Journal
MEDICINE
Volume 100, Issue 13, Pages -Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MD.0000000000025148
Keywords
autophagy; colorectal cancer; prognosis; regulatory network inference; subtype
Categories
Ask authors/readers for more resources
This study proposed and validated an autophagy-based signature for colorectal cancer, which showed promising prognostic capabilities. Network analysis of 6 core genes revealed that patients in the high-risk group had unfavorable prognosis. The autophagy-based signature demonstrated higher prognostic power compared to commercialized multigene signatures.
Background: The heterogeneity of colorectal cancer (CRC) poses a significant challenge to the precise treatment of patients. CRC has been divided into 4 consensus molecular subtypes (CMSs) with distinct biological and clinical characteristics, of which CMS4 has the mesenchymal identity and the highest relapse rate. Autophagy plays a vital role in CRC development and therapeutic response. Methods: The gene expression profiles collected from 6 datasets were applied to this study. Network analysis was applied to integrate the subtype-specific molecular modalities and autophagy signature to establish an autophagy-based prognostic signature for CRC (APSCRC). Results: Network analysis revealed that 6 prognostic autophagy genes (VAMP7, DLC1, FKBP1B, PEA15, PEX14, and DNAJB1) predominantly regulated the mesenchymal modalities of CRC. The APSCRC was constructed by these 6 core genes and applied for risk calculation. Patients were divided into high- and low-risk groups based on APSCRC score in all cohorts. Patients within the high-risk group showed an unfavorable prognosis. In multivariate analysis, the APSCRC remained an independent predictor of prognosis. Moreover, the APSCRC achieved higher prognostic power than commercialized multigene signatures. Conclusions: We proposed and validated an autophagy-based signature, which is a promising prognostic biomarker of CRC patients. Further prospective studies are warranted to test and validate its efficiency for clinical application.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available