A large-scale screening and functional sorting of tumour microenvironment prognostic genes for breast cancer patients.

PURPOSE

The aim of this study was to systematically establish a comprehensive tumour microenvironment (TME)-relevant prognostic gene and target miRNA network for breast cancer patients.

METHODS

Based on a large-scale screening of TME-relevant prognostic genes (760 genes) for breast cancer patients, the prognostic model was established. The primary TME prognostic genes were selected from the constructing database and verified in the testing database. The internal relationships between the potential TME prognostic genes and the prognosis of breast cancer patients were explored in depth. The associated miRNAs for the TME prognostic genes were generated, and the functions of each primary TME member were investigated in the breast cancer cell line.

RESULTS

Compared with sibling controls, breast cancer patients showed 55 differentially expressed TME prognostic genes, of which 31 were considered as protective genes, while the remaining 24 genes were considered as risk genes. According to the lambda values of the LASSO Cox analysis, the 15 potential TME prognostic genes were as follows: ENPEP, CCDC102B, FEZ1, NOS2, SCG2, RPLP2, RELB, RGS3, EMP1, PDLIM4, EPHA3, PCDH9, VIM, GFI1, and IRF1. Among these, there was a remarkable linear internal relationship for CCDC102B but non-linear relationships for others with breast cancer patient prognosis. Using the siRNA technique, we silenced the expression of each TME prognostic gene. Seven of the 15 TME prognostic genes (NOS2, SCG2, RGS3, EMP1, PDLIM4, PCDH9, and GFI1) were involved in enhancing cell proliferation, destroying cell apoptosis, promoting cell invasion, or migration in breast cancer. Six of them (CCDC102B, RPLP2, RELB, EPHA3, VIM, and IRF1) were favourable for maintaining cell invasion or migration. Only two of them (ENPEP and FEZ1) were favourable for the processes of cell proliferation and apoptosis.

CONCLUSIONS

This integrated study hypothesised an innovative TME-associated genetic functional network for breast cancer patients. The external relationships between these TME prognostic genes and the disease were measured. Meanwhile, the internal molecular mechanisms were also investigated.