Wei Sitian, Zhang Jun, Shi Rui, Yu Zhicheng, Chen Xingwei, Wang Hongbo
Department of Gynecology and Obstetrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Industrial engineering, Tsinghua University, Beijing, China.
Front Oncol. 2022 Sep 7;12:944000. doi: 10.3389/fonc.2022.944000. eCollection 2022.
In the worldwide, uterine corpus endometrial carcinoma (UCEC) is the sixth most common malignancy in women, and the number of women diagnosed is increasing. Kinase plays an important role in the occurrence and development of malignant tumors. However, the research about kinase in endometrial cancer is still unclear. Here, we first downloaded the gene expression data of 552 UCEC patients and 23 healthy endometrial tissues from The Cancer Genome Atlas (TCGA), obtained 538 kinase-related genes from the previous literature, and calculated 67 differentially expressed kinases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were referenced to identify multiple important biological functions and signaling pathways related to 67 differentially expressed kinases. Using univariate Cox regression and Least absolute shrinkage and selection operator (LASSO), seven kinases (ALPK2, CAMKV, TTK, PTK6, MAST1, CIT, and FAM198B) were identified to establish a prognostic model of endometrial cancer. Then, patients were divided into high- and low-risk groups based on risk scores. Receiver operating characteristic (ROC) curves were plotted to evaluate that the model had a favorable predictive ability. Kaplan-Meier survival analysis suggested that high-risk groups experienced worse overall survival than low-risk groups. qRT-PCR and ISH assays confirmed the consistency between predicted candidate genes and real sample contents. CIBERSORT algorithm and ssGSEA were adopted to investigate the relationship between this signature and tumor immune microenvironment, and revealed that in low- and high-risk groups, the types of tumor-infiltrating immune cells and the immune cell-related functions were significantly different. In summary, a seven-gene signature risk model has been constructed, and could accurately predict the prognosis of UCEC, which may offer ideas and breakthrough points to the kinase-associated development of UCEC.
在全球范围内，子宫内膜癌（UCEC）是女性中第六大常见恶性肿瘤，且确诊的女性人数正在增加。激酶在恶性肿瘤的发生和发展中起着重要作用。然而，关于激酶在子宫内膜癌中的研究仍不明确。在此，我们首先从癌症基因组图谱（TCGA）下载了552例UCEC患者和23例健康子宫内膜组织的基因表达数据，从先前的文献中获取了538个激酶相关基因，并计算出67个差异表达激酶。参考基因本体论（GO）和京都基因与基因组百科全书（KEGG）来识别与67个差异表达激酶相关的多个重要生物学功能和信号通路。使用单变量Cox回归和最小绝对收缩和选择算子（LASSO），鉴定出7种激酶（ALPK2、CAMKV、TTK、PTK6、MAST1、CIT和FAM198B）以建立子宫内膜癌的预后模型。然后，根据风险评分将患者分为高风险组和低风险组。绘制受试者工作特征（ROC）曲线以评估该模型具有良好的预测能力。Kaplan-Meier生存分析表明，高风险组的总生存期比低风险组更差。qRT-PCR和ISH检测证实了预测的候选基因与真实样本含量之间的一致性。采用CIBERSORT算法和ssGSEA来研究该特征与肿瘤免疫微环境之间的关系，并揭示在低风险组和高风险组中，肿瘤浸润免疫细胞的类型和免疫细胞相关功能存在显著差异。总之，构建了一个七基因特征风险模型，其能够准确预测UCEC的预后，这可能为UCEC与激酶相关的发展提供思路和突破点。
