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Updated: May 5, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Construction and experimental verification of a prognostic model based on tumor-infiltrating lymphocytes related
Jing Zhao1, Kunquan Su2, Shoubin Zhong3
1Department of Hematology, Weifang People's Hospital, Shandong Second Medical University, Weifang City, Shandong Province 261000, China.
Purpose:
Our research objective is to construct a prognostic model of tumor-infiltrating lymphocytes related genes (TILRGs) for predicting the survival of cases with diffuse large B-cell lymphoma (DLBCL).
Methods:
Based on the clinical data of DLBCL patients in GEO database, the TILRGs that were significantly associated with survival prognosis were screened. Cox regression analysis and LASSO analysis were used to construct prognostic risk models for TILRGs. Kaplan-Meier (K-M) curve and ROC curve were used to assess the predictive performance of the model. CIBERSORT algorithm and GDSC database were applied to quantify tumor immune infiltration and chemotherapy drugs. The levels of key genes were detected via qRT-PCR. Additionally, K-M curves were plotted to analyze the relationship between LCP2 expression and patient prognosis.
Results:
A prognostic risk model consisting of 20 TILRGs was constructed. This model has high accuracy in predicting the survival of DLBCL patients, and reveals significant differences in tumor immune infiltration and chemotherapy drugs between two groups. Low expression of LCP2 in DLBCL patients is obviously related to serum LDH, IPI score, stage and poor prognosis. Further analysis revealed that LCP2 overexpression inhibited the growth of DLBCL cells.
Conclusion:
In the current study, an accurate and reliable prognostic risk model was constructed based on 20 TILRGs. Low expression of LCP2 indicates poor survival of DLBCL cases and is a potential prognostic indicator.
