Triple negative breast cancer (TNBC) lacks well-defined molecular targets and is highly heterogenous, making treatment challenging. Using gene expression analysis, TNBC has been classified into four different subtypes: basal-like immune-activated (BLIA), basal-like immune-suppressed (BLIS), mesenchymal (MES), and luminal androgen receptor (LAR). However, there is currently no standardized method for classifying TNBC subtypes. We attempted to define a gene signature for each subtype, and to develop a classification method based on machine learning (ML) for TNBC subtyping. In these experiments, gene expression microarray data for TNBC patients were downloaded from the Gene Expression Omnibus database. Differentially expressed genes unique to 198 known TNBC cases were identified and selected as a training gene set to train in seven different classification models. We produced a training set consisting of 719 DEGs selected from uniquely expressed genes of all four subtypes. The highest average accuracy of classification of the BLIA, BLIS, MES, and LAR subtypes was achieved by the SVM algorithm (accuracy 95–98.8%; AUC 0.99–1.00). For model validation, we used 334 samples of unknown TNBC subtypes, of which 97 (29.04%), 73 (21.86%), 39 (11.68%) and 59 (17.66%) were predicted to be BLIA, BLIS, MES, and LAR, respectively. However, 66 TNBC samples (19.76%) could not be assigned to any subtype. These samples contained only three upregulated genes (EN1, PROM1, and CCL2). Each TNBC subtype had a unique gene expression pattern, which was confirmed by identification of DEGs and pathway analysis. These results indicated that our training gene set was suitable for development of classification models, and that the SVM algorithm could classify TNBC into four unique subtypes. Accurate and consistent classification of the TNBC subtypes is essential for personalized treatment and prognosis of TNBC.

A liquid biopsy is currently an interesting tool for measuring tumor material with the advantage of being non-invasive. The overexpression of vimentin and ezrin genes was associated with epithelial-mesenchymal transition (EMT), a key process in metastasis and progression in osteosarcoma (OS). In this study, we identified other OS-specific genes by calculating differential gene expression using the Gene Expression Omnibus (GEO) database, confirmed by using quantitative reverse transcription-PCR (qRT-PCR) to detect OS-specific genes, including VIM and ezrin in the buffy coat, which were obtained from the whole blood of OS patients and healthy donors. Furthermore, the diagnostic model for OS detection was generated by utilizing binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. The model incorporating VIM, ezrin, and COL5A2 genes exhibited outstanding discriminative ability, as determined by the receiver operating characteristic curve (AUC = 0.9805, 95% CI 0.9603, 1.000). At the probability cut-off value of 0.3366, the sensitivity and the specificity of the model for detecting OS were 98.63% (95% CI 90.5, 99.7) and 94.94% (95% CI 87.5, 98.6), respectively. Bioinformatic analysis and qRT-PCR, in our study, identified three candidate genes that are potential diagnostic and prognostic genes for OS.

Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease associated with late-stage diagnosis and high metastatic rates. However, a gene signature for reliable TNBC biomarkers is not available yet. We aimed to identify potential key genes and their association with poor prognosis in TNBC through integrated bioinformatics.Methods: Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database.Differentially expressed genes (DEGs) in TNBC vs. non-TNBC and TNBC vs. normal tissues were analyzed. Overlapping upregulated and downregulated DEGs were selected as inputs for Gene Ontology and pathway enrichment analyses using Metascape. Then, UALCAN and Kaplan-Meier plotter were employed to analyze the prognostic values of all overlapping DEGs. Results:We identified 21 upregulated and 24 downregulated overlapping DEGs in TNBC vs. non-TNBC and TNBC vs. normal breast tissue. The upregulated overlapping DEGs were mainly enriched in various pathways including chromosome segregation, cell cycle phase transition, and cell division, whereas overlapping DEGs were significantly downregulated in pathways, such as multicellular organismal homeostasis, tissue homeostasis, and negative regulation of cell population proliferation. Key genes were identified by association with poor overall survival (OS). Our results showed that high expression of CENPW and HORMAD1 was associated with poor OS of TNBC patients. Conversely, the low expression of PIP, APOD, and ZNF703 indicated worse OS. Conclusions:We identified key genes (CENPW, HORMAD1, APOD, PIP, and ZNF703) associated with poor OS. Thus, these genes might serve as candidate prognostic markers for TNBC.

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Background Current techniques to identify circulating-tumor cells (CTCs) in osteosarcoma (OS), which are an indication of a poor prognosis in cases of intermediate levels of metastasis, are complicated and time-consuming. This study investigated the efficacy of quantitative reverse transcription PCR (qRT-PCR), a molecular technique that is available in most laboratories, for detection of CTCs in buffy coat samples of OS patients and healthy donors. Methods Previously published reports on data-reviewing and retrieval of data by calculation of differential gene expression from the Gene Expression Omnibus (GEO) database repository were reviewed identify candidate genes. Following analysis of the expression of the candidate genes identified a diagnostic model for detection of specific gene expression was derived using binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. Results A model incorporating VIM, ezrin, COL1A2, and PLS3 exhibited an outstanding discriminative ability as determined by the receiver operating characteristic curve (AUC = 0.9896, 95%CI 0.9695, 1.000). At the probability cut-off value 0.2943, the sensitivity and the specificity of the model for detection of OS were 100% (95%CI 94.8, 100.0) and 96.49% (95%CI 87.9, 99.6), respectively. Conclusion The qRT-PCR can identify the existence of OS circulating cells by detection of potential candidate genes (VIM, Ezrin, COL1A2 and PLS3). Thus, these genes are worthy to be considered diagnostic biomarkers and alternative micro-metastasis predictors for OS.