W. Zhu

MiR-92 overexpression suppresses immune cell function in ovarian cancer via LATS2/YAP1/PD-L1 pathway

Increasing evidence suggested that microRNA plays an important role in ovarian cancer. In this study, the role of miR-92 in ovarian cancer was investigated. In this study, miR-92 expression in clinical sample was evaluated, role of miR-92 was investigated in vitro, and underlying mechanism was investigated using Chip, co-IP, and western blot. In this study, we show that miR-92 is overexpressed in ovarian cancer tissue compared with normal cancer tissue. Transfection of miR-92 increased proliferation of ovarian cancer cell, and increased migration capacity and colony formation were observed after miR-92 transfection; we found that expression of LATS2 was decreased by miR-92, and this was further confirmed by luciferase assay, which proved that miR-92 is targeting 3' of the endogenous LATS2 gene. Downregulation of LATS2 resulted in increased translocation of YAP1 and upregulation of PD-L1, which subsequently suppressed NK cell function and promoted T cell apoptosis. Moreover, co-transfection of YAP1-targeted shRNA could relieve miR-92-induced immune suppression effect. Mechanically, immunoprecipitation (IP) was used to show that LATS2 interacted with YAP1 and subsequently limited nuclear translocation of YAP1; chromatin immunoprecipitation (ChIP) was used to confirm that YAP1 could bind to enhancer region of PD-L1 to enhance transcription activity of PD-L1. Our data revealed a novel mechanism which finally resulted in immune suppression in ovarian cancer.

RETRACTED: Development of gene panel for predicting recurrence in early‐stage cervical cancer patients

Abstract Cervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus‐based gene panel using multi‐omics data that could effectively predict recurrence in early‐stage cervical cancer patients. We utilized the “Multi‐Omics Consensus Integration Analysis (MOVICS)” package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early‐stage CC. We identified the “resting and naive” tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning‐driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan–Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early‐stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.