Literature DB >> 31992246

Key genes with prognostic values in suppression of osteosarcoma metastasis using comprehensive analysis.

Mi Li1, Xin Jin2, Hao Li1, Gang Wu3, Shanshan Wang3, Caihong Yang4, Sisi Deng5.   

Abstract

BACKGROUND: Osteosarcoma is a primary malignant tumor originating from mesenchymal tissue, with a poor distant metastasis prognosis. The molecular mechanisms of osteosarcoma metastasis are extremely complicated.
METHODS: A public data series (GSE21257) was used to identify differentially expressed genes (DEGs) in osteosarcoma patients that did, or did not, develop metastases. Functional enrichment analysis, a protein-protein interaction network, and survival analysis of DEGs were performed. DEGs with a prognostic value were considered as candidate genes and their functional predictions, different expression in normal and malignant tissues, and immune infiltration were analyzed.
RESULTS: The DEGs were mainly enriched in the immune response. Three candidate genes (ALOX5AP, CD74, and FCGR2A) were found, all of which were expressed at higher levels in lungs and lymph nodes than in matched cancer tissues and were probably expressed in the microenvironment.
CONCLUSIONS: Candidate genes can help us understand the molecular mechanisms underlying osteosarcoma metastasis and provide targets for future research.

Entities:  

Keywords:  Differentially expressed genes; Metastasis; Osteosarcoma; Prognosis; Protein-protein interaction network

Year:  2020        PMID: 31992246     DOI: 10.1186/s12885-020-6542-z

Source DB:  PubMed          Journal:  BMC Cancer        ISSN: 1471-2407            Impact factor:   4.430


  8 in total

1.  Comprehensive Analysis of Key mRNAs and lncRNAs in Osteosarcoma Response to Preoperative Chemotherapy with Prognostic Values.

Authors:  Mi Li; Wei-Ting Cheng; Hao Li; Zhi Zhang; Xiao-Li Lu; Si-Si Deng; Jian Li; Cai-Hong Yang
Journal:  Curr Med Sci       Date:  2021-10-20

2.  Expression of immune-related genes as prognostic biomarkers for the assessment of osteosarcoma clinical outcomes.

Authors:  Junjie Guo; Xiaoyang Li; Shen Shen; Xuejian Wu
Journal:  Sci Rep       Date:  2021-12-16       Impact factor: 4.379

3.  MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data.

Authors:  Somayah Albaradei; Abdurhman Albaradei; Asim Alsaedi; Mahmut Uludag; Maha A Thafar; Takashi Gojobori; Magbubah Essack; Xin Gao
Journal:  Front Mol Biosci       Date:  2022-07-22

4.  Prognostic value of SOX9 in cervical cancer: Bioinformatics and experimental approaches.

Authors:  Huan Chen; Xupeng Chen; Fanhua Zeng; Aizhen Fu; Meiyuan Huang
Journal:  Front Genet       Date:  2022-08-08       Impact factor: 4.772

5.  ALOX5AP Predicts Poor Prognosis by Enhancing M2 Macrophages Polarization and Immunosuppression in Serous Ovarian Cancer Microenvironment.

Authors:  Xiang Ye; Limei An; Xiangxiang Wang; Chenyi Zhang; Wenqian Huang; Chenggong Sun; Rongrong Li; Hanlin Ma; Hongyan Wang; Min Gao
Journal:  Front Oncol       Date:  2021-05-19       Impact factor: 6.244

Review 6.  Breast cancer: Muscarinic receptors as new targets for tumor therapy.

Authors:  Alejandro Español; Agustina Salem; Yamila Sanchez; María Elena Sales
Journal:  World J Clin Oncol       Date:  2021-06-24

7.  Transcriptome Analysis Identifies Novel Prognostic Genes in Osteosarcoma.

Authors:  Junfeng Chen; Xiaojun Guo; Guangjun Zeng; Jianhua Liu; Bin Zhao
Journal:  Comput Math Methods Med       Date:  2020-10-06       Impact factor: 2.238

8.  Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis.

Authors:  Xingyu Chen; Hua Lan; Dong He; Runshi Xu; Yao Zhang; Yaxin Cheng; Haotian Chen; Songshu Xiao; Ke Cao
Journal:  Front Immunol       Date:  2021-07-19       Impact factor: 7.561

  8 in total

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