Differential Gene -Based Predictors of Neoadjuvant Chemotherapy Efficacy in Breast Cancer

Background and objectiveChemotherapy is the most common treatment in breast cancer , and neoadjuvant chemotherapy (NAC) is wildly used because of it’s efficiency and safety. To identify significantly differentially expressed genes and select the most suitable breast cancer patients for neoadjuvant chemotherapy (NAC) before treatment. MethodsWe collected a total of 60 breast cancer patient samples before and after NAC. All the samples were subjected to high-throughput RNA sequencing (RNA-seq). Then , we identified AHNAK, CIDEA, ADIPOQ, and AKAP12 as candidate genes related to tumour chemotherapeutic resistance. Next, we analysed the expression levels of AHNAK, CIDEA, ADIPOQ, and AKAP12 by logistic regression and based on the result, we constructed a predictive model visualized by a nomogram. ResultsThe RNA-seq results show that AHNAK, CIDEA, ADIPOQ and AKAP12 are upregulated in residual disease after NAC (P<0.05), and compared with the pathological complete response (pCR) group, the non-pCR group presented high AHNAK, CIDEA, ADIPOQ and AKAP12 expression levels (P<0.05). Logistic analysis showed that high AHNAK, CIDEA, ADIPOQ and AKAP12 expression levels significantly reduced the pCR rate of NAC for breast cancer (P<0.05). In addition, our prediction model , which included AHNAK, CIDEA, ADIPOQ and AKAP12 , showed a good fitting effect with the H1 test (χ2=6.3967, P=0.4945) and the receiver operating characteristic (ROC) curve (area under the curve (AUC) 0.8249, 95% CI 0.722–0.9271). ConclusionHigh expression of AHNAK, CIDEA, ADIPOQ and AKAP12 indicates poor treatment response in breast cancer patients treated with NAC . The efficacy prediction model based on these results is expected to be a new method to select the optimal population of breast cancer patients for NAC.