PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer

Xu Wang; Rui Sun; Shixin Chan; Lei Meng; Yuanmin Xu; Xiaomin Zuo; Zhenglin Wang; Xianyu Hu; Qijun Han; Longfei Dai; Tao Bai; Zhen Yu; Ming Wang; Wenqi Yang; Huabing Zhang; Wei Chen

doi:10.3389/fgene.2022.955355

PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer

Front Genet. 2022 Sep 14:13:955355. doi: 10.3389/fgene.2022.955355. eCollection 2022.

Authors

Xu Wang¹, Rui Sun¹, Shixin Chan¹, Lei Meng¹, Yuanmin Xu¹, Xiaomin Zuo¹, Zhenglin Wang¹, Xianyu Hu¹, Qijun Han¹, Longfei Dai¹, Tao Bai¹, Zhen Yu¹, Ming Wang¹, Wenqi Yang¹, Huabing Zhang^{2

3}, Wei Chen¹

Affiliations

¹ Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
² Affiliated Chuzhou Hospital of Anhui Medical University, First People's Hospital of Chuzhou, Chuzhou, Anhui, China.
³ Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, Anhui, China.

Abstract

PANoptosis is a newly-discovered cell death pathway that involves crosstalk and co-ordination between pyroptosis, apoptosis, and necroptosis processes. However, the roles of PANoptosis-related genes (PRGs) in prognosis and immune landscape of colon cancer remain widely unknown. Here, we performed a bioinformatics analysis of expression data of nineteen PRGs identified from previous studies and clinical data of colon cancer patients obtained from TCGA and GEO databases. Colon cancer cases were divided into two PRG clusters, and prognosis-related differentially expressed genes (PRDEGs) were identified. The patient data were then separated into two corresponding distinct gene clusters, and the relationship between the risk score, patient prognosis, and immune landscape was analyzed. The identified PRGs and gene clusters correlated with patient survival and immune system and cancer-related biological processes and pathways. A prognosis signature based on seven genes was identified, and patients were divided into high-risk and low-risk groups based on the calculated risk score. A nomogram model for prediction of patient survival was also developed based on the risk score and other clinical features. Accordingly, the high-risk group showed worse prognosis, and the risk score was related to immune cell abundance, cancer stem cell (CSC) index, checkpoint expression, and response to immunotherapy and chemotherapeutic drugs. Results of quantitative real-time polymerase chain reaction (qRT-PCR) showed that LGR5 and VSIG4 were differentially expressed between normal and colon cancer samples. In conclusion, we demonstrated the potential of PANoptosis-based molecular clustering and prognostic signatures for prediction of patient survival and tumor microenvironment (TME) in colon cancer. Our findings may improve our understanding of the role of PANoptosis in colon cancer, and enable the development of more effective treatment strategies.

Keywords: PANoptosis; colon cancer; immunotherapy; prognosis; tumor microenvironment.