Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features

POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning

Long non-coding RNAs (lncRNAs) are receiving increasing attention as biomarkers for cancer diagnosis and therapy. Although there are many computational methods to identify cancer lncRNAs, they do not comprehensively integrate multi-omics features for predictions or systematically evaluate the contribution of each omics to the multifaceted landscape of cancer lncRNAs. In this study, an algorithm, POCALI, is developed to identify cancer lncRNAs by integrating 44 omics features across six categories. The contributions of different omics are explored to identifying cancer lncRNAs and, more specifically, how each feature contributes to a single prediction. The model is evaluated and benchmarked POCALI with existing methods. Finally, the cancer phenotype and genomics characteristics of the predicted novel cancer lncRNAs are validated. POCALI identifies secondary structure and gene expression-related features as strong predictors of cancer lncRNAs, and epigenomic features as moderate predictors. POCALI performed better than other methods, especially in terms of sensitivity, and predicted more candidates. Novel POCALI-predicted cancer lncRNAs have strong relationships with cancer phenotypes, similar to known cancer lncRNAs. Overall, this study facilitates the identification of previously undetected cancer lncRNAs and the comprehensive exploration of the multifaceted feature contributions to cancer lncRNA prediction.

Element-specific estimation of background mutation rates in whole cancer genomes through transfer learning

Mutational burden tests are essential for detecting signals of positive selection in cancer driver discovery by comparing observed mutation rates with background mutation rates (BMRs). However, accurate BMR estimation is challenging due to the diversity of mutational processes across genomes, complicating driver discovery efforts. Existing methods rely on various genomic regions and features for BMR estimation but lack a model that integrates both intergenic intervals and functional genomic elements on a comprehensive set of genomic features. Here, we introduce eMET (element-specific Mutation Estimator with boosted Trees), which employs 1372 (epi)genomic features from intergenic data and fine-tunes it with element-specific data through transfer learning. Applied to PCAWG somatic mutations, eMET significantly improves BMR accuracy and has potential to enhance driver discovery. Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.

Localization is the key to action: regulatory peculiarities of lncRNAs

To understand the transcriptomic profile of an individual cell in a multicellular organism, we must comprehend its surrounding environment and the cellular space where distinct molecular stimuli responses are located. Contradicting the initial perception that RNAs were nonfunctional and that only a few could act in chromatin remodeling, over the last few decades, research has revealed that they are multifaceted, versatile regulators of most cellular processes. Among the various RNAs, long non-coding RNAs (LncRNAs) regulate multiple biological processes and can even impact cell fate. In this sense, the subcellular localization of lncRNAs is the primary determinant of their functions. It affects their behavior by limiting their potential molecular partner and which process it can affect. The fine-tuned activity of lncRNAs is also tissue-specific and modulated by their cis and trans regulation. Hence, the spatial context of lncRNAs is crucial for understanding the regulatory networks by which they influence and are influenced. Therefore, predicting a lncRNA's correct location is not just a technical challenge but a critical step in understanding the biological meaning of its activity. Hence, examining these peculiarities is crucial to researching and discussing lncRNAs. In this review, we debate the spatial regulation of lncRNAs and their tissue-specific roles and regulatory mechanisms. We also briefly highlight how bioinformatic tools can aid research in the area.

Melanoma-specific mutation hotspots in distal, non-coding, promoter-interacting regions implicate novel candidate driver genes

BACKGROUND

To develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored.

METHODS

We used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this "promoter-interaction network", alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity.

RESULTS

We identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers.

CONCLUSIONS

Our study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers.

Unveiling the intricacies of paraspeckle formation and function

Paraspeckle nuclear bodies form when the NEAT1 long noncoding RNA is transcribed and bound by multiple RNA-binding proteins. First described 20 years ago, in recent years a growing appreciation of paraspeckle dynamics has led to new understandings, in both structure and function. Structurally, paraspeckles form via distinct physico-chemical domains arising from the composition of key proteins, recruited to different parts of NEAT1. These domains interact, creating a core-shell structured paraspeckle via microphase separation. Functionally, many environmental, chemical, and mechanical triggers can alter paraspeckle abundance, with important consequences depending on the cell type, developmental stage, and trigger identity. Underpinning these insights are new tools for paraspeckle research, including screening assays, proximity-based identification tools, and RNA processing modulators. A picture is emerging of paraspeckles as gene regulatory condensates in many healthy and disease settings. Critically, however, paraspeckle functional importance is generally most apparent when cells and organisms face external stressors.

Aneuploidy as a driver of human cancer

Aneuploidy, an abnormal chromosome composition, is a major contributor to cancer development and progression and an important determinant of cancer therapeutic responses and clinical outcomes. Despite being recognized as a hallmark of human cancer, the exact role of aneuploidy as a 'driver' of cancer is still largely unknown. Identifying the specific genetic elements that underlie the recurrence of common aneuploidies remains a major challenge of cancer genetics. In this Review, we discuss recurrent aneuploidies and their function as drivers of tumor development. We then delve into the context-dependent identification and functional characterization of the driver genes underlying driver aneuploidies and examine emerging strategies to uncover these driver genes using cancer genomics data and cancer models. Lastly, we explore opportunities for targeting driver aneuploidies in cancer by leveraging the functional consequences of these common genetic alterations.

Long Non-Coding RNAs, Nuclear Receptors and Their Cross-Talks in Cancer-Implications and Perspectives

Long non-coding RNAs (lncRNAs) play key roles in various epigenetic and post-transcriptional events in the cell, thereby significantly influencing cellular processes including gene expression, development and diseases such as cancer. Nuclear receptors (NRs) are a family of ligand-regulated transcription factors that typically regulate transcription of genes involved in a broad spectrum of cellular processes, immune responses and in many diseases including cancer. Owing to their many overlapping roles as modulators of gene expression, the paths traversed by lncRNA and NR-mediated signaling often cross each other; these lncRNA-NR cross-talks are being increasingly recognized as important players in many cellular processes and diseases such as cancer. Here, we review the individual roles of lncRNAs and NRs, especially growth factor modulated receptors such as androgen receptors (ARs), in various types of cancers and how the cross-talks between lncRNAs and NRs are involved in cancer progression and metastasis. We discuss the challenges involved in characterizing lncRNA-NR associations and how to overcome them. Furthering our understanding of the mechanisms of lncRNA-NR associations is crucial to realizing their potential as prognostic features, diagnostic biomarkers and therapeutic targets in cancer biology.

The Traces of Dysregulated lncRNAs-Associated ceRNA Axes in Retinoblastoma: A Systematic Scope Review

PURPOSE

Long non-coding RNAs are an essential component of competing endogenous RNA regulatory axes and play their role by sponging microRNAs and interfering with the regulation of gene expression. Because of the broadness of competing endogenous RNA interaction networks, they may help investigate treatment targets in complicated disorders.

METHODS

This study performed a systematic scoping review to assess verified loops of competing endogenous RNAs in retinoblastoma, emphasizing the competing endogenous RNAs axis related to long non-coding RNAs. We used a six-stage approach framework and the PRISMA guidelines. A systematic search of seven databases was done to locate suitable papers published before February 2022. Two reviewers worked independently to screen articles and collect data.

RESULTS

Out of 363 records, fifty-one articles met the inclusion criteria, and sixty-three axes were identified in desired articles. The majority of the research reported several long non-coding RNAs that were experimentally verified to act as competing endogenous RNAs in retinoblastoma: XIST/NEAT1/MALAT1/SNHG16/KCNQ1OT1, respectively. At the same time, around half of the studies investigated unique long non-coding RNAs.

CONCLUSIONS

Understanding the many features of this regulatory system may aid in elucidating the unknown etiology of Retinoblastoma and providing novel molecular targets for therapeutic and clinical applications.

NEAT1 regulates VSMC differentiation and calcification in as long noncoding RNA NEAT1 enhances phenotypic and osteogenic switching of vascular smooth muscle cells in atherosclerosis via scaffolding EZH2

Atherosclerosis (AS) is a significant contributor to cardio-cerebrovascular ischemia diseases, resulting in high mortality rates worldwide. During AS, vascular smooth muscle cells (VSMCs) play a crucial role in plaque formation by undergoing phenotypic and osteogenic switching. Long noncoding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) has previously been identified as a nuclear regulator that promotes tumorigenesis and metastasis, but its role in regulating VSMCs in AS remains unclear. Our study aimed to investigate the biological functions and specific mechanisms of NEAT1 in regulating VSMCs in AS. We found that NEAT1 was upregulated in the aortas of AS mouse models and dedifferentiated primary VSMCs. Silencing NEAT1 in vitro attenuated the proliferation, migration, and osteogenic differentiation of VSMCs, while NEAT1 overexpression had the opposite effect. Furthermore, NEAT1 promoted VSMC osteogenic differentiation and vascular calcification in both in vivo and in vitro vascular calcification models. We also discovered that NEAT1 directly activates enhancer of zeste homolog 2 (EZH2), an epigenetic enzyme that suppresses the expression of senescence- and antimigration-related genes, by translocating it into the nucleus. CUT&Tag assay revealed that NEAT1 guides EZH2 to the promoters of senescence-related genes (P16, P21, and TIMP3), methylating local histones to reduce their transcription. Our findings suggest that NEAT1 functions in AS by modulating the epigenetic function of EZH2, which enhances the proliferation, migration, and osteogenic differentiation of VSMCs. This study provides new insights into the molecular mechanisms underlying the pathogenesis of AS and highlights the potential of NEAT1 as a therapeutic target of AS.NEW & NOTEWORTHY Our study demonstrates that the upregulation of long noncoding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) promotes proliferation and migration during phenotypic switching of vascular smooth muscle cells in atherosclerosis. We also provide in vivo and in vitro evidence that NEAT1 accelerates vascular calcification. Our findings identified the direct interaction between enhancer of zeste homolog 2 (EZH2) and NEAT1 during atherosclerosis. NEAT1 is necessary for EZH2 to translocate from the cytoplasm to the nucleus, where EZH2 epigenetically inhibits the expression of genes related to senescence and antimigration.

LncRNAs exhibit subtype-specific expression, survival associations, and cancer-promoting effects in breast cancer

Long non-coding RNAs (lncRNAs) play important roles in cancer progression, influencing processes such as invasion, metastasis, and drug resistance. Their reported cell type-dependent expression patterns suggest the potential for specialized functions in specific contexts. In breast cancer, lncRNA expression has been associated with different subtypes, highlighting their relevance in disease heterogeneity. However, our understanding of lncRNA function within breast cancer subtypes remains limited, warranting further investigation. We conducted a comprehensive analysis using the TANRIC dataset derived from the TCGA-BRCA cohort, profiling the expression, patient survival associations and immune cell type correlations of 12,727 lncRNAs across subtypes. Our findings revealed subtype-specific associations of lncRNAs with patient survival, tumor infiltrating lymphocytes and other immune cells. Targeting of lncRNAs exhibiting subtype-specific survival associations and expression in a panel of breast cancer cells demonstrated a selective reduction in cell proliferation within their associated subtype, supporting subtype-specific functions of certain lncRNAs. Characterization of HER2 + -specific lncRNA LINC01269 and TNBC-specific lncRNA AL078604.2 showed nuclear localization and altered expression of hundreds of genes enriched in cancer-promoting processes, including apoptosis, cell proliferation and immune cell regulation. This work emphasizes the importance of considering the heterogeneity of breast cancer subtypes and the need for subtype-specific analyses to fully uncover the relevance and potential impact of lncRNAs. Collectively, these findings demonstrate the contribution of lncRNAs to the distinct molecular, prognostic, and cellular composition of breast cancer subtypes.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases.

IMPLICATIONS

This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Novel PBMC LncRNA signatures as diagnostic biomarkers for colorectal cancer

The expression of long non-coding RNAs (LncRNAs) in peripheral blood mononuclear cell (PBMC) and its clinical relevance in colorectal cancer (CRC) remains largely uncharacterized. To address these gaps, we investigated the expression profiles of lncRNAs in PBMC from CRC and healthy controls (HC) by RNA sequencing. The expression level of differentially expressed lncRNAs (DElncRNAs) were evaluated by quantitative PCR in PBMC samples from CRC patients and HC. A total of 447 DElncRNAs were identified, with 178 elevated lncRNAs and 269 decreased lncRNAs in PBMC from CRC patients as compared with that from HC. RT-PCR results supported a significant elevation of NEAT1:11, lnc-PDZD8-1:5 and LINC00910:16 in 98 CRC patients and 82 HC. The clinical implication of NEAT1:11, lnc-PDZD8-1:5 and LINC00910:16 as CRC diagnostic biomarker were determined by receiver operating characteristic (ROC) curve, showing sensitivity 74.5% and specificity 84.5% for joint detection the three lncRNAs. Notably, NEAT1:11 was closely related with the size and extent of primary tumor, with higher relative expression of NEAT1:11 in higher T stage (P = 0.0047). Moreover, NEAT1:11 was related with grade (P = 0.012). Collectively, PBMC from patients with CRC show significantly variable expression profiles of lncRNAs, and detection of these differential expression lncRNAs may provide useful information for basic and clinical research.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

Noncoding RNA mutations in cancer

Cancer is driven by both germline and somatic genetic changes. Efforts have been devoted to characterizing essential genetic variations in cancer initiation and development. Most attention has been given to mutations in protein-coding genes and associated regulatory elements such as promoters and enhancers. The development of sequencing technologies and in silico and experimental methods has allowed further exploration of cancer predisposition variants and important somatic mutations in noncoding RNAs, mainly for long noncoding RNAs and microRNAs. Association studies including GWAS have revealed hereditary variations including SNPs and indels in lncRNA or miRNA genes and regulatory regions. These mutations altered RNA secondary structures, expression levels, and target recognition and then conferred cancer predisposition to carriers. Whole-exome/genome sequencing comparing cancer and normal tissues has revealed important somatic mutations in noncoding RNA genes. Mutation hotspots and somatic copy number alterations have been identified in various tumor-associated noncoding RNAs. Increasing focus and effort have been devoted to studying the noncoding region of the genome. The complex genetic network of cancer initiation is being unveiled. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

Integrative Genomic Analyses Identify LncRNA Regulatory Networks across Pediatric Leukemias and Solid Tumors

Long noncoding RNAs (lncRNA) play an important role in gene regulation and contribute to tumorigenesis. While pan-cancer studies of lncRNA expression have been performed for adult malignancies, the lncRNA landscape across pediatric cancers remains largely uncharted. Here, we curated RNA sequencing data for 1,044 pediatric leukemia and extracranial solid tumors and integrated paired tumor whole genome sequencing and epigenetic data in relevant cell line models to explore lncRNA expression, regulation, and association with cancer. A total of 2,657 lncRNAs were robustly expressed across six pediatric cancers, including 1,142 exhibiting histotype-elevated expression. DNA copy number alterations contributed to lncRNA dysregulation at a proportion comparable to protein coding genes. Application of a multidimensional framework to identify and prioritize lncRNAs impacting gene networks revealed that lncRNAs dysregulated in pediatric cancer are associated with proliferation, metabolism, and DNA damage hallmarks. Analysis of upstream regulation via cell type-specific transcription factors further implicated distinct histotype-elevated and developmental lncRNAs. Integration of these analyses prioritized lncRNAs for experimental validation, and silencing of TBX2-AS1, the top-prioritized neuroblastoma-specific lncRNA, resulted in significant growth inhibition of neuroblastoma cells, confirming the computational predictions. Taken together, these data provide a comprehensive characterization of lncRNA regulation and function in pediatric cancers and pave the way for future mechanistic studies.

SIGNIFICANCE

Comprehensive characterization of lncRNAs in pediatric cancer leads to the identification of highly expressed lncRNAs across childhood cancers, annotation of lncRNAs showing histotype-specific elevated expression, and prediction of lncRNA gene regulatory networks.

LncRNA NEAT1 suppresses cellular senescence in hepatocellular carcinoma via KIF11-dependent repression of CDKN2A

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. Therapeutic options for advanced HCC are limited, which is due to a lack of full understanding of pathogenesis. Cellular senescence is a state of cell cycle arrest, which plays important roles in the pathogenesis of HCC. Mechanisms underlying hepatocellular senescence are not fully understood. LncRNA NEAT1 acts as an oncogene and contributes to the development of HCC. Whether NEAT1 modulates hepatocellular senescence in HCC is unknown.

METHODS

The role of NEAT1 and KIF11 in cellular senescence and tumor growth in HCC was assessed both in vitro and in vivo. RNA pulldown, mass spectrometry, Chromatin immunoprecipitation (ChIP), luciferase reporter assays, RNA FISH and immunofluorescence (IF) staining were used to explore the detailed molecular mechanism of NEAT1 and KIF11 in cellular senescence of HCC.

RESULTS

We found that NEAT1 was upregulated in tumor tissues and hepatoma cells, which negatively correlated with a senescence biomarker CDKN2A encoding p16INK4a and p14ARF proteins. NEAT1 was reduced in senescent hepatoma cells induced by doxorubicin (DOXO) or serum starvation. Furthermore, NEAT1 deficiency caused senescence in cultured hepatoma cells, and protected against the progression of HCC in a mouse model. During senescence, NEAT1 translocated into cytosol and interacted with a motor protein KIF11, resulting in KIF11 protein degradation and subsequent increased expression of CDKN2A in cultured hepatoma cells. Furthermore, KIF11 knockdown caused senescence in cultured hepatoma cells. Genetic deletion of Kif11 in hepatocytes inhibited the development of HCC in a mouse model.

CONCLUSIONS

Conclusively, NEAT1 overexpression reduces senescence and promotes tumor progression in HCC tissues and hepatoma cells, whereas NEAT1 deficiency causes senescence and inhibits tumor progression in HCC. This is associated with KIF11-dependent repression of CDKN2A. These findings lay the foundation to develop potential therapies for HCC by inhibiting NEAT1 and KIF11 or inducing senescence.

Long Non-Coding RNAs as "MYC Facilitators"

In this article, we discuss a class of MYC-interacting lncRNAs (long non-coding RNAs) that share the following criteria: They are direct transcriptional targets of MYC. Their expression is coordinated with the expression of MYC. They are required for sustained MYC-driven cell proliferation, and they are not essential for cell survival. We refer to these lncRNAs as "MYC facilitators" and discuss two representative members of this class of lncRNAs, SNHG17 (small nuclear RNA host gene) and LNROP (long non-coding regulator of POU2F2). We also present a general hypothesis on the role of lncRNAs in MYC-mediated transcriptional regulation.

Long non-coding RNAs in lung cancer: Unraveling the molecular modulators of MAPK signaling

Lung cancer (LC) continues to pose a significant global medical burden, necessitating a comprehensive understanding of its molecular foundations to establish effective treatment strategies. The mitogen-activated protein kinase (MAPK) signaling system has been scientifically associated with LC growth; however, the intricate regulatory mechanisms governing this system remain unknown. Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of diverse cellular activities, including cancer growth. LncRNAs have been implicated in LC, which can function as oncogenes or tumor suppressors, and their dysregulation has been linked to cancer cell death, metastasis, spread, and proliferation. Due to their involvement in critical pathophysiological processes, lncRNAs are gaining attention as potential candidates for anti-cancer treatments. This article aims to elucidate the regulatory role of lncRNAs in MAPK signaling in LC. We provide a comprehensive review of the key components of the MAPK pathway and their relevance in LC, focusing on aberrant signaling processes associated with disease progression. By examining recent research and experimental findings, this article examines the molecular mechanisms through which lncRNAs influence MAPK signaling in lung cancer, ultimately contributing to tumor development.

Tumour mutations in long noncoding RNAs enhance cell fitness

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.

A hybrid approach to assess the structural impact of long noncoding RNA mutations uncovers key NEAT1 interactions in colorectal cancer

Long noncoding RNAs (lncRNAs) are emerging players in cancer and they entail potential as prognostic biomarkers or therapeutic targets. Earlier studies have identified somatic mutations in lncRNAs that are associated with tumor relapse after therapy, but the underlying mechanisms behind these associations remain unknown. Given the relevance of secondary structure for the function of some lncRNAs, some of these mutations may have a functional impact through structural disturbance. Here, we examined the potential structural and functional impact of a novel A > G point mutation in NEAT1 that has been recurrently observed in tumors of colorectal cancer patients experiencing relapse after treatment. Here, we used the nextPARS structural probing approach to provide first empirical evidence that this mutation alters NEAT1 structure. We further evaluated the potential effects of this structural alteration using computational tools and found that this mutation likely alters the binding propensities of several NEAT1-interacting miRNAs. Differential expression analysis on these miRNA networks shows upregulation of Vimentin, consistent with previous findings. We propose a hybrid pipeline that can be used to explore the potential functional effects of lncRNA somatic mutations.

Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.

Widespread perturbation of ETS factor binding sites in cancer

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

Dynamic transcriptome analyses reveal m6A regulated immune non-coding RNAs during dengue disease progression

Dengue infection is one of the most prevalent arthropod-borne viral diseases, which can result in severe complications. Identification of genes and long non-coding RNAs (lncRNAs) involved in dengue infection would help in deciphering potential mechanisms responsible for the disease progression. We comprehensively analyzed the dynamic transcriptome during dengue disease progression and identified critical genes and lncRNAs with expression perturbations. Our findings revealed that the expression of genes (i.e., CCR10 and GNG7) and lncRNAs (i.e., CTBP1-AS and MAFG-AS1) were potentially regulated by m6A RNA methylation. Interestingly, dengue viral proteins prevalently interact with genes or lncRNAs with expression perturbations, which are involved in cell cycle, inflammation signaling pathways and immune response. Dynamically expressed genes and lncRNAs were likely to locate in the central regions of human protein-protein network, which play crucial roles in mediating signaling spread and helping viral replication. Immune microenvironments analysis revealed that plasma cells levels were increased and T cells infiltrations were decreased during dengue disease progression. Dynamically expressed genes and lncRNAs were correlated with immune cell infiltrations. Moreover, network analysis reveals the associations between dengue viral infections and human complex diseases (i.e., digestive diseases and neoplasms). Our comprehensive transcriptome analysis of dengue disease progression identified potential gene and lncRNA biomarkers, providing novel insights for understanding the pathogenesis of and developing effective therapeutic strategies for dengue infection.

Evaluation of Apoptosis, Cell Proliferation and Cell Cycle Progression by Inactivation of the NEAT1 Long Noncoding RNA in a Renal Carcinoma Cell Line Using CRISPR/Cas9

Background

Long noncoding RNAs (lncRNAs) play an important role in cellular mechanisms including transcription, translation, and apoptosis. NEAT1 is one of the essential types of lncRNAs in humans that can bind to active genes and modify their transcription. NEAT1 upregulation in various forms of cancer such as kidney cancer has been reported. Kidney cancer accounts for approximately 3% of all cancers worldwide and occurs almost twice as often in men as in women.

Objectives

This study has been performed to knockout the NEAT1 gene using the CRISPR/Cas9 technique in the Renal Cell Carcinoma ACHN cell line and to evaluate its effects on cancer progression and apoptosis.

Material and Methods

Two specific (single guide RNA (sgRNA) sequences for the NEAT1 gene were designed by CHOPCHOP software. These sequences were then cloned into plasmid pSpcas9, and recombinant vectors PX459-sgRNA1 and PX459-sgRNA2 were generated. ACHN cells were transfected using recombinant vectors carrying sgRNA1 and sgRNA2. The expression level of apoptosis-related genes was assessed by real-time PCR. Annexin, MTT and cell scratch tests were performed to evaluate the survival, proliferation, and migration of the knocked out cells, respectively.

Results

The results have shown successful knockout of the NEAT1 gene in the cells of the treatment group. Expressions of P53, BAK, BAX and FAS genes in the cells of the treatment group (NEAT1 knockout) showed significant increases in expression compared to the cells of the control group (P <0.01). Additionally, decreased expression of BCL2 and survivin genes was observed in knockout cells compared to the control group (p <0.05). In addition, in the cells of the treatment group compared to control cells, a significant decrease in cell viability, ability to migrate and cell growth and proliferation was observed.

Conclusion

Inactivation of the NEAT1 gene in ACHN cell line using CRISPR/Cas9 technology elevated apoptosis and reduced cell survival and proliferation which makes it a novel target for kidney cancer therapeutics.

Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction

By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset.

Discovery and Validation of Clinically Relevant Long Non-Coding RNAs in Colorectal Cancer

Simple Summary

Recent efforts in biomedical research have focused on the identification of molecular biomarkers to improve the diagnosis, prognosis and eventually treatment of the most common human diseases worldwide, including cancer. In this context, a large number of studies point to a pivotal role of long non-coding RNAs (lncRNAs) in the pathophysiology of carcinogenesis, suggesting diagnostic or therapeutic potential. However, for most of them, supporting evidence is scarce and often based on a single large-scale analysis. Here, focusing on colorectal cancer (CRC), we present an overview of the main approaches for discovering and validating lncRNA candidate molecules, and provide a curated list of the most promising lncRNAs associated with this malignancy.

Abstract

Colorectal cancer (CRC) is the third most prevalent cancer worldwide, with nearly two million newly diagnosed cases each year. The survival of patients with CRC greatly depends on the cancer stage at the time of diagnosis, with worse prognosis for more advanced cases. Consequently, considerable effort has been directed towards improving population screening programs for early diagnosis and identifying prognostic markers that can better inform treatment strategies. In recent years, long non-coding RNAs (lncRNAs) have been recognized as promising molecules, with diagnostic and prognostic potential in many cancers, including CRC. Although large-scale genome and transcriptome sequencing surveys have identified many lncRNAs that are altered in CRC, most of their roles in disease onset and progression remain poorly understood. Here, we critically review the variety of detection methods and types of supporting evidence for the involvement of lncRNAs in CRC. In addition, we provide a reference catalog that features the most clinically relevant lncRNAs in CRC. These lncRNAs were selected based on recent studies sorted by stringent criteria for both supporting experimental evidence and reproducibility.

Emerging roles for lncRNA-NEAT1 in colorectal cancer

Colorectal cancer (CRC) is the third cause of cancer death in the world that arises from the glandular and epithelial cells of the large intestine, during a series of genetic or epigenetic alternations. Recently, long non-coding RNAs (lncRNAs) has opened a separate window of research in molecular and translational medicine. Emerging evidence has supported that lncRNAs can regulate cell cycle of CRC cells. LncRNA NEAT1 has been verified to participate in colon cancer development and progression. NEAT1 as a competing endogenous RNA could suppress the expression of miRNAs, and then regulate molecules downstream of these miRNAs. In this review, we summarized emerging roles of NEAT1 in CRC cells.

Designing libraries for pooled CRISPR functional screens of long noncoding RNAs

Human and other genomes encode tens of thousands of long noncoding RNAs (lncRNAs), the vast majority of which remain uncharacterised. High-throughput functional screening methods, notably those based on pooled CRISPR-Cas perturbations, promise to unlock the biological significance and biomedical potential of lncRNAs. Such screens are based on libraries of single guide RNAs (sgRNAs) whose design is critical for success. Few off-the-shelf libraries are presently available, and lncRNAs tend to have cell-type-specific expression profiles, meaning that library design remains in the hands of researchers. Here we introduce the topic of pooled CRISPR screens for lncRNAs and guide readers through the three key steps of library design: accurate annotation of transcript structures, curation of optimal candidate sets, and design of sgRNAs. This review is a starting point and reference for researchers seeking to design custom CRISPR screening libraries for lncRNAs.

Current and Developing Liquid Biopsy Techniques for Breast Cancer

Breast cancer is the most commonly diagnosed cancer and leading cause of cancer mortality among woman worldwide. The techniques of diagnosis, prognosis, and therapy monitoring of breast cancer are critical. Current diagnostic techniques are mammography and tissue biopsy; however, they have limitations. With the development of novel techniques, such as personalized medicine and genetic profiling, liquid biopsy is emerging as the less invasive tool for diagnosing and monitoring breast cancer. Liquid biopsy is performed by sampling biofluids and extracting tumor components, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free mRNA (cfRNA) and microRNA (miRNA), proteins, and extracellular vehicles (EVs). In this review, we summarize and focus on the recent discoveries of tumor components and biomarkers applied in liquid biopsy and novel development of detection techniques, such as surface-enhanced Raman spectroscopy (SERS) and microfluidic devices.

A network-based method for detecting cancer driver gene in transcriptional regulatory networks using the structure analysis of weighted regulatory interactions

Background:

The identification of genes that instigate cell anomalies and cause cancer in humans is an important field in oncology research. Abnormalities in these genes are transferred to other genes in the cell, disrupting its normal functionality. Such genes are known as cancer driver genes (CDGs). Various methods have been proposed for predicting CDGs, most of which are based on genomic data and computational methods. Some novel bioinformatic approaches have been developed.

Objective:

In this article, we propose a network-based algorithm, SalsaDriver (Stochastic approach for link-structure analysis to driver detection), which can calculate the receiving and influencing power of each gene using the stochastic analysis of regulatory interaction structures in gene regulatory networks.

Method:

First, regulatory networks related to breast, colon, and lung cancers were constructed using gene expression data and a list of regulatory interactions, the weights of which were then calculated using biological and topological features of the network. After that, the weighted regulatory interactions were used in the structure analysis of interactions achieved using two separate Markov chains on the bipartite graph taken from the main graph of the gene network and implementing the stochastic approach for link-structure analysis. The proposed algorithm categorizes higher-ranked genes as driver genes.

Results:

The proposed algorithm was compared with 24 other computational and network tools based on the F-measure value and the number of detected CDGs. The results were validated using four valid databases. The findings of this study show that SalsaDriver outperforms other methods and can identify a significant number of driver genes not identified using other methods.

Conclusion:

The SalsaDriver network-based approach is suitable for predicting CDGs and can be used as a complementary method along with other computational tools.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

Background

Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.

Results

Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.

Conclusions

Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02607-z.

Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions

Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

ceRNA Network of lncRNA/miRNA as Circulating Prognostic Biomarkers in Non-Hodgkin Lymphomas: Bioinformatic Analysis and Assessment of Their Prognostic Value in an NHL Cohort

Research has been focusing on identifying novel biomarkers to better stratify non-Hodgkin lymphoma patients based on prognosis. Studies have demonstrated that lncRNAs act as miRNA sponges, creating ceRNA networks to regulate mRNA expression, and its deregulation is associated with lymphoma development. This study aimed to identify novel circulating prognostic biomarkers based on miRNA/lncRNA-associated ceRNA network for NHL. Herein, bioinformatic analysis was performed to construct ceRNA networks for hsa-miR-150-5p and hsa-miR335-5p. Then, the prognostic value of the miRNA–lncRNA pairs’ plasma levels was assessed in a cohort of 113 NHL patients. Bioinformatic analysis identified MALAT1 and NEAT1 as hsa-miR-150-5p and has-miR-335-5p sponges, respectively. Plasma hsa-miR-150-5p/MALAT1 and hsa-miR335-5p/NEAT1 levels were significantly associated with more aggressive and advanced disease. The overall survival and progression-free survival analysis indicated that hsa-miR-150-5p/MALAT1 and hsa-miR335-5p/NEAT1 pairs’ plasma levels were remarkably associated with NHL patients’ prognosis, being independent prognostic factors in a multivariate Cox analysis. Low levels of hsa-miR-150-5p and hsa-miR-335-5p combined with high levels of the respective lncRNA pair were associated with poor prognosis of NHL patients. Overall, the analysis of ceRNA network expression levels may be a useful prognostic biomarker for NHL patients and could identify patients who could benefit from more intensive treatments.

Network control principles for identifying personalized driver genes in cancer

To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype-phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level. To provide comprehensive perspectives of network control methods on this timely topic, we first considered the cancer progression as a network control problem, in which the expected PDGs are altered genes by oncogene activation signals that can change the individual molecular network from one health state to the other disease state. Then, we reviewed the network reconstruction methods on single samples and introduced novel network control methods on single-sample networks to identify PDGs in cancer. Particularly, we gave a performance assessment of the network structure control-based PDGs identification methods on multiple cancer datasets from TCGA, for which the data and evaluation package also are publicly available. Finally, we discussed future directions for the application of network control methods to identify PDGs in cancer and diverse biological processes.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

Sperm Transcriptional State Associated with Paternal Transmission of Stress Phenotypes

Paternal stress can induce long-lasting changes in germ cells potentially propagating heritable changes across generations. To date, no studies have investigated differences in transmission patterns between stress-resilient and stress-susceptible mice. We tested the hypothesis that transcriptional alterations in sperm during chronic social defeat stress (CSDS) transmit increased susceptibility to stress phenotypes to the next generation. We demonstrate differences in offspring from stressed fathers that depend on paternal category (resilient vs susceptible) and offspring sex. Importantly, artificial insemination (AI) reveals that sperm mediates some of the behavioral phenotypes seen in offspring. Using RNA-sequencing (RNA-seq), we report substantial and distinct changes in the transcriptomic profiles of sperm following CSDS in susceptible versus resilient fathers, with alterations in long noncoding RNAs (lncRNAs) predominating especially in susceptibility. Correlation analysis revealed that these alterations were accompanied by a loss of regulation of protein-coding genes by lncRNAs in sperm of susceptible males. We also identify several co-expression gene modules that are enriched in differentially expressed genes (DEGs) in sperm from either resilient or susceptible fathers. Taken together, these studies advance our understanding of intergenerational epigenetic transmission of behavioral experience.SIGNIFICANCE STATEMENT This manuscript contributes to the complex factors that influence the paternal transmission of stress phenotypes. By leveraging the segregation of males exposed to chronic social defeat stress (CSDS) into either resilient or susceptible categories we were able to identify the phenotypic differences in the paternal transmission of stress phenotypes across generations between the two lineages. Importantly, this work also alludes to the significance of both long noncoding RNAs (lncRNAs) and protein coding genes (PCGs) mediating the paternal transmission of stress. The knowledge gained from these data are of particular interest in understanding the risk for the development of psychiatric disorders such as anxiety and depression.

Functional annotation of noncoding mutations in cancer

Recurrent regulatory mutations affecting transcription factor binding sites in 2,500 cancer samples.

In a cancer genome, the noncoding sequence contains the vast majority of somatic mutations. While very few are expected to be cancer drivers, those affecting regulatory elements have the potential to have downstream effects on gene regulation that may contribute to cancer progression. To prioritize regulatory mutations, we screened somatic mutations in the Pan-Cancer Analysis of Whole Genomes cohort of 2,515 cancer genomes on individual bases to assess their potential regulatory roles in their respective cancer types. We found a highly significant enrichment of regulatory mutations associated with the deamination signature overlapping a CpG site in the CCAAT/Enhancer Binding Protein β recognition sites in many cancer types. Overall, 5,749 mutated regulatory elements were identified in 1,844 tumor samples from 39 cohorts containing 11,962 candidate regulatory mutations. Our analysis indicated 20 or more regulatory mutations in 5.5% of the samples, and an overall average of six per tumor. Several recurrent elements were identified, and major cancer-related pathways were significantly enriched for genes nearby the mutated regulatory elements. Our results provide a detailed view of the role of regulatory elements in cancer genomes.

Cell-Free DNA Detection of Tumor Mutations in Heterogeneous, Localized Prostate Cancer Via Targeted, Multiregion Sequencing

Cell-free DNA (cfDNA) may allow for minimally invasive identification of biologically relevant genomic alterations and genetically distinct tumor subclones. Although existing biomarkers may detect localized prostate cancer, additional strategies interrogating genomic heterogeneity are necessary for identifying and monitoring aggressive disease. In this study, we aimed to evaluate whether circulating tumor DNA can detect genomic alterations present in multiple regions of localized prostate tumor tissue.

METHODS

Low-pass whole-genome and targeted sequencing with a machine-learning guided 2.5-Mb targeted panel were used to identify single nucleotide variants, small insertions and deletions (indels), and copy-number alterations in cfDNA. The majority of this study focuses on the subset of 21 patients with localized disease, although 45 total individuals were evaluated, including 15 healthy controls and nine men with metastatic castration-resistant prostate cancer. Plasma cfDNA was barcoded with duplex unique molecular identifiers. For localized cases, matched tumor tissue was collected from multiple regions (one to nine samples per patient) for comparison.

RESULTS

Somatic tumor variants present in heterogeneous tumor foci from patients with localized disease were detected in cfDNA, and cfDNA mutational burden was found to track with disease severity. Somatic tissue alterations were identified in cfDNA, including nonsynonymous variants in FOXA1, PTEN, MED12, and ATM. Detection of these overlapping variants was associated with seminal vesicle invasion (P = .019) and with the number of variants initially found in the matched tumor tissue samples (P = .0005).

CONCLUSION

Our findings demonstrate the potential of targeted cfDNA sequencing to detect somatic tissue alterations in heterogeneous, localized prostate cancer, especially in a setting where matched tumor tissue may be unavailable (ie, active surveillance or treatment monitoring).

Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs

Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance.

Low expression of TRAF3IP2-AS1 promotes progression of NONO-TFE3 translocation renal cell carcinoma by stimulating N6-methyladenosine of PARP1 mRNA and downregulating PTEN

Background

NONO-TFE3 translocation renal cell carcinoma (NONO-TFE3 tRCC) is one subtype of RCCs associated with Xp11.2 translocation/TFE3 gene fusions RCC (Xp11.2 tRCCs). Long non-coding RNA (lncRNA) has attracted great attention in cancer research. The function and mechanisms of TRAF3IP2 antisense RNA 1 (TRAF3IP2-AS1), a natural antisense lncRNA, in NONO-TFE3 tRCC remain poorly understood.

Methods

FISH and qRT-PCR were undertaken to study the expression, localization and clinical significance of TRAF3IP2-AS1 in Xp11.2 tRCC tissues and cells. The functions of TRAF3IP2-AS1 in tRCC were investigated by proliferation analysis, EdU staining, colony and sphere formation assay, Transwell assay and apoptosis analysis. The regulatory mechanisms among TRAF3IP2-AS1, PARP1, PTEN and miR-200a-3p/153-3p/141-3p were investigated by luciferase assay, RNA immunoprecipitation, Western blot and immunohistochemistry.

Results

The expression of TRAF3IP2-AS1 was suppressed by NONO-TFE3 fusion in NONO-TFE3 tRCC tissues and cells. Overexpression of TRAF3IP2-AS1 inhibited the proliferation, migration and invasion of UOK109 cells which were derived from cancer tissue of patient with NONO-TFE3 tRCC. Mechanistic studies revealed that TRAF3IP2-AS1 accelerated the decay of PARP1 mRNA by direct binding and recruitment of N⁶-methyladenosie methyltransferase complex. Meanwhile, TRAF3IP2-AS1 competitively bound to miR-200a-3p/153-3p/141-3p and prevented those from decreasing the level of PTEN.

Conclusions

TRAF3IP2-AS1 functions as a tumor suppressor in NONO-TFE3 tRCC progression and may serve as a novel target for NONO-TFE3 tRCC therapy. TRAF3IP2-AS1 expression has the potential to serve as a novel diagnostic and prognostic biomarker for NONO-TFE3 tRCC detection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13045-021-01059-5.

The Missing Link Between Cancer-Associated Variants and LncRNAs

Despite studies indicating that long noncoding RNAs, or lncRNAs, can act as proto-oncogenes, the implications of large numbers of cancer-associated variants found within noncoding RNA loci remain largely unknown. Here, we draw upon emerging studies to speculate on how variants of lncRNAs might play a role in cancer development.

Whole genome sequencing of metastatic colorectal cancer reveals prior treatment effects and specific metastasis features

In contrast to primary colorectal cancer (CRC) little is known about the genomic landscape of metastasized CRC. Here we present whole genome sequencing data of metastases of 429 CRC patients participating in the pan-cancer CPCT-02 study (NCT01855477). Unsupervised clustering using mutational signature patterns highlights three major patient groups characterized by signatures known from primary CRC, signatures associated with received prior treatments, and metastasis-specific signatures. Compared to primary CRC, we identify additional putative (non-coding) driver genes and increased frequencies in driver gene mutations. In addition, we identify specific genes preferentially affected by microsatellite instability. CRC-specific 1kb-10Mb deletions, enriched for common fragile sites, and LINC00672 mutations are associated with response to treatment in general, whereas FBXW7 mutations predict poor response specifically to EGFR-targeted treatment. In conclusion, the genomic landscape of mCRC shows defined changes compared to primary CRC, is affected by prior treatments and contains features with potential clinical relevance.

Tumor Profiling at the Service of Cancer Therapy

Cancer treatment options have evolved significantly in the past few years. From the initial surgical procedures, to the latest next-generation technologies, we are now in the position to analyze and understand tumors in a one-by-one basis and use that to our advantage to provide with individualized treatment options that may increase patient survival. In this review, we will focus on how tumor profiling has evolved over the past decades to deliver more efficient and personalized treatment options, and how novel technologies can help us envisage the future of precision oncology toward a better management and, ultimately, increased survival.

LncRNAs and Available Databases

Long noncoding RNAs (lncRNAs) are involved in many regulatory mechanisms in practically every step of the RNA cycle, from transcription to RNA stability and translation. They are a highly heterogeneous class of molecules in terms of site of production, interaction networks, and functions. More and more databases are available on the web with the aim to make public information about lncRNA accessible to the scientific community. Here we review the most interesting resources with the purpose to organize a compendium of useful tools to interrogate before studying a lncRNA of interest.

Annotation of Full-Length Long Noncoding RNAs with Capture Long-Read Sequencing (CLS)

Metazoan genomes produce thousands of long-noncoding RNAs (lncRNAs), of which just a small fraction have been well characterized. Understanding their biological functions requires accurate annotations, or maps of the precise location and structure of genes and transcripts in the genome. Current lncRNA annotations are limited by compromises between quality and size, with many gene models being fragmentary or uncatalogued. To overcome this, the GENCODE consortium has developed RNA capture long-read sequencing (CLS), an approach combining targeted RNA capture with third-generation long-read sequencing. CLS provides accurate annotations at high-throughput rates. It eliminates the need for noisy transcriptome assembly from short reads, and requires minimal manual curation. The full-length transcript models produced are of quality comparable to present-day manually curated annotations. Here we describe a detailed CLS protocol, from probe design through long-read sequencing to creation of final annotations.

Long non-coding RNA NEAT1 promotes bone metastasis of prostate cancer through N6-methyladenosine

Background

N6-methyladenosine (m6A) is the most prevalent messenger RNA modification in mammalian cells. However, the disease relevant function of m6A on specific oncogenic long non-coding RNAs (ncRNAs) is not well understood.

Methods

We analyzed the m6A status using patients samples and bone metastatic PDXs. Through m6A high-throughput sequencing, we identified the m6A sites on NEAT1–1 in prostate bone metastatic PDXs. Mass spec assay showed interaction among NEAT1–1, CYCLINL1 and CDK19. RNA EMSA, RNA pull-down, mutagenesis, CLIP, western blot, ChIP and ChIRP assays were used to investigate the molecular mechanisms underlying the functions of m6A on NEAT1–1. Loss-of function and rescued experiments were executed to detect the biological roles of m6A on NEAT1–1 in the PDX cell phenotypes in vivo.

Results

In this study, we identified 4 credible m6A sites on long ncRNA NEAT1–1. High m6A level of NEAT1–1 was related to bone metastasis of prostate cancer and m6A level of NEAT1–1 was a powerful predictor of eventual death. Transcribed NEAT1–1 served as a bridge to facility the binding between CYCLINL1 and CDK19 and promoted the Pol II ser2 phosphorylation. Importantly, depletion of NEAT1–1or decreased m6A of NEAT1–1 impaired Pol II Ser-2p level in the promoter of RUNX2. Overexpression of NEAT1–1 induced cancer cell metastasis to lung and bone; xenograft growth and shortened the survival of mice, but NEAT1–1 with m6A site mutation failed to do these.

Conclusion

Collectively, the findings indicate that m6A on ncRNA NEAT1–1 takes critical role in regulating Pol II ser2 phosphorylation and may be novel specific target for bone metastasis cancer therapy and diagnosis. New complex CYCLINL1/CDK19/NEAT1–1 might provide new insight into the potential mechanism of the pathogenesis and development of bone metastatic prostate cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-020-01293-4.

Functional Screening Techniques to Identify Long Non-Coding RNAs as Therapeutic Targets in Cancer

Simple Summary

Long non-coding RNAs (lncRNAs) are a recently discovered class of molecules in the cell, with potential to be utilized as therapeutic targets in cancer. A number of lncRNAs have been described to play important roles in tumor progression and drive molecular processes involved in cell proliferation, apoptosis or invasion. However, the vast majority of lncRNAs have not been studied in the context of cancer thus far. With the advent of CRISPR/Cas genome editing, high-throughput functional screening approaches to identify lncRNAs that impact cancer growth are becoming more accessible. Here, we review currently available methods to study hundreds to thousands of lncRNAs in parallel to elucidate their role in tumorigenesis and cancer progression.

Abstract

Recent technological advancements such as CRISPR/Cas-based systems enable multiplexed, high-throughput screening for new therapeutic targets in cancer. While numerous functional screens have been performed on protein-coding genes to date, long non-coding RNAs (lncRNAs) represent an emerging class of potential oncogenes and tumor suppressors, with only a handful of large-scale screens performed thus far. Here, we review in detail currently available screening approaches to identify new lncRNA drivers of tumorigenesis and tumor progression. We discuss the various approaches of genomic and transcriptional targeting using CRISPR/Cas9, as well as methods to post-transcriptionally target lncRNAs via RNA interference (RNAi), antisense oligonucleotides (ASOs) and CRISPR/Cas13. We discuss potential advantages, caveats and future applications of each method to provide an overview and guide on investigating lncRNAs as new therapeutic targets in cancer.

Challenges and achievements of liquid biopsy technologies employed in early breast cancer

Breast cancer is the most common cancer type in women worldwide and its early detection is crucial to curing the disease. Tissue biopsy, currently the method of choice to obtain tumour molecular information, is invasive and might be affected by tumour heterogeneity rendering it incapable to portray the complete molecular picture. Liquid biopsy permits to study disease features in a more comprehensive manner by sampling biofluids and extracting tumour components such as circulating-tumour DNA (ctDNA), circulating-tumour cells (CTCs), and/or circulating-tumour RNA (ctRNA) amongst others in a monitoring-compatible manner. In this review, we describe the recent progress in the utilization of the circulating tumour components using early breast cancer samples. We review the most important analytes and technologies employed for their study.