Open AccessIdentification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis
Author(s) -
Runmin Li,
Guosheng Wang,
ZhouJie Wu,
Huaguang Lu,
Gen Li,
Qi Sun,
Ming Cai
Publication year - 2021
Publication title -
experimental biology and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.012
H-Index - 146
eISSN - 1535-3702
pISSN - 1535-3699
DOI - 10.1177/1535370221992015
Subject(s) - gene , lasso (programming language) , computational biology , proportional hazards model , genomics , copy number variation , biology , feature selection , cancer , identification (biology) , bioinformatics , genetics , genome , medicine , computer science , botany , machine learning , world wide web
Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes ( P < 0.01), 336 copies of amplified genes ( P < 0.05), and 36 copies of deleted genes ( P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases' surviving state.