DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients

Mar 11, 2022·
Min-Yi LV
Min-Yi LV
Co-1st Author
,
Wei Wang
Co-1st Author
Min-Er ZHONG
Min-Er ZHONG
Co-1st Author
Du CAI
Du CAI
De-Jun FAN
De-Jun FAN
Cheng-Hang LI
Cheng-Hang LI
Wei-Bin KOU
Wei-Bin KOU
Ze-Ping HUANG
Ze-Ping HUANG
Xin DUAN
Xin DUAN
Chu-Ling HU
Chu-Ling HU
Qi-Qi ZHU
Qi-Qi ZHU
,
Xiao-Sheng He
Feng GAO
Feng GAO
Corresponding Author
· 0 min read
Abstract

Background: Increasing evidence have depicted that DNA repair-related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair-related gene signature (DRGS) in predicting the prognosis of CRC patients.

Methods: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set.

Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67-3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38-3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40-3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best.

Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy.

Type
Publication
Frontiers in Genetics
publication
Min-Yi LV
Authors
PhD Student
I am a PhD student at Guangzhou National Laboratory, focusing on colorectal cancer research, biostatistics, and evidence-driven clinical modeling.
Min-Er ZHONG
Authors
Postdoc
I am a surgeon and clinical researcher focused on deep learning and translational studies in colorectal cancer.
Du CAI
Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.
De-Jun FAN
Authors
Associate Professor
My research explores the intersection of gastrointestinal endoscopy (GIE) and artificial intelligence (AI), along with the biological mechanisms of colorectal cancer development.
Cheng-Hang LI
Authors
Research Assistant
I am a research assistant focusing on deep learning, multimodal feature fusion, and medical AI system development.
Wei-Bin KOU
Authors
Research Assistant
I focus on deep learning and computational pathology with a background in software engineering and AI development.
Ze-Ping HUANG
Authors
Medical Student
I am a medical trainee in colorectal surgery, focusing on bioinformatics and translational research in colorectal cancer.
Xin DUAN
Authors
Postdoc
I focus on medical image analysis and artificial intelligence for cancer research, including molecular subtyping and predictive modeling.
Chu-Ling HU
Authors
PhD Student
I am a PhD student focusing on AI-driven colorectal cancer research and clinically useful model development.
Qi-Qi ZHU
Authors
Surgeon
I am a surgeon focusing on colorectal cancer and translational bioinformatics in clinical practice.
Feng GAO
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.