Dysregulated expression of repetitive DNA in ER+/HER2- breast cancer

Exploring Regulatory Roles of Transposable Elements in EMT and MET through Data-Driven Analysis: Insights from regulaTER

Gene expression is regulated at the transcriptional and translational levels and a plethora of epigenetic mechanisms. Regulation of gene expression by transposable elements is well documented. However, a comprehensive analysis of their regulatory roles is challenging due to the lack of dedicated approaches to define their contribution. Here, we present regulaTER, a new R library dedicated to deciphering the regulatory potential of transposable elements in a given phenotype. regulaTER utilizes a variety of genomics data of any origin and combines gene expression level information to predict the regulatory roles of transposable elements. We further validated its capabilities using data generated from an epithelial-mesenchymal and mesenchymal-epithelial transition cellular model. regulaTER stands out as an essential asset for uncovering the impact of transposable elements on the regulation of gene expression, with high flexibility to perform a range of transposable element-focused analyses. Our results also provided insights on the contribution of the MIR and B element subfamilies in regulating EMT and MET through the FoxA transcription factor family. regulaTER is publicly available and can be downloaded from https://github.com/karakulahg/regulaTER.

Genome-wide repeat landscapes in cancer and cell-free DNA

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

Repetitive Sequence Transcription in Breast Cancer

Repetitive sequences represent about half of the human genome. They are actively transcribed and play a role during development and in epigenetic regulation. The altered activity of repetitive sequences can lead to genomic instability and they can contribute to the establishment or the progression of degenerative diseases and cancer transformation. In this work, we analyzed the expression profiles of DNA repetitive sequences in the breast cancer specimens of the HMUCC cohort. Satellite expression is generally upregulated in breast cancers, with specific families upregulated per histotype: in HER2-enriched cancers, they are the human satellite II (HSATII), in luminal A and B, they are part of the ALR family and in triple-negative, they are part of SAR and GSAT families, together with a perturbation in the transcription from endogenous retroviruses and their LTR sequences. We report that the background expression of repetitive sequences in healthy tissues of cancer patients differs from the tissues of non-cancerous controls. To conclude, peculiar patterns of expression of repetitive sequences are reported in each specimen, especially in the case of transcripts arising from satellite repeats.

A classical revival: Human satellite DNAs enter the genomics era

The classical human satellite DNAs, also referred to as human satellites 1, 2 and 3 (HSat1, HSat2, HSat3, or collectively HSat1-3), occur on most human chromosomes as large, pericentromeric tandem repeat arrays, which together constitute roughly 3% of the human genome (100 megabases, on average). Even though HSat1-3 were among the first human DNA sequences to be isolated and characterized at the dawn of molecular biology, they have remained almost entirely missing from the human genome reference assembly for 20 years, hindering studies of their sequence, regulation, and potential structural roles in the nucleus. Recently, the Telomere-to-Telomere Consortium produced the first truly complete assembly of a human genome, paving the way for new studies of HSat1-3 with modern genomic tools. This review provides an account of the history and current understanding of HSat1-3, with a view towards future studies of their evolution and roles in health and disease.

The Role of Transposable Elements in Sexual Development

Up to 50% of most mammalian genomes are made up of transposable elements (TEs) that have the potential to mobilize around the genome. Despite this prevalence, research on TEs is only beginning to gain traction within the field of neuroscience. While TEs have long been regarded as "junk" or parasitic DNA, it has become evident that they are adaptive DNA and RNA regulatory elements. In addition to their vital role in normal development, TEs can also interact with steroid receptors, which are key elements to sexual development. In this review, we provide an overview of the involvement of TEs in processes related to sexual development- from TE activity in the germline to TE accumulation in sex chromosomes. Moreover, we highlight sex differences in TE activity and their regulation of genes related to sexual development. Finally, we speculate on the epigenetic mechanisms that may govern TEs' role in sexual development. In this context, we emphasize the need to further the understanding of sexual development through the lens of TEs including in a variety of organs at different developmental stages, their molecular networks, and evolution.

Repeat expression is linked to patient survival and exhibits single nucleotide variation in pancreatic cancer revealing LTR70:r.879A>G

Despite an overwhelming number of cancer literature reporting the links between patient survival and the expression levels of genes or mutations/single nucleotide variations (SNVs) on them, there is only limited information on repeat elements, which make at least half the human genome. Here, we analysed RNA-seq data obtained from primary pancreatic cancer tissues of 51 patients and revealed that two transposons, HERVI-int and X6A_LINE, showed an upregulation trend in the patients who lived shorter, along with 56 other potential repeats which were linked to survival. We also detected expressed single nucleotide variations (SNVs) on repeats, among which LTR70:r.879A>G stands out with the effect of its presence on this particular repeat's expression levels and a significant link to overall patient survival. Interestingly, the expression of LTR70:r.879A>G correlated with different cancer genes in comparison to its reference version highlighting the involvement of BRAF and Fumerate Hydratase with this expressed SNV. This is one of the first studies revealing possible links between repeat expression and survival in cancer and it warrants further research in this avenue.

Identification of differentially expressed genomic repeats in primary hepatocellular carcinoma and their potential links to biological processes and survival

Hepatocellular carcinoma (HCC) is one of the deadliest cancers. Research on HCC so far primarily focused on genes and provided limited information on genomic repeats, which constitute more than half of the human genome and contribute to genomic stability. In line with this, repeat dysregulation was significantly shown to be pathological in various cancers and other diseases. In this study, we aimed to determine the full repeat expression profile of HCC for the first time. We utilised two independent RNA-seq datasets obtained from primary HCC tumours with matched normal tissues of 20 and 17 HCC patients, respectively. We quantified repeat expressions and analysed their differential expression. We also identified repeats that are cooperatively expressed with genes by constructing a gene coexpression network. Our results indicated that HCC tumours in both datasets harbour 24 differentially expressed repeats and even more elements were coexpressed with genes involved in various metabolic pathways. We discovered that two L1 elements (L1M3b, L1M3de) were downregulated and a handful of HERV subfamily repeats (HERV-Fc1-int, HERV3-int, HERVE_a-int, HERVK11D-int, HERVK14C-int, HERVL18-int) were upregulated with the exception of HERV1_LTRc, which was downregulated. Various LTR elements (LTR32, LTR9, LTR4, LTR52-int, LTR70) and MER elements (MER11C, MER11D, MER57C1, MER9a1, MER74C) were implicated along with few other subtypes including Charlie12, MLT2A2, Tigger15a, Tigger 17b. The only satellite repeat differentially expressed in both datasets was GSATII, whose expression was upregulated in 33 (>90%) out of 37 patients. Notably, GSATII expression correlated with HCC survival genes. Elements discovered here promise future studies to be considered for biomarker and HCC therapy research. The coexpression pattern of the GSATII satellite with HCC survival genes and the fact that it has been upregulated in the vast majority of patients make this repeat particularly stand out for HCC.

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.

Signature changes in the expressions of protein-coding genes, lncRNAs, and repeat elements in early and late cellular senescence

Replicative cellular senescence is the main cause of aging. It is important to note that early senescence is linked to tissue regeneration, whereas late senescence is known to trigger a chronically inflammatory phenotype. Despite the presence of various genome-wide studies, there is a lack of information on distinguishing early and late senescent phenotypes at the transcriptome level. Particularly, the changes in the noncoding RNA portion of the aging cell have not been fully elucidated. By utilising RNA sequencing data of fibroblasts, hereby, we are not only reporting changes in gene expression profiles and relevant biological processes in the early and late senescent phenotypes but also presenting significant differences in the expressions of many unravelled long noncoding RNAs (lncRNAs) and transcripts arisen from repetitive DNA. Our results indicate that, in addition to previously reported L1 elements, various LTR and DNA transposons, as well as members of the classical satellites including HSAT5 and α-satellites (ALR/Alpha), are expressed at higher levels in late senescence. Moreover, we revealed finer links between the expression levels of repeats with the genes located near them and known to be involved in cell cycle and senescence. Noncoding elements reported here provide a new perspective to be explored in further experimental studies.

Identification of hub genes and small molecule therapeutic drugs related to breast cancer with comprehensive bioinformatics analysis

Breast cancer is one of the most common malignant tumors among women worldwide and has a high morbidity and mortality. This research aimed to identify hub genes and small molecule drugs for breast cancer by integrated bioinformatics analysis. After downloading multiple gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, 283 overlapping differentially expressed genes (DEGs) significantly enriched in different cancer-related functions and pathways were obtained using LIMMA, VennDiagram and ClusterProfiler packages of R. We then analyzed the topology of protein–protein interaction (PPI) network with overlapping DEGs and further obtained six hub genes (RRM2, CDC20, CCNB2, BUB1B, CDK1, and CCNA2) from the network via STRING and Cytoscape. Subsequently, we conducted genes expression verification, genetic alterations evaluation, immune infiltration prediction, clinicopathological parameters analysis, identification of transcriptional and post-transcriptional regulatory molecules, and survival analysis for these hub genes. Meanwhile, 29 possible drug candidates (e.g., Cladribine, Gallium nitrate, Alvocidib, 1β-hydroxyalantolactone, Berberine hydrochloride, Nitidine chloride) were identified from the DGIdb database and the GSE85871 dataset. In addition, some transcription factors and miRNAs (e.g., E2F1, PTTG1, TP53, ZBTB16, hsa-miR-130a-3p, hsa-miR-204-5p) targeting hub genes were identified as key regulators in the progression of breast cancer. In conclusion, our study identified six hub genes and 29 potential drug candidates for breast cancer. These findings may advance understanding regarding the diagnosis, prognosis and treatment of breast cancer.

Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer

Background

Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and prognosis.

Material/Methods

Three independent data sets (GSE45827, GSE38959, GSE65194) were downloaded from the Gene Expression Omnibus (GEO). The R software packages were used to integrate the gene profiles and identify differentially expressed genes (DEGs). A variety of bioinformatics tools were used to explore the hub genes, including the DAVID database, STRING database and Cytoscape software. Reverse transcription quantitative PCR (RT-qPCR) was used to verify the hub genes in 14 pairs of TNBC paired tissues.

Results

In this study, we screened out 161 DEGs between 222 non-TNBC and 126 TNBC samples, of which 105 genes were up-regulated and 56 were down-regulated. These DEGs were enriched for 27 GO terms and two pathways. GO analysis enriched mainly in “cell division”, “chromosome, centromeric region” and “microtubule motor activity”. KEGG pathway analysis enriched mostly in “Cell cycle” and “Oocyte meiosis”. PPI network was constructed and then 10 top hub genes were screened. According to the analysis results of the Kaplan-Meier survival curve, the expression levels of only NUF2, FAM83D and CENPH were associated with the recurrence-free survival in TNBC samples (P < 0.05). RT-qPCR confirmed that the expression levels of NUF2 and FAM83D in TNBC tissues were indeed up-regulated significantly.

Conclusions

The comprehensive analysis showed that NUF2 and FAM83D could be used as potential biomarkers for diagnosis and prognosis of TNBC.

Personalized analysis of breast cancer using sample-specific networks

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

The Sophisticated Transcriptional Response Governed by Transposable Elements in Human Health and Disease

Transposable elements (TEs), which cover ~45% of the human genome, although firstly considered as “selfish” DNA, are nowadays recognized as driving forces in eukaryotic genome evolution. This capability resides in generating a plethora of sophisticated RNA regulatory networks that influence the cell type specific transcriptome in health and disease. Indeed, TEs are transcribed and their RNAs mediate multi-layered transcriptional regulatory functions in cellular identity establishment, but also in the regulation of cellular plasticity and adaptability to environmental cues, as occurs in the immune response. Moreover, TEs transcriptional deregulation also evolved to promote pathogenesis, as in autoimmune and inflammatory diseases and cancers. Importantly, many of these findings have been achieved through the employment of Next Generation Sequencing (NGS) technologies and bioinformatic tools that are in continuous improvement to overcome the limitations of analyzing TEs sequences. However, they are highly homologous, and their annotation is still ambiguous. Here, we will review some of the most recent findings, questions and improvements to study at high resolution this intriguing portion of the human genome in health and diseases, opening the scenario to novel therapeutic opportunities.

TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model

Introduction

Recent studies highlight the crucial regulatory roles of transposable elements (TEs) on proximal gene expression in distinct biological contexts such as disease and development. However, computational tools extracting potential TE -proximal gene expression associations from RNA-sequencing data are still missing.

Implementation

Herein, we developed a novel R package, using a linear regression model, for studying the potential influence of TE species on proximal gene expression from a given RNA-sequencing data set. Our R package, namely TEffectR, makes use of publicly available RepeatMasker TE and Ensembl gene annotations as well as several functions of other R-packages. It calculates total read counts of TEs from sorted and indexed genome aligned BAM files provided by the user, and determines statistically significant relations between TE expression and the transcription of nearby genes under diverse biological conditions.

Availability

TEffectR is freely available at https://github.com/karakulahg/TEffectR along with a handy tutorial as exemplified by the analysis of RNA-sequencing data including normal and tumour tissue specimens obtained from breast cancer patients.