Jing Zhang

RETRACTED: Refining molecular subtypes and risk stratification of ovarian cancer through multi‐omics consensus portfolio and machine learning

AbstractOvarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classification system despite extensive research efforts. This study integrated five distinct multi‐omics datasets from eight multicentric cohorts, applying a combination of ten clustering algorithms and ninety‐nine machine learning models. This methodology has enabled us to refine the molecular subtyping of OC, leading to the development of a novel Consensus Machine Learning‐driven Signature (CMLS). Our analysis delineated two prognostically significant cancer subtypes (CS), each marked by unique genetic and immunological signatures. Notably, CS1 is associated with an adverse prognosis. Leveraging a subtype classifier, we identified five key genes (CTHRC1, SPEF1, SCGB3A1, FOXJ1, and C1orf194) instrumental in constructing the CMLS. Patients classified within the high CMLS group exhibited a poorer prognosis and were characterized by a “cold tumor” phenotype, indicative of an immunosuppressive microenvironment rich in MDSCs, CAFs, and Tregs. Intriguingly, this group also presented higher levels of tumor mutation burden (TMB) and tumor neoantigen burden (TNB), factors that correlated with a more favorable response to immunotherapy compared to their low CMLS counterparts. In contrast, the low CMLS group, despite also displaying a “cold tumor” phenotype, showed a favorable prognosis and a heightened responsiveness to chemotherapy. This study's findings underscore the potential of targeting immune‐suppressive cells, particularly in patients with high CMLS, as a strategic approach to enhance OC prognosis. Furthermore, the redefined molecular subtypes and risk stratification, achieved through sophisticated multi‐omics analysis, provide a framework for the selection of therapeutic agents.

ATM immunohistochemistry as an effective screening method for POLE variants among endometrial carcinomas lacking mismatch repair deficiency and p53 abnormalities

Abstract The molecular classification of endometrial carcinomas (ECs) is now integrated into clinical practice. However, identification of polymerase‐ε ( POLE ) variants remains reliant on DNA sequencing, which limits broader implementation. Given the strong prognostic value of pathogenic POLE mutations and the established efficacy of immunohistochemistry (IHC) for detecting mismatch repair (MMR) deficiency and p53 abnormalities, there is a clear need for IHC‐based screening strategies to identify patients likely to carry POLE variants and prioritize them for confirmatory sequencing. In this study, we analyzed 24 cases with POLE pathogenic mutations ( POLE mut ECs), 3 with benign POLE variants, and 32 matched cases with no specific molecular profile (NSMP) from a cohort of 378 ECs. IHC evaluation of the ataxia telangiectasia mutated (ATM) protein revealed that POLE ‐mutated ECs (with pathogenic or benign POLE variants) exhibited significantly higher frequencies of non‐diffuse positive staining patterns, including null, heterogeneous positive, and subclonal loss, compared with NSMP cases. Targeted next‐generation sequencing of all exons across 474 cancer‐related genes in the 27 POLE ‐mutated ECs and 20 NSMP cases with ATM non‐diffuse positive staining patterns confirmed that POLE ‐mutated ECs typically had high tumor mutational burden and were enriched for ATM truncating variants. ATM molecular alterations, including various variant subtypes and multisite mutations, also closely correlated with these staining patterns. Based on these findings, we refined the ATM IHC interpretation framework to integrate staining patterns with sequencing data for improved molecular correlation. Specifically, the null and subclonal loss patterns showed high specificity (96.9%), positive predictive value (94.1%), and accuracy (79.7%) for identifying POLE variants. Notably, the null pattern appeared exclusively in ECs with pathogenic POLE mutations. These results suggest that ATM IHC staining is an effective screening tool for identifying patients who may benefit from confirmatory POLE sequencing among those lacking MMR deficiency or p53 abnormalities.

Tumor Stiffness Measurement at Multifrequency MR Elastography to Predict Lymphovascular Space Invasion in Endometrial Cancer

A diagnostic model combining tumor stiffness measured at MR elastography with clinical-radiologic variables showed the best performance for predicting lymphovascular space invasion in participants with endometrial cancer.

Cytoplasmic SIRT1 enhances the stemness of polyploid giant cancer cells by promoting β-catenin protein stability and nuclear accumulation in ovarian carcinoma upon neoadjuvant chemotherapy

Epithelial ovarian carcinoma, the deadliest gynecological malignancy, frequently develops treatment resistance through polyploid giant cancer cells (PGCCs) that typically emerge after carboplatin-paclitaxel chemotherapy. Accumulating evidence suggests that PGCCs exhibit traits similar to those of cancer stem cells (CSCs), including expression of stemness markers, self-renewal, and resistance to treatment. Although Wnt/β-catenin signaling is a major driver of stemness and chemoresistance in ovarian carcinoma, the specific mechanisms by which it activates cancer stemness of PGCCs remain unclear. This study investigates the role of SIRT1-mediated deacetylation of β-catenin in PGCC stemness, with a specific focus on SIRT1's subcellular localization. Immunohistochemical analysis of ovarian carcinoma samples from patients receiving neoadjuvant chemotherapy revealed that nuclear β-catenin staining in PGCCs correlated with SOX2-positive expression. Comparative proteomics further demonstrated the enrichment of differentially expressed proteins related to the Wnt pathway and stem cell programs in PGCCs compared to diploid tumor cells, highlighting the role of the Wnt/β-catenin pathway in the cancer stemness of PGCCs. Critically, PGCCs with overexpressed cytoplasmic SIRT1 (SIRT1

CircCDK17 promotes the proliferation and metastasis of ovarian cancer cells by sponging miR-22-3p to regulate CD147 expression

Abstract Ovarian cancer (OC) is a common malignancy in women of reproductive age. Circular RNAs (circRNAs) are emerging players in OC progression. We investigated the function and mechanism of circular RNA hsa_circ_0027803 (circCDK17) in OC pathogenesis. Real‑time PCR (RT-qPCR) and western blot were utilized for gene and protein expression analysis, respectively. Cell counting kit‑8 (CCK-8), EdU and Transwell assays investigated OC cell proliferation, migration and invasion. The associations between circCDK17, miR-22-3p and CD147 were examined by dual-luciferase reporter and RNA-protein immunoprecipitation (RIP) assays. The in vivo model of OC nude mice was constructed to explore the role of circCDK17. CircCDK17 was increased in OC tissue and cells, and patients with higher expression of circCDK17 had a shorter survival. CircCDK17 downregulation inhibited OC cell proliferation, migration and invasion, and reduced epithelial-mesenchymal transition (EMT)-related markers. In vivo experiments showed that circCDK17 silencing inhibited OC tumor growth and metastasis. CircCDK17 depletion reduced CD147 level via sponging miR-22-3p. MiR-22-3p knockdown overturned effect of circCDK17 depletion on OC cell proliferation, migration and invasion. Meanwhile, overexpressed CD147 restored functions of circCDK17 downregulation on OC development. CircCDK17 is an important molecule that regulates OC pathogenic process through miR-22-3p/CD147.

Cytoplasmic SIRT1 promotes paclitaxel resistance in ovarian carcinoma through increased formation and survival of polyploid giant cancer cells

AbstractTherapeutic resistance is a notable cause of death in patients with ovarian carcinoma. Polyploid giant cancer cells (PGCCs), commonly arising in tumor tissues following chemotherapy, have recently been considered to contribute to drug resistance. As a type III deacetylase, Sirtuin1 (SIRT1) plays essential roles in the cell cycle, cellular senescence, and drug resistance. Accumulating evidence has suggested that alteration in its subcellular localization via nucleocytoplasmic shuttling is a critical process influencing the functions of SIRT1. However, the roles of SIRT1 subcellular localization in PGCC formation and subsequent senescence escape remain unclear. In this study, we compared the differences in the polyploid cell population and senescence state of PGCCs following paclitaxel treatment between tumor cells overexpressing wild‐type SIRT1 (WT SIRT1) and those expressing nuclear localization sequence (NLS)‐mutated SIRT1 (SIRT1NLSmt). We investigated the involvement of cytoplasmic SIRT1 in biological processes and signaling pathways, including the cell cycle and cellular senescence, in ovarian carcinoma cells' response to paclitaxel treatment. We found that the SIRT1NLSmt tumor cell population contained more polyploid cells and fewer senescent PGCCs than the SIRT1‐overexpressing tumor cell population. Comparative proteomic analyses using co‐immunoprecipitation (Co‐IP) combined with liquid chromatography–mass spectrometry (LC‐MS)/MS showed the differences in the differentially expressed proteins related to PGCC formation, cell growth, and death, including CDK1 and CDK2, between SIRT1NLSmt and SIRT1 cells or PGCCs. Our results suggested that ovarian carcinoma cells utilize polyploidy formation as a survival mechanism during exposure to paclitaxel‐based treatment via the effect of cytoplasmic SIRT1 on PGCC formation and survival, thereby boosting paclitaxel resistance. © 2023 The Pathological Society of Great Britain and Ireland.

Determination of Potential Therapeutic Targets and Prognostic Markers of Ovarian Cancer by Bioinformatics Analysis

This study is to study the expression of CXCRs in ovarian cancer tissues and their value in prognosis. The expressions of CXCR1‐CXCR7 mRNA between ovarian tumor tissues and normal tissues and in different pathological types of ovarian tumor tissues were compared by ONCOMINE online tool. The relationship between the expression of CXCRs and clinical pathological staging was studied by GEPIA. Kaplan‐Meier plotter online tool was used to analyze prognosis. Finally, GO and KEGG analyses and protein interaction network analysis were performed for CXCRs by the DAVID software to predict their function, and cBioPortal was used to identify the key functional genes. The expression of CXCR3/4/7 mRNA in ovarian cancer tissues was higher than that in normal ovarian tissues, and the expression of CXCR4 was the highest (fold change = 306.413, P < 0.05). The expression of CXCR1/2/3/4/7 mRNA in different pathological types of ovarian tumors was significantly different (P < 0.05). Only CXCR5 expression level was associated with tumor staging. Survival analysis showed that high CXCR7 mRNA expression and low CXCR5/6 expression were associated with the shortening of overall survival. High CXCR4/7 expression and low CXCR5/6 expression were associated with the shortening of progression‐free survival. High CXCR2/4 expression and low CXCR5/6 expression were closely related to the shortening of postprogressing survival. Protein interaction network analysis showed that GNB1, PTK2, MAPK1, PIK3CA, GNB4, GNA11, KNG1, and ARNT proteins were closely related to the CXC receptor family. CXCR3/4/7 are potential therapeutic targets, and CXCR2/4/5/6/7 are new markers for the prognosis of ovarian cancer.

Construction of a diagnostic classifier for cervical intraepithelial neoplasia and cervical cancer based on XGBoost feature selection and random forest model

AbstractBackgroundThe pathological phenotype of early‐stage cervical cancer (CC) is similar to that of cervical intraepithelial neoplasia (CIN), which provides a challenge for the diagnosis of cervical precancerous lesions. Meanwhile, the existing diagnostic methods have certain subjectivity and limitations, resulting in the possibility of misdiagnosis or missed diagnosis. Hence, some methods are needed to assist diagnosis of CC and CIN.MethodsBased on the data of CIN and CC in gene expression omnibus (GEO) dataset, the eXtreme Gradient Boosting (XGBoost) algorithm was used to screen the feature genes between CIN and CC for constructing the classifier. Incremental feature selection (IFS) curve was also used for screening. The classifier was validated for reliability using principal component analysis (PCA) dimensionality reduction analysis and heat map analysis of gene expression. Then, differentially expressed genes of CIN and CC were intersected with the classifier genes. Genes in the intersection were used as seeds for protein–protein interaction network construction and restart random walk analysis. And the genes with the top 50 affinity coefficients were selected for gene ontology (GO) and kyoto encyclopedia of genes and genome (KEGG) enrichment analyses to observe the biological functions with differences between CIN and CC.ResultsThe peripheral blood genes of CIN and CC were analyzed, and seven genes were screened. Using this gene for classifier construction, IFS curve screening revealed that the three‐feature gene classifier constructed according to the random forest model had the best effect. The results of PCA dimensionality reduction analysis and gene expression heat map analysis showed that the three‐gene classifier could effectively distinguish CIN from CC.ConclusionA three‐gene diagnostic classifier can effectively distinguish CIN patients from CC patients and provide a reference for the clinical diagnosis of early CC.

Efficacy and Safety of the Anti–PD-L1 mAb Socazolimab for Recurrent or Metastatic Cervical Cancer: a Phase I Dose-Escalation and Expansion Study

Abstract Purpose: This study (ClinicalTrials.gov identifier, NCT03676959) is an open, phase I dose-escalation and expansion study investigating the safety and efficacy of the recombinant, fully human anti–programmed death ligand 1 (PD-L1) mAb socazolimab in patients diagnosed with recurrent or metastatic cervical cancer. Patients and Methods: Patients received socazolimab every 2 weeks until disease progression. The study was divided into a dose-escalation phase and a dose-expansion phase. Safety and tolerability were primary endpoints of the dose-escalation phase. The primary endpoints of the dose-expansion phase were safety and the objective response rate (ORR) of the 5 mg/kg dose. Efficacy was assessed by the third-party independent review committee (IRC) using the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1). Results: 104 patients were successfully enrolled into the study. Twelve patients were included in the dose-escalation phase, with one complete response and two partial responses in the 5 mg/kg treatment group. Ninety-two patients (5 mg/kg) were enrolled in the dose-expansion phase. Fifty-four patients (59.3%) had baseline PD-L1–positive tumor expression (combined positive score ≥1). ORR was 15.4% [95% confidence interval (CI), 8.7%–24.5%]. Median PFS was 4.44 months (95% CI, 2.37–5.75 months), and the median OS was 14.72 months (95% CI, 9.59–NE months). ORR of PD-L1–positive patients was 16.7%, and the ORR of PD-L1–negative patients was 17.9%. No treatment-related deaths occurred. Conclusions: Our study demonstrates that socazolimab has durable safety and efficacy for the treatment of recurrent or metastatic cervical cancer and exhibits a safety profile similar to other anti–PD-1/PD-L1 mAbs.

A Clinical Study of PD-L1 Antibody ZKAB001（Drug Code） in Recurrent or Metastatic Cervical Cancer

This is a Phase 1, open-label, dose-escalation, and multidose study, aiming to investigate the safety, tolerability and pharmacokinetics(PK) of ZKAB001 (a fully human monoclonal antibody targeting the Programmed Death - Ligand 1 (PD-L1) membrane receptor on T lymphocytes and other cells of the immune system) administered every 14 days in subjects with recurrent or metastatic cervical cancer.