Recent Multiomics Approaches in Endometrial Cancer

Fractal Dimension, Circularity, and Solidity of Cell Clusters in Liquid-Based Endometrial Cytology Are Potentially Useful for Endometrial Cancer Detection and Prognosis Prediction

Endometrial cancer (EC) in women is increasing globally, necessitating improved diagnostic methods and prognosis prediction. While endometrial histology is the conventional approach, liquid-based endometrial cytology may benefit from novel analytical techniques for cell clusters. A clinical study was conducted at the University of Fukui Hospital from 2012 to 2018, involving 210 patients with endometrial cytology. The liquid-based cytology images were analyzed using cell cluster analysis with Image J software. Logistic regression, ROC analysis, and survival analysis were employed to assess the diagnostic accuracy and prognosis between cell cluster analysis and EC/atypical endometrial hyperplasia (AEH). Circularity and fractal dimension demonstrated significant associations with EC and AEH, regardless of age and cytology results. The ROC analysis revealed improved diagnostic accuracy when combining fractal dimension with cytology, particularly in menopausal age groups. Lower circularity and solidity were independently associated with poor overall survival, while higher fractal dimension values correlated with poorer overall survival in Grades 2 and 3 endometrial cancers. The combination of circularity and fractal dimension with cytology improved diagnostic accuracy for both EC and AEH. Moreover, circularity, solidity, and fractal dimension may serve as prognostic indicators for endometrial cancer, contributing to the development of more refined screening and diagnostic strategies.

Comparative Assessment of miR-185-5p and miR-191-5p Expression: From Normal Endometrium to High-Grade Endometrial Cancer

Endometrial cancer (EC) is a significant cause of cancer-related deaths in women. MicroRNAs (miRs) play a role in cancer development, acting as oncogenes or tumor suppressors. This study evaluated the diagnostic potential of hsa-miR-185-5p and hsa-miR-191-5p in EC and their correlation with clinical and histopathological features. A cross-sectional study analyzed formalin-fixed, paraffin-embedded tissue samples from 59 patients: 18 with EC, 21 with endometrial hyperplasia (EH), 17 with normal endometrium (NE), and 3 with endometrial polyps (EPs). Quantitative reverse transcription-polymerase chain reaction and TaqMan probes were used for miR expression analysis. The Shapiro-Wilk test was used to analyze the normal distribution of the data. Subsequently, parametric or non-parametric tests were used to evaluate the associations between the expression levels of each miR and clinical parameters. Both miRs were underexpressed in some precursor and malignant lesions compared to certain NE subtypes and benign lesions. Specifically, hsa-miR-185-5p showed underexpression in grade 3 EC compared to some NE and EH subtypes (FC: -57.9 to -8.5, p < 0.05), and hsa-miR-191-5p was underexpressed in EH and EC compared to secretory endometrium and EPs (FC: -4.2 to -32.8, p < 0.05). SETD1B, TJP1, and MSI1 were common predicted target genes. In conclusion, hsa-miR-185-5p and hsa-miR-191-5p are underexpressed in EC tissues, correlating with histopathological grades, highlighting their potential as diagnostic biomarkers and their role as tumor suppressors in EC.

Upregulation of sperm-associated antigen 5 expression in endometrial carcinoma was associated with poor prognosis and immune dysregulation, and promoted cell migration and invasion

Sperm-associated antigen 5 (SPAG5) regulates cancer cell invasion and is involved in the progression of many cancers. However, the role of SPAG5 in endometrial carcinoma (EC) is still unknown. The purpose of this study was to explore the role of SPAG5 in EC and its potential molecular mechanism. The UALCAN tool and cBioPortal were used to analyze the expression and alterations of SPAG5 in EC, respectively. OncoLnc was used for survival analysis. We analyzed the effects of SPAG5 on immune cell infiltration and the expression levels of immune checkpoints. We also overexpressed and knocked down SPAG5 in EC cells to explore the effect of SPAG5 regulation on migration, invasion, apoptosis, and the cell cycle of EC cells. We found that SPAG5 was overexpressed and the SPAG5 gene was often mutated in EC. High SPAG5 expression was significantly associated with poor overall survival in patients with EC. SPAG5 also affected the level of immune cell infiltration in the TIME and the expression of immune checkpoints lymphocyte activating 3 (LAG3) and T cell immunoreceptor with Ig and ITIM domains (TIGIT) in patients with EC. It may also be involved in the immunotherapy response in these patients. In vitro experiments showed that SPAG5 promotes cancer cell migration and invasion. In conclusion, this study lays the foundation for further understanding the molecular mechanisms of EC involving SPAG5 and contributes to diagnosing and managing this disease.

Comparative Analysis of Shapley Values Enhances Transcriptomics Insights across Some Common Uterine Pathologies

Uterine pathologies pose a challenge to women's health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

The usefulness of p16 and COX-2 expression on the prediction of progression to endometrial cancer

OBJECTIVES

Endometrial cancer (EC) is the most commonly diagnosed gynecological cancer. Endometrial hyperplasia (EH) is a more common diagnosis than EC. Endometrial hyperplasia is found in approximately 1.5% of all women presenting with abnormal bleeding. Endometrial hyperplasia progresses to EC, and especially, cancer risk increases in cases with atypical hyperplasia. p16, one of the tumor suppressor proteins involved in the cell cycle, and COX-2, one of the key enzymes of prostaglandin synthesis, are important markers for the diagnosis of both EH and EC. There is lack of consensus in the classification, diagnosis and treatment of EH. The subject of changes in the cell cycle in the progression of endometrial pathologies may help to identify and prevent these affected pathways in the treatment stage. The aim of this study is to investigate the expression of p16 and COX-2 during the development of EC from EH.

MATERIAL AND METHODS

We investigated COX-2 and P16 expressions in patients with proliferative endometrium, complex/simple endometrial hyperplasia and endometrioid adenocarcinoma.

RESULTS

p16 expression increased in EH and EC (p<0.001). COX-2 expression was increased in endometrial cancer compared to other groups, but this increase was not found to be statistically significant. Although p16 and COX-2 expression were increased in patients with advanced grade/stage, lymphovascular invasion, and >50% of myometrial invasion, this increase was not statistically significant.

CONCLUSIONS

More detailed studies are needed to investigate the prognostic significance of the COX-2 molecule. COX-2 might be a potential biomarker for the prognosis of endometrial cancer and a potential therapeutic target for EC treatment. Also, it might be used to prevent the progression of precursor lesions to invasive EC.

One-pot method for preparing DNA, RNA, and protein for multiomics analysis

Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.

Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer

Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.

Application of patient-derived induced pluripotent stem cells and organoids in inherited retinal diseases

Inherited retinal diseases (IRDs) can induce severe sight-threatening retinal degeneration and impose a considerable economic burden on patients and society, making efforts to cure blindness imperative. Transgenic animals mimicking human genetic diseases have long been used as a primary research tool to decipher the underlying pathogenesis, but there are still some obvious limitations. As an alternative strategy, patient-derived induced pluripotent stem cells (iPSCs), particularly three-dimensional (3D) organoid technology, are considered a promising platform for modeling different forms of IRDs, including retinitis pigmentosa, Leber congenital amaurosis, X-linked recessive retinoschisis, Batten disease, achromatopsia, and best vitelliform macular dystrophy. Here, this paper focuses on the status of patient-derived iPSCs and organoids in IRDs in recent years concerning disease modeling and therapeutic exploration, along with potential challenges for translating laboratory research to clinical application. Finally, the importance of human iPSCs and organoids in combination with emerging technologies such as multi-omics integration analysis, 3D bioprinting, or microfluidic chip platform are highlighted. Patient-derived retinal organoids may be a preferred choice for more accurately uncovering the mechanisms of human retinal diseases and will contribute to clinical practice.

Preoperative Serum Levels of PDGF-AB, PDGF-BB, TGF-α, EGF and ANG-2 in the Diagnosis of Endometrial Cancer

(1) Background: It is relevant to find new diagnostic biomarkers for endometrial cancer. This study aimed to investigate whether PDGF-AB, PDGF-BB, TGF-α, EGF and ANG-2 could be considered new useful markers for diagnosis and survival of endometrial cancer. (2) Methods: A total of 93 women diagnosed with endometrial cancer (EC) and 66 patients with non-cancerous endometrial lesions (NCEL) were included in this study. (3) Results: Median serum levels of PDGF-AB, PDGF-BB, TGF-α, EGF and ANG-2 were significantly higher in the EC group compared to the NCEL group (for PDGF-AB, PDGF-BB, TGF-α and ANG-2, p = 0.0000; for EGF, p = 0.0186). The cut-off level of PDGF-AB was set at 127.69 pg/mL with a sensitivity of 87.1% and a specificity of 66.67% (AUC = 0.78, p < 0.000001). The cut-off level of PDGF-BB was set at 207.86 ng/L with a sensitivity of 82.8% and a specificity of 75.76% (AUC = 0.85, p < 0.000001). The cut-off level of TGF-α was set at 33.85 ng/L with a sensitivity of 82.8% and a specificity of 75.76% (AUC = 0.82, p < 0.000001). The cut-off level of EGF was set at 934.76 pg/mL with a sensitivity of 83.87% and a specificity of 28.79% (AUC = 0.61, p = 0.018472). The cut-off level of ANG-2 was set at 3120.68 pg/mL with a sensitivity of 72.04% and a specificity of 93.94% (AUC = 0.87, p < 0.000001). (4) Conlusion: It was concluded that all the proteins studied could be potential diagnostic markers in endometrial cancer.

Identification of Potentially Novel Molecular Targets of Endometrial Cancer Using a Non-Biased Proteomic Approach

The present study was aimed at identifying novel proteins in endometrial cancer (EC), employing proteomic analysis of tissues obtained after surgery. A differential MS-based proteomic analysis was conducted from whole tissues dissected from biopsies from post-menopausal women, histologically confirmed as endometrial cancer (two endometrioid and two serous; n = 4) or normal atrophic endometrium (n = 4), providing 888 differentially expressed proteins with 246 of these previously documented elsewhere as expressed in EC and 372 proteins not previously demonstrated to be expressed in EC but associated with other types of cancer. Additionally, 33 proteins not recorded previously in PubMed as being expressed in any forms of cancer were also identified, with only 26 of these proteins having a publication associated with their expression patterns or putative functions. The putative functions of the 26 proteins (GRN, APP, HEXA, CST3, CAD, QARS, SIAE, WARS, MYH8, CLTB, GOLIM4, SCARB2, BOD1L1, C14orf142, C9orf142, CCDC13, CNPY4, FAM169A, HN1L, PIGT, PLCL1, PMFBP1, SARS2, SCPEP1, SLC25A24 and ZC3H4) in other tissues point towards and provide a basis for further investigation of these previously unrecognised novel EC proteins. The developmental biology, disease, extracellular matrix, homeostatic, immune, metabolic (both RNA and protein), programmed cell death, signal transduction, molecular transport, transcriptional networks and as yet uncharacterised pathways indicate that these proteins are potentially involved in endometrial carcinogenesis and thus may be important in EC diagnosis, prognostication and treatment and thus are worthy of further investigation.

An EMT-related genes signature as a prognostic biomarker for patients with endometrial cancer

BACKGROUND

The epithelial-mesenchymal transition (EMT) plays an indispensable role in the development and progression of Endometrial cancer (EC). Nevertheless, little evidence is reported to uncover the functionality and application of EMT-related molecules in the prognosis of EC. This study aims to develop novel molecular markers for prognosis prediction in patients with EC.

METHODS

RNA sequencing profiles of EC patients obtained from The Cancer Genome Atlas (TCGA) database were used to screen differential expression genes (DEGs) between tumors and normal tissues. The Cox regression model with the LASSO method was utilized to identify survival-related DEGs and to establish a prognostic signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC) and calibration curve. Eventually, functional enrichment analysis and cellular experiments were performed to reveal the roles of prognosis-related genes in EC progression.

RESULTS

A total of 540 EMT-related DEGs in EC were screened, and subsequently a four-gene risk signature comprising SIRT2, SIX1, CDKN2A and PGR was established to predict overall survival of EC. This risk signature could serve as a meaningfully independent indicator for EC prognosis via multivariate Cox regression (HR = 2.002, 95%CI = 1.433-2.798; P < 0.001). The nomogram integrating the risk signature and clinical characteristics exhibited robust validity and performance at predicting EC overall survival indicated by ROC and calibration curve. Functional enrichment analysis revealed that the EMT-related genes risk signature was associated with extracellular matrix organization, mesenchymal development and cellular component morphogenesis, suggesting its possible relevance to epithelial-mesenchymal transition and cancer progression. Functionally, we demonstrated that the silencing of SIX1, SIRT2 and CDKN2A expression could accelerate the migratory and invasive capacities of tumor cells, whereas the downregulation of PGR dramatically inhibited cancer cells migration and invasion.

CONCLUSIONS

Altogether, a novel four-EMT-related genes signature was a potential biomarker for EC prognosis. These findings might help to ameliorate the individualized prognostication and therapeutic treatment of EC patients.

Potential biomarkers in endometrial cancer: a narrative review

CONTEXT

Every year, approximately 0.4 million women suffer from endometrial cancer (EC) worldwide and it has become the most common gynecological malignancy. Almost 66% of EC cases are diagnosed at an early stage and can be cured by performing surgery while those at an advanced stage turns out to be fatal. Biomarkers of endometrial cancer would be very valuable for screening of women who are at high risk and in detecting the chance of recurrence of disease.

OBJECTIVE

The current article has reviewed studies published on expression of biomarkers and susceptibility to EC.

METHODS

Google Scholar and PubMed were used as searching platforms and we have majorly considered the literature from last 10 years.

RESULTS

Potential biomarkers of EC identified from various studies were summarised.

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case−control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

Clinical Value and Molecular Function of Circulating MicroRNAs in Endometrial Cancer Regulation: A Systematic Review

This systematic review of literature highlights the different microRNAs circulating in the serum or plasma of endometrial cancer patients and their association with clinical and prognostic characteristics in endometrial cancer. This study also investigates the molecular functions of these circulating microRNAs. According to this systematic review, a total of 33 individual circulating miRs (-9, -15b, -20b-5p, -21, -27a, -29b, -30a-5p, -92a, -99a, -100, -135b, -141, -142-3p, -143-3p, -146a-5p, -150-5p, -151a-5p, -186, -195-5p, -199b, -200a, -203, -204, -205, -222, -223, -301b, -423-3p, -449, -484, -887-5p, -1228, and -1290) and 6 different panels of miRs (“miR-222/miR-223/miR-186/miR-204”, “miR-142-3p/miR-146a-5p/miR-151a-5p”, “miR-143-3p/miR-195-5p/miR-20b-5p/miR-204-5p/miR-423-3p/miR-484”, “mir-9/miR-1229”, “miR-9/miR-92a”, and “miR-99a/miR-199b”) had a significant expression variation in EC patients compared to healthy patients. Also, seven individual circulating miRs (-9, -21, -27a, -29b, -99a, -142-3p, and -449a) had a significant expression variation according to EC prognostic factors such as the histological type and grade, tumor size, FIGO stage, lymph node involvement, and survival rates. One panel of circulating miRs (“-200b/-200c/-203/-449a”) had a significant expression variation according to EC myometrial invasion. Further studies are needed to better understand their function and circulation.