Abstract
Metabolic change is the hallmark of cancer. In the present study, we aimed to develop a glycolysis-related gene signature to predict the prognosis of breast cancer patients. Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO database. A four-gene based signature (ALDH2, PRKACB, STMN1 and ZNF292) was developed to separate patients into high-risk and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Time-dependent ROC analysis demonstrated that the glycolysis-related gene signature had excellent prognostic accuracy. We further confirmed the expression of the four prognostic genes in breast cancer and paracancerous tissue samples using qRT-PCR analysis. Expression level of PRKACB was higher in paracancerous tissues, while STMN1 and ZNF292 were overexpressed in tumor samples, no significant difference was observed in ALDH2 expression level. Global proteome data of 105 TCGA breast cancer samples obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) were used to evaluate the prognostic value in protein levels. Consistently, high expression level of PRKACB protein was associated with favorable prognosis, while high ZNF292 and STMN1 protein expression levels indicated poor prognosis. The glycolysis-related gene signature might provide an effective prognostic predictor and a new insight for individualize management of breast cancer patients.
Highlights
Metabolic change is the hallmark of cancer
Time-dependent receiver operating characteristic (ROC) analysis demonstrated that the glycolysis-related gene signature had excellent prognostic accuracy
Expression level of PRKACB was higher in paracancerous tissues, while STMN1 and ZNF292 were overexpressed in tumor samples
Summary
Metabolic change is the hallmark of cancer. Even in the presence of oxygen, cancer cells reprogram their glucose metabolism to enhance glycolysis and reduce oxidative phosphorylation. We aimed to develop a glycolysis-related gene signature to predict the prognosis of breast cancer patients. Due to the improve of therapeutic strategies, breast cancer-related deaths have been observed to decrease in recent decades. Some breast cancer patients initially diagnosed with advanced stage are still incurable. Nearly 30% patients diagnosed at early stage disease will eventually develop locoregional or distant tumor recurrence[3]. In the majority of breast cancer patients, metastatic disease is the underlying cause of death and current clinical strategies fall short in accurately identifying patients at high risk of recurrence. It is important to understand the molecular mechanisms underlying the recurrent process of breast cancer and innovative biomarkers for prognosis predication
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