小桶
清脆的
基因
比例危险模型
坏死性下垂
Lasso(编程语言)
计算生物学
生物
弗雷明翰风险评分
转录组
Cas9
疾病
生物信息学
肿瘤科
医学
内科学
遗传学
基因表达
计算机科学
程序性细胞死亡
细胞凋亡
万维网
作者
Cheng Liu,Wei Xiong,Song Li,Gao Wang,Jie Zhou,Haoguang Li
摘要
The present study aimed to identify indispensable genes associated with tumor cell viability according to the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) datasets, which may support new therapeutic targets for patients with osteosarcoma.The transcriptome patterns between tumor and normal tissues, which were obtained from the Therapeutically Applicable Research to Generate Effective Treatments dataset, were overlapped with the genomics associated with cell viability screened by CRISPR-Cas9 technology. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were employed to determine enrichment pathways related to lethal genes. Least absolute shrinkage and selection operator (LASSO) regression was employed to construct a risk model related to lethal genes for predicting clinical outcomes of osteosarcoma. Univariate and multivariate Cox regressions were conducted to assess the prognostic value of this feature. Weighted gene co-expression network analysis was performed to identify modules associated with patients with high-risk score.In total, 34 lethal genes were identified in this investigation. These genes were enriched in the necroptosis pathway. The risk model based on LASSO regression algorithm distinguishes patients with high-risk score from patients with low-risk score. Compared with low-risk patients, high-risk patients showed a shorter overall survival rate in both the training and validation sets. The time-dependent receiver operating characteristic curves of 1, 3 and 5 years displayed that the risk score has great prediction performance. The necroptosis pathway represents the main difference in biological behavior between the high-risk group and the low-risk group. Meanwhile, CDK6 and SMARCB1 may serve as important targets for detecting osteosarcoma progression.The present study developed a predictive model that outperformed classical clinicopathological parameters for predicting the clinical outcomes of osteosarcoma patients and identified specific lethal genes, including CDK6 and SMARCB1, as well as the necroptosis pathway. These findings may serve as potential targets for future osteosarcoma treatments.
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