Identification of Novel Anoikis-Related Gene Signatures to Predict the Prognosis, Immune Microenvironment, and Drug Sensitivity of Breast Cancer Patients

Abstract

Introduction: Breast cancer is one of the most prevalent types of cancer and a leading cause of cancer-related death among females worldwide. Anoikis, a specific type of apoptosis that is triggered by the loss of anchoring between cells and the native extracellular matrix, plays a vital role in cancer invasion and metastasis. However, studies that focus on the prognostic values of anoikis-related genes (ARGs) in breast cancer are scarce.

Methods: Gene expression data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Five anoikis-related signatures (ARS) were selected from ARGs through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Subsequently, an ARGs risk score model was established, and breast cancer patients were divided into high and low risk groups. The correlation between risk groups and overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed. Moreover, RT-qPCR was performed to verify the gene expression levels of the five ARS in breast cancer tissues. Furthermore, a nomogram model was constructed based on ARGs risk score and clinicopathological factors.

Results: A novel ARGs risk score model was constructed based on five ARS (CEMIP, LAMB3, CD24, PTK6, and PLK1), and breast cancer patients were divided into high and low risk groups. Correlation analysis showed that the high and low risk groups had different OS, TMB, TME, stemness, and drug sensitivity. Both the ARGs risk score model and the nomogram showed promising prognosis predictive value in breast cancer.

Conclusion: ARS could be used as promising biomarkers for breast cancer prognosis predication and treatment options selection.

Keywords: anoikis; breast cancer; drug sensitivity; overall survival; tumor immune microenvironment.

Figure 1.

Determination of differentially expressed ARGs in breast cancer. Heatmap (A) and volcano plot (B) of differentially expressed ARGs between breast cancer and normal tissues. (C) KEGG enrichment analysis of differentially expressed ARGs. (D) GO pathway enrichment analysis of differentially expressed ARGs.

Figure 2.

Establishment and evaluation of ARGs risk score model based on differentially expressed ARGs. (A) Forest plot shows OS-associated ARGs screened through univariate Cox regression analysis. (B) The interaction network map of OS-associated ARGs. (C) Somatic mutation profile of OS-associated ARGs in breast cancer. (D) The localization and CNV of OS-associated ARGs on 23 chromosomes. (E) CNV frequency of OS-associated ARGs. (F) K-M survival curve analysis, risk score distribution, survival status of patients, and the expression of ARS in TCGA all sets. (G) K-M survival curve analysis, risk score distribution, survival status of patients, and the expression of ARS in TCGA train set. (H) K-M survival curve analysis, risk score distribution, survival status of patients, and the expression of ARS in TCGA test set. (I) K-M survival curve analysis, risk score distribution, survival status of patients, and the expression of ARS in GEO set. (J) K-M survival curve analysis, risk score distribution, survival status of patients, and the expression of ARS in METABRIC set.

Figure 3.

Comparison of TME, TMB and stemness between high and low risk groups. (A) Somatic mutation profile in high risk group. (B) Somatic mutation profile in low risk group. (C) Differences in tumor mutation burden between high-risk and low-risk groups. (D) The correlation between tumor mutation burden and risk score in breast cancer. (E) K-M survival curve analysis the prognosis difference between high and low TMB subgroups. (F) K-M survival curve analysis the prognosis differences between high and low risk groups combined with TMB. (G) ESTIMATE analysis between the high and low risk groups. (H) The correlation between risk score and stromal score in breast cancer. (I) The correlation between risk score and immune score in breast cancer. (J) The correlation between risk score and ESTIMATE score in breast cancer. (K) The relationship between risk score and immune checkpoints. (L) The correlation between the expression of 5 ARS, risk score and immune cell infiltration in breast cancer. (M) CIBERSORT algorithm to analysis the difference of immune cell infiltration between high and low risk groups of breast cancer patients. (N) Differences in stemness index between high-risk and low-risk groups. (O) The association between stemness index and risk score.

Figure 4.

Expression, stemness and immune correlation of 5 ARS in breast cancer. (A) Differential expression of 5 ARS between breast cancer and normal tissues. (B) RT-qPCR validation the mRNA expression of 5 ARS in cancerous tissues and the adjacent normal tissues (n = 10). (C) The correlation of ARS gene expression levels with stemness index, stromal score, immune score, and ESTIMATE score in breast cancer.

Figure 5.

Correlation of risk score with drug sensitivity and response to immunotherapy. (A-K). The relationship between risk score and drug sensitivity. (A) Tamoxifen. (B) Epirubicin. (C) Oxaliplatin. (D) Docetaxel. (E) Teniposide. (F) Palbociclib. (G) Ribociclib. (H) Olaparib. (I) Zoledronate. (J) Lapatinib. (K) Sapitinib. (L) Differences in response to immunotherapy between high-risk and low-risk groups.

Figure 6.

Construction and validation of the nomogram containing ARGs risk model. (A) Univariate Cox regression analysis of clinicopathological factors and risk score. (B) Multivariate Cox regression analysis of clinicopathological factors and risk score. (C) Nomogram model construction based on independent prognostic factors. (D) Calibration curves of nomogram model at 1, 3, 5 years. (E–G) The DCA assess the net benefit of nomogram and other indicators at 3, 5 and 8 years.

Figure 7.

Identification of breast cancer subtypes (A). K-M survival analysis between different subtypes in breast cancer. (B). Differential expression of ARS in breast cancer subtypes. (C). The distributions among ARGclusters, risk groups, and clinical status. (D). Differences in risk score between breast cancer subtypes. (E). Differences in tumor mutation burden between breast cancer subtypes. (F). The distributions among ARGclusters and molecular subtypes. (G). Differences in stemness index between breast cancer subtypes. (H-S). The correlation between ARGclusters and drug sensitivity. (H). Docetaxel. (I). Epirubicin. (J). Oxaliplatin. (K). Teniposide. (L). Cyclophosphamide. (M). Fulvestrant. (N). Zoledronate. (O). Palbociclib. (P). Ribociclib. (Q). Olaparib. (R). Lapatinib. (S). Sapitinib.