Cheng-Chang Chang

Loss of ten-eleven translocation 1 (TET1) expression as a diagnostic and prognostic biomarker of endometrial carcinoma

Endometrial carcinoma (EC) is the most common gynecological cancer. However, there is currently no routinely used biomarker for differential diagnosis of malignant and premalignant endometrial lesions. Ten-eleven translocation (TET) proteins, especially TET1, were found to play a significant role in DNA demethylation, via conversion of 5-methylcytosine (5-mC) to 5-hydroxymethylcytosine (5-hmC). TET1, 5-mC, and 5-hmC expression profiles in endometrial carcinogenesis are currently unclear. We conducted a hospital-based retrospective review of the immunohistochemical expression of TET1, 5-mC, and 5-hmC in 181 endometrial samples. A “high” TET1 and 5-hmC expression score was observed in all cases of normal endometrium (100.0% and 100.0%, respectively) and in most samples of endometrial hyperplasia without atypia (90.9% and 78.8%, respectively) and atypical hyperplasia (90.6% and 93.8%, respectively), but a “high” score was found in only less than half of the EC samples (48.8% and 46.5%, respectively). The TET1 and 5-hmC expression scores were significantly higher in normal endometrium and premalignant endometrial lesions than in ECs (p < 0.001). A “high” 5-mC expression score was observed more frequently for ECs (81.4%) than for normal endometrium (40.0%), endometrial hyperplasia without atypia (51.5%), and atypical hyperplasia (53.1%) (p < 0.001). We also found that TET1 mRNA expression was lower in ECs compared to normal tissues (p = 0.0037). TET1 immunohistochemistry (IHC) scores were highly proportional to the TET1 mRNA levels and we summarize that the TET1 IHC scoring can be used for biomarker determinations. Most importantly, a higher TET1 score in EC cases was associated with a good overall survival (OS) rate, with a hazard ratio (HR) of 0.31 for death (95% confidence interval: 0.11–0.84). Our findings suggest that TET1, 5-mC, and 5-hmC expression is a potential histopathology biomarker for the differential diagnosis of malignant and premalignant endometrial lesions. TET1 is also a potential prognostic marker for EC.

Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study

Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists’ diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.

The Potential Role of Complement System in the Progression of Ovarian Clear Cell Carcinoma Inferred from the Gene Ontology-Based Immunofunctionome Analysis

Ovarian clear cell carcinoma (OCCC) is the second most common epithelial ovarian carcinoma (EOC). It is refractory to chemotherapy with a worse prognosis after the preliminary optimal debulking operation, such that the treatment of OCCC remains a challenge. OCCC is believed to evolve from endometriosis, a chronic immune/inflammation-related disease, so that immunotherapy may be a potential alternative treatment. Here, gene set-based analysis was used to investigate the immunofunctionomes of OCCC in early and advanced stages. Quantified biological functions defined by 5917 Gene Ontology (GO) terms downloaded from the Gene Expression Omnibus (GEO) database were used. DNA microarray gene expression profiles were used to convert 85 OCCCs and 136 normal controls into to the functionome. Relevant offspring were as extracted and the immunofunctionomes were rebuilt at different stages by machine learning. Several dysregulated pathogenic functions were found to coexist in the immunopathogenesis of early and advanced OCCC, wherein the complement-activation-alternative-pathway may be the headmost dysfunctional immunological pathway in duality for carcinogenesis at all OCCC stages. Several immunological genes involved in the complement system had dual influences on patients’ survival, and immunohistochemistrical analysis implied the higher expression of C3a receptor (C3aR) and C5a receptor (C5aR) levels in OCCC than in controls.

Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics

The pathogenesis and molecular mechanisms of ovarian low malignant potential (LMP) tumors or borderline ovarian tumors (BOTs) have not been fully elucidated to date. Surgery remains the cornerstone of treatment for this disease, and diagnosis is mainly made by histopathology to date. However, there is no integrated analysis investigating the tumorigenesis of BOTs with open experimental data. Therefore, we first utilized a functionome-based speculative model from the aggregated obtainable datasets to explore the expression profiling data among all BOTs and two major subtypes of BOTs, serous BOTs (SBOTs) and mucinous BOTs (MBOTs), by analyzing the functional regularity patterns and clustering the separate gene sets. We next prospected and assembled the association between these targeted biomolecular functions and their related genes. Our research found that BOTs can be accurately recognized by gene expression profiles by means of integrative polygenic analytics among all BOTs, SBOTs, and MBOTs; the results exhibited the top 41 common dysregulated biomolecular functions, which were sorted into four major categories: immune and inflammatory response-related functions, cell membrane- and transporter-related functions, cell cycle- and signaling-related functions, and cell metabolism-related functions, which were the key elements involved in its pathogenesis. In contrast to previous research, we identified 19 representative genes from the above classified categories (IL6, CCR2 for immune and inflammatory response-related functions; IFNG, ATP1B1, GAS6, and PSEN1 for cell membrane- and transporter-related functions; CTNNB1, GATA3, and IL1B for cell cycle- and signaling-related functions; and AKT1, SIRT1, IL4, PDGFB, MAPK3, SRC, TWIST1, TGFB1, ADIPOQ, and PPARGC1A for cell metabolism-related functions) that were relevant in the cause and development of BOTs. We also noticed that a dysfunctional pathway of galactose catabolism had taken place among all BOTs, SBOTs, and MBOTs from the analyzed gene set databases of canonical pathways. With the help of immunostaining, we verified significantly higher performance of interleukin 6 (IL6) and galactose-1-phosphate uridylyltransferase (GALT) among BOTs than the controls. In conclusion, a bioinformatic platform of gene-set integrative molecular functionomes and biophysiological pathways was constructed in this study to interpret the complicated pathogenic pathways of BOTs, and these important findings demonstrated the dysregulated immunological functionome and dysfunctional metabolic pathway as potential roles during the tumorigenesis of BOTs and may be helpful for the diagnosis and therapy of BOTs in the future.

Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer

AbstractOvarian cancer is the leading cause of gynecologic cancer death among women. Regardless of the development made in the past two decades in the surgery and chemotherapy of ovarian cancer, most of the advanced-stage patients are with recurrent cancer and die. The conventional treatment for ovarian cancer is to remove cancerous tissues using surgery followed by chemotherapy, however, patients with such treatment remain at great risk for tumor recurrence and progressive resistance. Nowadays, new treatment with molecular-targeted agents have become accessible. Bevacizumab as a monotherapy in combination with chemotherapy has been recently approved by FDA for the treatment of epithelial ovarian cancer (EOC). Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors’ best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for EOC and peritoneal serous papillary carcinoma (PSPC). This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with EOC and PSPC to bevacizumab.