Sense-antisense gene-pairs in breast cancer and associated pathological pathways

FADS1/2 control lipid metabolism and ferroptosis susceptibility in triple-negative breast cancer

Triple-negative breast cancer (TNBC) has limited therapeutic options, is highly metastatic and characterized by early recurrence. Lipid metabolism is generally deregulated in TNBC and might reveal vulnerabilities to be targeted or used as biomarkers with clinical value. Ferroptosis is a type of cell death caused by iron-dependent lipid peroxidation which is facilitated by the presence of polyunsaturated fatty acids (PUFA). Here we identify fatty acid desaturases 1 and 2 (FADS1/2), which are responsible for PUFA biosynthesis, to be highly expressed in a subset of TNBC with a poorer prognosis. Lipidomic analysis, coupled with functional metabolic assays, showed that FADS1/2 high-expressing TNBC are susceptible to ferroptosis-inducing agents and that targeting FADS1/2 by both genetic interference and pharmacological approach renders those tumors ferroptosis-resistant while unbalancing PUFA/MUFA ratio by the supplementation of exogenous PUFA sensitizes resistant tumors to ferroptosis induction. Last, inhibiting lipid droplet (LD) formation and turnover suppresses the buffering capacity of LD and potentiates iron-dependent cell death. These findings have been validated in vitro and in vivo in mouse- and human-derived clinically relevant models and in a retrospective cohort of TNBC patients.

Analysis of m6A Methylation Modification Patterns and Tumor Immune Microenvironment in Breast Cancer

Increasing evidence indicates that the abnormal expression of N6-methyladenosine (m6A) modification is closely related to the epigenetic regulation of immune response in breast cancer (BC). However, the potential roles of m6A modification in the tumor microenvironment (TME) of BC remain unclear. For addressing this issue, we comprehensively analyzed the m6A modification patterns in 983 samples and correlated these modification patterns with TME immune cell infiltration, based on 23 kinds of m6A regulators. Principal component analysis (PCA) was used to construct the m6A scoring system to quantify the modification pattern of m6A of BC individuals. Consequently, three different m6A modification patterns were identified, and the infiltrating characteristics of TME cells were consistent with the three immune phenotypes, including immune rejection, immune inflammation, and immune desert. Besides, our analysis showed that the prognosis of patients could be predicted by evaluating the m6A modification pattern in the tumor. The low m6Ascore corresponded to increased mutation burden and immune activation, while stroma activation and lack of immune infiltration were observed in high m6Ascore subtypes. In addition, a low m6Ascore was associated with enhanced response to anti-PD-1/PD-L1 immunotherapy. In conclusion, the m6A modification pattern was closely related to the BC immune landscape. This well-validated score model of the m6A modification patterns will provide a valuable tool to depict the tumor immune state and guide effective tumor immunotherapy for combating BC.

Obscurin: A multitasking giant in the fight against cancer

Giant obscurins (720-870 kDa), encoded by OBSCN, were originally discovered in striated muscles as cytoskeletal proteins with scaffolding and regulatory roles. Recently though, they have risen to the spotlight as key players in cancer development and progression. Herein, we provide a timely prudent synopsis of the expanse of OBSCN mutations across 16 cancer types. Given the extensive work on OBSCN's role in breast epithelium, we summarize functional studies implicating obscurins as potent tumor suppressors in breast cancer and delve into an in silico analysis of its mutational profile and epigenetic (de)regulation using different dataset platforms and sophisticated computational tools. Lastly, we formally describe the OBSCN-Antisense-RNA-1 gene, which belongs to the long non-coding RNA family and discuss its potential role in modulating OBSCN expression in breast cancer. Collectively, we highlight the escalating involvement of obscurins in cancer biology and outline novel potential mechanisms of OBSCN (de)regulation that warrant further investigation.

Screening of core genes and pathways in breast cancer development via comprehensive analysis of multi gene expression datasets

Breast cancer has been the leading cause of cancer-associated mortality in women worldwide. Perturbation of oncogene and tumor suppressor gene expression is generally considered as the fundamental cause of cancer initiation and progression. In the present study, three gene expression datasets containing information of breast cancer and adjacent normal tissues that were detected using traditional gene microarrays were downloaded and batch effects were removed with R programming software. The differentially expressed genes between breast cancer and normal tissue groups were closely associated with cancer development pathways. Interestingly, five pathways, including ‘extracellular matrix-receptor interaction’, ‘peroxisome proliferator-activated receptors signaling pathway’, ‘propanoate metabolism’, ‘pyruvate metabolism’ and ‘regulation of lipolysis in adipocytes’, were thoroughly connected by 10 genes. Patients with upregulation of six of these hub genes (acetyl-CoA carboxylase β, acyl-CoA dehydrogenase medium chain, adiponectin, C1Q and collagen domain containing, acyl-CoA synthetase short chain family member 2, phosphoenolpyruvate carboxykinase 1 and perilipin 1) exhibited improved breast cancer prognosis. Additionally, breast cancer-specific network analysis identified several gene-gene interaction modules. These gene clusters had strong interactions according to the scoring in the whole network, which may be important to the development of breast cancer. In conclusion, the present study may improve the understanding of the mechanisms of breast cancer and provide several valuable prognosis and treatment signatures.

Grade-specific diagnostic and prognostic biomarkers in breast cancer

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.

Mechanisms of Antisense Transcription Initiation with Implications in Gene Expression, Genomic Integrity and Disease Pathogenesis

Non-coding antisense transcripts arise from the strand opposite the sense strand. Over 70% of the human genome generates non-coding antisense transcripts while less than 2% of the genome codes for proteins. Antisense transcripts and/or the act of antisense transcription regulate gene expression and genome integrity by interfering with sense transcription and modulating histone modifications or DNA methylation. Hence, they have significant pathological and physiological relevance. Indeed, antisense transcripts were found to be associated with various diseases including cancer, diabetes, cardiac and neurodegenerative disorders, and, thus, have promising potentials for prognostic and diagnostic markers and therapeutic development. However, it is not clearly understood how antisense transcription is initiated and epigenetically regulated. Such knowledge would provide new insights into the regulation of antisense transcription, and hence disease pathogenesis with therapeutic development. The recent studies on antisense transcription initiation and its epigenetic regulation, which are limited, are discussed here. Furthermore, we concisely describe how antisense transcription/transcripts regulate gene expression and genome integrity with implications in disease pathogenesis and therapeutic development.

Plasma Ceramides as Prognostic Biomarkers and Their Arterial and Myocardial Tissue Correlates in Acute Myocardial Infarction

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Targeted profiling of ceramides identified a 12-ceramide plasma signature that predicted 12-month cardiovascular death, MI, and stroke in 2 prospective cohorts of AMI patients.

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Among coronary artery bypass grafting patients, plasma ceramides were higher in those with recent AMI compared with those without recent acute MI.

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Analysis of rat ischemic myocardium revealed a consistent increase in ceramide levels and overexpression of 3 enzymes in ceramide biosynthesis.

We identified a plasma signature of 11 C14 to C26 ceramides and 1 C16 dihydroceramide predictive of major adverse cardiovascular events in patients with acute myocardial infarction (AMI). Among patients undergoing coronary artery bypass surgery, those with recent AMI, compared with those without recent AMI, showed a significant increase in 5 of the signature’s 12 ceramides in plasma but not simultaneously-biopsied aortic tissue. In contrast, a rat AMI model, compared with sham control, showed a significant increase in myocardial concentrations of all 12 ceramides and up-regulation of 3 ceramide-producing enzymes, suggesting ischemic myocardium as a possible source of this ceramide signature.

Large set data mining reveals overexpressed GPCRs in prostate and breast cancer: potential for active targeting with engineered anti-cancer nanomedicines

Over 800 G-protein-coupled receptors (GPCRs) are encoded by the human genome and many are overexpressed in tumors. GPCRs are triggered by ligand molecules outside the cell and activate internal signal transduction pathways driving cellular responses. The receptor signals are desensitized by receptor internalization and this mechanism can be exploited for the specific delivery of ligand-linked drug molecules directly into cells. Detailed expression analysis in cancer tissue can inform the design of GPCR-ligand decorated drug carriers for active tumor cell targeting. The active targeting process utilizes ligand receptor interactions leading to binding and in most cases internalization of the ligand-attached drug carrier resulting in effective targeting of cancer cells. In this report public microarray data from the Gene Expression Omnibus (GEO) repository was used to identify overexpressed GPCRs in prostate and breast cancer tissues. The analyzed data confirmed previously known cancer receptor associations and identified novel candidates for potential active targeting. Prioritization of the identified targeting receptors is also presented based on high expression levels and frequencies in cancer samples but low expression in healthy tissue. Finally, some selected examples were used in ligand docking studies to assess the feasibility for chemical conjugation to drug nanocarriers without interference of receptor binding and activation. The presented data demonstrate a large untapped potential to improve efficacy and safety of current and future anti-cancer compounds through active targeting of GPCRs on cancer cells.

Big data and computational biology strategy for personalized prognosis

The era of big data and precision medicine has led to accumulation of massive datasets of gene expression data and clinical information of patients. For a new patient, we propose that identification of a highly similar reference patient from an existing patient database via similarity matching of both clinical and expression data could be useful for predicting the prognostic risk or therapeutic efficacy.

Here, we propose a novel methodology to predict disease/treatment outcome via analysis of the similarity between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). Our analyses revealed that Euclidean distance rather correlation distance measure was effective in defining an unbiased similarity measure calculated between two PSVs.

We implemented our methods to high-grade serous ovarian cancer (HGSC) based on a 36-mRNA predictor that was previously shown to stratify patients into 3 distinct prognostic subgroups. We studied and revealed that patient's age, when converted into binary variable, was positively correlated with the overall risk of succumbing to the disease. When applied to an independent testing dataset, the inclusion of age into the molecular predictor provided more robust personalized prognosis of overall survival correlated with the therapeutic response of HGSC and provided benefit for treatment targeting of the tumors in HGSC patients.

Finally, our method can be generalized and implemented in many other diseases to accurately predict personalized patients’ outcomes.

Natural Antisense Transcripts: Molecular Mechanisms and Implications in Breast Cancers

Natural antisense transcripts are RNA sequences that can be transcribed from both DNA strands at the same locus but in the opposite direction from the gene transcript. Because strand-specific high-throughput sequencing of the antisense transcriptome has only been available for less than a decade, many natural antisense transcripts were first described as long non-coding RNAs. Although the precise biological roles of natural antisense transcripts are not known yet, an increasing number of studies report their implication in gene expression regulation. Their expression levels are altered in many physiological and pathological conditions, including breast cancers. Among the potential clinical utilities of the natural antisense transcripts, the non-coding|coding transcript pairs are of high interest for treatment. Indeed, these pairs can be targeted by antisense oligonucleotides to specifically tune the expression of the coding-gene. Here, we describe the current knowledge about natural antisense transcripts, their varying molecular mechanisms as gene expression regulators, and their potential as prognostic or predictive biomarkers in breast cancers.

Transcriptome-wide analysis of natural antisense transcripts shows their potential role in breast cancer

Non-coding RNAs (ncRNA) represent 1/5 of the mammalian transcript number, and 90% of the genome length is transcribed. Many ncRNAs play a role in cancer. Among them, non-coding natural antisense transcripts (ncNAT) are RNA sequences that are complementary and overlapping to those of either protein-coding (PCT) or non-coding transcripts. Several ncNATs were described as regulating protein coding gene expression on the same loci, and they are expected to act more frequently in cis compared to other ncRNAs that commonly function in trans. In this work, 22 breast cancers expressing estrogen receptors and their paired adjacent non-malignant tissues were analyzed by strand-specific RNA sequencing. To highlight ncNATs potentially playing a role in protein coding gene regulations that occur in breast cancer, three different data analysis methods were used: differential expression analysis of ncNATs between tumor and non-malignant tissues, differential correlation analysis of paired ncNAT/PCT between tumor and non-malignant tissues, and ncNAT/PCT read count ratio variation between tumor and non-malignant tissues. Each of these methods yielded lists of ncNAT/PCT pairs that were enriched in survival-associated genes. This work highlights ncNAT lists that display potential to affect the expression of protein-coding genes involved in breast cancer pathology.

Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma

Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro‐oncogenic pathways in primary tumors (PT) and adjacent non‐malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome‐wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24‐ribosomal gene‐based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10⁻⁶) and cross‐cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross‐validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c‐MYC in the pro‐oncogenic pattern of ribosomal biogenesis co‐regulation in PT and AT. Microarray, quantitative RT‐PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co‐transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor‐like metabolic redirection/assimilation in non‐malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for stratifying HCC patient risks. The adjacent non‐malignant liver tissue alone, or in combination with HCC tissue biopsy, could be an important target for developing predictive and monitoring strategies, as well as evidence‐based therapeutic interventions, that aim to reduce the risk of post‐surgery relapse in HCC patients.

Gene expression information improves reliability of receptor status in breast cancer patients

Immunohistochemical (IHC) determination of receptor status in breast cancer patients is frequently inaccurate. Since it directs the choice of systemic therapy, it is essential to increase its reliability.

We increase the validity of IHC receptor expression by additionally considering gene expression (GE) measurements. Crisp therapeutic decisions are based on IHC estimates, even if they are borderline reliable. We further improve decision quality by a responsibility function, defining a critical domain for gene expression. Refined normalization is devised to file any newly diagnosed patient into existing data bases. Our approach renders receptor estimates more reliable by identifying patients with questionable receptor status. The approach is also more efficient since the rate of conclusive samples is increased. We have curated and evaluated gene expression data, together with clinical information, from 2880 breast cancer patients. Combining IHC with gene expression information yields a method more reliable and also more efficient as compared to common practice up to now.

Several types of possibly suboptimal treatment allocations, based on IHC receptor status alone, are enumerated. A ‘therapy allocation check’ identifies patients possibly miss-classified. Estrogen: false negative 8%, false positive 6%. Progesterone: false negative 14%, false positive 11%. HER2: false negative 2%, false positive 50%. Possible implications are discussed.

We propose an ‘expression look-up-plot’, allowing for a significant potential to improve the quality of precision medicine.

Methods are developed and exemplified here for breast cancer patients, but they may readily be transferred to diagnostic data relevant for therapeutic decisions in other fields of oncology.

EGF hijacks miR-198/FSTL1 wound-healing switch and steers a two-pronged pathway toward metastasis

Exploring the parallels between wound healing and epithelial cancers, Sundaram et al. elucidate the mechanism by which cancer cells hijack the wound healing switch to enhance invasion and metastasis in head and neck squamous cell carcinoma.

Epithelial carcinomas are well known to activate a prolonged wound-healing program that promotes malignant transformation. Wound closure requires the activation of keratinocyte migration via a dual-state molecular switch. This switch involves production of either the anti-migratory microRNA miR-198 or the pro-migratory follistatin-like 1 (FSTL1) protein from a single transcript; miR-198 expression in healthy skin is down-regulated in favor of FSTL1 upon wounding, which enhances keratinocyte migration and promotes re-epithelialization. Here, we reveal a defective molecular switch in head and neck squamous cell carcinoma (HNSCC). This defect shuts off miR-198 expression in favor of sustained FSTL1 translation, driving metastasis through dual parallel pathways involving DIAPH1 and FSTL1. DIAPH1, a miR-198 target, enhances directional migration through sequestration of Arpin, a competitive inhibitor of Arp2/3 complex. FSTL1 blocks Wnt7a-mediated repression of extracellular signal–regulated kinase phosphorylation, enabling production of MMP9, which degrades the extracellular matrix and facilitates metastasis. The prognostic significance of the FSTL1-DIAPH1 gene pair makes it an attractive target for therapeutic intervention.

shinyGEO: a web-based application for analyzing gene expression omnibus datasets

The Gene Expression Omnibus (GEO) is a public repository of gene expression data. Although GEO has its own tool, GEO2R, for data analysis, evaluation of single genes is not straightforward and survival analysis in specific GEO datasets is not possible without bioinformatics expertise. We describe a web application, shinyGEO, that allows a user to download gene expression data sets directly from GEO in order to perform differential expression and survival analysis for a gene of interest. In addition, shinyGEO supports customized graphics, sample selection, data export and R code generation so that all analyses are reproducible. The availability of shinyGEO makes GEO datasets more accessible to non-bioinformaticians, promising to lead to better understanding of biological processes and genetic diseases such as cancer.

AVAILABILITY AND IMPLEMENTATION

Web application and source code are available from http://gdancik.github.io/shinyGEO/ CONTACT: dancikg@easternct.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells

Eukaryotic genomes contain a variety of structured patterns: repetitive elements, binding sites of DNA and RNA associated proteins, splice sites, and so on. Often, these structured patterns can be formalized as motifs and described using a proper mathematical model such as position weight matrix and IUPAC consensus. Two key tasks are typically carried out for motifs in the context of the analysis of genomic sequences. These are: identification in a set of DNA regions of over-represented motifs from a particular motif database, and de novo discovery of over-represented motifs. Here we describe existing methodology to perform these two tasks for motifs characterizing transcription factor binding. When applied to the output of ChIP-seq and ChIP-exo experiments, or to promoter regions of co-modulated genes, motif analysis techniques allow for the prediction of transcription factor binding events and enable identification of transcriptional regulators and co-regulators. The usefulness of motif analysis is further exemplified in this review by how motif discovery improves peak calling in ChIP-seq and ChIP-exo experiments and, when coupled with information on gene expression, allows insights into physical mechanisms of transcriptional modulation.