Abstract
BackgroundPrevious cancer prognostic prediction models often consider only the most important transcriptomic expressions, and their power is limited. It is unknown whether prediction power can be further improved when additional transcriptomic information is incorporated.MethodsTo integrate transcriptomes, four models are compared based on 32 types of cancer in the Cancer Genome Atlas, including the general Cox model with only clinical covariates, the Cox model with a lasso penalty (coxlasso), the Cox model with an elastic net penalty (coxenet), and the mixed-effects Cox model (coxlmm). Furthermore, we partition the survival variance into the relative contribution of clinical and transcriptomic components within the framework of coxlmm. Finally, the influence of different numbers of genes was evaluated in the context of coxlmm.ResultsCompared with the clinical covariates–only Cox model, the average prediction gain was 2.4% for coxlasso, 4.2% for coxenet, and 7.2% for coxlmm across 16 low-censored cancers; a significant elevation of prediction power was observed for SARC, SKCM, LGG, PAAD, and HNSC. Similar findings were observed for all 32 cancers with the average prediction gain of 2.7, 3.8, and 5.8% for coxlasso, coxenet, and coxlmm. Coxlmm always had comparable or better prediction performance relative to coxlasso and coxenet with an average of 2.8% prediction improvement across the 16 low-censored cancers. In addition, it is shown that the predictive accuracy of coxlmm generally increases with the number of genes included. The survival variance partition analysis demonstrates that the transcriptomic contribution was higher for some cancers (e.g., LGG, CESC, PAAD, SKCM, and SARC) and lower for others (e.g., BRCA, COAD, KIRC, and STAD).ConclusionThis study demonstrates that the integration of transcriptomic information can substantially improve prognostic prediction accuracy, but the prediction performance is cancer-specific and varies across cancer types. It further reveals that gene expression exhibits distinct contributions to survival variation across cancers.
Highlights
Cancer is one of the primary causes of death worldwide, leading to a growing severe threat to public health (Kyu et al, 2018; Roth et al, 2018; Siegel et al, 2019)
In terms of our literature review, we find many previous predictions only incorporate a small set of biomarkers into models in addition to clinical covariates (Supplementary Table S1); for example, only seven CpG-based methylation signatures were employed in Shen et al (2017a), and alternatively, only some important biomarker information was extracted with dimensional reduction methods and was employed for prediction
As it has been demonstrated that gene expressions possess the best predictive power for cancer prognostic assessment compared with other genomic measurements related to survival risk (Zhao et al, 2005, 2014; Zhu et al, 2017; Kim et al, 2018), in the present study, we only focus on this kind of omic information to explore how transcriptome data can be leveraged to improve prediction accuracy relative to prior sparse methods
Summary
Cancer is one of the primary causes of death worldwide, leading to a growing severe threat to public health (Kyu et al, 2018; Roth et al, 2018; Siegel et al, 2019). In terms of our literature review, we find many previous predictions only incorporate a small set of biomarkers into models in addition to clinical covariates (Supplementary Table S1); for example, only seven CpG-based methylation signatures were employed in Shen et al (2017a), and alternatively, only some important biomarker information was extracted with dimensional reduction methods (e.g., principal component analysis, partial least squares, or variable selection methods; Zhao et al, 2014; Tang et al, 2017a,b) and was employed for prediction. It is unknown whether prediction power can be further improved when additional transcriptomic information is incorporated
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