Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.
May 20, 2021·

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Qi-Qi ZHU
Du CAI
Wei Wang
Min-Er ZHONG
De-Jun FAN
Xuanhui Liu
Cheng-Hang LI
Ze-Ping HUANG
Min-Yi LV
Chu-Ling HU
Xin DUAN
Xiaojian Wu
Feng GAO
Abstract
Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.
Type
Publication
Journal of Clinical Oncology

Authors
Surgeon
I am a surgeon focusing on colorectal cancer and translational bioinformatics in clinical practice.

Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.

Authors
Postdoc
I am a surgeon and clinical researcher focused on deep learning and translational studies in colorectal cancer.

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.

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Research Assistant
I am a research assistant focusing on deep learning, multimodal feature fusion, and medical AI system development.

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Medical Student
I am a medical trainee in colorectal surgery, focusing on bioinformatics and translational research in colorectal cancer.

Authors
PhD Student
I am a PhD student at Guangzhou National Laboratory, focusing on colorectal cancer research, biostatistics, and evidence-driven clinical modeling.

Authors
PhD Student
I am a PhD student focusing on AI-driven colorectal cancer research and clinically useful model development.

Authors
Postdoc
I focus on medical image analysis and artificial intelligence for cancer research, including molecular subtyping and predictive modeling.

Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.