Jian Wu

High FGF18 expression levels predict poor prognosis in endometrial carcinoma patients and promote tumor growth and metastasis

Objective To investigate if fibroblast growth factor 18 (FGF18) expression plays an important role in endometrial carcinoma (EC). Methods The clinicopathological associations and prognostic value of FGF18 expression were retrospectively analyzed in 190 patients with EC. FGF18 expression was stably knocked down in EC cell lines. Changes in cell proliferation, migration, and invasion rates were examined via cell behavior experiments. Tumor growth was investigated using a xenograft mouse model. RNA sequencing (RNA-seq) was performed to identify differentially expressed genes (DEGs) in HEC-1-B cells after FGF18 knockdown, followed by pathway enrichment analysis of the DEGs. Results High FGF18 expression levels were closely correlated with EC clinicopathological features, such as histological subtype, FIGO stage, depth of myometrial invasion, and tumor size. Moreover, EC patients with high FGF18 expression levels had poorer overall survival. FGF18 knockdown in EC cells revealed its role in promoting tumor cell proliferation, migration, and invasion in vitro, as well as tumor growth in vivo. RNA-seq of HEC-1-B cells revealed that the DEGs were enriched in signaling pathways related to cell proliferation and migration. Conclusions Overexpression of FGF18 may serve as a prognostic biomarker for EC patients and is a potential therapeutic target for treating this disease.

Microbial and Metabolic Disorders in Cervical Cancer: Structural Insights, Biomarkers, Mechanisms, and Therapeutic Strategies

ABSTRACT The development of cervical cancer is strongly associated with persistent high‐risk HPV infection. Microbiota and metabolomics offer new perspectives. This article focuses on the role of microbial dysbiosis and metabolic reprogramming in the development of cervical cancer, revealing its synergistic regulation of the tumor immune microenvironment and treatment resistance. Machine learning and multi‐omics have identified new biomarkers, while treatment strategies include microbiota modulation, metabolic targeting, and combination therapies. Despite limitations such as small sample size and unclear mechanisms, this review proposes a multi‐target precision medicine framework. In the future, we should focus on multi‐center and multi‐omics research and optimized clinical trials.

A Corresponding Region Fusion Framework for Multi-Modal Cervical Lesion Detection

Cervical lesion detection (CLD) using colposcopic images of multi-modality (acetic and iodine) is critical to computer-aided diagnosis (CAD) systems for accurate, objective, and comprehensive cervical cancer screening. To robustly capture lesion features and conform with clinical diagnosis practice, we propose a novel corresponding region fusion network (CRFNet) for multi-modal CLD. CRFNet first extracts feature maps and generates proposals for each modality, then performs proposal shifting to obtain corresponding regions under large position shifts between modalities, and finally fuses those region features with a new corresponding channel attention to detect lesion regions on both modalities. To evaluate CRFNet, we build a large multi-modal colposcopic image dataset collected from our collaborative hospital. We show that our proposed CRFNet surpasses known single-modal and multi-modal CLD methods and achieves state-of-the-art performance, especially in terms of Average Precision.