Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

• Published in: Cancer cell international Vol. 20; no. 1; p. 259
• Main Authors: Zhu, Naiqiang, Hou, Jingyi, Ma, Guiyun, Guo, Shuai, Zhao, Chengliang, Chen, Bin
• Format: Journal Article
• Language: English
• Published: England BioMed Central 22.06.2020; BMC
• Subjects: Algorithms; Atrophy; Biomarkers; Bone cancer; Bone tumors; Diagnosis; Energy; Energy metabolism; Gene expression; Gene signature; Genomes; Kinases; Least absolute shrinkage and selection operator; Medical prognosis; Metabolism; Metastases; Metastasis; Molecular modelling; Osteosarcoma; Patients; Primary Research; Prognosis; Prognosis biomarker; Sarcoma; Survival analysis; Weighted co-expressed network analysis; Young adults; Least absolute shrinkage and selection operator; Energy metabolism; Gene signature; Prognosis biomarker; Osteosarcoma; Weighted co-expressed network analysis
• ISSN: 1475-2867; 1475-2867
• DOI: 10.1186/s12935-020-01352-2

Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown. Energy metabolism-related genes were obtained from the TARGET database. We applied the "NFM" algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model. Two subtypes associated with energy metabolism was determined using the "NFM" algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis. The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.