A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer.

BACKGROUND: Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. OBJECTIVE: This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. STUDY DESIGN: This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. RESULTS: In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. CONCLUSION: When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images.

We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin-stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin-stained whole slide images (WSIs) of primary cervical tumors from 564 patients were used in this retrospective, proof-of-concept study. Primary tumor sections (1161 WSIs) were obtained from 405 patients who underwent radical cervical cancer surgery at the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014; 165 and 240 patients were negative and positive for lymph node metastasis, respectively (including 166 with positive pelvic lymph nodes alone and 74 with positive pelvic and para-aortic lymph nodes). We constructed and trained a multi-instance deep convolutional neural network based on a multiscale attention mechanism, in which an internal independent test set (100 patients, 228 WSIs) from the FUSCC cohort and an external independent test set (159 patients, 363 WSIs) from the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program database were used to evaluate the predictive performance of the network. In predicting the occurrence of lymph node metastasis, our network achieved areas under the receiver operating characteristic curve of 0.87 in the cross-validation set, 0.84 in the internal independent test set of the FUSCC cohort, and 0.75 in the external test set of the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program. For patients with positive pelvic lymph node metastases, we retrained the network to predict whether they also had para-aortic lymph node metastases. Our network achieved areas under the receiver operating characteristic curve of 0.91 in the cross-validation set and 0.88 in the test set of the FUSCC cohort. Deep learning analysis based on pathological images of primary foci is very likely to provide new ideas for preoperatively assessing cervical cancer lymph node status; its true value must be validated with cervical biopsy specimens and large multicenter datasets.

Developing a nomogram for preoperative prediction of cervical cancer lymph node metastasis by multiplex immunofluorescence.

BACKGROUND: Most traditional procedures can destroy tissue natural structure, and the information on spatial distribution and temporal distribution of immune milieu in situ would be lost. We aimed to explore the potential mechanism of pelvic lymph node (pLN) metastasis of cervical cancer (CC) by multiplex immunofluorescence (mIF) and construct a nomogram for preoperative prediction of pLN metastasis in patients with CC. METHODS: Patients (180 IB1-IIA2 CC patients of 2009 FIGO (International Federation of Gynecology and Obstetrics)) were divided into two groups based on pLN status. Tissue microarray (TMA) was prepared and tumor-infiltrating immune markers were assessed by mIF. Multivariable logistic regression analysis and nomogram were used to develop the predicting model. RESULTS: Multivariable logistic regression analysis constructs a predictive model and the area under the curve (AUC) can reach 0.843. By internal validation with the remaining 40% of cases, a new ROC curve has emerged and the AUC reached 0.888. CONCLUSIONS: This study presents an immune nomogram, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CC.

Diffusion-Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High-Grade Serous Ovarian Carcinoma.

BACKGROUND: Preoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high-grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded. PURPOSE: To investigate the value of diffusion-weighted MRI (DW-MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients. STUDY TYPE: Prospective. SUBJECTS: A total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years). FIELDS STRENGTH/SEQUENCE: A 3.0 T, readout-segmented echo-planar DWI. ASSESSMENT: The MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI-PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI-PCI alone; and a fusion model combining the clinical-morphological information and DWI-PCI. STATISTICAL TESTS: Multivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P < 0.05 was considered to be statistically significant. RESULTS: Sixty-seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI-PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA-125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI-PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI. DATA CONCLUSIONS: The combination of two clinical factors, MRI morphological characteristics and DWI-PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram.

OBJECTIVE: This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS: In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS: Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS: The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS: • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.

RNA-binding proteins in ovarian cancer: a novel avenue of their roles in diagnosis and treatment.

Ovarian cancer (OC), an important cause of cancer-related death in women worldwide, is one of the most malignant cancers and is characterized by a poor prognosis. RNA-binding proteins (RBPs), a class of endogenous proteins that can bind to mRNAs and modify (or even determine) the amount of protein they can generate, have attracted great attention in the context of various diseases, especially cancers. Compelling studies have suggested that RBPs are aberrantly expressed in different cancer tissues and cell types, including OC tissues and cells. More specifically, RBPs can regulate proliferation, apoptosis, invasion, metastasis, tumorigenesis and chemosensitivity and serve as potential therapeutic targets in OC. Herein, we summarize what is currently known about the biogenesis, molecular functions and potential roles of human RBPs in OC and their prospects for application in the clinical treatment of OC.

SPOP promotes cervical cancer progression by inducing the movement of PD-1 away from PD-L1 in spatial localization.

BACKGROUND: Metastasis is a major obstacle in the treatment of cervical cancer (CC), and SPOP-mediated regulatory effects are involved in metastasis. However, the mechanisms have not been fully elucidated. METHODS: Proteomic sequencing and SPOP immunohistochemistry (IHC) were performed for the pelvic lymph node (pLN)-positive and non-pLN groups of CC patients. The corresponding patients were stratified by SPOP expression level for overall survival (OS) and relapse-free survival (RFS) analysis. In vitro and in vivo tests were conducted to verify the causal relationship between SPOP expression and CC metastasis. Multiplex immunofluorescence (m-IF) and the HALO system were used to analyse the mechanism, which was further verified by in vitro experiments. RESULTS: SPOP is upregulated in CC with pLN metastasis and negatively associated with patient outcome. In vitro and in vivo, SPOP promotes CC proliferation and metastasis. According to m-IF and HALO analysis, SPOP may promote CC metastasis by promoting the separation of PD-1 from PD-L1. Finally, it was further verified that SPOP can achieve immune tolerance by promoting the movement of PD-1 away from PD-L1 in spatial location and function. CONCLUSION: This study shows that SPOP can inhibit the immune microenvironment by promoting the movement of PD-1 away from PD-L1, thereby promoting pLN metastasis of CC and resulting in worse OS and RFS.

Homologous recombination deficiency in diverse cancer types and its correlation with platinum chemotherapy efficiency in ovarian cancer.

BACKGROUND: Homologous recombination deficiency (HRD) is a molecular biomarker for administrating PARP inhibitor (PARPi) or platinum-based (Pt) chemotherapy. The most well-studied mechanism of causing HRD is pathogenic BRCA1/2 mutations, while HRD phenotype is also present in patients without BRCA1/2 alterations, suggesting other unknown factors. METHODS: The targeted next-generation sequencing (GeneseeqPrime® HRD) was used to evaluate the HRD scores of 199 patients (Cohort I). In Cohort II, a total of 85 Pt-chemotherapy-treated high-grade serous ovarian cancer (HGSOC) patients were included for investigating the role of HRD score in predicting treatment efficacy. The concurrent genomic features analyzed along HRD score evaluation were studied in a third cohort with 416 solid tumor patients (Cohort III). RESULTS: An HRD score ≥ 38 was predefined as HRD-positive by analyzing Cohort I (range: 0-107). Over 95% of the BRCA1/2-deficient cases of Cohort I were HRD-positive under this threshold. In Cohort II, Pt-sensitive patients have significantly higher HRD scores than Pt-resistant patients (median: 54 vs. 34, p = 0.031) and a significantly longer PFS was observed in HRD-positive patients (median: 548 vs. 343 days, p = 0.003). Furthermore, TP53, NCOR1, and PTK2 alterations were enriched in HRD-positive patients. In Cohort III, impaired homologous recombination repair pathway was more frequently observed in HRD-positive patients without BRCA1/2 pathogenic mutations. The alteration enrichment of TP53, NCOR1, and PTK2 observed in Cohort II was also validated by the ovarian subgroup in Cohort III. CONCLUSIONS: Using an in-house HRD evaluation method, our findings show that overall HRR gene mutations account for a significant part of HRD in the absence of BRCA1/2 aberrations, and suggest that HRD positive status might be a predictive biomarker of Pt-chemotherapy.

Long noncoding RNA SNHG6 promotes carcinogenesis by enhancing YBX1-mediated translation of HIF1α in clear cell renal cell carcinoma.

Recent studies have showed that Small nucleolar RNA host genes (SNHGs) acted as a subset of long noncoding RNAs (lncRNAs) have critical roles in human cancer carcinogenesis. However, the biological functions of SNHGs in clear cell renal cell carcinoma (ccRCC) have not been fully investigated. In this study, we screened an oncogenic lncRNA termed SNHG6 using RNA-Seq data of ccRCC from The Cancer Genome Atlas (TCGA). Quantitative real-time PCR was then used to demonstrate the expression of SNHG6 in ccRCC tissues. SNHG6 overexpression is highly associated with malignant features in patients and is a prognostic indicator. SNHG6 significantly promotes ccRCC cell proliferation and metastasis in vitro and in vivo. Mechanistic investigations identified that SNHG6 exerts oncogenic effects by interacting with YBX1, and then, enhancing HIF1α translation. Taken together, SNHG6 promotes ccRCC progression by binding YBX1 and may serve as a novel molecular target for ccRCC therapy.

Long non-coding RNA SNHG6 promotes cell proliferation and migration through sponging miR-4465 in ovarian clear cell carcinoma.

Dysregulation of small nucleolar RNA host gene 6 (SNHG6) exerts critical oncogenic effects and facilitates tumourigenesis in human cancers. However, little information about the expression pattern of SNHG6 in ovarian clear cell carcinoma (OCCC) is available, and the contributions of this long non-coding RNA to the tumourigenesis and progression of OCCC are unclear. In the present study, we showed via quantitative real-time PCR that SNHG6 expression was abnormally up-regulated in OCCC tissues relative to that in unpaired normal ovarian tissues. High SNHG6 expression was correlated with vascular invasion, distant metastasis and poor survival. Further functional experiments demonstrated that knockdown of SNHG6 in OCCC cells inhibited cell proliferation, migration and invasion in vitro as well as tumour growth in vivo. Moreover, SNHG6 functioned as a competing endogenous RNA (ceRNA), effectively acting as a sponge for miR-4465 and thereby modulating the expression of enhancer of zeste homolog 2 (EZH2). Taken together, our data suggest that SNHG6 is a novel molecule involved in OCCC progression and that targeting the ceRNA network involving SNHG6 may be a treatment strategy in OCCC.

Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis.

Histopathology image classification is an important clinical task, and current deep learning-based whole-slide image (WSI) classification methods typically cut WSIs into small patches and cast the problem as multi-instance learning. The mainstream approach is to train a bag-level classifier, but their performance on both slide classification and positive patch localization is limited because the instance-level information is not fully explored. In this article, we propose a negative instance-guided, self-distillation framework to directly train an instance-level classifier end-to-end. Instead of depending only on the self-supervised training of the teacher and the student classifiers in a typical self-distillation framework, we input the true negative instances into the student classifier to guide the classifier to better distinguish positive and negative instances. In addition, we propose a prediction bank to constrain the distribution of pseudo instance labels generated by the teacher classifier to prevent the self-distillation from falling into the degeneration of classifying all instances as negative. We conduct extensive experiments and analysis on three publicly available pathological datasets: CAMELYON16, PANDA, and TCGA, as well as an in-house pathological dataset for cervical cancer lymph node metastasis prediction. The results show that our method outperforms existing methods by a large margin. Code will be publicly available.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

PARP1-stabilised FOXQ1 promotes ovarian cancer progression by activating the LAMB3/WNT/ß-catenin signalling pathway.

Metastasis is an important factor that causes ovarian cancer (OC) to become the most lethal malignancy of the female reproductive system, but its molecular mechanism is not fully understood. In this study, through bioinformatics analysis, as well as analysis of tissue samples and clinicopathological characteristics and prognosis of patients in our centre, it was found that Forkhead box Q1 (FOXQ1) was correlated with metastasis and prognosis of OC. Through cell function experiments and animal experiments, the results show that FOXQ1 can promote the progression of ovarian cancer in vivo and in vitro. Through RNA-seq, chromatin immunoprecipitation sequencing (ChIP-seq), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), Western blotting (WB), quantitative real-time polymerase chain reaction (qRT‒PCR), immunohistochemistry (IHC), luciferase assay, and ChIP-PCR, it was demonstrated that FOXQ1 can mediate the WNT/ß-catenin pathway by targeting the LAMB promoter region. Through coimmunoprecipitation (Co-IP), mass spectrometry (MS), ubiquitination experiments, and immunofluorescence (IF), the results showed that PARP1 could stabilise FOXQ1 expression via the E3 ubiquitin ligase Hsc70-interacting protein (CHIP). Finally, the whole mechanism pathway was verified by animal drug combination experiments and clinical specimen prognosis analysis. In summary, our results suggest that PARP1 can promote ovarian cancer progression through the LAMB3/WNT/ß-catenin pathway by stabilising FOXQ1 expression.

Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer.

OBJECTIVES: Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. METHODS: The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. RESULTS: Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. CONCLUSIONS: The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.

Preoperative prediction of miliary changes in the small bowel mesentery in advanced high-grade serous ovarian cancer using MRI radiomics nomogram.

PURPOSE: To develop and validate an MRI-based radiomics nomogram for the preoperative prediction of miliary changes in the small bowel mesentery (MCSBM) in advanced high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: One hundred and twenty-eight patients with pathologically proved  advanced HGSOC (training cohort: n = 91; validation cohort: n = 37) were retrospectively included. All patients were initially evaluated as MCSBM-negative by preoperative imaging modalities but were finally confirmed by surgery and histopathology (MCSBM-positive: n = 53; MCSBM-negative: n = 75). Five radiomics signatures were built based on the features from multisequence magnetic resonance images. Independent clinicoradiological factors and radiomics-fusion signature were further integrated to construct a radiomics nomogram. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves and clinical utility. RESULTS: Radiomics signatures, ascites, and tumor size were independent predictors of MCSBM. A nomogram integrating radiomics features and clinicoradiological factors demonstrated satisfactory predictive performance with areas under the curves (AUCs) of 0.871 (95% CI 0.801-0.941) and 0.858 (95% CI 0.739-0.976) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) revealed that the nomogram had a significantly improved ability compared with the clinical model in the training cohort (NRI = 0.343, p = 0.002; IDI = 0.299, p < 0.001) and validation cohort (NRI = 0.409, p = 0.015; IDI = 0.283, p = 0.001). CONCLUSION: Our proposed nomogram has the potential to serve as a noninvasive tool for the prediction of MCSBM, which is helpful for the individualized assessment of advanced HGSOC patients.

Effect of the Number of Removed Lymph Nodes on Survival in Patients with FIGO Stage IB-IIA Cervical Squamous Cell Carcinoma following Open Radical Hysterectomy with Pelvic Lymphadenectomy: A Retrospective Cohort Study.

OBJECTIVE: To determine whether the number of removed lymph nodes (RLN) is associated with survival in patients with International Federation of Gynecology and Obstetrics (FIGO) stage IB-IIA cervical squamous cell carcinoma (CSCC). METHODS: We reviewed the medical records of FIGO stage IB-IIA CSCC patients who underwent standardized radical hysterectomy with pelvic lymphadenectomy (RHPL) in our center between 2006 and 2014. The X-tile software was performed to calculate the optimal grouping of cutoff points for RLN. The impact of RLN on progression-free survival (PFS) and overall survival (OS) was analyzed using Cox regression analysis. RESULTS: Among 3,127 patients, the mean number of RLN was 22, and positive lymph node (LN) was found in 668 (21.4%) patients. X-tile plots identified "21" and "16" as the optimal cutoff value of RLN to divide the patients into two groups in terms of PFS and OS separately. In all patients, the number of RLN was not associated with PFS (P=0.182) or OS (P=0.193). Moreover, in both LN positive and negative patients, the number of RLN was not associated with either PFS (P=0.212 and P=0.540, respectively) or OS (P=0.173 and P=0.497, respectively). Cox regression analysis showed that the number of RLN was not an independent prognostic factor for PFS or OS. CONCLUSION: If standardized RHPL was performed, the number of RLN was not an independent prognostic factor for survival of patients with FIGO stage IB-IIA CSCC.

Genomic Profiling of Chinese Cervical Cancer Patients Reveals Prevalence of DNA Damage Repair Gene Alterations and Related Hypoxia Feature.

BACKGROUND: Cervical cancer is responsible for 10-15% of cancer-related deaths in women worldwide. In China, it is the most common cancer in the female genital tract. However, the genomic profiles of Chinese cervical cancer patients remain unclear. MATERIALS AND METHODS: A total of 129 cervical cancer patients were enrolled in this study (113 squamous, 12 adenocarcinoma, 2 adenosquamous, and 2 neuroendocrine carcinoma). To classify the clinical features and molecular characteristics of cervical cancer, the genomic alterations of 618 selected genes were analyzed in the samples of these patients, utilizing target next-generation sequencing (NGS) technology. Furthermore, the findings from the Chinese cohort were then compared with the data of Western patients downloaded from The Cancer Genome Atlas (TCGA) database, in terms of gene expression files, mutation data, and clinical information. RESULTS: All studied patients had valid somatic gene alterations, and the most frequently altered genes were PIK3C, TP53, FBXW7, ARID1A, ERBB2, and PTEN. Comparison of genomic profiling showed significantly different prevalence of genes, including TP53, KMT2C, and RET, between the Chinese and the TCGA cohorts. Moreover, 57 patients (44.19%) with 83 actionable alterations were identified in our cohort, especially in PI3K and DNA damage repair (DDR) pathways. After an in-depth analysis of cervical cancer data from the TCGA cohort, DDR alteration was found to be associated with extremely higher tumor mutation burden (TMB) (median mutation count: 149.5 vs 66, p <0.0001), and advanced stages (p <0.05). Additionally, DDR alteration, regardless of its function, was positively correlated with hypoxia feature and score. Moreover, patients with a high hypoxia score were positively correlated with a high abundance of mast cell resting, but lower abundance of CD8+ T cells and activated mast cell. Finally, CDHR5 was identified as the hub gene to be involved in the DDR-hypoxia network, which was negatively correlated with both the DDR alteration and hypoxia score. CONCLUSIONS: Overall, a unique genomic profiling of Chinese patients with cervical cancer was uncovered. Besides, the prevalent actionable variants, especially in PI3K and DDR pathways, would help promote the clinical management. Moreover, DDR alteration exerted the significant influence on the tumor microenvironment in cervical cancer, which could guide the clinical decisions for the treatment. CDHR5 was the first identified hub gene to be negatively correlated with DDR or hypoxia in cervical cancer, which had potential effects on the treatment of immune checkpoint inhibitors (ICIs).

The RNA-Binding Protein CELF2 Inhibits Ovarian Cancer Progression by Stabilizing FAM198B.

An increasing number of studies have clarified the functional roles of RNA-binding proteins (RBPs) in driving post-transcriptional mechanisms of cancer progression. In this study, we integrated data from the RBP database and Gene Expression Omnibus (GEO) data with RNA sequencing (RNA-seq) data from 10 ovarian cancer tissues and 8 normal ovarian tissues and identified an RBP, CUGBP- and ETR-3-like family 2 (CELF2). We found that CELF2 expression was downregulated in ovarian cancer and positively correlated with the overall survival (OS) and progression-free survival (PFS) of patients with ovarian cancer. Altered CELF2 expression led to changes in the proliferation, migration, and invasion of ovarian cancer cells in vitro and in vivo. CELF2 expression increased the stability of its target, FAM198B, by binding to AU/U-rich elements (AREs) in the 3' untranslated region (3' UTR). FAM198B knockdown restored the CELF2-mediated suppression of proliferation and migration. We also found that CELF2/FAM198B may repress ovarian cancer progression by inhibiting the mitogen-activated protein kinase/extracellular-regulated protein kinase (MAPK/ERK) signaling pathway. Finally, a curcumin-induced increase in CELF2 expression resulted in increased ovarian cancer cell sensitivity to cisplatin. Our study elucidated a novel mechanism by which the CELF2/FAM198B axis regulates proliferation and metastasis in ovarian cancer, providing novel, potential therapeutic targets for ovarian cancer.

HNRNPH1-stabilized LINC00662 promotes ovarian cancer progression by activating the GRP78/p38 pathway.

Numerous studies suggest an important role for copy number alterations (CNAs) in cancer progression. However, CNAs of long intergenic noncoding RNAs (lincRNAs) in ovarian cancer (OC) and their potential functions have not been fully investigated. Here, based on analysis of The Cancer Genome Atlas (TCGA) database, we identified in this study an oncogenic lincRNA termed LINC00662 that exhibited a significant correlation between its CNA and its increased expression. LINC00662 overexpression is highly associated with malignant features in OC patients and is a prognostic indicator. LINC00662 significantly promotes OC cell proliferation and metastasis in vitro and in vivo. Mechanistically, LINC00662 is stabilized by heterogeneous nuclear ribonucleoprotein H1 (HNRNPH1). Moreover, LINC00662 exerts oncogenic effects by interacting with glucose-regulated protein 78 (GRP78) and preventing its ubiquitination in OC cells, leading to activation of the oncogenic p38 MAPK signaling pathway. Taken together, our results define an oncogenic role for LINC00662 in OC progression mediated via GRP78/p38 signaling, with potential implications regarding therapeutic targets for OC.

Identification of Chemoresistance-Associated Key Genes and Pathways in High-Grade Serous Ovarian Cancer by Bioinformatics Analyses.

PURPOSE: High-grade serous ovarian cancer (HGSOC) is the leading cause of death among gynecological malignancies. This is mainly attributed to its high rates of chemoresistance. To date, few studies have investigated the molecular mechanisms underlying this resistance to treatment in ovarian cancer patients. In this study, we aimed to explore these molecular mechanisms using bioinformatics analysis. METHODS: We analyzed microarray data set GSE51373, which included 16 platinum-sensitive HGSOC samples and 12 platinum-resistant control samples. Differentially expressed genes (DEGs) were identified using RStudio. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using DAVID, and a DEG-associated protein-protein interaction (PPI) network was constructed using STRING. Hub genes in the PPI network were identified, and the prognostic value of the top ten hub genes was evaluated. MGP, one of the hub genes, was verified by immunohistochemistry. RESULTS: All samples were confirmed to be of high quality. A total of 109 DEGs were identified, and the top ten enriched GO terms and four KEGG pathways were obtained. Specifically, the PI3K-AKT signaling pathway and the Rap1 signaling pathway were identified as having significant roles in chemoresistance in HGSOC. Furthermore, based on the PPI network, KIT, FOXM1, FGF2, HIST1H4D, ZFPM2, IFIT2, CCNO, MGP, RHOBTB3, and CDC7 were identified as hub genes. Five of these hub genes could predict the prognosis of HGSOC patients. Positive immunostaining signals for MGP were observed in the chemoresistant samples. CONCLUSION: Taken together, the findings of this study may provide novel insights into HGSOC chemoresistance and identify important therapeutic targets.