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A Novel Signature Based on Crosstalk Between Anoikis and Pyroptosis Associated Genes for Prediction of Clinical Outcomes, Tumor Microenvironment (TME) and Treatment Response of Breast Cancer

Qian Liu, Fei Qu, Xue Fang Wu, Rongrong Lu, Wei Li

Background: Breast cancer is now the most prevalent malignant among female population worldwide. Anoikis is a key progress during genesis and metastasis of malignant cells. Pyroptosis is a newly defined type of programmed cell death reported to have a dual effect on the development of carcinomas and had been reported to have the potential to affect anti-tumor immunity. However, few studies investigated the connections between anoikis, pyroptosis and prognosis in breast cancer.

Methods: Anoikis and Pyroptosis related Genes (APGs) were achieved from GeneCards and Harmonizome portals database. Based on expression profiles of APGs of patients from TCGA-BRCA cohort, differentiated expressed APGs between normal and tumoral tissues are identified. Next, by univariate Cox regression analysis of combined data of TCGA and GSE cohorts, prognostic APGs was defined. Then patients from both TCGA and GEO cohort were classified into three clusters by consensus clustering algorithm. Overlapped APGs between three clusters were identified as intersecting genes, based on expression of which, individuals are again assigned to two different gene clusters. Eventually, we successfully developed a PCA scoring signature and a nomogram system to accurately predict the prognosis and immunotherapy efficacy of breast cancer patients.

Results: Patients were classified into three clusters based on APGs’ expression. Cluster A was featured by longest OS. According to the expression profile of 300 intersecting genes, patients were again divided into two different gene clusters. Subtype B is characterized with poorer diagnosis. Meanwhile, by means of principal component analysis, we successfully predicted clinical outcomes and treatment response to immunotherapy. Finally, we constructed an APG score-associated nomogram model to predict prognosis.

Conclusion: We successfully established a scoring system based on anoikis and pyroptosis-related genes, as well as combined with clinicopathological features, to serve as a biomarker for prediction of clinical outcomes and immunotherapy efficacy in breast cancer.