Machine learning identifies two autophagy-related genes as markers of recurrence in colorectal cancer

Author:

Wu Jianping1,Liu Sulai2,Chen Xiaoming1,Xu Hongfei1,Tang Yaoping1ORCID

Affiliation:

1. Hunan University of Science and Engineering, Yongzhou, China

2. Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China

Abstract

Objective Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis. Methods We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence. Results By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein–protein interactions identified SQSTM1, CASP8, HSP80AB1, FADD, and MAPK9 as core genes in CRC autophagy. Of 26 ARGs, BAX and PARP1 were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%. Conclusion These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.

Funder

the construct program of applied characteristic discipline in Hunan University of Science and Engineering

Publisher

SAGE Publications

Subject

Biochemistry (medical),Cell Biology,Biochemistry,General Medicine

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