In Journal of translational medicine
BACKGROUND : Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse.
RESULTS : Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models-NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used.
CONCLUSIONS : Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.
Kim Jinho, Kim Hyunjung, Lee Min-Seok, Lee Heetak, Kim Yeon Jeong, Lee Woo Yong, Yun Seong Hyeon, Kim Hee Cheol, Hong Hye Kyung, Hannenhalli Sridhar, Cho Yong Beom, Park Donghyun, Choi Sun Shim
Colorectal cancer, Elastic net-based machine learning, Normal tissues adjacent to tumors, Recurrence, Tumor-infiltrating immune cells