Han Jiawen, Lyu Lin
Department of Nutrition, Jinshan Hospital, Fudan University, 1508 Longhang Road, Jinshan District, Shanghai, 201508, China.
Discov Oncol. 2024 May 30;15(1):198. doi: 10.1007/s12672-024-01047-4.
Patients with ovarian cancer (OC) tend to face a poor prognosis due to a lack of typical symptoms and a high rate of recurrence and chemo-resistance. Therefore, identifying representative and reliable biomarkers for early diagnosis and prediction of chemo-therapeutic responses is vital for improving the prognosis of OC.
Expression levels, IHC staining, and subcellular distribution of eight ITGBs were analyzed using The Cancer Genome Atlas (TCGA)-Ovarian Serous Cystadenocarcinoma (OV) database, GEO DataSets, and the HPA website. PrognoScan and Univariate Cox were used for prognostic analysis. TIDE database, TIMER database, and GSCA database were used to analyze the correlation between immune functions and ITGBs. Consensus clustering analysis was performed to subtype OC patients in the TCGA database. LASSO regression was used to construct the predictive model. The Cytoscape software was used for identifying hub genes. The 'pRRophetic' R package was applied to predict chemo-therapeutic responses of ITGBs.
ITGBs were upregulated in OC tissues except ITGB1 and ITGB3. High expression of ITGBs correlated with an unfavorable prognosis of OC except ITGB2. In OC, there was a strong correlation between immune responses and ITGB2, 6, and 7. In addition, the expression matrix of eight ITGBs divided the TCGA-OV database into two subgroups. Subgroup A showed upregulation of eight ITGBs. The predictive model distinguishes OC patients from favorable prognosis to poor prognosis. Chemo-therapeutic responses showed that ITGBs were able to predict responses of common chemo-therapeutic drugs for patients with OC.
This article provides evidence for predicting prognosis, immuno-, and chemo-therapeutic responses of ITGBs in OC and reveals related biological functions of ITGBs in OC.
卵巢癌（OC）患者由于缺乏典型症状以及高复发率和化疗耐药性，往往预后较差。因此，识别具有代表性和可靠性的生物标志物用于早期诊断和预测化疗反应对于改善OC的预后至关重要。
使用癌症基因组图谱（TCGA）-卵巢浆液性囊腺癌（OV）数据库、GEO数据集和HPA网站分析8种整合素β（ITGBs）的表达水平、免疫组化染色和亚细胞分布。使用PrognoScan和单因素Cox进行预后分析。使用TIDE数据库、TIMER数据库和GSCA数据库分析免疫功能与ITGBs之间的相关性。对TCGA数据库中的OC患者进行一致性聚类分析以进行亚型划分。使用LASSO回归构建预测模型。使用Cytoscape软件识别枢纽基因。应用“pRRophetic”R包预测ITGBs的化疗反应。
除ITGB1和ITGB3外，ITGBs在OC组织中上调。除ITGB2外，ITGBs的高表达与OC的不良预后相关。在OC中，免疫反应与ITGB2、6和7之间存在强相关性。此外，8种ITGBs的表达矩阵将TCGA-OV数据库分为两个亚组。亚组A显示8种ITGBs上调。该预测模型可区分OC患者的预后良好与预后不良。化疗反应表明，ITGBs能够预测OC患者对常用化疗药物的反应。
本文为预测OC中ITGBs的预后、免疫和化疗反应提供了证据，并揭示了ITGBs在OC中的相关生物学功能。
