KLP-PI: a new prognostic index for luminal B HER-2-negative breast cancer

Systems biology comprehensive analysis on breast cancer for identification of key gene modules and genes associated with TNM-based clinical stages

Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein-protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan-Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan-Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.

ACLY: A biomarker of recurrence in breast cancer

OBJECTIVE

ACLY is a cytoplasmic metabolic enzyme involved in lipid synthesis. It also affects proliferation and metastasis of breast cancer. However, the correlation of ACLY expression with breast cancer recurrence is unclear.

METHODS

The Oncomine and TCGA databases were used to investigate the mRNA expression of ACLY in breast cancer. Immunohistochemistry (IHC) was used to evaluate ACLY expression level in tumor tissues and normal tissues from 127 breast cancer patients. Next, the prognostic role of ACLY was explored by analyzing the clinicopathological features and prognosis during follow-up. The role of ACLY in breast cancer cells drug resistance was further detected by CCK-8 assays and quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

ACLY mRNA and protein expression was significantly increased in the breast cancer tissues compared to normal tissues. Clinically, high ACLY levels were associated with ER status, PR status, tumor size, TNM stage, and lymph node invasion. Upregulated ACLY predicted worse tumor relapse-free survival (RFS) of breast cancer patients in univariate analyses and in multivariate models. In subgroup analysis, patients with high ACLY expression showed worse RFS in the TNM III or ER positive subgroups. Moreover, ACLY over-expression induced the resistance of breast cancer cells to docetaxel and promoted the expression of multi-drug resistant protein ABCB1/ABCG2.

CONCLUSIONS

Our study highlights the possibility of ACLY as a potential and independent biomarker for the recurrence prediction in breast cancer patients. It may be related to ACLY promoting drug resistance in breast cancer cells.