Development of a novel, clinically relevant anoikis-related gene signature to forecast prognosis in patients with prostate cancer

Abstract

Introduction: Anoikis is a specific form of programmed cell death and is related to prostate cancer (PC) metastasis. This study aimed to develop a reliable anoikis-related gene signature to accurately forecast PC prognosis. Methods: Based on anoikis-related genes and The Cancer Genome Atlas (TCGA) data, anoikis-related molecular subtypes were identified, and their differences in disease-free survival (DFS), stemness, clinical features, and immune infiltration patterns were compared. Differential expression analysis of the two subtypes and weighted gene co-expression network analysis (WGCNA) were employed to identify clinically relevant anoikis-related differentially expressed genes (DEGs) between subtypes, which were then selected to construct a prognostic signature. The clinical utility of the signature was verified using the validation datasets GSE116918 and GSE46602. A nomogram was established to predict patient survival. Finally, differentially enriched hallmark gene sets were revealed between the different risk groups. Results: Two anoikis-related molecular subtypes were identified, and cluster 1 had poor prognosis, higher stemness, advanced clinical features, and differential immune cell infiltration. Next, 13 clinically relevant anoikis-related DEGs were identified, and five of them (CKS2, CDC20, FMOD, CD38, and MSMB) were selected to build a prognostic signature. This gene signature had a high prognostic value. A nomogram that combined Gleason score, T stage, and risk score could accurately predict patient survival. Furthermore, gene sets closely related with DNA repair were differentially expressed in the different risk groups. Conclusion: A novel, clinically relevant five-anoikis-related gene signature was a powerful prognostic biomarker for PC.

Keywords: anoikis; gene signature; prognosis; prostate cancer; tumor microenvironment.

FIGURE 1

Two anoikis-related molecular subtypes were identified, which had different survival, mRNAsi, and clinical features. (A): Subtype clustering heatmap, cumulative distribution function (CDF) distribution curve, and Delta area line graph. (B): Kaplan–Meier (K–M) survival curves for samples in two subtypes. (C): The stemness signature of two subtypes. (D): The distribution of clinical features including Gleason score, TMB nonsynonymous, and N stage in the two subtypes.

FIGURE 2

Differences in immune infiltration patterns in different subtypes. (A): The infiltration proportion of 22 kinds of immune cells in two subtypes. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. (B): Differences in the stromal score, immune score, and ESTIMATE score of two subtypes. (C): The expression of immune checkpoint genes in the two subtypes.

FIGURE 3

Analysis of differentially expressed genes (DEGs) between subtypes and their functional enrichment analysis. (A): Heatmap of DEGs between subtypes. (B): GO and KEGG pathway enrichment results. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

FIGURE 4

Co-expression network analysis by weighted gene co-expression network analysis (WGCNA). (A): Analysis of network topology for various soft-threshold powers. (B): Module clustering result. (C): Results of gene module merging based on clustering and dynamic pruning methods. Each vertical line indicates a gene and each branch represents an expression module of highly interconnected genes. Below the dendrogram, different modules are given different colors. Gray indicated that genes are outside all modules. (D): The correlations of gene modules with multiple clinical features.

FIGURE 5

Construction and validation of the prognostic signature that was established by five anoikis-related genes. (A): Venn plot showed the overlapping DEGs related to both anoikis and clinical features of prostate cancer. (B): Univariate Cox regression analysis showed that the overlapping DEGs were all significantly correlated with DFS of patients. (C): The LASSO coefficient spectrum of the prognostic DEGs and optimized lambda determined in the LASSO regression model. (D): Kaplan–Meier (K–M) survival curves showed the survival differences between the two risk groups based on TCGA-PRAD training dataset and GSE116918 validation dataset. (E): The scatterplots showed the distribution of the risk score and recurred/progressed time of patients based on TCGA-PRAD training dataset and GSE116918 validation dataset. (F): ROC curves revealed the predictive performance of the gene signature in predicting 1-, 3-, and 5-year survival probabilities based on TCGA-PRAD training dataset and GSE116918 validation dataset. (G): Heatmap showed significant differences in the expression of the five model genes between the high- and low-risk samples in the TCGA-PRAD training dataset and GSE116918 validation dataset.

FIGURE 6

The anoikis-related gene signature was an independent prognostic factor and a nomogram was established. (A): Univariate Cox regression analysis shows the correlation between survival and risk score of the anoikis-related gene signature and various clinicopathological features. (B): Multivariate Cox regression analysis showed that Gleason score, T stage, and risk score were independent prognostic factors. (C): A nomogram was constructed for estimating the 1-, 3-, and 5-year survival probabilities of patients. (D): The calibration curves showed the concordance of the prediction probability and actual probability of 1-, 3-, and 5-year survival.

FIGURE 7

Gene set enrichment analysis (GSEA) of differentially enriched hallmark gene sets between different risk groups. (A): The up-regulated hallmark gene sets in the high-risk group. (B): The down-regulated hallmark gene sets in the high-risk group.

FIGURE 8

Prognostic signature for metastatic PC. (A): The mean risk score between metastasis group and primary group. (B–F): CKS2, CDC20, FMOD, MSMB and CD38 expression detected by RT-qPCR. *p < 0.05.