Novel Biomarker For Breast Cancer Research Articles

MiRNA-449c-5p regulates the JAK-STAT pathway in inhibiting cell proliferation and invasion in human breast cancer cells by targeting ERBB2.

Breast cancer is a highly prevalent disease worldwide, and early diagnosis and treatment could reduce the mortality rate of breast cancer patients. microRNAs (miRNA) have been shown to regulate the occurrences and progression of many types of cancers. Thus, it is crucial to find novel biomarkers in breast cancer. miR-449c-5p acted as a biomarker in non-small cell lung cancer, gastric carcinoma, and so forth. ERBB2 is an ideal target for breast cancer therapy. However, the molecular mechanisms between miR-449c-5p and ERBB2 in breast cancer remain poorly understood. Our study focused on the regulatory role of miR-449c-5p in breast cancer and its targeting relationship with ERBB2. The miR-449c-5p expression in breast cancer tissue and normal tissue was searched from the online database (Starbase). The clinical prognosis of miR-449c-5p and ERBB2 was predicted by using the Kaplan-Meier analysis method. The expression of miR-449c-5p mimics and inhibitors was measured by qRT-PCR. T47D cells were transfected with miR-449c-5p mimics and miR-449c-5p inhibitors. After that, CCK-8, colony formation assays and Transwell assays were used to evaluate the cell proliferation ability, migration and invasion. Whether ERBB2 was the target gene of the miR-449c-5p was predicted by Starbase and verified by dual-luciferase activity assay. In addition, protein levels and the relationship between signalling pathways were measured and validated using western blotting analysis. We confirmed that miR-449c-5p was highly expressed in breast cancer tissue, and its downregulation was linked with poor prognosis. Overexpression of miR-449c-5p inhibited the proliferation, migration and invasion of breast cancer cells. ERBB2 was a target of miR-449c-5p. The invasion, migration, and proliferation of breast cancer cells were inhibited by miR-449c-5p/ERBB2 through JAK-STAT. This study demonstrated that miR-449c-5p inhibits breast cancer cell proliferation, migration and invasion by targeting ERBB2 via JAK/STAT, which means miR-449c-5p, is a potential biomarker for breast cancer and provides a novel insight for diagnosis.

Screening of novel biomarkers for breast cancer based on WGCNA and multiple machine learning algorithms.

Breast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prognosis. We analyzed gene expression data from 99 normal and 1,081 BC tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using "limma" R package, and relevant modules were chosen through Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were obtained by matching DEGs to WGCNA module genes. Functional enrichment studies were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were screened via Protein-Protein Interaction (PPI) networks and multiple machine-learning algorithms. The Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer (UALCAN), and Human Protein Atlas (HPA) databases were employed to examine mRNA and protein expression of eight biomarkers. Kaplan-Meier mapper tool assessed their prognostic capabilities. Key biomarkers were analyzed via single-cell sequencing, and their relationship with immune infiltration was examined using Tumor Immune Estimation Resource (TIMER) database and "xCell" R package. Lastly, drug prediction was conducted based on the identified biomarkers. We identified 1,673 DEGs and 542 important genes through differential analysis and WGCNA, respectively. Intersection analysis revealed 76 genes, which play significant roles in immune-related viral infection and IL-17 signaling pathways. DIX domain containing 1 (DIXDC1), Dual specificity phosphatase 6 (DUSP6), Pyruvate dehydrogenase kinase 4 (PDK4), C-X-C motif chemokine ligand 12 (CXCL12), Interferon regulatory factor 7 (IRF7), Integrin subunit alpha 7 (ITGA7), NIMA related kinase 2 (NEK2), and Nuclear receptor subfamily 3 group C member 1 (NR3C1) were selected as BC biomarkers using machine-learning algorithms. NEK2 was the most critical gene for diagnosis. Prospective drugs targeting NEK2 include etoposide and lukasunone. Our study identified DIXDC1, DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, and NR3C1 as potential diagnostic biomarkers for BC, with NEK2 having the highest potential to aid in diagnosis and prognosis in clinical settings.

UBE2S and UBE2C confer a poor prognosis to breast cancer via downregulation of Numb.

Ubiquitin-conjugating enzymes E2S (UBE2S) and E2C (UBE2C), which mediate the biological process of ubiquitination, have been widely reported in various cancers. Numb, the cell fate determinant and tumor suppressor, was also involved in ubiquitination and proteasomal degradation. However, the interaction between UBE2S/UBE2C and Numb and their roles in the clinical outcome of breast cancer (BC) are not widely elucidated. Oncomine, Cancer Cell Line Encyclopedia (CCLE), the Human Protein Atlas (HPA) database, qRT-PCR, and Western blot analyses were utilized to analyze UBE2S/UBE2C and Numb expression in various cancer types and their respective normal controls, breast cancer tissues, and breast cancer cell lines. The expression of UBE2S, UBE2C, and Numb in BC patients with different ER, PR, and HER2 status, grades, stages, and survival status was compared. By Kaplan-Meier plotter, we further evaluated the prognostic value of UBE2S, UBE2C, and Numb in BC patients. We also explored the potential regulatory mechanisms underlying UBE2S/UBE2C and Numb through overexpression and knockdown experiments in BC cell lines and performed growth and colony formation assays to assess cell malignancy. In this study, we showed that UBE2S and UBE2C were overexpressed while Numb was downregulated in BC, and in BC of higher grade, stage, and poor survival. Compared to hormone receptor negative (HR-) BC cell lines or tissues, HR+ BC demonstrated lower UBE2S/UBE2C and higher Numb, corresponding to better survival. We also showed that increased UBE2S/UBE2C and reduced Numb predicted poor prognosis in BC patients, as well as in ER+ BC patients. In BC cell lines, UBE2S/UBE2C overexpression decreased the level of Numb and enhanced cell malignancy, while knocking down UBE2S/UBE2C demonstrated the opposite effects. UBE2S and UBE2C downregulated Numb and enhanced BC malignancy. The combination of UBE2S/UBE2C and Numb could potentially serve as novel biomarkers for BC.

Plectin as a putative novel biomarker for breast cancer: an in silico study

Plectin as a putative novel biomarker for breast cancer: an in silico study

COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation.

Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831-3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis.

RNA Sequencing-Based Total RNA Profiling; The Oncogenic MiR-191 Identification as a Novel Biomarker for Breast Cancer.

This study aims to screen the differential expression of total RNA transcripts in formalin-fixed paraffin-embedded tissues (FFPETs) in breast cancer (BRCA) and normal adjacent tissues (NATs) and identify miR-191 as a new biomarker for early diagnosing BRCA. Differentially expressed genes (DEGs) by MACE-Seq and differentially expressed ncRNAs (DEncRNAs) by the TrueQuant technique were examined in this study. The miR-191 expression level was measured by Real Time-qPCR. An average of 4,739 coding genes from 25,713 significantly down-regulated genes was identified, whereas 3,954 coding genes were significantly up-regulated in the BRCA against NAT. An average of 1450 ncRNAs, including up-regulated= 679 and down-regulated= 780, were differentially expressed in 7 paired samples of BRCA and NAT. Among the ncRNAs, 227 microRNAs, including unchanged= 152, down=53, and up=22, were differentially expressed. MiR-191 was one of the 22 significant up-regulation, with p=0.0001. RT-qPCR results confirmed that miR-191, p=0.003, was significantly over-expressed in 120 paired samples of BRCA and NAT. Furthermore, NextSeq 500 revealed that a single nucleotide polymorphism (C>T) newly occurred in the mature sequence of miR-191-5p seed region in BRCA samples. However, the putative target genes regulated by the miR-191-5p were recognized by the above ten computational programs for the prediction. MACE-Seq outcomes showed that the genes of CDK6(P=0.0001), DAPK1(P=0.02), MTC7(P=0.04), SETD1B(P=0.005), CALN1(P=0.01), and TMOD2(P=0.001) were significantly over-expressed in the BRCA against the NATs. The expression level of the targets was adversely related to the miR-191-5p.

Identification of Circular RNA hsa-circ-0006969 as a Novel Biomarker for Breast Cancer

Background: To investigate the characteristics of circular RNA hsa-circ-0006969 in breast cancer and identify it as a novel biomarker for breast cancer. Methods: Three breast cancer (BC) patient tissues were selected to perform human circRNA microarray analysis. GeneSpring 13.0 (Agilent) software was applied for analyzing the data. Another 116 BC patients were recruited for verification. Hsa-circ-0006969 was found as a potential circRNA for BC diagnostic biomarker. The structure of hsa-circ-0006969 was predicted by circPrimer1.2 software. MiRanda v3.3, RNA hybrid 2.1, and Cytoscape 3.6.0 were used for predicting the networks of circRNA-miRNA. T-test, Curve regression, and ROC analysis were applied to certify the diagnostic values of hsa-circ-0006969. Using SPSS25.0 software to perform the Statistical analysis. Results: 546 higher expression and 1475 lower expression circRNAs were identified. Four circRNAs were filtrated for further verification. Hsa-circ-0006969 was significantly low expressed in BC tissues and peripheral blood. Hsa-circ-0006969 was confirmed as higher diagnostic correlation with BC tissues (AUC = 0.965) and peripheral blood (AUC = 0.842), with Grade 1 (AUC = 0.639), ER-positive (AUC = 0.612), TNM I (AUC = 0.693), TNM II (AUC = 0.712), TNM III (AUC = 0.757), TNM early stage(I, II) (AUC = 0.709). Hsa-circ-0006969 presented more effective diagnostic values for tumor metastasis (AUC = 0.784) compared with CA153 (AUC = 0.752). Conclusion: Hsa-circ-0006969 could be a novel biomarker for the diagnosis and treatment of BC.

Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction.

The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology. The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed. The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues. Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection. This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.

Identification of novel biomarkers in breast cancer via integrated bioinformatics analysis and experimental validation

ABSTRACT Breast cancer (BC), an extremely aggressive malignant tumor, causes a large number of deaths worldwide. In this study, we pooled profile datasets from three cohorts to illuminate the underlying key genes and pathways of BC. Expression profiles GSE42568, GSE45827, and GSE124646, including 244 BC tissues and 28 normal breast tissues, were integrated and analyzed. Differentially expressed genes (DEGs) were screened out based on these three datasets. Functional analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plugin were utilized to visualize protein protein interaction (PPI) of these DEGs. The module with the highest connectivity of gene interactions was selected for further analysis. All of these hub genes had a significantly worse prognosis in BC by survival analysis. Additionally, four genes (CDK1, CDC20, AURKA, and MCM4) dramatically were enriched in oocyte meiosis and cell cycle pathways through re-analysis of DAVID. Moreover, the mRNA and protein levels of CDK1, CDC20, AURKA, and MCM4 were significantly increased in BC patients. In addition, knockdown of CDK1 and CDC20 by small interfering RNA remarkably suppressed cell migration and invasion in MCF-7 and MDA-MB-231 cells. In conclusion, our results suggested that CDK1, CDC20, AURKA, and MCM4 were reliable biomarkers of BC via bioinformatics analysis and experimental validation and may act as prospective targets for BC diagnosis and treatment.

Identification of the Upregulation of MRPL13 as a Novel Prognostic Marker Associated with Overall Survival Time and Immunotherapy Response in Breast Cancer.

Mitochondrial ribosomal protein (MRPL) genes have been reported to participate in many cellular processes, such as cell proliferation, apoptosis, and cell cycle. Meanwhile, the occurrence rate of breast cancer (BRCA) in China steadily increased. Exploring the prognostic value of MRPL genes in BRCA could provide novel biomarkers for BRCA. In this study, to identify prognosis-related genes in breast cancer, the P value and the hazard ratio (HR) of all genes are analyzed with TCGA database. We revealed higher expression level of CEL, PGK1, WNT3A, USP41, LINC02037, PCMT1, LRP11, MCTS1, TCP1, TMEM31, STK4-AS1, STXBP5, LOC100287036, SLC16A2, MRPL13, DERL1, and TARS was correlated to shorter OS time in BRCA. However, higher expression level of JCHAIN, KLRB1, and TNFRSF14 was correlated to longer OS time in BRCA. The further analysis demonstrated MRPL13 was overexpressed in BRCA. Subtype analysis showed that MRPL13 was overexpressed in luminal, HER2-positive BRCA, and TNBC samples and was highest in TNBC samples. Moreover, we revealed higher expression of MRPL13 was significantly correlated to shorter OS time and higher TMB levels in BRCA. Pan-cancer analysis further revealed the prognostic value of MRPL13 in human cancers. MRPL13 expression was significantly increased in multiple human cancers, such as bladder cancer, colon cancer, liver cancer, and prostate cancer. Pan-cancer TMB and overall survival time showed dysregulation of MRPL13 is significantly related to the OS and TMB levels in various cancers. These results further proved that MRPL13 may be a pan-cancer biomarker for predicting prognosis and the response to immunotherapy.

Polypeptide-GalNAc-Transferase-13 Shows Prognostic Impact in Breast Cancer.

Simple SummaryThe knowledge of the molecular mechanisms underlying breast tumorigenesis has allowed the identification of an increasing number of biomarkers, which have been correlated with cancer prognosis or used as predictors for specific treatment responses, thus improving the ability to individualize therapy. Protein O-glycosylation is dysregulated in breast cancer cells. Abnormal O-glycans have functional importance in cell adhesion, invasion, and metastasis. Polypeptide N-acetylgalactosaminyltransferases (GalNAc-Ts) family enzymes regulate the initial steps of mucin type O-glycosylation and may be responsible for the altered glycosylation observed in cancer. Previous reports have related GalNAc-T13 expression to cancer aggressiveness. In the present work, we produced a specific monoclonal antibody against GalNAc-T13 which is capable of recognizing this enzyme on formalin-fixed tissues. We observed a significant higher expression of this enzyme in metastatic samples compared with the corresponding primary tumors. Significantly, a high GalNAc-T13 score was associated with worse patient survival rates, thus supporting its prognostic potential.Breast cancer is a public health concern and is currently the fifth cause of mortality worldwide. Identification of different biological subtypes is essential for clinical management; therefore, the role of pathologists is essential and useful tools for immunohistochemistry diagnosis are needed. Polypeptide-GalNAc-transferases are emerging novel biomarkers related to cancer behavior and GalNAc-T13, correlated with aggressiveness in some tumors, is an interesting candidate. Few monoclonal antibodies reacting with native proteins, and not affected by fixation and paraffin embedding, have been reported. The aim of this work was to develop a useful monoclonal antibody anti-GalNAc-T13 and to assess its potential significance in breast cancer diagnosis. We evaluated 6 human breast cancer cell lines, 338 primary breast tumors and 48 metastatic lymph nodes and looked for clinical significance correlating GalNAc-T13 expression with patients’ clinical features and survival. We found high GalNAc-T13 expression in 43.8% of the cases and observed a significant higher expression in metastatic lymph nodes, correlating with worse overall survival. We hypothesized several possible molecular mechanisms and their implications. We conclude that GalNAc-T13 may be a novel biomarker in breast cancer, useful for routine pathological diagnosis. Elucidation of molecular mechanisms related to aggressiveness should contribute to understand the role of GalNAc-T13 in breast cancer biology.

Evaluation of Oncogene NUP37 as a Potential Novel Biomarker in Breast Cancer.

PurposeThere is an urgent need to identify oncogenes that may be beneficial to diagnose and develop target therapy for breast cancer.MethodsBased on the GEO database, DECenter was used to screen the differentially overexpressed genes in breast cancer samples. Search Tool for the Retrieval of Interacting Genes and Cytoscape were performed to construct the PPI network to predict the hub gene. Functional and pathway enrichment were performed based on GO analysis. GEO2R, Oncomine, human tissue microarray staining, and western blot were applied to confirm the expression of NUP37. The association between NUP37 expression and prognosis in patients with breast cancer were assessed using the Kaplan–Meier plotter online tool and OncoLnc. siRNAs were used to knock down NUP37 and evaluate proliferation, migration, and stemness in breast cancer cells.ResultsWe found that 138 genes were differentially upregulated in breast cancer samples, mainly comprising components of the nucleus and involved in the cell cycle process. NUP37 was identified as a hub gene that is upregulated in breast cancer patients related to a significantly worse survival rate. Furthermore, we confirmed that the downregulation of NUP37 in breast cancer cells results in the inhibition of cell growth, migration, and stemness.ConclusionsHigh expression of NUP37 in breast cancer patients is associated with a poorer prognosis and promotion of cell growth, migration, and stemness. The multiple bioinformatics and experimental analysis help provide a comprehensive understanding of the roles of NUP37 as a potential marker for diagnosis and prognosis and as a novel therapeutic target in breast cancer.

In silico analysis of differentially expressed genesets in metastatic breast cancer identifies potential prognostic biomarkers

BackgroundIdentification of specific biological functions, pathways, and appropriate prognostic biomarkers is essential to accurately predict the clinical outcomes of and apply efficient treatment for breast cancer patients.MethodsTo search for metastatic breast cancer-specific biological functions, pathways, and novel biomarkers in breast cancer, gene expression datasets of metastatic breast cancer were obtained from Oncomine, an online data mining platform. Over- and under-expressed genesets were collected and the differentially expressed genes were screened from four datasets with large sample sizes (N > 200). They were analyzed for gene ontology (GO), KEGG pathway, protein-protein interaction, and hub gene analyses using online bioinformatic tools (Enrichr, STRING, and Cytoscape) to find enriched functions and pathways in metastatic breast cancer. To identify novel prognostic biomarkers in breast cancer, differentially expressed genes were screened from the entire twelve datasets with any sample sizes and tested for expression correlation and survival analyses using online tools such as KM plotter and bc-GenExMiner.ResultsCompared to non-metastatic breast cancer, 193 and 144 genes were differentially over- and under-expressed in metastatic breast cancer, respectively, and they were significantly enriched in regulating cell death, epidermal growth factor receptor signaling, and membrane and cytoskeletal structures according to the GO analyses. In addition, genes involved in progesterone- and estrogen-related signalings were enriched according to KEGG pathway analyses. Hub genes were identified via protein-protein interaction network analysis. Moreover, four differentially over-expressed (CCNA2, CENPN, DEPDC1, and TTK) and three differentially under-expressed genes (ABAT, LRIG1, and PGR) were further identified as novel biomarker candidate genes from the entire twelve datasets. Over- and under-expressed biomarker candidate genes were positively and negatively correlated with the aggressive and metastatic nature of breast cancer and were associated with poor and good prognosis of breast cancer patients, respectively.ConclusionsTranscriptome datasets of metastatic breast cancer obtained from Oncomine allow the identification of metastatic breast cancer-specific biological functions, pathways, and novel biomarkers to predict clinical outcomes of breast cancer patients. Further functional studies are needed to warrant validation of their roles as functional tumor-promoting or tumor-suppressing genes.

MACE-Seq-based coding RNA and TrueQuant-based small RNA profile in breast cancer: tumor-suppressive miRNA-1275 identified as a novel marker

IntroductionDisruption of cellular processes in the breast by abnormally expressed miRNA is characterized to develop cancer. We aimed to identify the differential expression of small RNAs (sRNAs) and mRNAs in formalin-fixed paraffin-embedded (FFPE) tissue of the breast cancer (BC) and normal adjacent tissue (NAT). Another aim is to determine the differential expression of miR-1275 as a novel biomarker for BC and also identify its target genes.MethodsTrueQuant method for analysis of sRNA expression and MACE-sequencing method for analysis of gene expression were used analyzing. The RT-qPCR technique was used to confirm miR-1275 down expression. Target genes of miR-1275 were computationally identified using target prediction sites and also the expression level of them was experimentally determined among the expressed genes.ResultsTrueQuant findings showed that 1400 sRNAs were differentially expressed in the FFPE tissue of two Kurdish cases with BC, as compared to NAT. Among the sRNAs, 29 small RNAs were shown to be significantly downregulated in BC cells. The RT-qPCR results confirmed that miR-1275 was significantly down-expressed in 20 Kurdish cases with BC compared to NAT. However, Overall survival (OS) analysis revealed that the correlation between the expression level of miR-1275 and clinical significance was highly corrected in cases with BC (OS rate: P = 0.0401). The MACE-seq results revealed that 26,843 genes were differentially expressed in the BC tissue compared to NAT, but 7041 genes were displayed in a scatter plot. Furthermore, putative target genes (DVL3, PPP2R2D, THSD4, CREB1, SYT7, and PRKACA) were computationally identified as direct targets of miR-1275 in several target predicted sites. The MACE-seq results revealed that the expression level of these targets was increased in BC tissue compared to NAT. The level of these targets was negatively associated with miR-1275 expression. Finally, the role of down-regulated miR-1275 on its targets in biological mechanisms of BC cells was identified; including cell growth, proliferation, movement, invasion, metastasis, and apoptosis.ConclusionDown-expressed miR-1275, a tumor suppressor, is a novel biomarker for early detection of BC. DVL3, PPP2R2D, THSD4, CREB1, SYT7, and PRKACA are newly identified to be targeted by miR-1275.

Discovery and Proof-of-Concept Study of Nuclease Activity as a Novel Biomarker for Breast Cancer Tumors

Simple SummaryA diagnostic biomarker for the detection of breast cancer remains an unmet clinical need despite decades of intensive research efforts. Herein, we describe, for the first time, the use of nuclease activity as a biomarker to discriminate between healthy and cancer biopsy samples. We have identified a panel of three nucleic acid probes able to target nucleases derived from breast cancer tumors with high sensitivity and specificity. These results are in good agreement with histopathological analysis as the diagnostic gold standard. Moreover, these findings support nuclease activity as a potential adjacent diagnostic tool and shed light on the use of nuclease activity as a detection biomarker in breast cancer.Breast cancer is one of the most common pathologies diagnosed in the clinical practice. Despite major advancements in diagnostic approaches, there is no widely accepted biomarker in the clinical practice that can diagnose breast malignancy. Confirmatory diagnosis still relies on the pathological assessment of tissue biopsies by expert pathologists. Thus, there is an unmet need for new types of biomarkers and novel platform technologies that can be easily and robustly integrated into the clinic and that can assist pathologists. Herein, we show that nuclease activity associated to malignant tumors can be used as a novel biomarker in breast cancer, which can be detected via specific degradation of nucleic acid probes. In this study we have identified a set of three chemically modified nucleic acid probes that can diagnose malignancy in biopsy samples with high accuracy (89%), sensitivity (82%) and specificity (94%). This work represents a breakthrough for the potential clinical use of nuclease activity as biomarker, which can be detected via nucleic acids probes, for the clinical diagnosis of malignancy in breast tissue biopsies. This platform technology could be readily implemented into the clinic as adjunct to histopathological diagnostic.

Coexpression Module Construction by Weighted Gene Coexpression Network Analysis and Identify Potential Prognostic Markers of Breast Cancer.

Background: Breast cancer (BC) is a malignant tumor with the highest morbidity among women, disrupting millions of their lives worldwide each year. However, the molecular mechanisms underlying remain unclear. Materials and Methods: The RNA-Sequencing and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene coexpression network analysis (WGCNA). Additionally, coexpressed modules were used to detect their correlation with the clinical traits of BC. Next, nodes of the most significant coexpression modules were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, mRNA-lncRNA coexpression network and survival analyses. Results: In total, 2056 differentially expressed mRNAs (DEmRNAs) and 297 differentially expressed lncRNAs (DElncRNAs) were identified and subjected to WGCNA analysis, and 12 coexpression modules were generated. The top five significant modules (turquoise, green, red, brown, and blue modules) were related to one or more clinical traits of BC. In particular, the turquoise and green modules were chosen for further analysis. Next, by lncRNA-mRNA coexpression analysis of the turquoise and green modules, 12 DEmRNAs and 2 DElncRNAs were identified as hub nodes. The lncRNA-associated mRNAs of the networks were commonly related to several cancer-related pathways. Moreover, these networks also revealed central roles for RP11-389C8.2 and TGFBR2 in the turquoise module and MYLK, KIT, and RP11-394O4.5 in the green module. Furthermore, 16 DEmRNAs and 3 DElncRNAs in these two modules were significantly correlated with the overall survival of BC patients. Conclusions: The authors' study identified some prognostic biomarkers that might play important roles in the development and treatment of BC. In particular, lncRNAs AC016995.3, RP1-193H18.2, and RP11-166D19.1 were novel biomarkers for BC.

Identification of Modulated MicroRNAs Associated with Breast Cancer, Diet, and Physical Activity.

Simple SummaryHealthy diet and physical activity are able to induce beneficial molecular modifications that have been associated with a lower risk of breast cancer (BC) incidence and a better prognosis for BC patients. Although the beneficial effects of healthy lifestyle have been described, the beneficial epigenetic modifications induced by dietary and exercise intervention in BC patients have not been elucidated yet. On these bases, the aim of the present study was to computationally identify microRNAs (miRNAs) strictly associated with BC progression and with dietary and exercise interventions. Through several computational approaches, a set of miRNAs modulated by diet and exercise and useful as diagnostic and prognostic biomarkers for BC was identified. The results obtained represent the starting point for further validation analyses performed on BC patients undergoing lifestyle interventions to propose the miRNAs here identified as novel biomarkers for BC management.Background: Several studies have shown that healthy lifestyles prevent the risk of breast cancer (BC) and are associated with better prognosis. It was hypothesized that lifestyle strategies induce microRNA (miRNA) modulation that, in turn, may lead to important epigenetic modifications. The identification of miRNAs associated with BC, diet, and physical activity may give further insights into the role played by lifestyle interventions and their efficacy for BC patients. To predict which miRNAs may be modulated by diet and physical activity in BC patients, the analyses of different miRNA expression datasets were performed. Methods: The GEO DataSets database was used to select miRNA expression datasets related to BC patients, dietary interventions, and physical exercise. Further bioinformatic approaches were used to establish the value of selected miRNAs in BC development and prognosis. Results: The analysis of datasets allowed the selection of modulated miRNAs associated with BC development, diet, and physical exercise. Seven miRNAs were also associated with the overall survival of BC patients. Conclusions: The identified miRNAs may play a role in the development of BC and may have a prognostic value in patients treated with integrative interventions including diet and physical activity. Validation of such modulated miRNAs on BC patients undergoing lifestyle strategies will be mandatory.

Circular RNA; a new biomarker for breast cancer: A systematic review.

Circular RNAs (circRNAs) were recently discovered as a looped subset of competing endogenous RNAs, with an ability to regulate gene expression by microRNA sponging. There are several studies on their potential roles in cancer development, such as colorectal cancer and basal cell carcinoma. However, there is still a significant gap in the knowledge about circRNA functions in breast cancer (BC) progression. The current study systematically reviewed circRNA biogenesis and their potential roles as a novel biomarker in BC on published studies of the MEDLINE®/PubMed, Cochrane®, and Scopus® databases. The obtained results showed a general dysregulation of circRNAs expression in BC cells with a cell-type and stage-specific manner. The potential connection between circRNAs and BC cell proliferation, apoptosis, metastasis, and chemotherapy sensitivity and resistance were discussed.

Profiling and integrated analysis of differentially expressed circRNAs as novel biomarkers for breast cancer.

Breast cancer (BC) is a globally common cancer with the highest and increasing morbidity and mortality among females. Novel biomarkers are warranted to be discovered for the early detection, treatment, and prognosis of BC. In this study, we investigated the profiles of differentially expressed (DE) circular RNAs (circRNAs) by competing endogenous RNAs (ceRNA) microarray to construct a genome-wide circRNA profile. Then, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis of the host genes (HGs) of circRNAs. A total of 4,370 DE circRNAs were detected and GO and KEGG analysis showed that they were significantly associated with cell cycle, DNA replication, BC, and familial BC. We validated the differential circRNAs and relevant HGs through quantitative real-time polymerase chain reaction and constructed a putative circRNA-microRNA-messenger RNA regulatory network. Eight circRNAs, including hsa_circ_0069094, hsa_circ_0062558, hsa_circ_0074026, hsa_circ_0079876, hsa_circ_0017536, hsa_circ_0023302, hsa_circ_0017650, and hsa_circ_0017545, were validated significantly DE in BC tissue and associated with TNM staging, lymph node infiltration, and Ki67. Hsa_circ_0069094, hsa_circ_0079876, hsa_circ_0017650, and hsa_circ_0017526 were upregulated in plasma. This study revealed the general expression characteristics of specific DE circRNAs in BC and hsa_circ_0069094, hsa_circ_0079876, hsa_circ_0017650, and hsa_circ_0017526 might be promising candidate targets.

Bioinformatics-Based Discovery of CKLF-Like MARVEL Transmembrane Member 5 as a Novel Biomarker for Breast Cancer.

Chemokine-like factor (CKLF)-like MARVEL transmembrane members (CMTMs) represent a novel protein family linking the chemokine and transmembrane-4 superfamily families, which potentially play several roles in diverse physiological and pathological processes. The detailed functions and underlying molecular mechanisms of CMTMs remain elusive in breast cancer. Herein, we performed a comprehensive bioinformatic analysis to investigate the prognostic effect, potential functions, and biomolecular regulatory network of CMTMs in breast cancer. The mRNA expression level of CMTM5, in particular, was significantly downregulated in breast cancer; moreover, high mRNA expression level of CMTM5 was significantly associated with better relapse-free survival. DNA promoter hypermethylation of CMTM5 was negatively correlated with its mRNA expression levels. Furthermore, CMTM5 strongly associated with pathway in MARVEL domains, chemotaxis, cytokines, transmembrane structures, and integral component of membrane. For example, genes related to MARVEL domains, transmembrane structures, and chemokines were significantly enriched. Our findings indicate that CMTM5 can be used as a prognostic biomarker and potential therapeutic target for breast cancer.