Construction of an original anoikis-related prognostic model closely related to immune infiltration in gastric cancer

Abstract

Background: Anoikis is considered as a particular type of programmed cell death, the weakness or resistance of which contributes greatly to the development and progression of most malignant solid tumors. However, the latent impact of anoikis-related genes (ARGs) on gastric cancer (GC) is still ambiguous. Based on these, this study established an anoikis-related prognostic model of GC to identify the prognosis of patients and provide more effective treatment in clinical practice.Methods: First, we extracted four public datasets containing the gene expression and clinicopathological information of GC, which were worked as the training and validating sets, separately. Then, an anoikis-related survival-predicted model of GC was developed via Lasso and COX regression analyses and verified by using the Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) curve analyses. Next, we assigned GC patients to two groups characterized by the risk score calculated and analyzed somatic mutation, functional pathways, and immune infiltration between the different two groups. Finally, a unique nomogram was offered to clinicians to forecast the personal survival probability of GC patients.Results: Based on seven anoikis-related markers screened and identified, a carcinogenic model of risk score was produced. Patients placed in the high-score group suffered significantly worse overall survival (OS) in four cohorts. Additionally, the model revealed a high sensitivity and specificity to prognosticate the prognoses of GC patients [area under the ROC curve (AUC) at 5-year = 0.713; GSE84437, AUC at 5-year = 0.639; GSE15459, AUC at 5-year = 0.672; GSE62254, AUC at 5-year = 0.616]. Apart from the excellent predictive performance, the model was also identified as an independent prediction factor from other clinicopathological characteristics. Combining anoikis-related prognostic model with GC clinical features, we built a more comprehensive nomogram to foresee the likelihood of survival of GC patients in a given year, showing a well-accurate prediction performance.Conclusion: In summary, this study created a new anoikis-related signature for GC, which has potentially provided new critical insights into survival prediction and individualized therapy development.

Identification and Validation of an Anoikis-Related Gene Signature to Predict Prognosis in Colorectal Cancer

“…Under physiological or pathological conditions, anoikis facilitates tissue homeostasis through the elimination of misplaced or detached cells (Adeshakin, Adeshakin et al 2021). Anoikis was rst identi ed in epithelial and endothelial cells, and has been found to be an important mechanism of cancer invasion and metastasis (Zhao, Li et al 2022). The occurrence of anoikis resistance could help detached cells circumvent death signaling pathways, allowing cells to survive under adverse conditions (Zhou, Wang et al 2022).…”

A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer

“…In contrast, angiopoietin-like protein 2, an inflammatory factor, inhibits peritoneal metastasis of OC cells by suppressing anoikis resistance ( 12 ). Recently, some prognostic models based on anoikis-related genes (ARGs) using RNA-sequencing data have been developed for predicting the clinical outcomes of several cancers, including hepatocellular carcinoma ( 13 ), cutaneous melanoma ( 14 ), gastric cancer ( 15 ), lung adenocarcinoma ( 16 ), glioblastoma ( 17 ), and colorectal cancer ( 18 ). However, few studies have systematically evaluated the prognostic value of ARGs in OC.…”