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KLHL14：一种新型的卵巢癌预后生物标志物及治疗靶点

KLHL14: A Novel Prognostic Biomarker and Therapeutic Target for Ovarian Cancer.

作者信息

Wang Xingwei, Sun Ru, Hong Xia, Chen Chen, Ding Yan

机构信息

Department of Obstetrics and Gynecology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225001, China.

Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210008, China.

出版信息

J Oncol. 2022 Aug 22;2022:9799346. doi: 10.1155/2022/9799346. eCollection 2022.

DOI:10.1155/2022/9799346
PMID:36046368
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9423958/
Abstract

Ovarian cancer (OV) is a gynaecological malignancy that poses a serious risk to the health status of women. To date, effective molecular markers are unavailable for the diagnosis and management of ovarian malignancies. In this study, we aimed to investigate the molecular markers associated with the development of this cancer. We used bioinformatic analysis to determine the molecules and genes related to ovarian cancer using the gene expression profiling interactive analysis (GEPIA) and the cancer genome Atlas (TCGA) databases. In addition, we examined the genes and mechanisms underlying ovarian cancer. Our results showed that the KLHL14 gene is overexpressed in some cancers. An increase in the KLHL14 expression indicates a poor prognosis in patients with ovarian cancer. Results of immune cell infiltration analysis and half-maximal inhibitory concentration (IC50) analysis provide novel insights into the treatment of ovarian cancer. KLHL14 is anticipated to emerge as a novel molecular marker specifically for ovarian carcinoma.

摘要

卵巢癌（OV）是一种妇科恶性肿瘤，对女性健康构成严重威胁。迄今为止，尚无有效的分子标志物可用于卵巢恶性肿瘤的诊断和治疗。在本研究中，我们旨在探究与这种癌症发生相关的分子标志物。我们利用生物信息学分析，通过基因表达谱交互分析（GEPIA）和癌症基因组图谱（TCGA）数据库来确定与卵巢癌相关的分子和基因。此外，我们还研究了卵巢癌的相关基因和机制。我们的结果表明，KLHL14基因在某些癌症中过度表达。KLHL14表达的增加表明卵巢癌患者预后不良。免疫细胞浸润分析和半数最大抑制浓度（IC50）分析的结果为卵巢癌的治疗提供了新的见解。预计KLHL14将成为一种专门针对卵巢癌的新型分子标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/dd74a0b725d8/JO2022-9799346.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/26f73a4b9114/JO2022-9799346.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/e299ec1deb9b/JO2022-9799346.005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/dd74a0b725d8/JO2022-9799346.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/26f73a4b9114/JO2022-9799346.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/f808ef58c349/JO2022-9799346.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/85c9464aabef/JO2022-9799346.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/133f84f315f5/JO2022-9799346.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/e299ec1deb9b/JO2022-9799346.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/987fbcb5a078/JO2022-9799346.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d22/9423958/dd74a0b725d8/JO2022-9799346.007.jpg

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PTH2R is related to cell proliferation and migration in ovarian cancer: a multi-omics analysis of bioinformatics and experiments.
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