SCTR regulates cell cycle-related genes toward anti-proliferation in normal breast cells while having pro-proliferation activity in breast cancer cells

Bayesian Multi-View Clustering given complex inter-view structure

Multi-view datasets are becoming increasingly prevalent. These datasets consist of different modalities that provide complementary characterizations of the same underlying system. They can include heterogeneous types of information with complex relationships, varying degrees of missingness, and assorted sample sizes, as is often the case in multi-omic biological studies. Clustering multi-view data allows us to leverage different modalities to infer underlying systematic structure, but most existing approaches are limited to contexts in which entities are the same across views or have clear one-to-one relationships across data types with a common sample size. Many methods also make strong assumptions about the similarities of clusterings across views. We propose a Bayesian multi-view clustering approach (BMVC) which can handle the realities of multi-view datasets that often have complex relationships and diverse structure. BMVC incorporates known and complex many-to-many relationships between entities via a probabilistic graphical model that enables the joint inference of clusterings specific to each view, but where each view informs the others. Additionally, BMVC estimates the strength of the relationships between each pair of views, thus moderating the degree to which it imposes dependence constraints. We benchmarked BMVC on simulated data to show that it accurately estimates varying degrees of inter-view dependence when inter-view relationships are not limited to one-to-one correspondence. Next, we demonstrated its ability to capture visually interpretable inter-view structure in a public health survey of individuals and households in Puerto Rico following Hurricane Maria. Finally, we showed that BMVC clusters integrate the complex relationships between multi-omic profiles of breast cancer patient data, improving the biological homogeneity of clusters and elucidating hypotheses for functional biological mechanisms. We found that BMVC leverages complex inter-view structure to produce higher quality clusters than those generated by standard approaches. We also showed that BMVC is a valuable tool for real-world discovery and hypothesis generation.

A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue

The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes.

Transcription profiling of feline mammary carcinomas and derived cell lines reveals biomarkers and drug targets associated with metabolic and cell cycle pathways

The molecular heterogeneity of feline mammary carcinomas (FMCs) represents a prognostic and therapeutic challenge. RNA-Seq-based comparative transcriptomic profiling serves to identify recurrent and exclusive differentially expressed genes (DEGs) across sample types and molecular subtypes. Using mass-parallel RNA-Seq, we identified DEGs and performed comparative function-based analysis across 15 tumours (four basal-like triple-negative [TN], eight normal-like TN, and three luminal B fHER2 negative [LB fHER2-]), two cell lines (CL, TiHo-0906, and TiHo-1403) isolated from the primary tumours (LB fHER2-) of two cats included in this study, and 13 healthy mammary tissue controls. DEGs in tumours were predominantly upregulated; dysregulation of CLs transcriptome was more extensive, including mostly downregulated genes. Cell-cycle and metabolic-related DEGs were upregulated in both tumours and CLs, including therapeutically-targetable cell cycle regulators (e.g. CCNB1, CCNB2, CDK1, CDK4, GTSE1, MCM4, and MCM5), metabolic-related genes (e.g. FADS2 and SLC16A3), heat-shock proteins (e.g. HSPH1, HSP90B1, and HSPA5), genes controlling centrosome disjunction (e.g. RACGAP1 and NEK2), and collagen molecules (e.g. COL2A1). DEGs specifically upregulated in basal-like TN tumours were involved in antigen processing and presentation, in normal-like TN tumours encoded G protein-coupled receptors (GPCRs), and in LB fHER2- tumours were associated with lysosomes, phagosomes, and endosomes formation. Downregulated DEGs in CLs were associated with structural and signalling cell surface components. Hence, our results suggest that upregulation of genes enhancing proliferation and metabolism is a common feature among FMCs and derived CLs. In contrast, the dissimilarities observed in dysregulation of membrane components highlight CLs' disconnection with the tumour microenvironment. Furthermore, recurrent and exclusive DEGs associated with dysregulated pathways might be useful for the development of prognostically and therapeutically-relevant targeted panels.

Berberine as a potential agent for breast cancer therapy

Breast cancer (BC) is a common malignancy that mainly occurred in women and it has become the most diagnosed cancer annually since 2020. Berberine (BBR), an alkaloid extracted from the Berberidacea family, has been found with broad pharmacological bioactivities including anti-inflammatory, anti-diabetic, anti-hypertensive, anti-obesity, antidepressant, and anticancer effects. Mounting evidence shows that BBR is a safe and effective agent with good anticancer activity against BC. However, its detailed underlying mechanism in BC treatment remains unclear. Here, we will provide the evidence for BBR in BC therapy and summarize its potential mechanisms. This review briefly introduces the source, metabolism, and biological function of BBR and emphasizes the therapeutic effects of BBR against BC via directly interacting with effector proteins, transcriptional regulatory elements, miRNA, and several BBR-mediated signaling pathways. Moreover, the novel BBR-based therapeutic strategies against BC improve biocompatibility and water solubility, and the efficacies of BBR are also briefly discussed. Finally, the status of BBR in BC treatment and future research directions is also prospected.

Ginsenoside Rg3 Prevents Oncogenic Long Noncoding RNA ATXN8OS from Inhibiting Tumor-Suppressive microRNA-424-5p in Breast Cancer Cells

Ginsenoside Rg3 exerts antiproliferation activity on cancer cells by regulating diverse noncoding RNAs. However, little is known about the role of long noncoding RNAs (lncRNAs) or their relationship with competitive endogenous RNA (ceRNA) in Rg3-treated cancer cells. Here, a lncRNA (ATXN8OS) was found to be downregulated via Rg3-mediated promoter hypermethylation in MCF-7 breast cancer cells. SiRNA-induced downregulation of ATXN8OS decreased cell proliferation but increased apoptosis, suggesting that the noncoding RNA possessed proproliferation activity. An in silico search for potential ATXN8OS-targeting microRNAs (miRs) identified a promising candidate (miR-424-5p) based on its high binding score. As expected, miR-424-5p suppressed proliferation and stimulated apoptosis of the MCF-7 cells. The in silico miR-target-gene prediction identified 200 potential target genes of miR-424-5p, which were subsequently narrowed down to seven that underwent hypermethylation at their promoter by Rg3. Among them, three genes (EYA1, DACH1, and CHRM3) were previously known oncogenes and were proven to be oppositely regulated by ATXN8OS and miR-424-5p. When taken together, Rg3 downregulated ATXN8OS that inhibited the tumor-suppressive miR-424-5p, leading to the downregulation of the oncogenic target genes.

Berberine Inhibits the Expression of SCT through miR-214-3p Stimulation in Breast Cancer Cells

In this study, we aimed to evaluate the suppressive abilities of berberine (BBR) on MCF-7 and MDA-MB-231 cells and confirm its underlying mechanisms on miR-214-3p. We first built a panel of 18 miRNAs and 9 lncRNAs that were reported to participate in the mechanism of breast cancer. The RT-qPCR results suggested that BBR illustrated a dosage-dependent pattern in the stimulation to miR-214-3p in both MCF-7 and MDA-MB-231 cells. Then, we performed gain-and-lose function tests to validate the role of miR-214-3p contributing to the anticancer effects of BBR. Both BBR and miR-214-3p mimic reduced the cell viability, repressed migration and invasion capacities, increased rates of total apoptotic cells and ratio of Bax/Bcl-2, and increased the percentage of G2/M cells of MCF-7 and MDA-MB-231 cells by colony formation and CKK8 assay, scratch wound healing and gelatin-based 3D conformation assay, transwell invasion assay, and cell cycle analysis, respectively. However, miR-214-3p inhibitor counteracted all these effects of BBR. Based on the bioinformatics analysis and dual-luciferase reporter test, we identified binding sites between SCT and miR-214-3p. We further confirmed that BBR massively and dose-dependently reduced the mRNA expression and protein levels of SCT in both MCF-7 and MDA-231 cells. We testified that both miR-214-3p mimic and BBR could decrease the mRNA expression and protein levels of SCT, while miR-214-3p inhibitor weakened these reductions. In conclusion, BBR suppressed MCF-7 and MDA-MB-231 breast cancer cells by upregulating miR-214-3p and increasing its inhibition to SCT.

Pan-cancer analysis of differential DNA methylation patterns

Background

DNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.

Methods

In this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.

Results

PanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.

Conclusions

PanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html.

Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer

Using a machine learning method, this study aimed to identify unique causal networks of genes associated with bone, brain, and lung metastasis of breast cancer. Bayesian network analysis identified differentially expressed genes in primary breast cancer tissues, in bone, brain, and lung breast cancer metastatic tissues, and the clinicopathological features of patients obtained from the Gene Expression Omnibus microarray datasets. We evaluated the causal Bayesian networks of breast metastasis to distant sites (bone, brain, or lung) by (i) measuring how well the structures of each specific type of breast cancer metastasis fit the data, (ii) comparing the structures with known experimental evidence, and (iii) reporting predictive capabilities of the structures. We report for the first time that the molecular gene signatures are specific to the different types of breast cancer metastasis. Several genes, including CHPF, ARC, ANGPTL4, NR2E1, SH2D1A, CTSW, POLR2J4, SPTLC1, ILK, ALDH3B1, PDE6A, SCTR, ADM, HEY1, KCNF1, and UVRAG, were found to be predictors of the risk for site-specific metastasis of breast cancer. Expression of POLR2JA, SPTLC1, ILK, ALDH3B1, and the estrogen receptor was significantly associated with breast cancer bone metastasis. Expression of PDE6A and NR2E1 was causally linked to breast cancer brain metastasis. Expression of HEY1, KCNF1, UVRAG, and the estrogen and progesterone receptors was strongly associated with breast cancer lung metastasis. The causal Bayesian network structures of these genes identify potential interactions among the genes in distant metastases of breast cancer, including to the bone, brain, and lung, and may serve as target candidates for treatment of breast cancer metastasis.

DEPDC1, negatively regulated by miR-26b, facilitates cell proliferation via the up-regulation of FOXM1 expression in TNBC

Triple negative breast cancer (TNBC), characterized by lack of estrogen receptors, progesterone hormone receptors, and HER2 overexpression, is a more aggressive high grade tumor and not sensitive to current targeted drugs. The clinical prognosis of TNBC is poorer than other types of breast cancer, and there is no effective therapy strategy until now. Thus, it is necessary to determine important factors involved in regulating the progression of TNBC. In this study, we found DEPDC1 was up-regulated in the tissues of TNBC compared with their paired peritumoral tissues. DEPDC1 over-expression facilitated cell proliferation and tumor growth through increasing the expression of FOXM1 in TNBC cells. Conversely, knockdown of DEPDC1 had the opposite effects. Moreover, miR-26b, acting as a tumor suppressor in TNBC, directly repressed the expression of DEPDC1 and mitigated its promotive effects on cell growth and colony formation. These results indicate that DEPDC1, negatively regulated by miR-26b, promotes cell proliferation and tumor growth via up-regulating FOXM1 expression, implying an important underlying mechanism of regulating the progression of TNBC.

Weighted gene co-expression network analysis in identification of metastasis-related genes of lung squamous cell carcinoma based on the Cancer Genome Atlas database

BACKGROUND

Lung squamous cell carcinoma (lung SCC) is a common type of malignancy. Its pathogenesis mechanism of tumor development is unclear. The aim of this study was to identify key genes for diagnosis biomarkers in lung SCC metastasis.

METHODS

We searched and downloaded mRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA expression of primary tumor tissues from lung SCC with and without metastasis. Gene co-expression network analysis, protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and quantitative real-time polymerase chain reactions (qRT-PCR) were used to explore the biological functions of the identified dysregulated genes.

RESULTS

Four hundred and eighty-two differentially expressed genes (DEGs) were identified between lung SCC with and without metastasis. Nineteen modules were identified in lung SCC through weighted gene co-expression network analysis (WGCNA). Twenty-three DEGs and 26 DEGs were significantly enriched in the respective pink and black module. KEGG pathway analysis displayed that 26 DEGs in the black module were significantly enriched in bile secretion pathway. Forty-nine DEGs in the two gene co-expression module were used to construct PPI network. CFTR in the black module was the hub protein, had the connectivity with 182 genes. The results of qRT-PCR displayed that FIGF, SFTPD, DYNLRB2 were significantly down-regulated in the tumor samples of lung SCC with metastasis and CFTR, SCGB3A2, SSTR1, SCTR, ROPN1L had the down-regulation tendency in lung SCC with metastasis compared to lung SCC without metastasis.

CONCLUSIONS

The dysregulated genes including CFTR, SCTR and FIGF might be involved in the pathology of lung SCC metastasis and could be used as potential diagnosis biomarkers or therapeutic targets for lung SCC.