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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
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A predictive model based on immune related genes for diffuse large B cell lymphoma (DLBCL)
Ruixin Sun1, Pengcheng Liu1, Zizhen Xu1
1Department of Laboratory Medicine, College of Health Science and Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Translational Cancer Research
|July 21, 2025
Summary
This study introduces an immune-related gene risk model for early diffuse large B cell lymphoma (DLBCL) diagnosis and treatment guidance. The validated model predicts patient survival and identifies potential novel therapies.
Area of Science:
- Oncology
- Immunology
- Genetics
Background:
- Anti-tumor immunity is crucial for malignancy response and may aid in early diffuse large B cell lymphoma (DLBCL) diagnosis.
- Integrating immune-related genes offers novel insights for DLBCL predictive modeling and therapeutic strategies.
Purpose of the Study:
- To develop a predictive model using immune-related genes for early DLBCL diagnosis.
- To establish a risk assessment tool to guide DLBCL therapy and predict patient survival.
Main Methods:
- Immune-related genes in DLBCL were identified and selected using LASSO regression.
- A validated risk model and nomogram were created, incorporating clinical factors for survival probability evaluation.
- Drug sensitivity analysis and a competing endogenous RNA (ceRNA) network were constructed for treatment guidance and biomarker exploration.
Main Results:
- An immune-related gene-based risk model was successfully established and validated for predicting DLBCL survival and progression.
- The risk score correlated with advanced disease stages and age over 60.
- Four novel second-line chemotherapies were identified for patients with varying risk scores.
Conclusions:
- A robust predictive risk model based on immune-related genes was developed for clinical application in DLBCL diagnosis and treatment.
- This model aids physicians in early DLBCL detection and personalized therapeutic decisions.
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