Abstract

Identification of key biomarkers involved in osteosarcoma using altered modules

The aim of this study was to screen for key biomarkers of osteosarcoma (OS) by tracking altered modules. Protein-protein interaction (PPI) networks of OS and normal groups were constructed and re-weighted using the Pearson correlation coefficient (PCC), respectively. The condition-specific modules were explored from OS and normal PPI networks using a clique-merging algorithm. Altered modules were identified by a maximum weight bipartite-matching method. The important biological pathways in OS were identified by a pathway-enrichment analysis using genes from disrupted modules. The most important genes in these pathways were selected as key biomarkers. Finally, the mRNA and protein expressions of hub genes in OS bone tissues were analyzed using reverse transcription-polymerase chain reaction and western blotting, respectively. We identified 703 and 2270 modules in normal and disease networks, respectively; 150 altered modules were identified from among these and explored. We identified 10 important pathways based on gene pairs with altered PCC > 1 in the disrupted modules (P < 0.01), and PCNA, ATP6V1C2, ATP6V1G3,FEN1, CDC7, and RPA3 (expressed in these pathways) were selected as key genes of OS. We observed that these genes (and the proteins they encoded) were differentially expressed between normal and OS samples (P < 0.01) (excluding ATP6V1C2, whose protein expression did not differ significantly). Therefore, we identified 5 gene signatures that may be potential biomarkers for the detection and effective therapy of OS.

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