Diagnostic Biomarker For Breast Cancer Research Articles

Down regulation of C1q tumor necrosis factor-related protein 6 is associated with increased risk of breast cancer

C1q tumor necrosis factor-related protein 6 (CTRP6), a member of the C1q tumor necrosis factor-related protein (CTRP) family, is reported to be associated with the progression of different malignancies, however, its expression levels and role in breast cancer (BC) are yet unknown. In this study, we investigated the levels of circulating CTRP6 in BC patients and evaluated its role as a potential diagnostic biomarker in BC patients. Then we investigated the effect of recombinant CTRP6 on cellular viability in MCF-7 cells along with its effects on the expression of inflammatory cytokines, interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α) in addition to the expression of vascular endothelial growth factor (VEGF) as a marker of angiogenesis. Our results showed decreased expression of circulating CTRP6 in BC patients with an inverse correlation between CTRP6 and IL-6, TNF-α and VEGF levels. Besides, Receiver operating characteristic (ROC) curve showed that the assessment of CTRP6 levels could be used to predict BC. Moreover, treatment of MCF-7 cells with recombinant CTRP6 protein reduced cellular viability and decreased IL-6, TNF-α and VEGF expression. In conclusion, these results provide new insights into the role of CTRP6 in BC pathogenesis and suggest its potential use as a novel diagnostic biomarker of BC.

Spatial and single-cell explorations uncover prognostic significance and immunological functions of mitochondrial calcium uniporter in breast cancer

The mitochondrial calcium uniporter (MCU) is a transmembrane protein facilitating the entry of calcium ions into mitochondria from the cell cytosol. Maintaining calcium balance is crucial for enhancing cellular energy supply and regulating cell death. The interplay of calcium balance through MCU and the sodium-calcium exchanger is known, but its regulation in the breast cancer tumor microenvironment remains elusive. Further investigations are warranted to explore MCU’s potential in BRCA clinical pathology, tumor immune microenvironment, and precision oncology. Our study, employing a multi-omics approach, identifies MCU as an independent diagnostic biomarker for breast cancer (BRCA), correlated with advanced clinical status and poor overall survival. Utilizing public datasets from GEO and TCGA, we discern differentially expressed genes in BRCA and examine their associations with immune gene expression, overall survival, tumor stage, gene mutation status, and infiltrating immune cells. Spatial transcriptomics is employed to investigate MCU gene expression in various regions of BRCA, while spatial transcriptomics and single-cell RNA-sequencing methods explore the correlation between MCUs and immune cells. Our findings are validated through the analysis of 59 BRCA patient samples, utilizing immunohistochemistry and bioinformatics to examine the relationship between MCU expression, clinicopathological features, and prognosis. The study uncovers the expression of key gene regulators in BRCA associated with genetic variations, deletions, and the tumor microenvironment. Mutations in these regulators positively correlate with different immune cells in six immune datasets, playing a pivotal role in immune cell infiltration in BRCA. Notably, high MCU performance is linked to CD8 + T cells infiltration in BRCA. Furthermore, pharmacogenomic analysis of BRCA cell lines indicates that MCU inactivation is associated with increased sensitivity to specific small molecule drugs. Our findings suggest that MCU alterations may be linked to BRCA progression, unveiling new diagnostic and prognostic implications for MCU in BRCA. The study underscores MCU's role in the tumor immune microenvironment and cell cycle progression, positioning it as a potential tool for BRCA precision medicine and drug screening.

UTRN as a potential biomarker in breast cancer: a comprehensive bioinformatics and in vitro study

Utrophin (UTRN), known as a tumor suppressor, potentially regulates tumor development and the immune microenvironment. However, its impact on breast cancer’s development and treatment remains unstudied. We conducted a thorough examination of UTRN using both bioinformatic and in vitro experiments in this study. We discovered UTRN expression decreased in breast cancer compared to standard samples. High UTRN expression correlated with better prognosis. Drug sensitivity tests and RT-qPCR assays revealed UTRN’s pivotal role in tamoxifen resistance. Furthermore, the Kruskal–Wallis rank test indicated UTRN’s potential as a valuable diagnostic biomarker for breast cancer and its utility in detecting T stage of breast cancer. Additionally, our results demonstrated UTRN’s close association with immune cells, inhibitors, stimulators, receptors, and chemokines in breast cancer (BRCA). This research provides a novel perspective on UTRN’s role in breast cancer’s prognostic and therapeutic value. Low UTRN expression may contribute to tamoxifen resistance and a poor prognosis. Specifically, UTRN can improve clinical decision-making and raise the diagnosis accuracy of breast cancer.

Serum miR-16 and CA15.3 combination as early diagnostic biomarkers for breast cancer in Egyptian females

Serum miR-16 and CA15.3 combination as early diagnostic biomarkers for breast cancer in Egyptian females

Plasma Thioredoxin Reductase as a Potential Diagnostic Biomarker for Breast Cancer

BackgroundEarly diagnosis of breast cancer is critical to the treatment and prognosis of breast cancer patients. Our aim is to explore more practical and effective diagnostic methods to facilitate early treatment and improve prognosis for breast cancer patients. Materials and MethodsThe Mann–Whitney U test, receiver operating characteristic curve, Youden index, Chi-square test, and Fisher's exact test were used to determine whether plasma thioredoxin reductase (TrxR) could be used for the clinical diagnosis of breast cancer. The Wilcoxon signed-rank test was used to validate the prognostic potential of plasma TrxR activity assessment. ResultsA total of 761 patients were included, including 537 cases of breast cancer and 224 cases of benign breast diseases. Plasma TrxR activity in the breast cancer group [8.0 (6.0, 9.45) U/mL] was significantly higher than that in the benign group [3.05 (1.20, 6.275) U/mL]. The diagnostic efficiency of TrxR for breast cancer was higher than that of other conventional breast cancer biomarkers, with an area under the curve of 0.821 (95% CI = 0.791-0.852). In addition, TrxR can be used in combination with conventional tumor markers to further improve the diagnostic efficiency. The optimal TrxR threshold for identifying benign and malignant diseases is 7.45 U/mL. We detected plasma TrxR activity and serum tumor markers before and after antitumor therapies in 333 breast cancer patients and found that their trends were basically the same, with a significant decrease in plasma TrxR activity after treatment. ConclusionPlasma TrxR activity can be used as a suitable biomarker for breast cancer diagnosis and efficacy assessment.

Integrative Bioinformatics Analysis Reveals Potential Target Genes and PTEN Signaling in Breast Cancer and Effect of Zingiber officinale (Ginger) and Allium sativum (Garlic) extract on It.

Breast cancer is the most common type of cancer in women. The construction of a competing gene network is an important step in the identification of the role of hub genes in breast cancers. In the current research, we used a number of bioinformatics tools to construct this network in breast cancer and investigated the combined effect of garlic and ginger on mice model of breast cancer. We chose female mice weighing 18-20 g that were divided into 7 groups including; the cancer group receiving normal saline, different doses of ginger extract (100 and 500 mg/kg), different doses of garlic (50 and 100 mg/kg), tamoxifen (10 mg/ kg) and simultaneous garlic (100 mg/kg) and ginger (500 mg/kg) for 3 weeks intraperitoneal. Then we anesthetized the mice, isolated the tumor, and determined its size. Glutathione reductase and peroxidase levels and HER2, PTEN, and Cullin3 genes expression were measured. We identified 20 hub genes for breast cancer. In animal phase we found that tumor size in all mice receiving garlic and ginger showed a significant decrease compared to the control. Glutathione reductase showed a significant increase in all groups, especially in ginger 500 and combined groups. Glutathione peroxidase increased almost in all groups, especially in ginger 500. Expression of HER2 decreased in all treated groups. Expression of PTEN increased just in the combined group. Taken together, we introduce a number of novel promising diagnostic biomarkers for breast cancer. The use of garlic and ginger in the treatment of cancer can be useful. This action is probably through the antioxidant mechanism, and regulation of the expression of cancer related genes such as PTEN.

Exosomal microRNA-92b Is a Diagnostic Biomarker in Breast Cancer and Targets Survival-Related MTSS1L to Promote Tumorigenesis.

Exosomal microRNAs (miRNAs) are novel, non-invasive biomarkers for facilitating communication and diagnosing cancer. However, only a few studies have investigated their function and role in the clinical diagnosis of breast cancer. To address this gap, we established a stable cell line, MDA-MB-231-CD63-RFP, and recruited 112 female participants for serum collection. We screened 88 exosomal miRNAs identified through microarray analysis of 231-CD63 and literature screening using real-time PCR; only exosomal miR-92b-5p was significantly increased in patients with breast cancer. It had a significant correlation with stage and discriminated patients from the control with an AUC of 0.787. Exosomal miR-92b-5p impacted the migration, adhesion, and spreading ability of normal human mammary epithelial recipient cells through the downregulation of the actin dynamics regulator MTSS1L. In clinical breast cancer tissue, the expression of MTSS1L was significantly inversely correlated with tissue miR-92b-5p, and high expression of MTSS1L was associated with better 10-year overall survival rates in patients undergoing hormone therapy. In summary, our studies demonstrated that exosomal miR-92b-5p might function as a non-invasive body fluid biomarker for breast cancer detection and provide a novel therapeutic strategy in the axis of miR-92b-5p to MTSS1L for controlling metastasis and improving patient survival.

Comparative profiling by data-independent acquisition mass spectrometry reveals featured plasma proteins in breast cancer: a pilot study.

Breast cancer is known to be influenced by genetic and environmental factors, and several susceptibility genes have been discovered. Still, the majority of genetic contributors remain unknown. We aimed to analyze the plasma proteome of breast cancer patients in comparison to healthy individuals to identify differences in protein expression profiles and discover novel biomarkers. This pilot study was conducted using bioresources from Seoul National University Bundang Hospital's Human Bioresource Center. Serum samples from 10 breast cancer patients and 10 healthy controls were obtained. Liquid chromatography-mass spectrometry analysis was performed to identify differentially expressed proteins. We identified 891 proteins; 805 were expressed in the breast cancer group and 882 in the control group. Gene set enrichment and differential expression analysis identified 30 upregulated and 100 downregulated proteins in breast cancer. Among these, 10 proteins were selected as potential biomarkers. Three proteins were upregulated in breast cancer patients, including cluster of differentiation 44, eukaryotic translation initiation factor 2-α kinase 3, and fibronectin 1. Seven proteins downregulated in breast cancer patients were also selected: glyceraldehyde-3-phosphate dehydrogenase, α-enolase, heat shock protein member 8, integrin-linked kinase, tissue inhibitor of metalloproteinases-1, vasodilator-stimulated phosphoprotein, and 14-3-3 protein gamma. All proteins had been previously reported to be related to tumor development and progression. The findings suggest that plasma proteome profiling can reveal potential diagnostic biomarkers for breast cancer and may contribute to early detection and personalized treatment strategies. A further validation study with a larger sample cohort of breast cancer patients is planned.

Comprehensive evaluation of cell death-related genes as novel diagnostic biomarkers for breast cancer

BackgroundBreast cancer (BRCA) ranks first among cancers in terms of incidence and mortality rates in women, primarily owing to metastasis, chemo-resistance, and heterogeneity. To predict long-term prognosis and design novel therapies for BRCA, more sensitive markers need to be explored. MethodsData from 1089 BRCA patients were downloaded from TCGA database. Pearson's correlation analysis and univariate and multivariate Cox regression analyses were performed to assess the role of cell death-related genes (CDGs) in predicting BRCA prognosis. Kaplan–Meier survival curves were generated to compare the overall survival in the two subgroups. A nomogram was constructed using risk scores based on the five CDGs and other clinicopathological features. CCK-8, EdU incorporation, and colony formation assays were performed to verify the inhibitory effect of NFKBIA on BRCA cell proliferation. Transwell assay, flow cytometry, and immunohistochemistry analyses were performed to ascertain the biological function of NFKBIA. ResultsFive differentially expressed CDGs were detected among 156 CDGs. The risk score for each patient was then calculated based on the expression levels of the five CDGs. Distinct differences in immune infiltration, expression of immune-oncological targets, mutation status, and half-maximal inhibitory concentration values of some targeted drugs were observed between the high- and low-risk groups. Finally, in vitro cell experiments verified that NFKBIA overexpression suppresses the proliferation and migration of BRCA cells. ConclusionsOur study revealed that some CDGs, especially NFKBIA, could serve as sensitive markers for predicting the prognosis of patients with BRCA and designing more personalized clinical therapies.

Identification of early diagnostic biomarkers for breast cancer through bioinformatics analysis.

In the realm of clinical practice, there is currently an insufficiency of distinct biomarkers available for the detection of breast cancer. It is of utmost importance to promptly employ bioinformatics methodologies to investigate prospective biomarkers for breast cancer, with the ultimate goal of achieving early diagnosis of the disease. The initial phase of this investigation involved the identification of 2 breast cancer gene chips meeting the specified criteria within the gene expression omnibus database. Subsequently, paired data analysis was conducted on these datasets, leading to the identification of differentially expressed genes (DEGs). In addition, this study executed Gene Ontology enrichment analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. The subsequent stage involved the construction of a protein-protein interaction network graph using the STRING website and Cytoscape software, facilitating the calculation of Hub genes. Lastly, the UALCAN database and Kaplan-Meier survival plots were utilized to perform differential expression and survival analysis on the selected Hub genes. A total of 733 DEGs were identified from the combined analysis of 2 datasets. Among these DEGs, 441 genes were found to be downregulated, while 292 genes were upregulated. The selected DEGs underwent comprehensive analysis, including gene ontology enrichment analysis, Kyoto encyclopedia of genes and genomes pathway enrichment analysis, and establishing a protein-protein interaction network. As a result, 10 Hub genes closely associated with early diagnosis of breast cancer were identified: PDZ-binding kinase, cell cycle protein A2, cell division cycle-associated protein 8, maternal embryonic leucine zipper kinase, nucleolar and spindle-associated protein 1, BIRC5, cell cycle protein B2, hyaluronan-mediated motility receptor, mitotic arrest deficient 2-like 1, and protein regulator of cytokinesis 1. The findings of this study unveiled the significant involvement of the identified 10 Hub genes in facilitating the growth and proliferation of cancer cells, particularly cell cycle protein A2, cell division cycle-associated protein 8, maternal embryonic leucine zipper kinase, nucleolar and spindle-associated protein 1, hyaluronan-mediated motility receptor, and protein regulator of cytokinesis 1, which demonstrated a more pronounced connection with the onset and progression of breast cancer. Further analysis through differential expression and survival analysis reaffirmed their strong correlation with the incidence of breast cancer. Consequently, the investigation of these 10 pertinent Hub genes presents novel prospects for potential biomarkers and valuable insights into the early diagnosis of breast cancer.

GLS as a diagnostic biomarker in breast cancer: in-silico, in-situ, and in-vitro insights.

Recently, a novel programmed cell death mechanism, Cuproptosis, has been discovered and found to play an important role in the development and progression of diverse tumors. In the present study, we comprehensively investigated the core gene of this mechanism, GLS, in breast cancer. Bulk RNA sequencing data were curated from the TCGA repository to investigate the aberrant expression of GLS over diverse cancer types. Then, we examined its efficacy as a diagnostic biomarker in breast cancer by Area Under Curve (AUC) of the Receiver Operative Characteristic (ROC) curve. Furthermore, by applying siRNA technique, we knocked down the GLS expression level in cancerous cell lines, measuring the corresponding effects on cell proliferation and metastasis. Afterward, we explored the potential implications of GLS expression in the tumor immune microenvironment quantitatively by using several R packages and algorithms, including ESTIMATE, CIBERSORT, etc. Pan-cancer analysis suggested that GLS was aberrantly over-expressed in many cancer types, with breast cancer being typical. More in-depth analyses revealed the expression of GLS exerted a high ROC-AUC value in breast cancer diagnosis. Through the knock-down of GLS expression, it was found that GLS expression was strongly relevant to the growth and metastasis of tumor. Furthermore, it was also found to be correlated with the immune tumor microenvironment. We highlighted that GLS expression might be applicable as a diagnostic biomarker in breast cancer and possess significant implications in the growth and metastasis of tumor and the immune tumor microenvironment, sharing new insights into ontological and personalized medicine.

Identification of four candidates circulating microRNAs in plasma as a potential biomarker for early-stage breast cancer diagnosis in Vietnamese women

Breast cancer is the most commonly occurring malignant neoplasm among the female population. This study aims to ascertain novel biomarkers that may prove advantageous in the early detection of breast cancer. The present investigation involved a comparative analysis of the plasma expression of four distinct microRNA (namely, miR-425-5p, miR-142-3p, miR-9-3p and miR-15b-5p) in a cohort of 80 Vietnamese patients who were newly diagnosed with breast cancer. The expression levels of these microRNA were compared with those of 80 healthy individuals who served as controls. The plasma total RNA was extracted and the miRNA expression levels were assessed using Stem-loop RT-qPCR followed by statistical analysis. According to the findings of our study, the plasma levels of four microRNAs namely miR-425-5p, miR-142-3p, miR-9-3p and miR-15b-5p, were significantly reduced in both breast cancer patients in stage 0-I and the general breast cancer patient population as compared to healthy controls. The diagnostic accuracy of both miR-9-3p levels and miR-15b-5p levels was satisfactory with 74% and 88% values respectively. The combined effect of miR-425-5p/miR-142-3p/miR-9-3p/miR-15b-5p revealed the highest level of diagnostic precision, attaining a rate of 98% and an area under the curve value of 0.98 (with a sensitivity of 87.5% and specificity of 100%). In summary, the microRNAs miR-425-5p, miR-142-3p, miR-9-3p and miR-15b-5p exhibit potential as non-invasive diagnostic biomarkers for breast cancer, particularly in the initial stages of the disease.

Peripheral blood mononuclear cell derived biomarker detection using eXplainable Artificial Intelligence (XAI) provides better diagnosis of breast cancer

The incidence and mortality rate of breast cancer increases yearly by an average of 1.44 % and 0.23 %, respectively. Till 2021, there were 7.8 million women who had been diagnosed with breast cancer within 5 years. Biopsies of tumors are often expensive and invasive and raise the risk of serious complications like infection, excessive bleeding, and puncture damage to nearby tissues and organs. Early detection biomarkers are often variably expressed in different patients and may even be below the detection level at an early stage. Hence PBMC that shows alteration in gene profile as a result of interaction with tumor antigens may serve as a better early detection biomarker. Also, such alterations in immune gene profile in PBMCs are more prone to detection despite variability in different breast cancer mutants.This study aimed to identify potential diagnostic biomarkers for breast cancer using eXplainable Artificial Intelligence (XAI) on XGBoost machine learning (ML) models trained on a binary classification dataset containing the expression data of PBMCs from 252 breast cancer patients and 194 healthy women.After effectively adding SHAP values further into the XGBoost model, ten important genes related to breast cancer development were discovered to be effective potential biomarkers. Our studies showed that SVIP, BEND3, MDGA2, LEF1-AS1, PRM1, TEX14, MZB1, TMIGD2, KIT, and FKBP7 are key genes that impact model prediction. These genes may serve as early, non-invasive diagnostic and prognostic biomarkers for breast cancer patients.

Identification of Serum miR-501-3p and miR-338-3p as Novel Diagnostic Biomarkers for Breast Cancer and Their Target Genes Associated with Immune Infiltration.

MicroRNAs influence the growth and metastasis of breast cancer (BC) by regulating their target genes. Our study aims to screen and identify miRNAs that are closely related to the development of breast cancer, and explore the role of these miRNAs and their target genes in breast cancer. Bioinformatics tools were applied to screen breast cancer-associated miRNAs and predict their potential target genes. Serum miRNAs were measured using RT-PCR. The correlation between miRNA expression and different clinicopathological features of BC patients was analyzed. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value. GEPIA, Kaplan-Meier Plotter, TIMER, and TISIDB databases were used to validate the expression levels and their prognostic value, as well as their target gene associated with immune infiltrating cells and immune checkpoints. Breast cancer-associated serum miR-338-3p and miR-501-3p were screened and verified for the first time. Serum miR-501-3p was elevated in BC and was closely linked to the ki-67 index and histological grade. CDKN2C, as a potential target gene of miR-501-3p, was enriched in the cGMP-PKG signaling pathway. Serum miR-338-3p was reduced in BC and was strongly linked to lymph node metastasis and histological grading. ACTR2, CDH1, COL1A1, RBBP5, RRM1, and TPM3, as potential target genes of miR-338-3p, were enriched in MAPK, PI3K-Akt, and RAS signaling pathways. These target genes were found to be linked to breast cancer prognosis, immune infiltrating cells, and immune checkpoint inhibitors. Analysis of ROC curve showed that serum miR-501-3p combined with serum miR-338-3p had a high diagnostic value in breast cancer (AUC: 0.89, 95% CI: 0.821-0.958). Serum miR-501-3p combined with serum miR-338-3p show obvious clinical significance in the diagnosis and prognosis of breast cancer, which suggests that they may act as novel diagnostic biomarkers for breast cancer.

Direct, precise, enzyme-free detection of miR-103–3p in real samples by microgels with highly specific molecular beacons

Low abundance, small size, and sequence similarities render microRNA (miRNAs) detection challenging, particularly in real samples, where quantifying weakly expressed miRNAs can be arduous due to interference of more abundant molecules. The standard quantitative reverse transcription polymerase chain reaction (qRT-PCR) requires multiple steps, thermal cycles, and costly enzymatic reactions that can negatively affect results. Here we present a direct, precise, enzyme-free assay based on microgels particles conjugating molecular beacons (MB) capable of optically detecting low abundant miRNAs in real samples. We validate the applicability of microgels assay using qRT-PCR as a reference technology. As a relevant case, we chose miR-103–3p, a valuable diagnostic biomarker for breast cancer, both in serum samples and MCF7 cells. As a result, microgels assay quantifies miRNA molecules at room temperature in a single step, 1 h (vs. 4 hrs for qRT-PCR) without complementary DNA synthesis, amplification, or expensive reagents. Microgels assay exhibits femtomolar sensitivity, single nucleotide specificity, and a wide linear range (102–107 fM) (wider than qRT-PCR), with low sample consumption (2 μL) and excellent linearity (R2= 0.98). To test the selectivity of the microgel assay in real samples, MCF7 cells were considered where the pool of 8 other miRNAs were further upregulated with respect to miRNA 103–3p. In such complex environments, microgels assay selectively detects the miRNA target, mainly due to MB advanced stability and specificity as well as high microgel antifouling properties. These results show the reliability of microgels assay to detect miRNAs in real samples.

Identifying Diagnostic Biomarkers of Breast Cancer Based on Gene Expression Data and Ensemble Feature Selection

Background: In recent years, the identification of biomarkers or signatures based on gene expression profiling data has attracted much attention in bioinformatics. The successful discovery of breast cancer (BRCA) biomarkers will be beneficial in reducing the risk of BRCA among patients for early detection. Methods: This paper proposes an Ensemble Feature Selection method to screen biomarkers (abbreviat-ed as EFSmarker) for BRCA from publically available gene expression data. Firstly, we employ twelve filter feature selection methods, namely median, variance, Chi-square, Relief, Pearson and Spearman correlation, mutual information, minimal-redundancy-maximal-relevance criterion, ridge regression, decision tree and random forest with Gini index and accuracy index, to calculate the importance (weights or coefficients) of all features on the training dataset. Secondly, we apply the logistic regres-sion classifier on the test dataset to calculate the classification AUC value of each feature subset indi-vidually selected by twelve methods. Thirdly, we provide an ensemble feature selection method by ag-gregating feature importance with classification AUC value. In particular, we establish a feature im-portance score (FIS) to evaluate the importance of each feature underlying all feature selection methods. Finally, the features with higher FIS are taken as identified biomarkers. Results: With the direction of the FIS index induced by the EFSmarker method, 12 genes (COL10A1, COL11A1, MMP11, LOC728264, FIGF, GJB2, INHBA, CD300LG, IGFBP6, PAMR1, CXCL2 and FXYD1) are regarded as diagnostic biomarkers for BRCA. Especially, COL10A1, ranked first with a FIS value of 0.663, is identified as the most credible biomarker. The findings justified via gene and protein expression validation, functional enrichment analysis, literature checking and independent dataset validation verify the effectiveness and efficiency of these selected biomarkers. Conclusion: Our proposed biomarker discovery strategy not only utilizes the feature contribution but also considers the prediction accuracy simultaneously, which may also serve as a model for identifying unknown biomarkers for other diseases from high-throughput gene expression data. The source code and data are available at https://github.com/zpliulab/EFSmarker.

Identification of Hub Genes Associated with Breast Cancer Using Integrated Gene Expression Data with Protein-Protein Interaction Network

Breast cancer (BC) is the most incident cancer type among women. BC is also ranked as the second leading cause of death among all cancer types. Therefore, early detection and prediction of BC are significant for prognosis and in determining the suitable targeted therapy. Early detection using morphological features poses a significant challenge for physicians. It is therefore important to develop computational techniques to help determine informative genes, and hence help diagnose cancer in its early stages. Eight common hub genes were identified using three methods: the maximal clique centrality (MCC), the maximum neighborhood component (MCN), and the node degree. The hub genes obtained were CDK1, KIF11, CCNA2, TOP2A, ASPM, AURKB, CCNB2, and CENPE. Enrichment analysis revealed that the differentially expressed genes (DEGs) influenced multiple pathways. The most significant identified pathways were focal adhesion, ECM-receptor interaction, melanoma, and prostate cancer pathways. Additionally, survival analysis using Kaplan–Meier was conducted, and the results showed that the obtained eight hub genes are promising candidate genes to serve as prognostic and diagnostic biomarkers for BC. Furthermore, a correlation study between the clinicopathological factors in BC and the eight hub genes was performed. The results showed that all eight hub genes are associated with the clinicopathological variables of BC. Using an integrated analysis of RNASeq and microarray data, a protein-protein interaction (PPI) network was developed. Eight hub genes were identified in this study, and they were validated using previous studies. Additionally, Kaplan-Meier was used to verify the prognostic value of the obtained hub genes.

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations.

The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

Bioinformatics analysis of the diagnostic significance and functions of RDH5 in breast cancer

Objective: RDH5 (Retinol dehydrogenase 5) is one of the member of the short-chain dehydrogenase/reductase (SDR) family and plays a critical role in a variety of tumor processes. In this study, we analyzed the expression, diagnostic significance and gene function of RDH5 in breast cancer. Methods: The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to determine the diagnostic and prognostic value of RDH5 and the University of Alabama at Birmingham (UALCAN) database analyzed its expression in different subtypes, clinical stages and altered signal pathways in breast cancer. Then we explored the co-expression gene of RDH5 in breast cancer and constructed its corresponding network through the cBioPortal database. The String database was used to determine its interactions with other proteins. Results: RDH5 expression was differentiated in various tumours compared to normal tissue, and was down-regulated in breast cancer tissue and different subtypes of breast cancer. There was no correlation between RDH5 and the survival of breast cancer. RDH5 expression was significantly associated with different clinical parameters. Additionally, the protein expression of RDH5 in breast cancer with altered pathways indicated it was closely associated with the SWI-SNF (switching/sucrose non-fermenting) complex status and the mammalian Target of Rapamycin (mTOR), WNT (Wingless/Integrated), MYC/MYCN (Myelocytomatosis oncogene/euroblastoma derived MYC), RTK (receptor Tyrosine kinases), and p53/Rb (tumor protein p53/Retinoblastoma) pathways. Interaction analysis with RDH5 and its co-expression genes showed that its main related functions were ethanol oxidation, regulation of heart contraction and negative regulation of tumor necrosis factor. Lastly, the Protein-protein interaction (PPI) of RDH5 showed that the most relevant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were retinol metabolism, vitamin digestion and absorption and metabolic pathways. Conclusion: These results implied that RDH5 might be a larvaceous diagnostic biomarker for breast cancer and was closely associated with its metabolism. However, more studies are needed to confirm our findings and support the clinical importance of RDH5 in breast cancer.

HER2 status of CTCs by peptide-functionalized nanoparticles as the diagnostic biomarker of breast cancer and predicting the efficacy of anti-HER2 treatment.

Efficacy of anti-human epidermal growth factor receptor 2 (HER2) treatment is impacted by tissue-based evaluation bias due to tumor heterogeneity and dynamic changes of HER2 in breast cancer. Circulating tumor cell (CTC)-based HER2 phenotyping provides integral and real-time assessment, benefiting accurate HER2 diagnosis. This study developed a semi-quantitative fluorescent evaluation system of HER2 immunostaining on CTCs by peptide-functionalized magnetic nanoparticles (Pep@MNPs) and immunocytochemistry (ICC). 52 newly-diagnosed advanced breast cancer patients were enrolled for blood samples before and/or after first-line treatment, including 24 patients who were diagnosed with HER2+ tumors and treated with anti-HER2 drugs. We enumerated CTCs and assessed levels of HER2 expression on CTCs in 2.0 ml whole blood. Enumerating CTCs at baseline could distinguish cancer patients (sensitivity, 69.2%; specificity, 100%). 80.8% (42/52) of patients had at least one CTCs before therapy. Patients with <3 CTCs at baseline had significantly longer progression-free survival (medians, 19.4 vs. 9.2 months; log-rank p = 0.046) and overall survival (medians, not yet reached; log-rank p = 0.049) than those with ≥3 CTCs. Both HER2+ and HER2-low patients could be detected with HER2 overexpression on CTCs (CTC-HER2+) (52.6%, 44.4%, respectively), whereas all the HER2-negative patients had no CTC-HER2+ phenotype. Among HER2+ patients with ≥3 CTCs at baseline, objective response only appeared in pretherapeutic CTC-HER2+ cohort (60.0%), rather than in CTC-HER2– cohort (0.0%) (p = 0.034). In conclusion, we demonstrate the significance of CTC enumeration in diagnosis and prognosis of first-line advanced breast cancer, and highlight the value of CTC-HER2 status in predicting efficacy of anti-HER2 treatment.