G2/M checkpoint plays a vital role at the early stage of HCC by analysis of key pathways and genes.

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We downloaded the gene expression file dataset GSE6764 from GEO, adopted the Robust Multi-array Average (RMA) algorithm to preprocess the raw file. 797 differentially expressed genes (DEGs) were screened out based on the SAM method using R language. Ingenuity Pathway Analysis (IPA) was used to perform canonical pathway analysis in order to calculate the most significantly changed pathways and predict the upstream regulators. In order to confirm the results from the DEGs which based on the individual gene level, the gene set enrichment analysis (GSEA) was done from the gene set level and the leading edge analysis was performed to find out the most appeared genes in several gene sets. The PPI network was built using GeneMANIA and the key genes were calculated using cytoHubba plugin based on cytoscape 3.4.0. We found that the Cell Cycle: G2/M DNA damage checkpoint regulation is the top-ranked pathways at the early stage of HCC by IPA. The high expression of several genes including CCNB1, CDC25B, XPO1, GMPS, KPNA2 and MELK is correlated with high risk, poor prognosis and shorter overall survival time in HCC patients by use of Kaplan-Meier Survival analysis. Taken together, our study showed that the G2/M checkpoint plays a vital role at the early HCC and the genes participate in the process may serve as biomarkers for the diagnosis and prognosis.", "claims": [{"id": "claim-method", "statement": "Once the signal value for each probe set calculated in every microarray, the t-test based significance analysis of microarrays (SAM) which make use of permutations to simulate for every gene a situation in which there is no difference between the two groups was utilized to determine the DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29100313", "locator": "sections[14] \"Microarray data analysis and identification of differentially expressed genes (DEGs) using SAM\" paragraph 2", "quote": "Once the signal value for each probe set calculated in every microarray, the t-test based significance analysis of microarrays (SAM) which make use of permutations to simulate for every gene a situation in which there is no difference between the two groups was utilized to determine the DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression dataset GSE6764 were downloaded from the Gene Expression Omnibus (GEO) database by Wurmbach E et.al (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29100313", "locator": "sections[13] \"Data source\" paragraph 2", "quote": "The gene expression dataset GSE6764 were downloaded from the Gene Expression Omnibus (GEO) database by Wurmbach E et.al (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:29100313"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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