Background This study intended to determine important genes related to the prognosis and recurrence of breast cancer. Methods Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed. Results Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors. Conclusion ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

BackgroundCancer is a genetic disease; its development and metastasis depend on the function of many proteins. Human serum contains thousands of proteins; it is a window for the homeostasis of individual’s health. Many of the proteins found in the human serum could be potential biomarkers for cancer early detection and drug efficacy evaluation.MethodsIn this study, a functional proteomics technology was used to systematically monitor metabolic enzyme and protease activities from resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP. All the experiments were repeated at least twice to ensure the validity of the findings.ResultsFor the first time, significant differences were found between breast cancer patient serum and normal serum in two families of enzymes known to be involved in cancer development and metastasis: metabolic enzymes and proteases. Multiple enzyme species were identified in the serum assayed directly or after enrichment. Both qualitative and quantitative differences in the metabolic enzyme and protease activity were detected between breast cancer patient and control group, providing excellent biomarker candidates for breast cancer diagnosis and drug development.ConclusionsThis study identified several potential functional protein biomarkers from breast cancer patient serum. It also demonstrated that the functional proteomics technology, PEP, can be applied to the analysis of any functional proteins in human serum which contains thousands of proteins. The study indicated that the functional domain of the human serum could be unlocked with the PEP technology, pointing to a novel alternative for the development of diagnosis biomarkers for breast cancer and other diseases.

Purpose This work aims to determine the feasibility of using a computer-aided diagnosis system to differentiate benign and malignant breast tumors on magnetic resonance diffusion-weighted image (DWI). Materials and Methods Institutional review board approval was obtained. This retrospective study included 76 patients who underwent breast magnetic resonance imaging before neoadjuvant chemotherapy from March 10, 2017, to October 12, 2017, with a total of 80 breast tumors including 40 cases of breast cancers and 40 cases of benign breast tumors. The textural features of DWI images were analyzed. The area under the receiver operating characteristic curve was calculated to evaluate the diagnostic efficiency of texture parameters. Multiple linear regression analysis was used to determine the efficiency of texture parameters for distinguishing the 2 types of breast tumors. Results Computer vision algorithms were applied to extract 67 imaging features from lesions indicated by a breast radiologist on DWI images. A total of 19 texture feature parameters, such as variance, standard deviation, intensity, and entropy, out of 67 texture parameters were statistically significant in the 2 sets of data (P < 0.05). By comparing the receiver operating characteristic curves, we found that the mean and relative deviations exhibited high diagnostic values in differentiating between benign and malignant tumors. The accuracy of Fisher discriminant analysis for the 2 types of breast tumors was 92.5%. Conclusions Breast lesions exhibit certain characteristic features in DWI images that can be captured and quantified with computer-aided diagnosis, which enables good discrimination of benign and malignant breast tumors.

Clinical treatment of triple negative breast cancer (TNBC) is very poor for lack of effective treatment combination selection. Protein C receptor (PROCR) is a novel cancer stem marker in TNBC patients tumor tissues. Developed based on peptide BP10 with affinity to PROCR as a targeting element, constructing a peptide drug conjugate of BP10 covalently coupling doxorubicin with disulfide bonds. This study demonstrated that the constructed BP10-DOX can selectively target Triplenegative breast cancer cells expressing PROCR and controlled release of DOX in response to the GSH environment. Moreover, BP10-DOX improves the therapeutic efficiency on MDA-MB-231 cells in vitro. Further evidence obtained from in vivo xenograft experiments revealed that administration of BP10-DOX enhanced the antitumor efficacy. This study developed a promising chemotherapy strategy for TNBC.

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