Identification of cell adhesion-related subtypes and construction of risk model to predict breast cancer prognostic and immunological properties.

BACKGROUND

Breast invasive carcinoma is the most common form of breast cancer, often resulting in recurrence or metastasis in patients. Cell adhesion molecules play a crucial role in modulating the interactions between tumor cells and surrounding cells. The study aims to identify breast cancer subtypes related to cell adhesion and develop prognostic models that are essential for evaluating the prognostic risk and immunological profile of breast cancer.

METHODS

Transcriptome and clinical data were obtained from The Cancer Genome Atlas (TCGA) database, while cell adhesion-related genes (CARGs) from the MSigDB database. Molecular subtyping was performed using NMF clustering. Cox regression and Least absolute shrinkage and selection operator (LASSO) regression analyses were employed to construct a risk model for predicting patient prognosis. This model was validated in independent Gene Expression Omnibus (GEO) datasets, specifically GSE20685 and GSE42568. Immune cell infiltration was explored utilizing the CIBERSORT algorithm. Subsequently, we analyzed tumor mutation burden (TMB). Finally, potential drugs and drug sensitivity was evaluated using pRRobhetic algorithm.

RESULTS

Based on the expression levels of 39 genes related to cell adhesion, we identified 3 distinct subtypes, and LASSO regression analysis identified 8 genes that could be used as prognostic markers. Receiver operating characteristic (ROC) curves demonstrated that these cell adhesion genes were effective in predicting patient prognosis. Compared to the high-risk group, the low-risk group had a more favorable prognosis and a greater response to immunotherapy. These prognostic genes were found to be closely associated with immune cell infiltration and the response to immunotherapy. Furthermore, their significant associations with breast cancer sensitivities to anti-cancer drugs were revealed.

CONCLUSION

We developed a risk model focused on cell adhesion-related genes. This model accurately predicts the prognosis for breast cancer patients. It may also offer new insights for clinical decisions and immunotherapy.