Establishment and validation of the prognostic risk model based on the anoikis related genes in esophageal squamous cell carcinoma

Abstract

Abstract Background Esophageal squamous cell carcinoma (ESCC) is a malignant condition in humans. Anoikis related genes (ARGs) are crucial to cancer progression. Therefore, more studies on the relationship between ARGs and ESCC are warranted. Methods The study acquired ESCC-related transcriptome data from the cancer genome atlas (TCGA). Differentially expressed ARGs (DE-ARGs) were obtained by performing differential analysis on the training set, and candidates were filtered out by survival analysis of high and low expression groups of DE-ARGs. Prognostic genes were determined by univariate and multivariate Cox and lasso regression based on candidate genes and were analyzed by gene set enrichment analysis (GSEA). A risk model was constructed on the basis of prognostic gene expressions. To find out how these genes contribute to ESCC development, immune infiltration study was done. Based on prognostic genes, the miRNA-mRNA-TF regulation network was constructed. IC50 test was adopted to assess the clinical response of chemotherapy drug. Single-cell analysis was performed on the GSE145370 dataset. Moreover, with the use of quantitative reverse transcription (qRT)-PCR, we verified prognostic gene expressions. Results 53 DE-ARGs (46 upregulated; 7 downregulated) were screened by differential analysis. Survival analysis yielded four candidate genes consisting of PBK, LAMC2, TNFSF10 and KL. The two prognostic genes, TNFSF10 and PBK, were determined by univariate and multivariate Cox and lasso regression. In terms of hallmark, TNFSF10 was involved in 32 pathways, and PBK was partake in 34 pathways. In immunomic signatures, 4558 enrichment entries were associated with TNFSF10, like genes downregulated CD8 T cells, PBK enriched to 4262 pathways, such as genes downregulated in B cells. Immuno-infiltration analysis revealed positive associations of PBK with Macrophages M0 cells, and TNFSF10 with Macrophages M1 cells. miRNA-mRNA-TF network was generated with prognostic genes, which contained hsa-miR-562-TNFSF10-FOXO3, hsa-miR-216b-5p-PBK-ATM regulatory relationship pairs, etc. The result of chemotherapy drug susceptibility analysis showed that IC50 values of predicted drugs, in the case of Tozasertib 1096 and WIKI4 1940, were significantly variant between risk groups. Single-cell analysis revealed that TNFSF10 and PBK levels were higher in epithelial cells than in other cells. The prognostic genes expression results by qRT-PCR were compatible with the dataset analysis. Conclusion The study explored the biomarkers related to anoikis based on bioinformatics technology and established a prognosis model of ESCC. It provided a reference for the research of ARGs in ESCC.