Anoikis Patterns in Cervical Cancer: Identification of Subgroups and Construction of a Novel Risk Model for Predicting Prognosis and Immune Response

Abstract

Background: Cervical cancer has high morbidity and intratumor heterogeneity. Anoikis, a form of programmed cell death preventing detached cancer cells from readhering, may serve as a potential prognostic signature for cervical cancer. This study aimed to assess the predictive performance of anoikis patterns in cervical cancer prognosis. Methods: Differentially expressed anoikis-related genes (DEARGs) were identified between normal and cancer samples using data from the Gene Expression Omnibus database with the elucidation of mutation status and bio-function. Novel anoikis molecular subtypes were defined in The Cancer Genome Atlas (TCGA) cohort with consensus clustering analysis. A multigene prognostic signature was constructed through least absolute shrinkage and selection operator (LASSO) Cox analysis with internal and external validation. The nomogram-based survival probability of cervical cancer over 3 and 5 years was predicted and assessed with calibration, receiver operating characteristic, decision curve analysis, and Kaplan-Meier curves. Additionally, mutation, function, and immune analysis were conducted among different risk groups. Results: We identified 77 DEARGs between normal and cervical cancer tissues and explored their mutation status and functions. The TCGA cohort could be categorized into two subtypes based on these genes. Furthermore, seven prognostic signature genes were constructed, and the nomogram involving DEARGs and clinicopathological characteristics showed satisfactory predictive performance. Functional analysis indicated that immune-related genes were enriched, and immune status, as well as sensitivity of chemotherapies and targeting drugs, were correlated with the risk model. Conclusions: Anoikis patterns play important roles in tumor immunity and can be used to predict the prognosis of cervical cancers.