Breast Heterogeneity: Obstacles to Developing Universal Biomarkers of Breast Cancer Initiation and Progression

A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma

Background: Invasive ductal carcinoma (IDC) is the most common type of metastatic breast cancer. Due to the lack of valuable molecular biomarkers, the diagnosis and prognosis of IDC remain a challenge. A large number of studies have confirmed that coagulation is positively correlated with angiogenesis-related factors in metastatic breast cancer. Therefore, the purpose of this study was to construct a COAGULATION-related genes signature for IDC using the bioinformatics approaches. Methods: The 50 hallmark gene sets were obtained from the molecular signature database (MsigDB) to conduct Gene Set Variation Analysis (GSVA). Gene Set Enrichment Analysis (GSEA) was applied to analyze the enrichment of HALLMARK_COAGULATION. The COAGULATION-related genes were extracted from the gene set. Then, Limma Package was used to identify the differentially expressed COAGULATION-related genes (DECGs) between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) samples in GSE26340 data set. A total of 740 IDC samples from The Cancer Genome Atlas (TCGA) database were divided into a training set and a validation set (7:3). The univariate and multivariate Cox regression analyses were performed to construct a risk signature, which divided the IDC samples into the high- and low-risk groups. The overall survival (OS) curve and receiver operating characteristic (ROC) curve were drawn in both training set and validation set. Finally, a nomogram was constructed to predict the 1-, 2-, 3-, 4-, and 5-year survival rates of IDC patients. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic genes. Results: The "HALLMARK_COAGULATION" was significantly activated in IDC. There was a significant difference in the clinicopathological parameters between the DCIS and IDC patients. Twenty-four DECGs were identified, of which five genes (SERPINA1, CAPN2, HMGCS2, MMP7, and PLAT) were screened to construct the prognostic model. The high-risk group showed a significantly lower survival rate than the low-risk group both in the training set and validation set (p=3.5943e-06 and p=0.014243). The risk score was demonstrated to be an independent predictor of IDC prognosis. A nomogram including risk score, pathological_stage, and pathological_N provided a quantitative method to predict the survival probability of 1-, 2-, 3-, 4-, and 5-year in IDC patients. The results of decision curve analysis (DCA) further demonstrated that the nomogram had a high potential for clinical utility. Conclusion: This study established a COAGULATION-related gene signature and showed its prognostic value in IDC through a comprehensive bioinformatics analysis, which may provide a potential new prognostic mean for patients with IDC.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.