Literature DB >> 33563042

Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis.

Runmin Li1, Guosheng Wang2, ZhouJie Wu1, HuaGuang Lu1, Gen Li1, Qi Sun1, Ming Cai1.   

Abstract

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.

Entities:  

Keywords:  Bioinformatics; copy amount variation; gene expression omnibus; osteosarcoma; prognostic marker

Mesh:

Substances:

Year:  2021        PMID: 33563042      PMCID: PMC8283252          DOI: 10.1177/1535370221992015

Source DB:  PubMed          Journal:  Exp Biol Med (Maywood)        ISSN: 1535-3699


  39 in total

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Review 3.  Future directions in the treatment of osteosarcoma.

Authors:  Michael W Bishop; Katherine A Janeway; Richard Gorlick
Journal:  Curr Opin Pediatr       Date:  2016-02       Impact factor: 2.856

Review 4.  Translational biology of osteosarcoma.

Authors:  Maya Kansara; Michele W Teng; Mark J Smyth; David M Thomas
Journal:  Nat Rev Cancer       Date:  2014-10-16       Impact factor: 60.716

Review 5.  Osteosarcoma.

Authors:  Drew D Moore; Hue H Luu
Journal:  Cancer Treat Res       Date:  2014

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Journal:  Cancer Commun (Lond)       Date:  2018-04-09

Review 7.  Comparative review of human and canine osteosarcoma: morphology, epidemiology, prognosis, treatment and genetics.

Authors:  Siobhan Simpson; Mark David Dunning; Simone de Brot; Llorenç Grau-Roma; Nigel Patrick Mongan; Catrin Sian Rutland
Journal:  Acta Vet Scand       Date:  2017-10-24       Impact factor: 1.695

8.  A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma.

Authors:  Feng Liu; Lu Xing; Xiaoqian Zhang; Xiaoqi Zhang
Journal:  Genes (Basel)       Date:  2019-05-29       Impact factor: 4.096

9.  MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32.

Authors:  Andrew D Kelly; Benjamin Haibe-Kains; Katherine A Janeway; Katherine E Hill; Eleanor Howe; Jeffrey Goldsmith; Kyle Kurek; Antonio R Perez-Atayde; Nancy Francoeur; Jian-Bing Fan; Craig April; Hal Schneider; Mark C Gebhardt; Aedin Culhane; John Quackenbush; Dimitrios Spentzos
Journal:  Genome Med       Date:  2013-01-22       Impact factor: 11.117

10.  Super enhancer inhibitors suppress MYC driven transcriptional amplification and tumor progression in osteosarcoma.

Authors:  Demeng Chen; Zhiqiang Zhao; Zixin Huang; Du-Chu Chen; Xin-Xing Zhu; Yi-Ze Wang; Ya-Wei Yan; Shaojun Tang; Subha Madhavan; Weiyi Ni; Zhan-Peng Huang; Wen Li; Weidong Ji; Huangxuan Shen; Shuibin Lin; Yi-Zhou Jiang
Journal:  Bone Res       Date:  2018-04-04       Impact factor: 13.567

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  1 in total

Review 1.  Application of Multi-Omics Approach in Sarcomas: A Tool for Studying Mechanism, Biomarkers, and Therapeutic Targets.

Authors:  Zijian Zou; Wei Sun; Yu Xu; Wanlin Liu; Jingqin Zhong; Xinyi Lin; Yong Chen
Journal:  Front Oncol       Date:  2022-07-08       Impact factor: 5.738

  1 in total

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