Characterizing PANoptosis gene signature in prognosis and chemosensitivity of colorectal cancer.

Abstract

PANoptosis is a cell death pathway involved in pyroptosis, apoptosis and necrosis, and plays a key role in the development of malignant tumors. However, the molecular signature of PANoptosis in colorectal cancer (CRC) prognosis has not been thoroughly explored. The present study aimed to develop a novel prognostic model based on PANoptosis-related genes in CRC. We initially included transcriptome data of 404 CRC samples from The Cancer Genome Atlas (TCGA) cohort and identified differentially expressed genes related to PANoptosis. We then employed Cox, least absolute shrinkage and selection operator (LASSO) regression, and Random Forest methods to determine the prognostic value and constructed a PANoptosis prognostic model, followed by the validation on both internal (TCGA) and external datasets [Nanjing Colorectal Cancer (NJCRC) and Gene Expression Omnibus (GEO), n=635]. We performed immune infiltration analysis and gene set enrichment analysis to reveal biological processes and pathways against differential risk score. Ultimately, we carried out drug sensitivity analysis to predict the response of CRC patients to diverse treatment strategies. We constructed a predictive model based on four PANoptosis-related genes (TIMP1, CDKN2A, CAMK2B, and TLR3), with a high performance [area under the curve (AUC)1-year =0.702, AUC3-year =0.725, AUC5-year =0.668] and being an independent prognostic factor in predicting the prognosis of CRC patients. Notably, colorectal tumor with high PANoptosis risk score performed higher levels of macrophage infiltration and immune scores, but a greater reduction of Tumor Microenvironment Score (TMEscore) and DNA replication. Particularly, patients in high-risk group exhibited higher sensitivity to fluorouracil, oxaliplatin and lapatinib compared to the low-risk group. This study highlights the prognostic potential of PANoptosis-related features in CRC, demonstrating their role as key biomarkers significantly associated with patient survival and aiding in the identification of high-risk patients, thereby advancing immunotherapy approaches.