Study of Gene Expression Profiles of Breast Cancers in Indian Women

Beyond the Usual Suspects: Examining the Role of Understudied Histone Variants in Breast Cancer

The incorporation of histone variants has structural ramifications on nucleosome dynamics and stability. Due to their unique sequences, histone variants can alter histone-histone or histone-DNA interactions, impacting the folding of DNA around the histone octamer and the overall higher-order structure of chromatin fibers. These structural modifications alter chromatin compaction and accessibility of DNA by transcription factors and other regulatory proteins to influence gene regulatory processes such as DNA damage and repair, as well as transcriptional activation or repression. Histone variants can also generate a unique interactome composed of histone chaperones and chromatin remodeling complexes. Any of these perturbations can contribute to cellular plasticity and the progression of human diseases. Here, we focus on a frequently overlooked group of histone variants lying within the four human histone gene clusters and their contribution to breast cancer.

An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

ABERRANT EXPRESSION OF COL14A1, CELRS3, and CTHRC1 IN BREAST CANCER СELLS

BACKGROUND

Collagens, which are the major components of the extracellular matrix involved in the regulation of tumor microenvironment, could be differentially expressed in breast cancer (BC) with different transcriptome profiling.

AIM

To analyze the transcript level expression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, COL14A1, CTHRC1, and CELRS3 genes and the clinical relevance of their differential expression in BC.

MATERIALS AND METHODS

The transcript level expression of the genes was analyzed using the quantitative real-time PCR (qPCR) in tumor tissue of 60 BC patients.

RESULTS

Overexpression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, CTHRC, and CELRS3 anddown-regulated expression of COL14A1 were observed. COL14A1 down-regulation was associated with aggressive, basal, and Her-2/neu BC subtypes (p = 0.031). Overexpression of CELSR3 was found to be associated with the older age of the patients (> 55 years, p = 0.049). Further analysis with the TCGA BC data set has shown a concordance in the differential expression of the above genes. Furthermore, overexpression of CTHRC1 was associated with poor overall survival (OS), particularly with poor prognosis (p = 0.00042) for the luminal BC subtype. On the other hand, CELSR3 overexpression was associated with mucinous tumors and poor prognosis in post-menopausal women. In silicotarget prediction identified several BC-associated miRNAs and members of miR-154, -515, and -10 families to perform a likely regulatory role in the above ECM genes.

CONCLUSION

The present study shows that the expression of COL14A1 and CTHRC1 may serve as potential biological markers for the detection of basal BC and the prognosis of survival for patients with the luminal subtype of BC.

Topological biclustering ARTMAP for identifying within bicluster relationships

Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation. Therefore, to improve the quality of biclustering and module extraction, this work combines the adaptive resonance theory (ART)-based methods of biclustering ARTMAP (BARTMAP) and topological ART (TopoART), to produce TopoBARTMAP. The latter inherits the ability to detect topological associations while performing data reduction. The capabilities of TopoBARTMAP were benchmarked using 35 real world cancer datasets and contrasted with other (bi)clustering methods, where it showed a statistically significant improvement over the other assessed methods on ordered and shuffled data experiments. In experiments with 12 synthetic datasets, the method was observed to perform better at identifying constant, scale, shift, and shift scale type biclusters. The produced graphical representation was refined to represent gene bicluster associations and was assessed on the NCBI GSE89116 dataset containing expression levels of 39,326 probes sampled over 38 observations.

Comparing the Biology of Young versus Old Age Estrogen-Receptor-Positive Breast Cancer through Gene and Protein Expression Analyses

Background: Breast cancer developed at a young age (≤45 years) is hypothesized to have unique biology; however, findings in this field are controversial. Methods: We compared the whole transcriptomic profile of young vs. old-age breast cancer using DNA microarray. RNA was extracted from 13 fresh estrogen receptor (ER)-positive primary breast cancer tissues of untreated patients (7 = young age ≤45 years and 6 = old age ≥55 years). In silico validation for the differentially expressed genes (DEGs) by young-age patients was conducted using The Cancer Genome Atlas (TCGA) database. Next, we analyzed the protein expression encoded by two of the significantly down-regulated genes by young-age patients, Glycine N-acyltransferase-like 1 (GLYATL-1) and Ran-binding protein 3 like (RANBP3L), using immunohistochemical analysis in an independent cohort of 56 and 74 ER-positive pre-therapeutic primary breast cancer tissues, respectively. Results: 12 genes were significantly differentially expressed by young-age breast cancers (fold change >2 or <2- with FDR p-value < 0.05). TCGA data confirmed the differential expression of six genes. Protein expression analysis of GLYATL-1 and RANBP3L did not show heterogeneous expression between young and old-age breast cancer tissues. Loss of expression of GLYATL-1 was significantly (p-value 0.005) associated with positive lymph node status. Higher expression of RANBP3L was significantly associated with breast cancers with lower histopathological grades (p-value 0.038). Conclusions: At the transcriptomic level, breast cancer developed in young and old age patients seems homogenous. The variation in the transcriptomic profiles can be attributed to the other clinicopathological characteristics rather than the age of the patient.

Aquaporins in Tumor

Recent researches have demonstrated that aquaporins (AQPs), including water-selective channels, aquaglyceroporins and superaquaporins, are generally expressed in various tumors, such as lung, colorectal, liver, brain, breast tumors, etc. Therefore, it is imperative to study the accurate relationship between AQPs and tumor, which may provide innovative approaches to treat and prevent tumor development. In this chapter, we mainly reviewed the expression and pathophysiological function of AQPs in tumor, and summarize recent work on AQPs in tumor. Although, the underlying mechanism of AQP in tumor is not very clear, growing evidences suggest that cell migration, adhesion, angiogenesis, and division contribute to tumor development, in which AQPs might be involved. Therefore, it is still necessary to conduct further studies to determine the specific roles of AQPs in the tumor.

Transcriptomic profiling of Indian breast cancer patients revealed subtype-specific mRNA and lncRNA signatures

Breast cancer (BC) is one of the leading causes of cancer-associated death in women. Despite the progress in therapeutic regimen, resistance and recurrence of breast cancer have affected the overall survival of patients. The present signatures, such as PAM50 and Oncotype DX, do not segregate the Indian breast samples based on molecular subtypes. This study aims at finding signatures of long noncoding RNA (lncRNA) and mRNA in Indian breast cancer patients using RNA-seq. We have analyzed the survival based on the menopausal and hormone status of 380 Indian breast cancer patients, and of these, we have sequenced and analyzed matched tumor–normal transcriptome of 17 (pre- and postmenopausal) Indian breast cancer patients representing six different subtypes, namely, four patients in triple-positive, three patients in estrogen receptor–positive (ER+ve), three patients in estrogen and progesterone receptors–positive (ER+ve, PR+ve), two patients in human epidermal growth factor receptor (Her2+ve), three patients in triple-negative, and one patient in ER+ve and Her2+ve subtypes. We have identified a 25 mRNA–27 lncRNA gene set, which segregated the subtypes in our data. A pathway analysis of the differentially expressed genes revealed downregulated ECM interaction and upregulated immune regulation, cell cycle, DNA damage response and repair, and telomere elongation in premenopausal women. Postmenopausal women showed downregulated metabolism, innate immune system, upregulated translation, sumoylation, and AKT2 activation. A Kaplan–Meier survival analysis revealed that menopausal status, grade of the tumor, and hormonal status displayed statistically significant effects (p < 0.05) on the risk of mortality due to breast cancer. Her2+ve patients showed low overall survival. One of the unique lncRNA-mRNA pairs specific to the EP-subtype, SNHG12 and EPB41, showed interaction, which correlates with their expression level; SNHG12 is downregulated and EPB41 is upregulated in EP samples.

A gene expression signature in HER2+ breast cancer patients related to neoadjuvant chemotherapy resistance, overall survival, and disease-free survival

Breast cancer ranks first in terms of mortality and incidence rates worldwide among women. The HER2+ molecular subtype is one of the most aggressive subtypes; its treatment includes neoadjuvant chemotherapy and the use of a HER2 antibody. Some patients develop resistance despite positive results obtained using this therapeutic strategy. Objective. To identify prognostic markers for treatment and survival in HER2+ patients. Methods. Patients treated with neoadjuvant chemotherapy were assigned to sensitive and resistant groups based on their treatment response. Differentially expressed genes (DEGs) were identified using RNA-seq analysis. KEGG pathway, gene ontology, and interactome analyses were performed for all DEGs. An enrichment analysis Gene set enrichment analysis was performed. All DEGs were analyzed for overall (OS) and disease-free survival (DFS). Results. A total of 94 DEGs were related to treatment resistance. Survival analysis showed that 12 genes (ATF6B, DHRS13, DIRAS1, ERAL1, GRIN2B, L1CAM, IRX3, PRTFDC1, PBX2, S100B, SLC9A3R2, and TNXB) were good predictors of disease-free survival, and eight genes (GNG4, IL22RA2, MICA, S100B, SERPINF2, HLA-A, DIRAS1, and TNXB) were good predictors of overall survival (OS). Conclusion: We highlighted a molecular expression signature that can differentiate the treatment response, overall survival, and DFS of patients with HER2+ breast cancer.

Ecdysoneless Overexpression Drives Mammary Tumorigenesis through Upregulation of C-MYC and Glucose Metabolism

Ecdysoneless (ECD) protein is essential for embryogenesis, cell-cycle progression, and cellular stress mitigation with an emerging role in mRNA biogenesis. We have previously shown that ECD protein as well as its mRNA are overexpressed in breast cancer and ECD overexpression predicts shorter survival in patients with breast cancer. However, the genetic evidence for an oncogenic role of ECD has not been established. Here, we generated transgenic mice with mammary epithelium-targeted overexpression of an inducible human ECD transgene (ECDTg). Significantly, ECDTg mice develop mammary hyperplasia, preneoplastic lesions, and heterogeneous tumors with occasional lung metastasis. ECDTg tumors exhibit epithelial to mesenchymal transition and cancer stem cell characteristics. Organoid cultures of ECDTg tumors showed ECD dependency for in vitro oncogenic phenotype and in vivo growth when implanted in mice. RNA sequencing (RNA-seq) analysis of ECDTg tumors showed a c-MYC signature, and alterations in ECD levels regulated c-MYC mRNA and protein levels as well as glucose metabolism. ECD knockdown-induced decrease in glucose uptake was rescued by overexpression of mouse ECD as well as c-MYC. Publicly available expression data analyses showed a significant correlation of ECD and c-MYC overexpression in breast cancer, and ECD and c-MYC coexpression exhibits worse survival in patients with breast cancer. Taken together, we establish a novel role of overexpressed ECD as an oncogenesis driver in the mouse mammary gland through upregulation of c-MYC-mediated glucose metabolism.

IMPLICATIONS

We demonstrate ECD overexpression in the mammary gland of mice led to the development of a tumor progression model through upregulation of c-MYC signaling and glucose metabolism.

Breast cancer stage prediction: a computational approach guided by transcriptome analysis

Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics.

Screening of Specific and Common Pathways in Breast Cancer Cell Lines MCF-7 and MDA-MB-231 Treated with Chlorophyllides Composites

Our previous findings have shown that the chlorophyllides composites have anticancer activities to breast cancer cell lines (MCF-7 and MDA-MB-231). In the present study, microarray gene expression profiling was utilized to investigate the chlorophyllides anticancer mechanism on the breast cancer cells lines. Results showed that chlorophyllides composites induced upregulation of 43 and 56 differentially expressed genes (DEG) in MCF-7 and MDA-MB-231 cells, respectively. In both cell lines, chlorophyllides composites modulated the expression of annexin A4 (ANXA4), chemokine C-C motif receptor 1 (CCR1), stromal interaction molecule 2 (STIM2), ethanolamine kinase 1 (ETNK1) and member of RAS oncogene family (RAP2B). Further, the KEGG annotation revealed that chlorophyllides composites modulated DEGs that are associated with the endocrine system in MCF-7 cells and with the nervous system in MDA-MB-231 cells, respectively. The expression levels of 9 genes were validated by quantitative reverse transcription PCR (RT-qPCR). The expression of CCR1, STIM2, ETNK1, MAGl1 and TOP2A were upregulated in both chlorophyllides composites treated-MCF-7 and MDA-MB-231 cells. The different expression of NLRC5, SLC7A7 and PKN1 provided valuable information for future investigation and development of novel cancer therapy.

Alpha1-antitrypsin combined fatty acids induced angiopoietin-like protein 4, expression in breast cancer: A pilot study

INTRODUCTION

The effect of nutrition on inflammation and breast cancer (BC) prognosis is still inconclusive. Mechanism of data suggests that different types of fatty acids (FAs) play an essential role in carcinogenesis, and binding of alpha 1 antitrypsin (A1AT) may modulate carcinogenesis. The increased expression in the bound form of A1AT and release of Angiopoietin-like protein 4 (Angptl4) targets the gene of peroxisome proliferator-activated receptor-gamma (PPAR-γ). Our aim of the study was to compare the effect of FA-free (A1AT-0) and FAs bound forms of A1AT on levels of IL-1β, PPAR-gamma, and Angplt4 in breast cancer and control women.

METHODOLOGY

10 women with breast cancer and ten control women within the age group 25-60 years with normal (Pi) M allele A1AT were recruited. Mononuclear cells were isolated and treated with different A1AT and FAs on the various combinations (linoleic acid, alpha-linolenic acid) for time-dependent study (2,4,18 and 24 h) and analyzed for the interleukin -1 beta(IL-1b), PPAR-gamma, and Angiopoietin-like protein 4 (Angptl4) expression by using ELISA method and gas chromatography for analyzing FAs. One-way ANOVA combined with multiple comparisons is used to compare the means.

RESULTS

100% of the study subjects were homozygous for the normal allele of A1AT. Time-dependent effects of A1AT and A1AT conjugated fatty acids on IL-I b, PPAR-g and Angptl4 showed statistically significant P = 0.07, P = 0.001, and P = 0.02 respectively, compared to those of the former study subjects. But within the groups, PPAR-g levels in case group (F(15,40)1.606, P = 0.003) and Angptl4 in the control group (F(15,32)0.64, P = 0.043) differed significantly.

CONCLUSION

To the best of our knowledge, it's the first kind of study, and we speculate that the A1AT complex with different types of FAs results in a new form of A1AT having a solid capability to regulate the inflammation-induced synthesis, processing, and release of an active form of IL-1β. Our experimental data shows that the anti-inflammatory property of A1AT combined FAs likely to be mediated PPAR γand Angptl4 activation, thereby inhibiting the IL-1b. These findings may be worth assessing BC's biological effects and therapeutic effectiveness.

Lift the Veil of Breast Cancers Using 4 or Fewer Critical Genes

Known genes in the breast cancer study literature could not be confirmed whether they are vital to breast cancer formations due to lack of convincing accuracy, although they may be biologically directly related to breast cancer based on present biological knowledge. It is hoped vital genes can be identified with the highest possible accuracy, for example, 100% accuracy and convincing causal patterns beyond what has been known in breast cancer. One hope is that finding gene-gene interaction signatures and functional effects may solve the puzzle. This research uses a recently developed competing linear factor analysis method in differentially expressed gene detection to advance the study of breast cancer formation. Surprisingly, 3 genes are detected to be differentially expressed in TNBC and non-TNBC (Her2, Luminal A, Luminal B) samples with 100% sensitivity and 100% specificity in 1 study of triple-negative breast cancers (TNBC, with 54 675 genes and 265 samples). These 3 genes show a clear signature pattern of how TNBC patients can be grouped. For another TNBC study (with 54 673 genes and 66 samples), 4 genes bring the same accuracy of 100% sensitivity and 100% specificity. Four genes are found to have the same accuracy of 100% sensitivity and 100% specificity in 1 breast cancer study (with 54 675 genes and 121 samples), and the same 4 genes bring an accuracy of 100% sensitivity and 96.5% specificity in the fourth breast cancer study (with 60 483 genes and 1217 samples). These results show the 4-gene-based classifiers are robust and accurate. The detected genes naturally classify patients into subtypes, for example, 7 subtypes. These findings demonstrate the clearest gene-gene interaction patterns and functional effects with the smallest numbers of genes and the highest accuracy compared with findings reported in the literature. The 4 genes are considered to be essential for breast cancer studies and practice. They can provide focused, targeted researches and precision medicine for each subtype of breast cancer. New breast cancer disease types may be detected using the classified subtypes, and hence new effective therapies can be developed.

Fabrication of eco-friendly chitosan functionalized few-layered WS2 nanocomposite implanted with ruthenium nanoparticles for in vitro antibacterial and anticancer activity: Synthesis, characterization, and pharmaceutical applications

The abundance of two-dimensional (2D) components has provided them with a broad material platform for building nano and atomic-level applications. So, 2D nanomaterials are unique because of their physicochemical properties. Over many years, graphene is a conventional 2D layered element that has significant attention in the scientific community. In recent years numerous new 2D nanomaterials other than graphene have been reported. The study of 2D nanomaterials is also in its infant stages, with the majority of research focusing on the explanation of special material properties, but very few articles are focusing on the biological applications of 2D nanomaterials. As a result, we focused on the transition metal dichalcogenides (TMDCs) such as MoS₂ and WS₂, which were emerging and exciting groups of elements with display great opportunities in several fields, such as cancer nanomedicine. Herein, we synthesized biologically active CS/WS₂/Ru composite by liquid exfoliation approach. The CS/WS₂/Ru composites exhibit significant antibacterial action towards (S. aureus, and E. coli) bacteria. Also, the composite suggests synergetic anticancer action against MCF-7 cancer cells. These reports are possible to explore the innovative aspects of biological outcomes in carcinological applications.

Comprehensive analysis based on DNA methylation and RNA-seq reveals hypermethylation of the up-regulated WT1 gene with potential mechanisms in PAM50 subtypes of breast cancer

Background

Breast cancer (BC), one of the most widespread cancers worldwide, caused the deaths of more than 600,000 women in 2018, accounting for about 15% of all cancer-associated deaths in women that year. In this study, we aimed to discover potential prognostic biomarkers and explore their molecular mechanisms in different BC subtypes using DNA methylation and RNA-seq.

Methods

We downloaded the DNA methylation datasets and the RNA expression profiles of primary tissues of the four BC molecular subtypes (luminal A, luminal B, basal-like, and HER2-enriched), as well as the survival information from The Cancer Genome Atlas (TCGA). The highly expressed and hypermethylated genes across all the four subtypes were screened. We examined the methylation sites and the downstream co-expressed genes of the selected genes and validated their prognostic value using a different dataset (GSE20685). For selected transcription factors, the downstream genes were predicted based on the Gene Transcription Regulation Database (GTRD). The tumor microenvironment was also evaluated based on the TCGA dataset.

Results

We found that Wilms tumor gene 1 (WT1), a transcription factor, was highly expressed and hypermethylated in all the four BC subtypes. All the WT1 methylation sites exhibited hypermethylation. The methylation levels of the TSS200 and 1stExon regions were negatively correlated with WT1 expression in two BC subtypes, while that of the gene body region was positively associated with WT1 expression in three BC subtypes. Patients with low WT1 expression had better overall survival (OS). Five genes including COL11A1, GFAP, FGF5, CD300LG, and IGFL2 were predicted as the downstream genes of WT1. Those five genes were dysregulated in the four BC subtypes. Patients with a favorable 6-gene signature (low expression of WT1 and its five predicted downstream genes) exhibited better OS than that with an unfavorable 6-gene signature. We also found a correlation between WT1 and tamoxifen using STITCH. Higher infiltration rates of CD8 T cells, plasma cells, and monocytes were found in the lower quartile WT1 group and the favorable 6-gene signature group. In conclusion, we demonstrated that WT1 is hypermethylated and up-regulated in the four BC molecular subtypes and a 6-gene signature may predict BC prognosis.

Long non-coding RNA ARAP1-AS1 accelerates cell proliferation and migration in breast cancer through miR-2110/HDAC2/PLIN1 axis

Breast cancer (BC) poses a great threaten to women health. Numerous evidences suggest the important role of long non-coding RNAs (lncRNAs) in BC development. In the present study, we intended to investigate the role of ARAP1-AS1 in BC progression. First of all, the GEPIA data suggested that ARAP1-AS1 was highly expressed in breast invasive carcinoma (BRAC) tissues compared with the normal breast tissues. Meanwhile, the expression of ARAP1-AS1 was greatly up-regulated in BC cell lines. ARAP1-AS1 knockdown led to repressed proliferation, strengthened apoptosis and blocked migration of BC cells. Moreover, ARAP1-AS1 could boost HDAC2 expression in BC through sponging miR-2110 via a ceRNA mechanism. Of note, the UCSC predicted that HDAC2 was a potential transcriptional regulator of PLIN1, an identified tumor suppressor in BC progression. Moreover, we explained that the repression of HDAC2 on PLIN1 was owing to its deacetylation on PLIN1 promoter. More importantly, depletion of PLIN1 attenuated the mitigation function of ARAP1-AS1 silence on the malignant phenotypes of BC cells. To sum up, ARAP1-AS1 serves a tumor-promoter in BC development through modulating miR-2110/HDAC2/PLIN1 axis, which may help to develop novel effective targets for BC treatment.

Bioinformatics analysis of prognostic significance of COL10A1 in breast cancer

Background: Collagen type X alpha 1 (COL10A1) is overexpressed in diverse tumors and displays vital roles in tumorigenesis. However, the prognostic value of COL10A1 in breast cancer remains unclear.

Methods: The expression of COL10A1 was analyzed by the Oncomine database and UALCAN cancer database. The relationship between COL10A1 expression level and clinical indicators including prognostic data in breast cancer were analyzed by the Kaplan–Meier Plotter, PrognoScan, and Breast Cancer Gene-Expression Miner (bc-GenExMiner) databases.

Results: COL10A1 was up-regulated in different subtypes of breast cancer. Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2) status and nodal status were positively correlated with COL10A1 expression. Conversely, age, the Scarff–Bloom–Richardson (SBR) grade, basal-like status, and triple-negative status were negatively related to COL10A1 level in breast cancer samples compared with normal tissues. Patients with increased COL10A1 expression level showed worse overall survival (OS), relapse-free survival (RFS), distant metastasis-free survival (DMFS) and disease-free survival (DFS). COL10A1 was positively correlated with metastatic relapse-free survival. GSEA analysis revealed that enrichment of TGF-β signaling pathway. 15-leucine-rich repeat containing membrane protein (LRRC15) is a correlated gene of COL10A1.

Conclusion: Bioinformatics analysis revealed that COL10A1 might be considered as a predictive biomarker for prognosis of breast cancer. Further experiments and clinical trials are essential to elucidate the value of COL10A1 in breast cancer treatment.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis

Background:

Breast cancer is the most commonly diagnosed malignancy and a major cause of cancer-related deaths in women globally. Identification of novel prognostic and pathogenesis biomarkers play a pivotal role in the management of the disease.

Methods:

Three data sets from the GEO database were used to identify differentially expressed genes (DEGs) in breast cancer. Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway analyses were performed to elucidate the functional roles of the DEGs. Besides, we investigated the translational and protein expression levels and survival data of the DEGs in patients with breast cancer from the Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases. The corresponding change in the expression level of microRNAs in the DEGs was also predicted using miRWalk and TargetScan, and the expression profiles were analyzed using OncomiR. Finally, the expression of novel DEGs were validated in Chinese breast cancer tissues by RT-qPCR.

Results:

A total of 46 DEGs were identified, and GO analysis revealed that these genes were mainly associated with biological processes involved in fatty acid, lipid localization, and regulation of lipid metabolism. Two novel biomarkers, ADH1A and IGSF10, and 4 other genes (APOD, KIT, RBP4, and SFRP1) that were implicated in the prognosis and pathogenesis of breast cancer, exhibited low expression levels in breast cancer tissues. Besides, 14/25 microRNAs targeting 6 genes were first predicted to be associated with breast cancer prognosis. RT-qPCR results of ADH1A and IGSF10 expression in Chinese breast cancer tissues were consistent with the database analysis and showed significant down-regulation.

Conclusion:

ADH1A, IGSF10, and the 14 microRNAs were found to be potential novel biomarkers for the diagnosis, treatment, and prognosis of breast cancer.

Matrix metalloproteinase (MMP)-1 and MMP-3 gene variations affect MMP-1 and -3 serum concentration and associates with breast cancer

Matrix metallopeptidases (MMPs) 1 and 3 have been shown to contribute to the initiation, and progression of different cancers, including breast cancer (BC). In this study, we aimed to examine the relations between polymorphisms of MMP1 (rs1799750) and MMP3 (rs632478) and their circulating levels with BC. The polymorphisms were genotyped by PCR-based Restriction Fragment Length Polymorphism (RFLP) and Allele-Specific PCR (AS-PCR) among 100 patients and 100 controls. MMP1 and MMP3 serum levels were measured by enzyme-linked immunosorbent assay (ELISA). Genotype distributions of MMP1 and MMP3 genes showed significant difference between patients and controls. The distribution of 2G/2G, 1G/2G and 1G/1G genotypes for MMP1 was 74%, 2% and 24% in the patients and 38%, 2% and 60% in the controls, respectively (P = 0.0001). For MMP3 the distribution of C/C, A/C and A/A genotypes was 28%, 54% and 18% in patients and 48%, 40% and 12% in controls, respectively (P = 0.01). For MMP1, the 2G/2G genotype was linked with a higher risk of BC when compared with that of the 1G/1G genotype (OR = 4.86; 95% CI = 2.63-8.99; P = 0.0001). For MMP3, in co-dominant model, there was a higher risk of BC in A/A and A/C genotype carriers (A/A: OR = 2.57; 95% CI = 1.08-6.11; P = 0.03) (A/C: OR = 2.31 95% CI = 1.24-4.30; P = 0.008). We also showed that MMP1 and MMP3 serum level was significantly increased in BC patients compared to controls. MMP1 and MMP3 genetic variations and their circulating levels are both significantly related to BC.

MELK/MPK38 in cancer: from mechanistic aspects to therapeutic strategies

Maternal embryonic leucine zipper kinase (MELK)/Murine protein serine-threonine kinase 38 (MPK38) is a member of the AMP-related serine-threonine kinase family, which has been reported to be involved in the regulation of many cellular events, including cell proliferation, apoptosis, and metabolism, partly by phosphorylation and regulation of several signaling molecules. The abnormal expression of MELK has been associated with tumorigenesis and malignant progression in various types of cancer. Currently, several small-molecule inhibitors of MELK are under investigation although only OTS167 has entered clinical trials. In this review, we elaborate on the relative contributions of MELK pathways in the physiological process, their oncogenic role in carcinogenesis, and targeted agents under development for the treatment of cancer.

Screening of Prognostic Factors in Early-Onset Breast Cancer

Background:

Gene expression profiles from early-onset breast cancer and normal tissues were analyzed to explore the genes and prognostic factors associated with breast cancer.

Methods:

GSE109169 and GSE89116 were obtained from the database of Gene Expression Omnibus. We firstly screened the differentially expressed genes between tumor samples and normal samples from patients with early-onset breast cancer. Based on database for annotation, visualization and intergrated discovery (DAVID) tool, functional analysis was calculated. Transcription factor-target regulation and microRNA-target gene network were constructed using the tool of transcriptional regulatory relatitionships unraveled by sentence-based text mining (TRRUST) and miRWalk2.0, respectively. The prognosis-related survival information was compiled based on The Cancer Genome Atlas breast cancer clinical data.

Results:

A total of 708 differentially expressed genes from GSE109169 data sets and 358 differentially expressed genes from GSE89116 data sets were obtained, of which 122 common differentially expressed genes including 102 uniformly downregulated genes and 20 uniformly upregulated genes were screened. Protein–protein interaction network with a total of 83 nodes and 157 relationship pairs was obtained, and genes in protein–protein interaction, such as peroxisome proliferator-activated receptor γ, FGF2, adiponectin, and PCK1, were recognized as key nodes in protein–protein interaction. In total, 66 transcription factor–target relationship pairs were obtained, and peroxisome proliferator-activated receptor γ was the only one downregulated transcription factor. MicroRNA-target gene network contained 368 microRNA-target relationship pairs. Moreover, 16 differentially expressed genes, including 2 upregulations and 14 downregulations, were related to a significant correlation with the prognosis, including SQLE and peroxisome proliferator-activated receptor γ.

Conclusions:

SQLE and peroxisome proliferator-activated receptor γ might be important prognostic factors in breast cancers, and adiponectin might be important in breast cancer pathogenesis regulated by peroxisome proliferator-activated receptor γ.

Association Between Episodic Memory and Genetic Risk Factors for Alzheimer's Disease in South Asians from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD)

BACKGROUND/OBJECTIVES

Genetic factors play an important role in Alzheimer's disease (AD) and cognitive aging. However, it is unclear whether risk loci identified in European ancestry (EA) populations have similar effects in other groups, such as South Asians.

DESIGN

We investigated the allelic distribution and cognitive associations of 56 known AD risk single-nucleotide polymorphisms (SNPs) identified from three EA genome-wide association studies (EA-GWASs) in a South Asian population. Single SNP and genetic risk score (GRS) associations with measures of episodic memory were assessed.

SETTING

The Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD).

PARTICIPANTS

A total of 906 LASI-DAD participants from diverse states in India.

MEASUREMENTS

Participants were genotyped using the Illumina Global Screening Array and imputed with 1000G Phase 3v5. Cognitive measures included total learning and delayed word recall.

RESULTS

Although only a few SNPs were significantly associated with memory scores (P < .05), effect estimates from the EA-GWAS and the LASI-DAD showed moderate correlation (0.35-0.88) in the expected direction. GRSs were also associated with memory scores, although percentage variation explained was small (0.1%-0.6%).

CONCLUSIONS

Discrepancies in allele frequencies and cognitive association results suggest that genetic factors found predominantly through EA-GWASs may play a limited role in South Asians. However, the extent of differences in the genetic architecture of AD and cognition in EA and South Asians remains uncertain. There is also a critical need to perform a more comprehensive assessment of the mutational spectrum of South Asia to identify novel genetic variants associated with AD and cognition in this population. J Am Geriatr Soc 68:S45-S53, 2020.

Prognostic Values for the mRNA Expression of the ADAMTS Family of Genes in Gastric Cancer

The “A Disintegrin and Metalloproteinase with Thrombospondin Motif” (ADAMTS) family of genes is involved in the occurrence and development of different cancers. However, the prognostic value of these genes in gastric cancer (GC) has not been revealed. The present study was thus conducted to determine the prognostic value for the ADAMTS family of genes in GC. First, we evaluated the mRNA expression levels of the ADAMTS family in GC patients using a GEPIA dataset. Thereafter, we determined the prognostic value of these genes by analyzing their mRNA level using the Kaplan–Meier Plotter database. The mRNA expression level of ADAMTS12 was randomly validated by qRT-PCR and meta-analysis while its coexpression genes were derived using Coexpedia. Finally, we performed Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the OmicShare Tools. Compared to normal tissues, expression of ADAMTS2 and 12 was significantly higher while that of ADAMTS1, 13, and 15 was significantly lower in GC tissues. According to the RNA-seq and gene chip data, the ADAMTS family (6, 7, 12, 15, and 18) of genes was closely related to the prognosis of GC, and their high expression levels were associated with poor prognosis and survival time. In addition, ADAMTS12 was highly expressed in 20 pairs of GC tissues based on RT-PCR (P=0.016) and meta-analysis (SMD: 0.73, 95% CI: 0.32–1.14, P < 0.001). GO and KEGG pathway analyses indicated that the ADAMTS12 coexpressed genes were enriched in the pathways of extracellular matrix organization, extracellular matrix structural constituent, extracellular matrix, and protein digestion and absorption. Herein, we discovered the prognostic values and biological roles of the ADAMTS genes in GC.

Systems biology comprehensive analysis on breast cancer for identification of key gene modules and genes associated with TNM-based clinical stages

Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein-protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan-Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan-Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.