Dataset Information

A Novel Nomogram to Predict the Postoperative Overall Survival in Gastrointestinal Cancer Patients

ABSTRACT: This multicenter retrospectively observational cohort study was conducted on participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery in 11 medical centers in China from August 1, 2015, to June 31, 2018. Baseline clinicopathologic data and nutritional status assessments including Nutrition Risk Screening 2002 (NRS 2002) score and Patient-generated Subjective Global Assessment (PG-SGA) rating were collected. Variables will be screened using the least absolute shrinkage and selection operator (LASSO) regression model and Cox regression analysis. Internal and external validations will be performed via the receiver operating curve (ROC), the area under the curve (AUC), the concordance index (C-index), calibration plots, decision curve analysis (DCA), and Five folds cross-validation by 200 times.

DISEASE(S): Prognosis,Gastrointestinal Neoplasms,Gastrointestinal Cancers - Colorectal,Survival Analysis,Gastrointestinal Cancer,Gastrointestinal Cancers - Stomach,Nutrition Assessment

PROVIDER: 211 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

Dataset Information

A Novel Nomogram to Predict the Postoperative Overall Survival in Gastrointestinal Cancer Patients

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