Research Paper Volume 11, Issue 13 pp 4478—4509
Integrated tumor stromal features of hepatocellular carcinoma reveals two distinct subtypes with prognostic/predictive significance
- 1 Department of Liver Surgery and Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu 610041, China
- 2 Department of Critical Care Medicine, Sichuan Provincial Hospital for Women and Children, Chengdu 610045, Sichuan Province, China
received: April 14, 2019 ; accepted: June 25, 2019 ; published: July 12, 2019 ;https://doi.org/10.18632/aging.102064
How to Cite
Copyright: Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Current clinical classification of hepatocellular carcinoma (HCC) is unable to predict prognosis efficiently. Our aim is to classify HCC into clinically/biologically relevant subtypes according to stromal factors. We detected seven types of stromal features in tumors from 161 HCC patients by immunohistochemical staining and Hematoxylin-eosin staining. Five stromal features were selected out of seven types of stromal features to construct stromal type based on LASSO COX regression model. Then, integrating multiple clinicopathologic characteristics and stromal type, we built two nomograms for overall survival (OS) and disease-free survival (DFS). Further validation of the stromal type and nomograms were performed in the testing cohort (n = 160) and validation cohort (n = 120). Using the LASSO model, we classified HCC patients into stromal type A subgroup (CD34lowTIL-stromal-ratiohighStromal-tumor-ratiolowα-SMAweakStromamature) and stromal type B subgroup (CD34highTIL-stromal-ratiolowStromal-tumor-ratiohighα-SMAstrongStromaimmature). The stromal type was an independent prognostic factor for OS and DFS in the training, testing and validation cohorts. Two nomograms (for OS and DFS) that integrated the stromal type and clinicopathologic risk factors also showed good predictive accuracy and discriminatory power. In addition, immune cell recruitment in the tumor microenvironment (TME) was conditioned by the tumor stromal type. In conclusion, the newly developed tumor stromal type was an effective predictor of OS and DFS. Furthermore, the stromal type is associated with the immune phenotype in the TME.