Department of Emergency Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Department of Operation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Sci Rep. 2021 Jul 22;11(1):14949. doi: 10.1038/s41598-021-94485-x.
Sarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.
肉瘤是一种预后不良的罕见恶性肿瘤。越来越多的证据表明，异常的选择性剪接（AS）事件通常与癌症的发病机制有关。本研究旨在确定 AS 相关生存基因作为潜在生物标志物的预后价值，并强调 AS 事件在肉瘤中的功能作用。从癌症基因组图谱（TCGA）肉瘤队列和 TCGA SpliceSeq 分别下载了 RNA-seq 和 AS 事件数据集。进一步使用单因素分析评估了与生存相关的 AS 事件。还进行了多变量 Cox 回归分析，以建立一个用于预测患者生存的生存基因特征，并使用曲线下面积方法评估预后可靠性。使用 KOBAS 3.0 和 Cytoscape 对 AS 相关基因进行功能注释，并评估它们的网络相互作用。我们从 236 个肉瘤样本中的 40,184 个基因中检测到 9674 个 AS 事件，然后使用 15 个最重要的基因构建了一个生存回归模型。我们进一步验证了十个潜在的与生存相关的基因（TUBB3、TRIM69、ZNFX1、VAV1、KCNN2、VGLL3、AK7、ARMC4、LRRC1 和 CRIP1）在肉瘤的发生和发展中的作用。还进行了多变量生存模型分析，并验证了使用这十个基因的模型可以很好地对患者的预后进行分类。本研究增加了我们对肉瘤中 AS 事件的理解，并且基于基因的使用 AS 相关事件的模型可能作为预测肉瘤患者生存的潜在预测因子。
