Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.
Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.
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DOI：
10.3389/fimmu.2022.868067
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发表时间：
2022
影响因子：
7.3
通讯作者：
Shen Y
中科院分区：
文献类型：
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作者：
Feng S;Xia T;Ge Y;Zhang K;Ji X;Luo S;Shen Y
低氧微环境参与卵巢癌的肿瘤发生。因此，我们的目标是开发一种非侵入性放射基因组学方法来识别具有潜在应用于患者预后的缺氧模式。通过对不同氧条件下培养的OC细胞株进行rna测序，鉴定出特定的缺氧相关基因（sHRGs）。同时，通过无监督共识分析和LASSO-Cox回归分析，鉴定出多种缺氧相关亚型。随后，采用多种生物信息学算法探索不同亚型的免疫微环境、预后、生物通路改变和药物敏感性。最后，通过机器学习算法开发了用于预测患者风险状态的最佳放射基因组学生物标志物。鉴定出140个sHRGs和3种与缺氧相关的亚型。其中，低氧簇b型、基因簇b型和高危亚型的生存结局较差。各亚型之间关系密切，低氧簇- b和基因簇- b具有较高的低氧风险评分。值得注意的是，低风险亚型具有活跃的免疫微环境，可能受益于免疫治疗。最后，构建了四特征放射基因组学模型来揭示低氧风险状态，该模型在训练组和测试组的曲线下面积（AUC）分别为0.900和0.703。作为一种非侵入性的方法，基于计算机断层扫描的放射基因组学生物标志物可以对OC患者的缺氧模式、预后、治疗效果和免疫微环境进行预处理预测。
The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a non-invasive radiogenomics approach to identify a hypoxia pattern with potential application in patient prognostication. Specific hypoxia-related genes (sHRGs) were identified based on RNA-seq of OC cell lines cultured with different oxygen conditions. Meanwhile, multiple hypoxia-related subtypes were identified by unsupervised consensus analysis and LASSO–Cox regression analysis. Subsequently, diversified bioinformatics algorithms were used to explore the immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms. One hundred forty sHRGs and three types of hypoxia-related subtypes were identified. Among them, hypoxia-cluster-B, gene-cluster-B, and high-risk subtypes had poor survival outcomes. The subtypes were closely related to each other, and hypoxia-cluster-B and gene-cluster-B had higher hypoxia risk scores. Notably, the low-risk subtype had an active immune microenvironment and may benefit from immunotherapy. Finally, a four-feature radiogenomics model was constructed to reveal hypoxia risk status, and the model achieved area under the curve (AUC) values of 0.900 and 0.703 for the training and testing cohorts, respectively. As a non-invasive approach, computed tomography-based radiogenomics biomarkers may enable the pretreatment prediction of the hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in patients with OC.
影响因子：
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作者：
Shang X;Yuan B;Li J;Xi F;Mao J;Zhang C;Jiang H;Liu G
通讯作者：
Liu G