Genes and functions from breast cancer signatures

A PET-Surrogate Signature for the Interrogation of the Metabolic Status of Breast Cancers

Metabolic alterations in cancers can be exploited for diagnostic, prognostic, and therapeutic purposes. This is exemplified by 18F-fluorodeoxyglucose (FDG)-positron emission tomography (FDG-PET), an imaging tool that relies on enhanced glucose uptake by tumors for diagnosis and staging. By performing transcriptomic analysis of breast cancer (BC) samples from patients stratified by FDG-PET, a 54-gene signature (PETsign) is identified that recapitulates FDG uptake. PETsign is independently prognostic of clinical outcome in luminal BCs, the most common and heterogeneous BC molecular subtype, which requires improved stratification criteria to guide therapeutic decision-making. The prognostic power of PETsign is stable across independent BC cohorts and disease stages including the earliest BC stage, arguing that PETsign is an ab initio metabolic signature. Transcriptomic and metabolomic analysis of BC cells reveals that PETsign predicts enhanced glycolytic dependence and reduced reliance on fatty acid oxidation. Moreover, coamplification of PETsign genes occurs frequently in BC arguing for their causal role in pathogenesis. CXCL8 and EGFR signaling pathways feature strongly in PETsign, and their activation in BC cells causes a shift toward a glycolytic phenotype. Thus, PETsign serves as a molecular surrogate for FDG-PET that could inform clinical management strategies for BC patients.

Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer

High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list. Such gene lists can be obtained as a set of features (genes) that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. Several studies have identified predictive gene lists for patient prognosis in breast cancer, but these lists are unstable and have only a few genes in common. Instability of feature selection impedes biological interpretability: genes that are relevant for cancer pathology should be members of any predictive gene list obtained for the same clinical type of patients. Stability and interpretability of selected features can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are methods to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to construct feature sets by aggregating individual explanations. We suggest a methodology to systematically and quantitatively analyze the stability, the impact on the classification performance, and the interpretability of the selected feature sets. We used this methodology to compare GCNN+LRP to GCNN+SHAP and to more classical ML-based feature selection approaches. Utilizing a large breast cancer gene expression dataset we show that, while feature selection with SHAP is useful in applications where selected features have to be impactful for classification performance, among all studied methods GCNN+LRP delivers the most stable (reproducible) and interpretable gene lists.

Research on the toxicological prognostic significance of age-related genes in endometrial cancer unveiling key factors in patient prognosis

This study investigates the influence of aging-related genes on endometrial cancer, a prominent gynecological malignancy with rising incidence and mortality. By analyzing gene expression differences between cancerous and normal endometrial tissues, 42 aging-related genes were identified as differentially expressed. Utilizing the TCGA-UCEC sample, consensus clustering divided the samples into two molecular subgroups, Aging low and Aging high, based on their expression profiles. These subgroups showed distinct prognoses and survival rates, with the Aging high group associated with DNA repair and cell cycle pathways, and the Aging low group showing suppressed metabolic pathways and increased immune cell infiltration, suggesting a potential for better immunotherapy outcomes. Mutation analysis did not find significant differences in mutation frequencies between the groups, but a high Tumor Mutation Burden (TMB) correlated with better prognosis. A risk score model was also developed, showcasing significant prognostic power. Further analysis of the SIX1 gene revealed its overexpression in cancer cells. Drug sensitivity tests indicated that the low-risk group might respond better to chemotherapy. This research underscores the significance of aging-related genes in endometrial cancer, offering insights into their prognostic value and therapeutic potential, which could lead to personalized treatment approaches and enhanced patient management.

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification

BACKGROUND

In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately.

RESULTS

This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature.

CONCLUSIONS

The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer

Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.

Molecular phenotyping of malignant canine mammary tumours: Detection of high-risk group and its relationship with clinicomolecular characteristics

Canine mammary gland tumours (CMTs) constitute the most common cancer in female dogs and comprise approximately 50% of all canine cancers. With the advent of high-throughput technologies such as microarray and next-generation sequencing, the molecular phenotyping (classification) of various cancers has been extensively developed. The present study used a canine RNA-sequencing dataset, namely GSE119810, to classify 113 malignant CMTs and 64 matched normal samples via an unsupervised hierarchical algorithm with a view to evaluating the association between the resulting subtypes (clusters) (n = 4) and clinical and molecular characteristics. Finally, a molecular classifier was developed, and it detected 1 high-risk molecular subtype in the training dataset (GSE119810) and 2 independent validation datasets (GSE20718 and GSE22516). Our results revealed four molecular subtypes (C2-C5) in malignant CMTs. Furthermore, the normal samples constituted a distinct group in the clustering analysis. Marked significant associations were observed between the molecular subtypes (especially C5) and clinical/molecular features, including positive lymphatic invasion, high tumour grades, histopathology diagnoses, short survival and high TP53 mutation rates (ps <.05). The high-risk subtype (C5) was further characterized through the development of a cell cycle-based gene signature, which comprised 37 proliferation-related genes according to the support vector machine algorithm. This signature identified the high-risk group in both training and validation datasets (ps <.001). In the validation analysis, our potential classifier robustly predicted patients with positive lymphatic invasion, metastases and short survival.

RNA interference: Promising approach for breast cancer diagnosis and treatment

Today, cancer is one of the main health-related challenges, and in the meantime, breast cancer (BC) is one of the most common cancers among women, with an alarming number of incidences and deaths every year. For this reason, the discovery of novel and more effective approaches for the diagnosis, treatment, and monitoring of the disease are very important. In this regard, scientists are looking for diagnostic molecules to achieve the above-mentioned goals with higher accuracy and specificity. RNA interference (RNAi) is a posttranslational regulatory process mediated by microRNA intervention and small interfering RNAs. After transcription and edition, these two noncoding RNAs are integrated and activated with the RNA-induced silencing complex (RISC) and AGO2 to connect the target mRNA by their complementary sequence and suppress their translation, thus reducing the expression of their target genes. These two RNAi categories show different patterns in different BC types and stages compared to healthy cells, and hence, these molecules have high diagnostic, monitoring, and therapeutic potentials. This article aims to review the RNAi pathway and diagnostic and therapeutic potentials with a special focus on BC.

scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes

A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

Muscarinic Receptors Associated with Cancer

Simple Summary

Recently, cancer research has described the presence of the cholinergic machinery, specifically muscarinic receptors, in a wide variety of cancers due to their activation and signaling pathways associated with tumor progression and metastasis, providing a wide overview of their contribution to different cancer formation and development for new antitumor targets. This review focused on determining the molecular signatures associated with muscarinic receptors in breast and other cancers and the need for pharmacological, molecular, biochemical, technological, and clinical approaches to improve new therapeutic targets.

Abstract

Cancer has been considered the pathology of the century and factors such as the environment may play an important etiological role. The ability of muscarinic agonists to stimulate growth and muscarinic receptor antagonists to inhibit tumor growth has been demonstrated for breast, melanoma, lung, gastric, colon, pancreatic, ovarian, prostate, and brain cancer. This work aimed to study the correlation between epidermal growth factor receptors and cholinergic muscarinic receptors, the survival differences adjusted by the stage clinical factor, and the association between gene expression and immune infiltration level in breast, lung, stomach, colon, liver, prostate, and glioblastoma human cancers. Thus, targeting cholinergic muscarinic receptors appears to be an attractive therapeutic alternative due to the complex signaling pathways involved.

Deletion of arylamine N-acetyltransferase 1 in MDA-MB-231 human breast cancer cells reduces primary and secondary tumor growth in vivo with no significant effects on metastasis

Arylamine N-acetyltransferase 1 (NAT1) is frequently upregulated in breast cancer. Previous studies showed that inhibition or depletion of NAT1 in breast cancer cells diminishes anchorage-independent growth in culture, suggesting that NAT1 contributes to breast cancer growth and metastasis. To further investigate the contribution of NAT1 to growth and cell invasive/migratory behavior, we subjected parental and NAT1 knockout (KO) breast cancer cell lines (MDA-MB-231, MCF-7, and ZR-75-1) to multiple assays. The rate of cell growth in suspension was not consistently decreased in NAT1 KO cells across the cell lines tested. Similarly, cell migration and invasion assays failed to produce reproducible differences between the parental and NAT1 KO cells. To overcome the limitations of in vitro assays, we tested parental and NAT1 KO cells in vivo in a xenograft model by injecting cells into the flank of immunocompromised mice. NAT1 KO MDA-MB-231 cells produced primary tumors smaller than those formed by parental cells, which was contributed by an increased rate of apoptosis in KO cells. The frequency of lung metastasis, however, was not altered in NAT1 KO cells. When the primary tumors of the parental and NAT1 KO cells were allowed to grow to a pre-determined size or delivered directly via tail vein, the number and size of metastatic foci in the lung did not differ between the parental and NAT1 KO cells. In conclusion, NAT1 contributes to primary and secondary tumor growth in vivo in MDA-MB-231 breast cancer cells but does not appear to affect its metastatic potential.

Developing ZNF Gene Signatures Predicting Radiosensitivity of Patients with Breast Cancer

Adjuvant radiotherapy is one of the main treatment methods for breast cancer, but its clinical benefit depends largely on the characteristics of the patient. This study aimed to explore the relationship between the expression of zinc finger (ZNF) gene family proteins and the radiosensitivity of breast cancer patients. Clinical and gene expression data on a total of 976 breast cancer samples were obtained from The Cancer Genome Atlas (TCGA) database. ZNF gene expression was dichotomized into groups with a higher or lower level than the median level of expression. Univariate and multivariate Cox regression analyses were used to evaluate the relationship between ZNF gene expression levels and radiosensitivity. The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database was used for validation. The results revealed that 4 ZNF genes were possible radiosensitivity markers. High expression of ZNF644 and low expression levels of the other 3 genes (ZNF341, ZNF541, and ZNF653) were related to the radiosensitivity of breast cancer. Hierarchical cluster, Cox, and CoxBoost analysis based on these 4 ZNF genes indicated that patients with a favorable 4-gene signature had better overall survival on radiotherapy. Thus, this 4-gene signature may have value for selecting those patients most likely to benefit from radiotherapy. ZNF gene clusters could act as radiosensitivity signatures for breast cancer patients and may be involved in determining the radiosensitivity of cancer.

Hubness weighted svm ensemble for prediction of breast cancer subtypes

BACKGROUND

Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Cancer subtype prediction from gene expression data can provide more information for cancer treatment decisions.

OBJECTIVE

Gene expressions are complex for analysis due to its high dimensional nature. Machine learning algorithms such as k-Nearest Neighbors, Support Vector Machine (SVM) and Random Forest are used with selection of features for prediction of breast cancer subtypes. Prediction accuracy of the existing methods are affected due to high dimensional nature of gene expressions. The objective of the work is to propose an efficient algorithm for the prediction of breast cancer subtypes from gene expression.

METHODS

For subtype prediction, a novel Hubness Weighted Support Vector machine algorithm (HWSVM) using bad hubness score as a weight measure to handle the outliers in the data has been proposed. Based on the various subtypes, features are projected into seven different feature sets and Ensemble based Hubness Aware Weighted Support Vector Machine (HWSVMEns) is implemented for breast cancer subtype prediction.

RESULTS

The proposed algorithms have been compared with the classical SVM and other traditional algorithms such as Random Forest, k-Nearest Neighbor algorithms and also with various gene selection methods.

CONCLUSIONS

Experimental results show that the proposed HWSVM outperforms other algorithms in terms of accuracy, precision, recall and F1 score due to the hubness weightage scheme and the ensemble approach. The experiments have shown an average accuracy of 92% across various gene expression datasets.

Effect arylamine N-acetyltransferase 1 on morphology, adhesion, migration, and invasion of MDA-MB-231 cells: role of matrix metalloproteinases and integrin αV

Reducted arylamine N-acetyltransferase (NAT1) in breast cancers is associated with poor patient survival. NAT1 has also been associated with changes in cancer cell survival and invasion both invitro and invivo. Here, we report the effects of NAT1 in cancer cell invasion by addressing its role in adherence, migration, and invasion in vitro. The NAT1 gene was deleted in MDA-MB-231, HT-29 and HeLa cells using CRISPR/Cas9 gene editing. Loss of NAT1 increased adherence to collagen in all three cell-lines but migration was unaffected. NAT1 deletion decreased invasion and induced changes to cell morphology. These effects were independent of matrix metalloproteinases but were related to integrin ITGαV expression. The data suggest NAT1 is important in adhesion and invasion through integrin expression.

Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows

In cancer research, genomic profiles are often extracted from homogenized macrodissections of tissues, with the histological context lost and a large fraction of material underutilized. Pertinently, the spatial genomic landscape provides critical complementary information in deciphering disease heterogeneity and progression. Microscale sampling methods such as microdissection to obtain such information are often destructive to a sizeable fraction of the biopsy sample, thus showing limited multiplexability and adaptability to different assays. A modular microfluidic technology is here implemented to recover cells at the microscale from tumor tissue sections, with minimal disruption of unsampled areas and tailored to interface with genome profiling workflows, which is directed here toward evaluating intratumoral genomic heterogeneity. The integrated workflow-GeneScape-is used to evaluate heterogeneity in a metastatic mammary carcinoma, showing distinct single nucleotide variants and copy number variations in different tumor tissue regions, suggesting the polyclonal origin of the metastasis as well as development driven by multiple location-specific drivers.

Galgo: a bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types

MOTIVATION

Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival.

RESULTS

To compare the predictive power of the signatures obtained by Galgo with previously studied subtyping methods, we used a meta-analytic approach testing a total of 35 large population-based transcriptomic biobanks of four different cancer types. Galgo-generated colorectal and lung adenocarcinoma signatures were stronger predictors of patient survival compared to published molecular classification schemes. One Galgo-generated breast cancer signature outperformed PAM50, AIMS, SCMGENE and IntClust subtyping predictors. In high-grade serous ovarian cancer, Galgo signatures obtained similar predictive power to a consensus classification method. In all cases, Galgo subtypes reflected enrichment of gene sets related to the hallmarks of the disease, which highlights the biological relevance of the partitions found.

AVAILABILITY AND IMPLEMENTATION

The open-source R package is available on www.github.com/harpomaxx/galgo.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Unleash multifunctional role of long noncoding RNAs biomarker panel in breast cancer: a predictor classification model

Aim: We aimed to explore the circulating expression profile of nine lncRNAs (MALAT1, HOTAIR, PVT1, H19, ROR, GAS5, ANRIL, BANCR, MIAT) in breast cancer (BC) patients relative to normal and risky individuals. Methods: Serum relative expressions of the specified long non-coding RNAs were quantified in 155 consecutive women, using quantitative reverse-transcription PCR. Random Forest (RF) and decision tree were also applied. Results: Significant MALAT1 upregulation and GAS5 downregulation could discriminate risky women from healthy controls. Overexpression of the other genes showed good diagnostic performances. Lower GAS5 levels were associated with metastasis and recurrence. RF model revealed a better performance when combining gene expression patterns with risk factors. Conclusion: The studied panel could be utilized as diagnostic/prognostic biomarkers in BC, providing promising epigenetic-based therapeutic targets.

Eight immune-related genes predict survival outcomes and immune characteristics in breast cancer

Advancements in immunotherapy have improved our understanding of the immune characteristics of breast cancer. Here, we analyzed gene expression profiles and clinical data obtained from The Cancer Genome Atlas database to identify genes that were differentially expressed between breast tumor tissues and normal breast tissues. Comparisons with the Immunology Database and Analysis Portal (ImmPort) indicated that many of the identified differentially expressed genes were immune-related. Risk scores calculated based on an eight-gene signature constructed from these immune-related genes predicted both overall survival and relapse-free survival outcomes in breast cancer patients. The predictive value of the eight-gene signature was validated in different breast cancer subtypes using external datasets. Associations between risk score and breast cancer immune characteristics were also identified; in vitro experiments using breast cancer cell lines confirmed those associations. Thus, the novel eight-gene signature described here accurately predicts breast cancer survival outcomes as well as immune checkpoint expression and immune cell infiltration processes.

Heterogeneity at the invasion front of triple negative breast cancer cells

Identifying better predictive and prognostic biomarkers for the diagnosis and treatment of triple negative breast cancer (TNBC) is complicated by tumor heterogeneity ranging from responses to therapy, mutational burden, and clonal evolution. To overcome the gap in our understanding of tumor heterogeneity, we hypothesized that isolating and studying the gene expression profile of invasive tumor cell subpopulations would be a crucial step towards achieving this goal. In this report, we utilized a fluidic device previously reported to be capable of supporting long-term three-dimensional growth and invasion dynamics of cancer cells. Live invading and matched non-invading SUM149 inflammatory breast cancer cells were enriched using this device and these two functionally distinct subpopulations were tested for differences in gene expression using a gene expression microarray. 305 target genes were identified to have altered expression in the invading cells compared to the non-invading tumoroid cells. Gene ontology analysis of the gene panel identified multiple biological roles ranging from extracellular matrix reorganization to modulation of the immune response and Rho signaling. Interestingly, the genes associated with the invasion front differ between different samples, consistent with inter- and intra-tumor heterogeneity. This work suggests the impact of heterogeneity in biomarker discovery should be considered as cancer therapy increasingly heads towards a personalized approach.

Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity

Multiplexed RNA in situ hybridization for the analysis of gene expression patterns plays an important role in investigating development and disease. Here, we present a method for multiplexed RNA-ISH to detect spatial tumor heterogeneity in tissue sections. We made use of a microfluidic chip to deliver ISH-probes locally to regions of a few hundred micrometers over time periods of tens of minutes. This spatial multiplexing method can be combined with ISH-approaches based on signal amplification, with bright field detection and with the commonly used format of formalin-fixed paraffin-embedded tissue sections. By using this method, we analyzed the expression of HER2 with internal positive and negative controls (ActB, dapB) as well as predictive biomarker panels (ER, PgR, HER2) in a spatially multiplexed manner on single mammary carcinoma sections. We further demonstrated the applicability of the technique for subtype differentiation in breast cancer. Local analysis of HER2 revealed medium to high spatial heterogeneity of gene expression (Cohen effect size r = 0.4) in equivocally tested tumor tissues. Thereby, we exemplify the importance of using such a complementary approach for the analysis of spatial heterogeneity, in particular for equivocally tested tumor samples. As the method is compatible with a range of ISH approaches and tissue samples, it has the potential to find broad applicability in the context of molecular analysis of human diseases.

A Tyrosine Kinase Expression Signature Predicts the Post-Operative Clinical Outcome in Triple Negative Breast Cancers

Triple negative breast cancer (TNBC) represent 15% of breast cancers. Histoclinical features and marketed prognostic gene expression signatures (GES) failed to identify good- and poor-prognosis patients. Tyrosine kinases (TK) represent potential prognostic and/or therapeutic targets for TNBC. We sought to define a prognostic TK GES in a large series of TNBC. mRNA expression and histoclinical data of 6379 early BCs were collected from 16 datasets. We searched for a TK-based GES associated with disease-free survival (DFS) and tested its robustness in an independent validation set. A total of 1226 samples were TNBC. In the learning set of samples (N = 825), we identified a 13-TK GES associated with DFS. This GES was associated with cell proliferation and immune response. In multivariate analysis, it outperformed the previously published GESs and classical prognostic factors in the validation set (N = 401), in which the patients classified as "low-risk" had a 73% 5-year DFS versus 53% for "high-risk" patients (p = 1.85 × 10-3). The generation of 100,000 random 13-gene signatures by a resampling scheme showed the non-random nature of our classifier, which was also prognostic for overall survival in multivariate analysis. We identified a robust and non-random 13-TK GES that separated TNBC into subgroups of different prognosis. Clinical and functional validations are warranted.

MCM10 facilitates the invaded/migrated potentials of breast cancer cells via Wnt/β-catenin signaling and is positively interlinked with poor prognosis in breast carcinoma

The minichromosome maintenance protein 10 (MCM10) is one of the MCM proteins that initiate DNA replication by interacting with CDC45-MCM2-7. It has been reported that MCM10 has a role in breast cancer progression. However, MCM10 in breast cancer is still not comprehensively studied and further research is needed. This study was aimed at investigating the potential effects of MCM10 on metastasis, the prognosis of breast carcinoma, and its underlying mechanisms. Using the ONCOMINE database and the Kaplan-Meier Plotter, MCM10 was significantly overexpressed in cancers, and high expression of MCM10 was involved in the poor prognosis of breast carcinoma. MCM10 can promote the proliferation, migration, and invasion of MDA-MB-231 cells. MCM10 knockdown brought about a radical reversal in cell behaviors. Meanwhile, decreased expression of β-catenin and cyclin Dl was detected in MCM10 short hairpin RNA cells, implying that MCM10 might induce breast cancer metastasis via the Wnt/β-catenin pathway.MCM10 can be defined as a potential diagnostic tool and a promising target for breast carcinoma.