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Identification of an Immune Cell Gene Signature for Predicting Response and Prognosis in Rectal Cancer Patients
Tengfei Yan1, Limin Jin1, Yi Shen2
1Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Background:
The objective of this research was to develop a robust classification system that accurately predicts the prognosis and therapeutic efficacy of neoadjuvant chemoradiotherapy (NCRT) in patients with rectal cancer.
Methods:
We generated an immune cell gene signature (ICGS) model employing univariate Cox regression and Lasso regression methods. Subsequently, the predictive capabilities of the ICGS model were assessed and validated for NCRT efficacy, survival outcomes, and immunotherapy benefits. Furthermore, we conducted functional annotation, investigated genomic alterations, and explored potential immune escape mechanisms.
Results:
Initially, we developed three ICGS models with prognostic implications: the B cell signature, macrophage signature, and combined signature. The immune cell-related genes were highly associated with immune function, immune response, and immunological pathways. The ROC analysis revealed that the ICGS model demonstrated a relatively strong predictive capability for the response to NCRT. Patients exhibiting high ICGS scores experienced a significantly reduced overall survival. Furthermore, multivariate Cox regression analysis verified the ICGS as an independent factor, and we constructed a predictive nomogram. The AUC value confirmed that the ICGS model was comparable to and superior to the clinical stage in predicting overall patient survival. Additionally, the low-risk group for the macrophage signature showed a better survival advantage from immunotherapy. Finally, we systematically correlated the ICGS model with genomic alterations and uncovered potential immune escape mechanisms.
Conclusion:
The ICGS model can be considered a reliable tool for evaluating the efficacy of neoadjuvant therapy in rectal cancer and can further assist in guiding risk stratification and treatment decisions.
