RERE-AS1 enhances the effect of CDK4/6 inhibitor Ribociclib and suppresses malignant phenotype in breast cancer via MEK/ERK pathway.

BACKGROUND

Currently, there is a lack of biomarkers to identify breast cancer (BC) patients who would benefit from CDK4/6 inhibitors. This study combined machine learning (ML) algorithms based on transcriptomic data with both in vivo and in vitro experiments to identify therapeutic efficacy-related biomarkers of the CDK4/6 inhibitor ribociclib from the perspective of long non-coding RNA (lncRNA).

METHODS

We used the Genomics of Drug Sensitivity in Cancer database along with the "oncoPredict" algorithm to calculate the half maximal inhibitory concentration (IC50) values for ribociclib based on transcriptome data. ML algorithms were utilized to select key lncRNAs related to ribociclib and to establish a model which could be used for selection of potential beneficiaries of ribociclib. Cellular experiments were conducted to validate the ML analysis and explore the potential biological mechanisms by which RERE-AS1 influences ribociclib efficacy and malignant phenotype of BC cells. Correlation analysis with clinical pathological factors, RT-qPCR experiments on tissue specimens, and pan-cancer analysis were carried out to explore the expression pattern, and the prognostic and diagnostic potential of RERE-AS1 in cancers.

RESULTS

We have identified 11 key ribociclib-related lncRNAs and constructed an artificial neural network model (ANNM) based on lncRNA. Cellular experiments demonstrated that overexpression of RERE-AS1 promoted the anti-tumor activity of ribociclib in BC cells. Furthermore, RERE-AS1 is crucial in suppressing the malignant traits of BC cells through the reduction of MEK and ERK phosphorylation levels. Patients with smaller primary tumors and lower pathological stage exhibited higher levels of RERE-AS1 expression. Lastly, a pan-cancer analysis revealed that RERE-AS1 exhibits distinctly abnormal expression patterns, prognostic significance, and clinical diagnostic value in BC, compared to other cancers.

CONCLUSIONS

The ANNM established through ML algorithms can serve as predictive indicators for the efficacy of ribociclib in BC patients. LncRNA RERE-AS1, a newly discovered biomarker, holds significant promise for diagnosis, treatment, and enhancing the therapeutic response to ribociclib in BC.