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.

Esophageal squamous cell carcinoma is a severe malignancy for its high mortality and poor prognosis. Mainstay chemotherapies cause serious side effects for their ways of inducing cell death. Oridonin is the main bioactive constituent from natural plants that has anticancer ability and weak side effects. The proteomics method is efficient to understand the anticancer mechanism. However, proteins identified by proteomics aimed at understanding oridonin’s anticancer mechanism is seldom overlapped by different groups. This study used proteomics based on two-dimensional electrophoresis sodium dodecyl sulfate-polyacrylamide gel electrophoresis (2-DE SDS-PAGE) integrated with mass spectrometry and Gene Set Enrichment Analysis (GSEA) to understand the anticancer mechanism of oridonin on esophageal squamous cell carcinoma (ESCC). The results showed that oridonin induced ESCC cell death via apoptosis by decreasing the protein expression of LASP1 and PDLIM1.