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干细胞相关特征有助于预测卵巢浆液性囊腺癌的诊断和预后。

Stem Cell-Associated Signatures Help to Predict Diagnosis and Prognosis in Ovarian Serous Cystadenocarcinoma.

作者信息

Li Li, Zhang Weiwei, Qiu Jinxin, Zhang Weiling, Lu Mengmeng, Wang Jiaqian, Jin Yunfeng, Xi Qinghua

机构信息

Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China.

Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu 226001, China.

出版信息

Stem Cells Int. 2023 Apr 30;2023:4500561. doi: 10.1155/2023/4500561. eCollection 2023.

DOI:10.1155/2023/4500561
PMID:37168445
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10164874/
Abstract

Ovarian serous cystadenocarcinoma (OV) is a fatal gynecologic cancer with a five-year survival rate of only 46%. Resistance to platinum-based chemotherapy is a prevalent factor in OV patients, leading to increased mortality. The platinum resistance in OV is driven by transcriptome heterogeneity and tumor heterogeneity. Studies have indicated that ovarian cancer stem cells (OCSCs), which are chemoresistant and help in disease recurrence, are enriched by platinum-based chemotherapy. Stem cells have a significant influence on the OV progression and prognosis of OV patients and are key pathology mediators of OV. However, the molecular mechanisms and targets of OV have not yet been fully understood. In this study, systematic research based on the TCGA-OV dataset was conducted for the identification and construction of key stem cell-related diagnostic and prognostic models for the development of multigene markers of OV. A six-gene diagnostic and prognostic model (C19orf33, CBX2, CSMD1, INSRR, PRLR, and SLC38A4) was developed based on the differentially expressed stem cell-related gene model, which can act as a potent diagnostic biomarker and can characterize the clinicopathological properties of OV. The key genes related to stem cells were identified by screening the genes differentially expressed in OV and control samples. The mRNA-miRNA-TF molecular network for the six-gene model was constructed, and the potential biological significance of this molecular model and its impact on the infiltration of immune cells in the OV tumor microenvironment were elucidated. The differences in immune infiltration and stem cell-related biological processes were determined using gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) for the selection of molecular treatment options and providing a reference for elucidating the posttranscriptional regulatory mechanisms in OV.

摘要

卵巢浆液性囊腺癌（OV）是一种致命的妇科癌症，五年生存率仅为46%。对铂类化疗耐药是OV患者中普遍存在的因素，导致死亡率增加。OV中的铂耐药是由转录组异质性和肿瘤异质性驱动的。研究表明，卵巢癌干细胞（OCSCs）具有化疗抗性并有助于疾病复发，铂类化疗会使其富集。干细胞对OV患者的病情进展和预后有重大影响，是OV的关键病理介质。然而，OV的分子机制和靶点尚未完全明确。在本研究中，基于TCGA-OV数据集进行了系统研究，以识别和构建与干细胞相关的关键诊断和预后模型，用于开发OV的多基因标志物。基于差异表达的干细胞相关基因模型开发了一个六基因诊断和预后模型（C19orf33、CBX2、CSMD1、INSRR、PRLR和SLC38A4），该模型可作为一种有效的诊断生物标志物，并能表征OV的临床病理特征。通过筛选OV和对照样本中差异表达的基因，确定了与干细胞相关的关键基因。构建了六基因模型的mRNA-miRNA-TF分子网络，并阐明了该分子模型的潜在生物学意义及其对OV肿瘤微环境中免疫细胞浸润的影响。使用基因集变异分析（GSVA）和单样本基因集富集分析（ssGSEA）确定免疫浸润和干细胞相关生物学过程的差异，以选择分子治疗方案，并为阐明OV中的转录后调控机制提供参考。

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