AIM To evaluate the prognostic power of different molecular data in liver cancer.METHODS Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variab...AIM To evaluate the prognostic power of different molecular data in liver cancer.METHODS Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis.RESULTS The m RNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index(C-index) of 0.61. For the copy number variation, methylation and mi RNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone(P < 0.05). On the other hand, the combination of clinical data with methylation, mi RNA and m RNA data could significantly boost the prediction accuracy of the clinical data itself(P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models.CONCLUSION In all kinds of omics data in liver cancer, the m RNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.展开更多
文摘AIM To evaluate the prognostic power of different molecular data in liver cancer.METHODS Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis.RESULTS The m RNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index(C-index) of 0.61. For the copy number variation, methylation and mi RNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone(P < 0.05). On the other hand, the combination of clinical data with methylation, mi RNA and m RNA data could significantly boost the prediction accuracy of the clinical data itself(P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models.CONCLUSION In all kinds of omics data in liver cancer, the m RNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.
