95), 8.59 (95% CI 5.32-13.86), 0.36 (95% CI 0.30-0.44), 23.80 (95% CI 13.86-40.86), and 0.85 (95% CI 0.82-0.88), respectively.

CONCLUSION

This Meta-analysis found that HE4 alone or in combination with CA125 showed better diagnostic efficacy than CA125, regardless of clinical stage and pathological type. HE4 + CA125 had slightly higher diagnostic efficiency than HE4, but did not show significant advantages. While the studies were heterogeneous, the credibility of the findings needs to be further confirmed by more homogeneous, prospective, and large sample size studies.

Integrated analysis identifies RAC3 as an immune-related prognostic biomarker associated with chemotherapy sensitivity in endometrial cancer

Endometrial cancer (EC) is one of the most common gynaecological malignant tumours with a high incidence, leading to urgent demands for exploring novel carcinogenic mechanisms and developing rational therapeutic strategies. The rac family of small GTPase 3 (RAC3) functions as an oncogene in various human malignant tumours and plays an important role in tumour development. However, the critical roles of RAC3 in the progression of EC need further investigation. Based on TCGA, single-cell RNA-Seq, CCLE and clinical specimens, we revealed that the RAC3 was specifically distributed in EC tumour cells compared to normal tissues and functioned as an independent diagnostic marker with a high area under curve (AUC) score. Meanwhile, the RAC3 expression in EC tissues was also correlated with a poor prognosis. In detail, the high levels of RAC3 in EC tissues were reversely associated with CD8+ T cell infiltration and orchestrated an immunosuppressive microenvironment. Furthermore, RAC3 accelerated tumour cell proliferation and inhibited its apoptosis, without impacting cell cycle stages. Importantly, silencing RAC3 improved the sensitivity of EC cells to chemotherapeutic drugs. In this paper, we revealed that RAC3 was predominantly expressed in EC and significantly correlated with the progression of EC via inducing immunosuppression and regulating tumour cell viability, providing a novel diagnostic biomarker and a promising strategy for sensitizing chemotherapy to EC.

Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer

Background

Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC.

Methods

One hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI).

Results

In total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754-0.920), 0.875 (95% CI: 0.797-0.952), and 0.923 (95% CI: 0.869-0.977) for the training set; 0.857 (95% CI: 0.741-0.973), 0.785 (95% CI: 0.592-0.979), and 0.914 (95% CI: 0.827-0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214-1.061) and 0.657 (0.079-1.394), and IDIs were 0.115 (0.077-0.306) and 0.053 (0.027-0.357) in the training set and validation set, respectively.

Conclusion

The radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.

Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma

RATIONALE AND OBJECTIVES

To establish a radiomics nomogram for detecting deep myometrial invasion (DMI) in early stage endometrioid adenocarcinoma (EAC).

MATERIALS AND METHODS

A total of 266 patients with stage I EAC were divided into training (n = 185) and test groups (n = 81). Logistic regression were used to identify clinical predictors. Radiomics features were extracted and selected from multiparameter MR images. The important clinical factors and radiomics features were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram. Two radiologists evaluated MR images with or without the help of the nomogram to detect DMI. The clinical benefit of using the nomogram was evaluated by decision curve analysis (DCA) and by calculating net reclassification index (NRI) and integrated discrimination index (IDI).

RESULTS

Age and CA125 were independent clinical predictors. The area under the curves of the clinical parameters, radiomics signature and nomogram in evaluating DMI were 0.744, 0.869 and 0.883, respectively. The accuracies of the two radiologists increased from 79.0% and 80.2% to 90.1% and 92.5% when they used the nomogram. The NRI of the two radiologists were 0.262 and 0.318, and the IDI were 0.322 and 0.405. According to DCA, the nomogram showed a higher net benefit than the radiomics signature or unaided radiologists. Cross-validation showed the outcome of radiomics analysis may not be influenced by changes in field strength.

CONCLUSION

The radiomics nomogram based on radiomics features and clinical factors can help radiologists evaluate DMI and improve their accuracy in predicting DMI in early stage EAC.

Roles and mechanisms of miR-195-5p in human solid cancers

Cancer persists as a worldwide disease that contributes to high morbidity and mortality rates. As a class of non-coding RNA, microRNAs (miRNAs) are one kind of important regulators in cancer and frequently implicated in tumor development and progression. Emerging experiments have suggested that miRNA-195-5p (miR-195-5p) can regulate neoplastic processes in many pathways. For instance, miR-195-5p can not only regulate proliferation, migration and invasion of tumor cells but also promote tumor cell apoptosis. Furthermore, low expression of miR-195-5p could induce drug resistance. Our review focuses on the expression of miR-195-5p in various tumors and elucidates the related mechanisms of which miR-195-5p participates in tumor biology, as well as summarizes the roles of miR-195-5p in tumor progression. We believe that miR-195-5p might have potential utility as a novel diagnostic biomarker and therapeutic target for cancer.

Radiomics Nomogram in Assisting Lymphadenectomy Decisions by Predicting Lymph Node Metastasis in Early-Stage Endometrial Cancer

Background

Lymph node metastasis (LNM) is an important risk factor affecting treatment strategy and prognosis for endometrial cancer (EC) patients. A radiomics nomogram was established in assisting lymphadenectomy decisions preoperatively by predicting LNM status in early-stage EC patients.

Methods

A total of 707 retrospective clinical early-stage EC patients were enrolled and randomly divided into a training cohort and a test cohort. Radiomics features were extracted from MR imaging. Three models were built, including a guideline-recommended clinical model (grade 1-2 endometrioid tumors by dilatation and curettage and less than 50% myometrial invasion on MRI without cervical infiltration), a radiomics model (selected radiomics features), and a radiomics nomogram model (combing the selected radiomics features, myometrial invasion on MRI, and cancer antigen 125). The predictive performance of the three models was assessed by the area under the receiver operating characteristic (ROC) curves (AUC). The clinical decision curves, net reclassification index (NRI), and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of the clinical model and the radiomics nomogram were calculated.

Results

The predictive ability of the clinical model, the radiomics model, and the radiomics nomogram between LNM and non-LNM were 0.66 [95% CI: 0.55-0.77], 0.82 [95% CI: 0.74-0.90], and 0.85 [95% CI: 0.77-0.93] in the training cohort, and 0.67 [95% CI: 0.56-0.78], 0.81 [95% CI: 0.72-0.90], and 0.83 [95% CI: 0.74-0.92] in the test cohort, respectively. The decision curve analysis, NRI (1.06 [95% CI: 0.81-1.32]), and IDI (0.05 [95% CI: 0.03-0.07]) demonstrated the clinical usefulness of the radiomics nomogram.

Conclusions

The predictive radiomics nomogram could be conveniently used for individualized prediction of LNM and assisting lymphadenectomy decisions in early-stage EC patients.

Gel-Based Proteomic Identification of Suprabasin as a Potential New Candidate Biomarker in Endometrial Cancer

Endometrial cancer (EC) is the most frequent gynaecologic cancer in postmenopausal women. We used 2D-DIGE and mass spectrometry to identify candidate biomarkers in endometrial cancer, analysing the serum protein contents of 10 patients versus 10 control subjects. Using gel-based proteomics, we identified 24 candidate biomarkers, considering only spots with a fold change in volume percentage ≥ 1.5 or intensity change ≤ 0.6, which were significantly different between cases and controls (p < 0.05). We used Western blotting analysis both in the serum and tissue of 43 patients for data validation. Among the identified proteins, we selected Suprabasin (SBSN), an oncogene previously associated with poor prognosis in different cancers. SBSN principal isoforms were subjected to Western blotting analysis in serum and surgery-excised tissue: both isoforms were downregulated in the tissue. However, in serum, isoform 1 was upregulated, while isoform 2 was downregulated. Data-mining on the TCGA and GTEx projects, using the GEPIA2.0 interface, indicated a diminished SBSN expression in the Uterine Corpus Endometrial Cancer (UCEC) database compared to normal tissue, confirming proteomic results. These results suggest that SBSN, specifically isoform 2, in tissue or serum, could be a potential novel biomarker in endometrial cancer.

Two Dimensional-Difference in Gel Electrophoresis (2D-DIGE) Proteomic Approach for the Identification of Biomarkers in Endometrial Cancer Serum

Endometrial cancer is the most common gynecologic malignancy arising from the endometrium. Identification of serum biomarkers could be beneficial for its early diagnosis. We have used 2D-Difference In Gel Electrophoresis (2D-DIGE) coupled with Mass Spectrometry (MS) procedures to investigate the serum proteome of 15 patients with endometrial cancer and 15 non-cancer subjects. We have identified 16 proteins with diagnostic potential, considering only spots with a fold change in %V ≥ 1.5 or ≤0.6 in intensity, which were statistically significant (p < 0.05). Western blotting data analysis confirmed the upregulation of CLU, ITIH4, SERPINC1, and C1RL in endometrial and exosome cancer sera compared to those of control subjects. The application of the logistic regression constructed based on the abundance of these four proteins separated the controls from the cancers with excellent levels of sensitivity and specificity. After a validation phase, our findings support the potential of using the proposed algorithm as a diagnostic tool in the clinical stage.