Prediction of radiosensitive patients with gastric cancer by developing gene signature.

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It is necessary to develop a method to predict radiosensitive patients who are most likely to benefit from radiotherapy. Using the publicly available data of gastric cancer from The Cancer Genome Atlas (TCGA), we developed a gene signature that predicts radiosensitive patients through estimating a new index, nominal HR (nHR) (HR product of sensitive genes), for each patient. In this study, we provided several results to validate our prediction. Cross-validation results showed that the predicted radiosensitive patients who received radiotherapy had significantly better survival than predicted radiosensitive patients who did not receive radiotherapy. After adjusting for other clinical factors, including age, sex, target therapy, histologic diagnosis, tumor stage, the benefit of radiotherapy on predicted radiosensitive patient remained significant. In addition, predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of disease progression. Taken together, we have obtained a set of genes, to identify radiosensitive patients with gastric cancer. These genes may be potential biomarkers for diagnosis and treatment of gastric cancer, which could give new insight for revealing the underlying mechanism of radiosensitivity of gastric cancer.", "claims": [{"id": "claim-method", "statement": "The results of Chi-square analyses are summarized in Fig. 4A for the new tumor event and in Fig. 4B for the progressive disease.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28902346", "locator": "sections[11] \"Association among radiotherapy and clinical assessments after adjuvant treatments\" paragraph 2", "quote": "The results of Chi-square analyses are summarized in Fig. 4A for the new tumor event and in Fig. 4B for the progressive disease."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data including clinical information and RNAseq expression were downloaded from The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/, update at March 2016).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28902346", "locator": "sections[3] \"Study samples\" paragraph 2", "quote": "All data including clinical information and RNAseq expression were downloaded from The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/, update at March 2016)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used an internal cross-validated adaptive signature design with a proportional hazards model to develop a radiosensitive gene signature from gastric-cancer RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:28902346", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "We obtained the RNA sequence data for GC from The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/)."}, {"asset": "source:pmid:28902346", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "An internal cross validation was developed using the cross-validated adaptive signature design."}, {"asset": "source:pmid:28902346", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "Using this novel idea of combination as a guide, we extended the approach to the proportional hazard model and developed a radiosensitive gene signature for predicting radiosensitive patients with GC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study used an internal cross-validated adaptive signature design with a proportional hazards model to develop a radiosensitive gene signature from gastric-cancer RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA).", "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:28902346"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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