The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes

Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim to identify a pseudogene-based prognosis signature for HNSCC by machine learning. RNA-seq data were downloaded from The Cancer Genome Atlas, and 700 differentially-expressed pseudogenes were identified. The survival-related pseudogenes were screened through COX-regression analysis, which includes univariate regression, least absolute shrinkage and selection operator regression, and multivariate regression, and a five-pseudogene signature was constructed. The value of prediction for the signature was validated in multiple subgroups in terms of survival. Gene set enrichment analysis (GSEA) and coexpression analysis were used to determine the underlying biological functions. Seven hundred dysregulated pseudogenes were identified, and the five-pseudogene signature can distinguish the low-risk and high-risk patients for both training and testing sets and predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different genders, ages, stages, and grades. Coexpression analysis revealed that the five-pseudogene is associated with immune system. GSEA showed cancer-related biological process and pathways the five-pseudogene involved in. The five-pseudogene signature is not only a novel marker for prognosis but also a promising signature for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

PURPOSE

Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample.

METHODS

We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression.

RESULTS

We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.

CONCLUSION

This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

Re-recognition of pseudogenes: From molecular to clinical applications

Pseudogenes were initially regarded as "nonfunctional" genomic elements that did not have protein-coding abilities due to several endogenous inactivating mutations. Although pseudogenes are widely expressed in prokaryotes and eukaryotes, for decades, they have been largely ignored and classified as gene "junk" or "relics". With the widespread availability of high-throughput sequencing analysis, especially omics technologies, knowledge concerning pseudogenes has substantially increased. Pseudogenes are evolutionarily conserved and derive primarily from a mutation or retrotransposon, conferring the pseudogene with a "gene repository" role to store and expand genetic information. In contrast to previous notions, pseudogenes have a variety of functions at the DNA, RNA and protein levels for broadly participating in gene regulation to influence the development and progression of certain diseases, especially cancer. Indeed, some pseudogenes have been proven to encode proteins, strongly contradicting their "trash" identification, and have been confirmed to have tissue-specific and disease subtype-specific expression, indicating their own value in disease diagnosis. Moreover, pseudogenes have been correlated with the life expectancy of patients and exhibit great potential for future use in disease treatment, suggesting that they are promising biomarkers and therapeutic targets for clinical applications. In this review, we summarize the natural properties, functions, disease involvement and clinical value of pseudogenes. Although our knowledge of pseudogenes remains nascent, this field deserves more attention and deeper exploration.

PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers

Background

Long thought “relics” of evolution, not until recently have pseudogenes been of medical interest regarding regulation in cancer. Often, these regulatory roles are a direct by-product of their close sequence homology to protein-coding genes. Novel pseudogene-gene (PGG) functional associations can be identified through the integration of biomedical data, such as sequence homology, functional pathways, gene expression, pseudogene expression, and microRNA expression. However, not all of the information has been integrated, and almost all previous pseudogene studies relied on 1:1 pseudogene–parent gene relationships without leveraging other homologous genes/pseudogenes.

Results

We produce PGG families that expand beyond the current 1:1 paradigm. First, we construct expansive PGG databases by (i) CUDAlign graphics processing unit (GPU) accelerated local alignment of all pseudogenes to gene families (totaling 1.6 billion individual local alignments and >40,000 GPU hours) and (ii) BLAST-based assignment of pseudogenes to gene families. Second, we create an open-source web application (PseudoFuN [Pseudogene Functional Networks]) to search for integrative functional relationships of sequence homology, microRNA expression, gene expression, pseudogene expression, and gene ontology. We produce four “flavors” of CUDAlign-based databases (>462,000,000 PGG pairwise alignments and 133,770 PGG families) that can be queried and downloaded using PseudoFuN. These databases are consistent with previous 1:1 PGG annotation and also are much more powerful including millions of de novo PGG associations. For example, we find multiple known (e.g., miR-20a-PTEN-PTENP1) and novel (e.g., miR-375-SOX15-PPP4R1L) microRNA-gene-pseudogene associations in prostate cancer. PseudoFuN provides a “one stop shop” for identifying and visualizing thousands of potential regulatory relationships related to pseudogenes in The Cancer Genome Atlas cancers.

Conclusions

Thousands of new PGG associations can be explored in the context of microRNA-gene-pseudogene co-expression and differential expression with a simple-to-use online tool by bioinformaticians and oncologists alike.

Aberrant hTERT promoter methylation predicts prognosis in Chinese patients with acral and mucosal melanoma: A CONSORT-compliant article

BACKGROUND

To evaluate the methylation levels of human telomerase reverse transcriptase (hTERT) promoter three CpG island (CGIs) regions and its prognostic impact in Chinese patients with acral and mucosal melanoma.

METHODS

Bioinformatics software was used to analyze hTERT gene promoter. Fresh frozen tissues were taken from 14 patients with melanoma (6 acral melanoma and 8 mucosal melanoma) and 14 pigmented nevus as control subjects (14 acral pigmented nevus). Bisulfite sequencing PCR (BSP) combined TA clone sequencing was used to assess the methylation levels of hTERT promoter CGIs regions. The relative expression level of hTERT mRNA was measured by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

CGIs-1 (-1392--1098 bp), CGIs-2 (-945--669 bp), and CGIs-3 (-445--48 bp) were selected for our study. Our results indicated that the methylation levels of hTERT promotor CGIs regions in melanoma were greater than pigmented nevus (CGIs-1: 69.3 ± 18.7% vs 46.8 ± 20.4%, t = 3.048 P = .005; CGIs-2: 73.8 ± 14.7% vs 55.6 ± 16.0%, t = 3.120 P = .004; CGIs-3: 5.8 ± 2.2% vs 2.2 ± 1.3%, t = 5.164 P < .001). The relative expression level of hTERT in melanoma was greater than in pigmented nevus (50.39 ± 9.16 vs 26.10 ± 7.25, t = 7.778, P < .001). Linear regression analysis showed that the methylation level of CGIs-2 in melanoma was positively correlated with the relative expression level of hTERT mRNA (R = .490, F = 13.478, P = .003). Combined with the analysis of clinicopathological features, the methylation level of CGIs-2 in melanoma with lymph node metastasis was greater than in melanoma without lymph node metastasis, and the methylation level of CGIs-2 increased with TNM staging.

CONCLUSION

CGIs-2 methylation level was associated with the relative expression level of hTERT mRNA, lymph node metastasis and TNM staging, suggesting that CGIs-2 hypermethylation might be used to evaluate the prognosis in Chinese patients with acral and mucosal melanoma.

Systems Biology of Cancer Metastasis

Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.

dreamBase: DNA modification, RNA regulation and protein binding of expressed pseudogenes in human health and disease

Although thousands of pseudogenes have been annotated in the human genome, their transcriptional regulation, expression profiles and functional mechanisms are largely unknown. In this study, we developed dreamBase (http://rna.sysu.edu.cn/dreamBase) to facilitate the investigation of DNA modification, RNA regulation and protein binding of potential expressed pseudogenes from multidimensional high-throughput sequencing data. Based on ∼5500 ChIP-seq and DNase-seq datasets, we identified genome-wide binding profiles of various transcription-associated factors around pseudogene loci. By integrating ∼18 000 RNA-seq data, we analysed the expression profiles of pseudogenes and explored their co-expression patterns with their parent genes in 32 cancers and 31 normal tissues. By combining microRNA binding sites, we demonstrated complex post-transcriptional regulation networks involving 275 microRNAs and 1201 pseudogenes. We generated ceRNA networks to illustrate the crosstalk between pseudogenes and their parent genes through competitive binding of microRNAs. In addition, we studied transcriptome-wide interactions between RNA binding proteins (RBPs) and pseudogenes based on 458 CLIP-seq datasets. In conjunction with epitranscriptome sequencing data, we also mapped 1039 RNA modification sites onto 635 pseudogenes. This database will provide insights into the transcriptional regulation, expression, functions and mechanisms of pseudogenes as well as their roles in biological processes and diseases.

Retroposed copies of RET gene: a somatically acquired event in medullary thyroid carcinoma

BACKGROUND

Different pathogenic germline mutations in the RET oncogene are identified in MEN 2, a hereditary syndrome characterized by medullary thyroid carcinoma (MTC) and other endocrine tumors. Although genetic predisposition is recognized, not all RET mutation carriers will develop the disease during their lifetime or, likewise, RET mutation carriers belonging to the same family may present clinical heterogeneity. It has been suggested that a single germline mutation might not be sufficient for development of MEN 2-associated tumors and a somatic bi-allelic alteration might be required. Here we investigated the presence of somatic second hit mutation in the RET gene in MTC.

METHODS

We integrated Multiplex Ligation-dependent Probe Amplification (MLPA) and whole exome sequencing (WES) to search for copy number alteration (CNA) in the RET gene in MTC samples and medullary thyroid cell lines (TT and MZ-CR-1). We next found reads spanning exon-exon boundaries on RET, an indicative of retrocopy. We subsequently searched for RET retrocopies in the human reference genome (GRCh37) and in the 1000 Genomes Project data, by looking for reads reporting joined exons in the RET locus or distinct genomic regions. To determine RET retrocopy specificity and recurrence, DNA isolated from sporadic and MEN 2-associated MTC (n = 37), peripheral blood (n = 3) and papillary thyroid carcinomas with RET fusion (n = 10) samples were tested using PCR-sequencing methodology.

RESULTS

Through MLPA we have found evidence of CNA in the RET gene in MTC samples and MTC cell lines. WES analysis reinforced the presence of the CNA and hinted for a retroposed copy of RET not found in the human reference genome and 1.000 Genomes Project. Extended analysis confirmed the presence of a somatic MTC-related retrocopy of RET in both sporadic and hereditary tumors. We further unveiled a recurrent (28%) novel point mutation (p.G548 V) found exclusively in the retrocopy of RET. The mutation was also found in cDNA of mutated samples, suggesting it might be functional.

CONCLUSION

We here report a somatic specific RET retroposed copy in MTC samples and cell lines. Our results support the idea that generation of retrocopies in somatic cells is likely to contribute to MTC genesis and progression.

Pseudogene RACGAP1P activates RACGAP1/Rho/ERK signalling axis as a competing endogenous RNA to promote hepatocellular carcinoma early recurrence

Accumulating evidence has indicated crucial roles for pseudogenes in human cancers. However, the roles played by pseudogenes in the pathogenesis of HCC, particularly HCC early recurrence, still incompletely elucidated. Herein, we identify a novel early recurrence related pseudogene RACGAP1P which was significantly upregulated in HCC and was associated with larger tumour size, advanced clinical stage, abnormal AFP level and shorter survival time. In vitro and in vivo experiments have shown that RACGAP1P is a prerequisite for the development of malignant characteristics of HCC cells, including cell growth and migration. Mechanistic investigations indicated that RACGAP1P elicits its oncogenic activity as a ceRNA to sequestrate miR-15-5p from its endogenous target RACGAP1, thereby leading to the upregulation of RACGAP1 and the activation of RhoA/ERK signalling. These results may provide new insights into the functional crosstalk of the pseudogene/miRNA/parent-gene genetic network during HCC early relapse and may contribute to improving the clinical intervention for this subset of HCC patients.

A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma

Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients’ RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44–0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14–0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35–2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37–0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

High Expression of Pseudogene PTTG3P Indicates a Poor Prognosis in Human Breast Cancer

Pseudogenes play pivotal roles in tumorigenesis. Previous studies have suggested that pituitary tumor-transforming 3, pseudogene (PTTG3P), serves as an oncogene in human cancers. However, its expression pattern, biological function, and underlying mechanism in breast cancer remain unknown. In this study, we demonstrated an elevated expression of PTTG3P in breast cancer and discovered that PTTG3P expression correlated negatively with estrogen receptor (ER) and progesterone receptor (PR) status, but linked positively to basal-like status, triple-negative breast cancer status, Nottingham prognostic index (NPI), and Scarff-Bloom-Richardson grade. High expression of PTTG3P was also found to be associated with a poor prognosis of breast cancer. To explore the potential mechanisms of PTTG3P, a PTTG3P-microRNA (miRNA)-mRNA regulatory network was established. Co-expressed genes of PTTG3P were also obtained. Enrichment analysis for these co-expressed genes revealed that they were significantly enriched in mitotic nuclear division and cell cycle. Subsequent research on mechanism of PTTG3P indicated that its expression correlated positively with PTTG1 expression. However, no significant expression correlation between PTTG3P and PTTG2 was observed. Taken together, our findings suggest that increased expression of pseudogene PTTG3P may be used as a promising prognostic biomarker and novel therapeutic target for breast cancer.

Identification of a Five-Pseudogene Signature for Predicting Survival and Its ceRNA Network in Glioma

Background: Glioma is the most common primary brain tumor with a dismal prognosis. It is urgent to develop novel molecular biomarkers and conform to individualized schemes. Methods: Differentially expressed pseudogenes between low grade glioma (LGG) and glioblastoma multiforme (GBM) were identified in the training cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards regression analyses were used to select pseudogenes associated with prognosis of glioma. A risk signature was constructed based on the selected pseudogenes for predicting the survival of glioma patients. A pseudogene-miRNA-mRNA regulatory network was established and visualized using Cytoscape 3.5.1. Gene Oncology (GO) and signaling pathway analyses were performed on the targeted genes to investigate functional roles of the risk signature. Results: Five pseudogenes (ANXA2P2, EEF1A1P9, FER1L4, HILS1, and RAET1K) correlating with glioma survival were selected and used to establish a risk signature. Time-dependent receiver operating characteristic (ROC) curves revealed that the risk signature could accurately predict the 1, 3, and 5-year survival of glioma patients. GO and signaling pathway analyses showed that the risk signature was involved in regulation of proliferation, migration, angiogenesis, and apoptosis in glioma. Conclusions: In this study, a risk signature with five pseudogenes was constructed and shown to accurately predict 1-, 3-, and 5-year survival for glioma patient. The risk signature may serve as a potential target against glioma.

Pseudogenes as Functionally Significant Elements of the Genome

Pseudogene is a gene copy that has lost its original function. For a long time, pseudogenes have been considered as "junk DNA" that inevitably arises as a result of ongoing evolutionary process. However, experimental data obtained during recent years indicate this understanding of the nature of pseudogenes is not entirely correct, and many pseudogenes perform important genetic functions. In the review, we have addressed classification of pseudogenes, methods of their detection in the genome, and the problem of their evolutionary conservatism and prevalence among species belonging to different taxonomic groups in the light of modern data. The mechanisms of gene expression regulation by pseudogenes and the role of pseudogenes in pathogenesis of various human diseases are discussed.

Competing endogenous RNA (ceRNA) cross talk and language in ceRNA regulatory networks: A new look at hallmarks of breast cancer

Breast cancer (BC) is the most frequently occurring malignancy in women worldwide. Despite the substantial advancement in understanding the molecular mechanisms and management of BC, it remains the leading cause of cancer death in women. One of the main reasons for this obstacle is that we have not been able to find the Achilles heel for the BC as a highly heterogeneous disease. Accumulating evidence has revealed that noncoding RNAs (ncRNAs), play key roles in the development of BC; however, the involving of complex regulatory interactions between the different varieties of ncRNAs in the development of this cancer has been poorly understood. In the recent years, the newly discovered mechanism in the RNA world is "competing endogenous RNA (ceRNA)" which proposes regulatory dialogues between different RNAs, including long ncRNAs (lncRNAs), microRNAs (miRNAs), transcribed pseudogenes, and circular RNAs (circRNAs). In the latest BC research, various studies have revealed that dysregulation of several ceRNA networks (ceRNETs) between these ncRNAs has fundamental roles in establishing the hallmarks of BC development. And it is thought that such a discovery could open a new window for a better understanding of the hidden aspects of breast tumors. Besides, it probably can provide new biomarkers and potential efficient therapeutic targets for BC. This review will discuss the existing body of knowledge regarding the key functions of ceRNETs and then highlights the emerging roles of some recently discovered ceRNETs in several hallmarks of BC. Moreover, we propose for the first time the "ceRnome" as a new term in the present article for RNA research.

Maximizing the Utility of Cancer Transcriptomic Data

Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.

A Pan-cancer Analysis of the Expression and Clinical Relevance of Small Nucleolar RNAs in Human Cancer

Increasing evidence has demonstrated that small nucleolar RNAs (snoRNAs) play important roles in tumorigenesis. We systematically investigated the expression landscape and clinical relevance of snoRNAs in >10,000 samples across 31 cancer types from The Cancer Genome Atlas. We observed overall elevated expression of snoRNAs and their ribonucleoproteins in multiple cancer types. We showed complex regulation of snoRNA expression by their host genes, copy number variation, and DNA methylation. Unsupervised clustering revealed that the snoRNA expression subtype is highly concordant with other molecular/clinical subtypes. We further identified 46 clinically relevant snoRNAs and experimentally demonstrated functional roles of SNORD46 in promoting cell proliferation, migration, and invasion. We developed a user-friendly data portal, SNORic, to benefit the research community. Our study highlights the significant roles of snoRNAs in the development and implementation of biomarkers or therapeutic targets for cancer and provides a valuable resource for cancer research.

Pan-cancer analysis of somatic mutations and transcriptomes reveals common functional gene clusters shared by multiple cancer types

To discover functional gene clusters across cancers, we performed a systematic pan-cancer analysis of 33 cancer types. We identified genes that were associated with somatic mutations and were the cores of a co-expression network. We found that multiple cancer types have relatively exclusive hub genes individually; however, the hub genes cooperate with each other based on their functional relationship. When we built a protein-protein interaction network of hub genes and found nine functional gene clusters across cancer types, the gene clusters divided not only the region of the network map, but also the function of the network by their distinct roles related to the development and progression of cancer. This functional relationship between the clusters and cancers was underpinned by the high expression of module genes and enrichment of programmed cell death, and known candidate cancer genes. In addition to protein-coding hub genes, non-coding hub genes had a possible relationship with cancer. Overall, our approach of investigating cancer genes enabled finding pan-cancer hub genes and common functional gene clusters shared by multiple cancer types based on the expression status of the primary tumour and the functional relationship of genes in the biological network.

The Genomic Landscape and Pharmacogenomic Interactions of Clock Genes in Cancer Chronotherapy

Cancer chronotherapy, treatment at specific times during circadian rhythms, endeavors to optimize anti-tumor effects and to lower toxicity. However, comprehensive characterization of clock genes and their clinical relevance in cancer is lacking. We systematically characterized the alterations of clock genes across 32 cancer types by analyzing data from The Cancer Genome Atlas, Cancer Therapeutics Response Portal, and The Genomics of Drug Sensitivity in Cancer databases. Expression alterations of clock genes are associated with key oncogenic pathways, patient survival, tumor stage, and subtype in multiple cancer types. Correlations between expression of clock genes and of other genes in the genome were altered in cancerous versus normal tissues. We identified interactions between clock genes and clinically actionable genes by analyzing co-expression, protein-protein interaction, and chromatin immunoprecipitation sequencing data and also found that clock gene expression is correlated to anti-cancer drug sensitivity in cancer cell lines. Our study provides a comprehensive analysis of the circadian clock across different cancer types and highlights potential clinical utility of cancer chronotherapy.

Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow

Proteogenomics enable the discovery of novel peptides (from unannotated genomic protein-coding loci) and single amino acid variant peptides (derived from single-nucleotide polymorphisms and mutations). Increasing the reliability of these identifications is crucial to ensure their usefulness for genome annotation and potential application as neoantigens in cancer immunotherapy. We here present integrated proteogenomics analysis workflow (IPAW), which combines peptide discovery, curation, and validation. IPAW includes the SpectrumAI tool for automated inspection of MS/MS spectra, eliminating false identifications of single-residue substitution peptides. We employ IPAW to analyze two proteomics data sets acquired from A431 cells and five normal human tissues using extended (pH range, 3–10) high-resolution isoelectric focusing (HiRIEF) pre-fractionation and TMT-based peptide quantitation. The IPAW results provide evidence for the translation of pseudogenes, lncRNAs, short ORFs, alternative ORFs, N-terminal extensions, and intronic sequences. Moreover, our quantitative analysis indicates that protein production from certain pseudogenes and lncRNAs is tissue specific.

Proteogenomics enables the discovery of protein coding regions and disease-relevant mutations but their verification remains challenging. Here, the authors combine peptide discovery, curation and validation in an integrated proteogenomics workflow, robustly identifying unknown coding regions and mutations.

Integrated analysis of long non-coding RNAs in human colorectal cancer

Accumulating evidence highlights the role of long non-coding RNAs (lncRNAs) in tumors. However, the genome-wide expression and roles of lncRNAs in colorectal cancer (CRC) remain unknown. Here, we systematically examined the global gene expressions in primary and synchronous liver metastases CRC tissue, in which thousands of aberrantly expressed lncRNAs were characterized. Co-expression analysis revealed that some lncRNAs correlated to their neighboring mRNAs in expression levels, whereas others formed networks with protein-coding genes in trans. We observed H3K4me3 was enriched at expressed lncRNA transcription start sites (TSSs) and correlated to dysregulated lncRNAs. Furthermore, we identified primary and metastasis tumor linked lncRNA signatures positively correlated with poor-prognosis gene set. Finally, functional experiments demonstrated two candidate lncRNAs were required for proliferation and migration of CRC cells. In summary, we provided a new framework for lncRNA associated clinical prognosis evaluation and target selection of gene therapy in CRC.

The pseudogene DUXAP10 promotes an aggressive phenotype through binding with LSD1 and repressing LATS2 and RRAD in non small cell lung cancer

Pseudogenes have been considered as non-functional transcriptional relics of human genomic for long time. However, recent studies revealed that they play a plethora of roles in diverse physiological and pathological processes, especially in cancer, and many pseudogenes are transcribed into long noncoding RNAs and emerging as a novel class of lncRNAs. However, the biological roles and underlying mechanism of pseudogenes in the pathogenesis of non small cell lung cancer are still incompletely elucidated. This study identifies a putative oncogenic pseudogene DUXAP10 in NSCLC, which is located in 14q11.2 and 2398 nt in length. Firstly, we found that DUXAP10 was significantly up-regulated in 93 human NSCLC tissues and cell lines, and increased DUXAP10 was associated with patients poorer prognosis and short survival time. Furthermore, the loss and gain of functional studies including growth curves, migration, invasion assays and in vivo studies verify the oncogenic roles of DUXAP10 in NSCLC. Finally, the mechanistic experiments indicate that DUXAP10 could interact with Histone demethylase Lysine specific demethylase1 (LSD1) and repress tumor suppressors Large tumor suppressor 2 (LATS2) and Ras-related associated with diabetes (RRAD) transcription in NSCLC cells. Taken together, these findings demonstrate DUXAP10 exerts the oncogenic roles through binding with LSD1 and epigenetic silencing LATS2 and RRAD expression. Our investigation reveals the novel roles of pseudogene in NSCLC, which may serve as new target for NSCLC diagnosis and therapy.

Downregulated pseudogene CTNNAP1 promote tumor growth in human cancer by downregulating its cognate gene CTNNA1 expression

Accumulating evidence indicates that deregulation of cancer-associated pseudogene is involved in the pathogenesis of cancer. In the study, we demonstrated that pseudogene CTNNAP1, for the CTNNA1 gene, was dysregulated in colorectal cancer and the degree of dysregulation was remarkably associated with tumor node metastasis (TNM) stage (P<0.05). The mechanistic experiments revealed that pseudogene CTNNAP1 played a pivotal role in the regulation of its cognate gene CTNNA1 by competition for microRNA-141. Moreover, gain-of-function approaches showed that overexpression of CTNNAP1 or CTNNA1 significantly inhibited cell proliferation and tumor growth in vitro and in vivo by inducing G0/G1 cell cycle arrest. Our findings add a new regulatory circuit via competing endogenous RNA (ceRNA) cross-talk between pseudogene CTNNAP1 and its cognate gene CTNNA1, and provide new insights into potential diagnostic biomarker for monitoring human colorectal cancer.

Pseudogene BMI1P1 expression as a novel predictor for acute myeloid leukemia development and prognosis

The BMI1P1 levels of 144 de novo AML patients and 36 healthy donors were detected by real-time quantitative PCR (RQ-PCR). BMI1P1 was significantly down-regulated in AML compared with control (P < 0.001). A receiver operating characteristic (ROC) curve revealed that BMI1P1 expression could differentiate patients with AML from control subjects (AUC = 0.895, 95% CI: 0.835–0.954, P < 0.001). The percentage of blasts in bone marrow (BM) was significantly lower in BMI1P1 high-expressed group versus low-expressed group (P = 0.008). BMI1P1 high-expressed cases had significantly higher complete remission (CR) than BMI1P1 low-expressed cases (P = 0.023). Furthermore, Kaplan–Meier demonstrated that both whole AML cohort and non-M3-AML patients with low BMI1P1 expression showed shorter leukemia free survival (LFS, P = 0.002 and P = 0.01, respectively) and overall survival (OS, P < 0.001 and P = 0.011, respectively) than those with high BMI1P1 expression. Multivariate analysis also showed that BMI1P1 over-expression was an independent favorable prognostic factor for OS in both whole and non-M3 cohort of AML patients (HR = 0.462, 95% CI = 0.243–0.879, P = 0.019 and HR = 0.483, 95% CI = 0.254–0.919, P = 0.027). To further investigate the significance of BMI1P1 expression in the follow-up of AML patients, we monitored the BMI1P1 level in 26 de novo AML patients and found that the BMI1P1 level increased significantly from the initial diagnosis to post-CR (P < 0.001). These results indicated that BMI1P1 might contribute to the diagnosis of AML and the assessment of therapeutic effect.

Over-expression of oncigenic pesudogene DUXAP10 promotes cell proliferation and invasion by regulating LATS1 and β-catenin in gastric cancer

BACKGROUND

Recently, the pesudogenes have emerged as critical regulators in human cancers tumorigenesis and progression, and been identified as a key revelation in post-genomic biology. However, the expression pattern, biological function and mechanisms responsible for these molecules in human gastric cancer (GC) are not fully understood.

METHODS

In this study, we globally assessed the transcriptomic differences of pesudogenes in gastric cancer using publicly available microarray data. DUXAP10 expression levels in GC tissues and cells was detected using quantitative real-time PCR (qPCR). DUXAP10 siRNAs and over-expression vector were transfected into GC cells to down-regulate or up-regulate DUXAP10 expression. Loss- and gain-of function assays were performed to investigate the role of DUXAP10 in GC cells cell proliferation, and invasion. RIP, RNA pulldown, and ChIP assays were used to determine the mechanism of DUXAP10's regulation of underlying targets.

RESULTS

The pesudogene DUXAP10 is the only pseudogene that significantly over-expressed in all four GEO datasets, and frequently over-expressed in many other cancers including Liver Hepatocellular carcinoma, Bladder cancer, and Esophageal Cancer. High DUXAP10 expression is associated with GC patients poor prognosis, and knockdown of DUXAP10 significantly inhibits cells proliferation, migration and invasion in GC. Mechanistic investigation shows that DUXAP10 can interact with PRC2 and LSD1 to repress LATS1 expression at transcriptional level, and bind with HuR to maintain the stability of β-catenin mRNA and increase its protein levels at post-transcriptional level.

CONCLUSIONS

Overall, our findings illuminate how increased DUXAP10 confers an oncogenic function in GC development and progression that may serve as a candidate prognostic biomarker and target for clinical management of GC.

Involvement of miRNAs and Pseudogenes in Cancer

Our understanding of cancer pathways has been changed by the determination of noncoding transcripts in the human genome in recent years. miRNAs and pseudogenes are key players of the noncoding transcripts from the genome, and alteration of their expression levels provides clues for significant biomarkers in pathogenesis of diseases. Especially, miRNAs and pseudogenes have both oncogenic and tumor-suppressive roles in each step of cancer tumorigenesis. In this current study, association between oncogenes and miRNAs-pseudogenes was reviewed and determined in human cancer by the CellMiner web-tool.

Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes

We studied the transcriptome landscape of skin cutaneous melanoma (SKCM) using 103 primary tumor samples from TCGA, and measured the expression levels of both protein coding genes and non-coding RNAs (ncRNAs). In particular, we emphasized pseudogenes potentially relevant to this cancer. While cataloguing the profiles based on the known biotypes, all the employed RNA-Seq methods generated just a small consensus of significant biotypes. We thus designed an approach to reconcile the profiles from all methods following a simple strategy: we selected genes that were confirmed as differentially expressed by the ensemble predictions obtained in a regression model. The main advantages of this approach are: 1) Selection of a high-confidence gene set identifying relevant pathways; 2) Use of a regression model whose covariates embed all method-driven outcomes to predict an averaged profile; 3) Method-specific assessment of prediction power and significance. Furthermore, the approach can be generalized to any biological system for which noisy RNA-Seq profiles are computed. As our analyses concerned bio-annotations of both high-quality protein coding genes and ncRNAs, we considered the associations between pseudogenes and parental genes (targets). Among the candidate targets that were validated, we identified PINK1, which is studied in patients with Parkinson and cancer (especially melanoma).

Energy stress-induced lncRNA FILNC1 represses c-Myc-mediated energy metabolism and inhibits renal tumor development

The roles of long non-coding RNAs in cancer metabolism remain largely unexplored. Here we identify FILNC1 (FoxO-induced long non-coding RNA 1) as an energy stress-induced long non-coding RNA by FoxO transcription factors. FILNC1 deficiency in renal cancer cells alleviates energy stress-induced apoptosis and markedly promotes renal tumor development. We show that FILNC1 deficiency leads to enhanced glucose uptake and lactate production through upregulation of c-Myc. Upon energy stress, FILNC1 interacts with AUF1, a c-Myc mRNA-binding protein, and sequesters AUF1 from binding c-Myc mRNA, leading to downregulation of c-Myc protein. FILNC1 is specifically expressed in kidney, and is downregulated in renal cell carcinoma; also, its low expression correlates with poor clinical outcomes in renal cell carcinoma. Together, our study not only identifies FILNC1 as a negative regulator of renal cancer with potential clinical value, but also reveals a regulatory mechanism by long non-coding RNAs to control energy metabolism and tumor development.FoxO are commonly down-regulated transcription factors and tumor suppressors in renal cell cancer (RCC). Here, the authors show that upon energy stress FoxOs induce the expression of the long non-coding RNA FILNC1, which inhibits survival of RCC by downregulating c-Myc and c-Myc-dependent metabolic rewiring.

Pseudogene PDIA3P1 promotes cell proliferation, migration and invasion, and suppresses apoptosis in hepatocellular carcinoma by regulating the p53 pathway

Pseudogenes are a subclass of long non-coding (lnc) RNAs that arose from protein-coding genes, but have lost the ability to produce proteins. Pseudogenes play an important role in the pathogenesis of various tumors; however, the role of pseudogenes in hepatocellular carcinoma (HCC) is poorly understood. In this study, we investigated a novel pseudogene, PDIA3P1, which was upregulated in HCC tissues compared with paired normal adjacent tissues. The expression of PDIA3P1 was significantly correlated with tumor size, metastasis, TNM stage, and overall stage. Knockdown of PDIA3P1decreased proliferation, migration, and invasion of HCC cells. PDIA3P1 knockdown also promoted cell apoptosis and inhibited tumor growth in vivo. We performed a GeneChip assay to investigate the underlying mechanism of PDIA3P1 action on biological function, and our results suggested that PDIA3P1 may promote proliferation and inhibit apoptosis of liver cancer cells by inhibiting the p53 pathway. Thus, our data suggest that PDIA3P1 acts as an oncogene in HCC and could be a potential prognostic marker and therapeutic target for HCC.

A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data

BACKGROUND

The Cancer Genome Atlas (TCGA) has generated comprehensive molecular profiles. We aim to identify a set of genes whose expression patterns can distinguish diverse tumor types. Those features may serve as biomarkers for tumor diagnosis and drug development.

METHODS

Using RNA-seq expression data, we undertook a pan-cancer classification of 9,096 TCGA tumor samples representing 31 tumor types. We randomly assigned 75% of samples into training and 25% into testing, proportionally allocating samples from each tumor type.

RESULTS

We could correctly classify more than 90% of the test set samples. Accuracies were high for all but three of the 31 tumor types, in particular, for READ (rectum adenocarcinoma) which was largely indistinguishable from COAD (colon adenocarcinoma). We also carried out pan-cancer classification, separately for males and females, on 23 sex non-specific tumor types (those unrelated to reproductive organs). Results from these gender-specific analyses largely recapitulated results when gender was ignored. Remarkably, more than 80% of the 100 most discriminative genes selected from each gender separately overlapped. Genes that were differentially expressed between genders included BNC1, FAT2, FOXA1, and HOXA11. FOXA1 has been shown to play a role for sexual dimorphism in liver cancer. The differentially discriminative genes we identified might be important for the gender differences in tumor incidence and survival.

CONCLUSIONS

We were able to identify many sets of 20 genes that could correctly classify more than 90% of the samples from 31 different tumor types using TCGA RNA-seq data. This accuracy is remarkable given the number of the tumor types and the total number of samples involved. We achieved similar results when we analyzed 23 non-sex-specific tumor types separately for males and females. We regard the frequency with which a gene appeared in those sets as measuring its importance for tumor classification. One third of the 50 most frequently appearing genes were pseudogenes; the degree of enrichment may be indicative of their importance in tumor classification. Lastly, we identified a few genes that might play a role in sexual dimorphism in certain cancers.

DNA methylation markers for diagnosis and prognosis of common cancers

The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.

Ultraconserved long non-coding RNA uc.63 in breast cancer

Transcribed-ultraconserved regions (T-UCRs) are long non-coding RNAs (lncRNA) encoded by a subset of long ultraconserved stretches in the human genome. Recent studies revealed that the expression of several T-UCRs is altered in cancer and growing evidences underline the importance of T-UCRs in oncogenesis, offering also potential new strategies for diagnosis and prognosis. We found that overexpression of one specific T-UCRs named uc.63 is associated with bad outcome in luminal A subtype of breast cancer patients. uc.63 is localized in the third intron of exportin-1 gene (XPO1) and is transcribed in the same orientation of its host gene. Interestingly, silencing of uc.63 induces apoptosis in vitro. However, silencing of host gene XPO1 does not cause the same effect suggesting that the transcription of uc.63 is independent of XPO1. Our results reveal an important role of uc.63 in promoting breast cancer cells survival and offer the prospect to identify a signature associated with poor prognosis.

Aberrant promoter methylation profiles and association with survival in patients with hepatocellular carcinoma

The aim of this study was to investigate the prognostic and diagnostic value of genes with promoter methylation in hepatocellular carcinoma (HCC) patients. On the basis of The Cancer Genome Atlas data, we identified genes with differentially methylated promoters in HCC tissues and adjacent non-tumor tissues, using the linear models for microarray data approach. Cox proportional hazard regression analysis was applied to access the prognostic value of identified differentially methylated genes. The diagnostic value of the genes was evaluated through receiver operating characteristic. Pathway analyses were performed to illustrate biological functions of the identified genes. Compared to adjacent tissues, 77 genes with hypermethylated promoters and 2,412 genes with hypomethylated promoters were identified in HCC. The promoter hypomethylations of RNA5SP38, IL21, SDC4P, and MIR4439 were found to be associated with HCC patient survival (P=0.035, 0.040, 0.004, and 0.024, respectively). Hypomethylated SDC4P was associated with a better prognosis (hazard ratio, 0.482; 95% confidence interval [CI], −0.147–1.110; P=0.007). The combination of the promoter hypomethylations with RNA5SP38, IL21, and SDC4P showed an area under receiver operating characteristic curves of 0.975 (95% CI, 0.962–0.989; P=4.811E-25). Several pathways, including olfactory transduction, cytokine–cytokine receptor interaction, natural killer cell–mediated cytotoxicity, as well as inflammation mediated by chemokine and cytokine signaling pathway, were annotated with the hypomethylated promoter genes. SDC4P promoter hypomethylation may be a potential prognosis biomarker. A panel of promoter methylations in RNA5SP38, IL21, and SDC4P was proven a novel approach to diagnosis HCC. The pathway analysis defined the extensive functional role of DNA hypomethylation in cancer.

The pseudogene-derived long noncoding RNA SFTA1P is down-regulated and suppresses cell migration and invasion in lung adenocarcinoma

Pseudogenes were once considered to be genomic fossils without biological function. Interestingly, recent evidence showed that a lot of pseudogenes are transcribed in human cancers, and their alterations contribute to multiple cancer development and progression. It is apparent that many pseudogenes transcribe noncoding RNAs and contribute to the role noncoding genome plays in human cancers. On this basis, some pseudogene transcripts are currently ranked among the classes of long noncoding RNAs. In this study, we identified a new pseudogene-derived long noncoding RNA termed SFTA1P by analyzing the microarray data of non-small cell lung cancer from Gene Expression Omnibus datasets. We found that SFTA1P expression was significantly decreased in non-small cell lung cancer tissues compared with normal tissues in non-small cell lung cancer microarray data. Moreover, decreased SFTA1P expression is only correlated with lung adenocarcinoma patients' poor survival time but not with lung squamous cell carcinoma patients' survival. In addition, gain-of-function studies including growth curves, migration, invasion assays, and in vivo studies were performed to verify the tumor suppressor role of SFTA1P in non-small cell lung cancer. Finally, the potential underlying pathways involved in SFTA1P were investigated by analyzing the SFTA1P-correlated genes in The Cancer Genome Atlas lung adenocarcinoma and normal tissues RNA sequencing data. Taken together, these findings demonstrate that pseudogene-derived long noncoding RNA SFTA1P exerts the tumor suppressor functions in human lung adenocarcinoma. Our investigation reveals the novel roles of pseudogene in lung adenocarcinoma, which may serve as a new target for lung adenocarcinoma diagnosis and therapy.

The Pseudogene DUXAP8 Promotes Non-small-cell Lung Cancer Cell Proliferation and Invasion by Epigenetically Silencing EGR1 and RHOB

Recently, the non-protein-coding functional elements in the human genome have been identified as key regulators in postgenomic biology, and a large number of pseudogenes as well as long non-coding RNAs (lncRNAs) have been found to be transcribed in multiple human cancers. However, only a small proportion of these pseudogenes has been functionally characterized. In this study, we screened for pseudogenes associated with human non-small-cell lung cancer (NSCLC) by comparative analysis of several independent datasets from the GEO. We identified a transcribed pseudogene named DUXAP8 that is upregulated in tumor tissues. Patients with higher DUXAP8 expression exhibited shorter survival, suggesting DUXAP8 as a new candidate prognostic marker for NSCLC patients. Knockdown of DUXAP8 impairs cell growth, migration, and invasion, and induces apoptosis both in vitro and in vivo. Mechanistically, DUXAP8 represses the tumor suppressors EGR1 and RHOB by recruiting histone demethylase LSD1 and histone methyltransferase EZH2, thereby promoting cell proliferation, migration, and invasion. These findings indicate that the pseudogene DUXAP8 may act as an oncogene in NSCLC by silencing EGR1 and RHOB transcription by binding with EZH2 and LSD1, which may offer a novel therapeutic target for this disease.

oncoNcRNA: A Web Portal for Exploring the Non-Coding RNAs with Oncogenic Potentials in Human Cancers

Non-coding RNAs (ncRNAs) have been shown to contribute to tumorigenesis and progression. However, the functions of the majority of ncRNAs remain unclear. Through integrating published large-scale somatic copy number alterations (SCNAs) data from various human cancer types, we have developed oncoNcRNA, a user-friendly web portal to explore ncRNAs with oncogenic potential in human cancers. The portal characterizes the SCNAs of over 58,000 long non-coding RNAs (lncRNAs), 34,000 piwi-interacting RNAs (piRNAs), 2700 microRNAs (miRNAs), 600 transfer RNAs (tRNAs) and 400 small nucleolar RNAs (snoRNAs) in 64 human cancer types. It enables researchers to rapidly and intuitively analyze the oncogenic potential of ncRNAs of interest. Indeed, we have discovered a large number of ncRNAs which are frequently amplified or deleted within and across tumor types. Moreover, we built a web-based tool, Correlations, to explore the relationships between gene expression and copy number from ~10,000 tumor samples in 36 cancer types identified by The Cancer Genome Atlas (TCGA). oncoNcRNA is a valuable tool for investigating the function and clinical relevance of ncRNAs in human cancers. oncoNcRNA is freely available at http://rna.sysu.edu.cn/onconcrna/.

Pseudogenes regulate parental gene expression via ceRNA network

The concept of competitive endogenous RNA (ceRNA) was first proposed by Salmena and colleagues. Evidence suggests that pseudogene RNAs can act as a 'sponge' through competitive binding of common miRNA, releasing or attenuating repression through sequestering miRNAs away from parental mRNA. In theory, ceRNAs refer to all transcripts such as mRNA, tRNA, rRNA, long non-coding RNA, pseudogene RNA and circular RNA, because all of them may become the targets of miRNA depending on spatiotemporal situation. As binding of miRNA to the target RNA is not 100% complementary, it is possible that one miRNA can bind to multiple target RNAs and vice versa. All RNAs crosstalk through competitively binding to miRNAvia miRNA response elements (MREs) contained within the RNA sequences, thus forming a complex regulatory network. The ratio of a subset of miRNAs to the corresponding number of MREs determines repression strength on a given mRNA translation or stability. An increase in pseudogene RNA level can sequester miRNA and release repression on the parental gene, leading to an increase in parental gene expression. A massive number of transcripts constitute a complicated network that regulates each other through this proposed mechanism, though some regulatory significance may be mild or even undetectable. It is possible that the regulation of gene and pseudogene expression occurring in this manor involves all RNAs bearing common MREs. In this review, we will primarily discuss how pseudogene transcripts regulate expression of parental genes via ceRNA network and biological significance of regulation.

Integrated methylome and transcriptome analysis reveals novel regulatory elements in pediatric acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is the most common cancer diagnosed in children under the age of 15. In addition to genetic aberrations, epigenetic modifications such as DNA methylation are altered in cancer and impact gene expression. To identify epigenetic alterations in ALL, genome-wide methylation profiles were generated using the methylated CpG island recovery assay followed by next-generation sequencing. More than 25,000 differentially methylated regions (DMR) were observed in ALL patients with ∼90% present within intronic or intergenic regions. To determine the regulatory potential of the DMR, whole-transcriptome analysis was performed and integrated with methylation data. Aberrant promoter methylation was associated with the altered expression of genes involved in transcriptional regulation, apoptosis, and proliferation. Novel enhancer-like sequences were identified within intronic and intergenic DMR. Aberrant methylation in these regions was associated with the altered expression of neighboring genes involved in cell cycle processes, lymphocyte activation and apoptosis. These genes include potential epi-driver genes, such as SYNE1, PTPRS, PAWR, HDAC9, RGCC, MCOLN2, LYN, TRAF3, FLT1, and MELK, which may provide a selective advantage to leukemic cells. In addition, the differential expression of epigenetic modifier genes, pseudogenes, and non-coding RNAs was also observed accentuating the role of erroneous epigenetic gene regulation in ALL.

Pan-Cancer Analyses Reveal Long Intergenic Non-Coding RNAs Relevant to Tumor Diagnosis, Subtyping and Prognosis

Long intergenic noncoding RNAs (lincRNAs) are a relatively new class of non-coding RNAs that have the potential as cancer biomarkers. To seek a panel of lincRNAs as pan-cancer biomarkers, we have analyzed transcriptomes from over 3300 cancer samples with clinical information. Compared to mRNA, lincRNAs exhibit significantly higher tissue specificities that are then diminished in cancer tissues. Moreover, lincRNA clustering results accurately classify tumor subtypes. Using RNA-Seq data from thousands of paired tumor and adjacent normal samples in The Cancer Genome Atlas (TCGA), we identify six lincRNAs as potential pan-cancer diagnostic biomarkers (PCAN-1 to PCAN-6). These lincRNAs are robustly validated using cancer samples from four independent RNA-Seq data sets, and are verified by qPCR in both primary breast cancers and MCF-7 cell line. Interestingly, the expression levels of these six lincRNAs are also associated with prognosis in various cancers. We further experimentally explored the growth and migration dependence of breast and colon cancer cell lines on two of the identified lncRNAs. In summary, our study highlights the emerging role of lincRNAs as potentially powerful and biologically functional pan-cancer biomarkers and represents a significant leap forward in understanding the biological and clinical functions of lincRNAs in cancers.

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LincRNAs exhibit significantly higher tissue specificities that mRNAs, which are then diminished in cancer tissues.

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LincRNAs are highly deregulated in cancers and their expression strongly correlates with molecular subtypes

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A panel of diagnostic lincRNA biomarkers are discovered using the pan-cancer samples of The Cancer Genome Atlas (TCGA), and further validated with multiple independent data sets.

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Knocking down experiments of some pan-cancer up-regulated lincRNAs slow down the cell growth and migration in some cancer cell lines, suggesting that lincRNAs may be biologically functional.

Most of the work on cancer characterization, diagnosis, prognosis and treatment have been focused on the protein coding genes. Long intergenic non-coding RNAs (lincRNAs) are a relatively new class of RNA molecules that are understudied for their biological and clinical functions. This report aims to expand our understanding on the roles of lincRNA. Specifically, we demonstrate the relevance of lincRNAs to tumor diagnosis, subtyping and prognosis. We further propose a panel of lincRNAs as potentially robust pan-cancer diagnostic biomarkers.

A pan-cancer analysis of prognostic genes

Numerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene’s association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently recapitulated by both unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes, and for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

Proteogenomics for understanding oncology: recent advances and future prospects

The concept of proteogenomics has emerged rapidly as a valuable approach to integrate mass spectrometry-derived proteomic data with genomic and transcriptomic data. It is used to harness the full potential of the former dataset in the discovery of potential biomarkers, therapeutic targets and novel proteins associated with various biological processes including diseases. Proteogenomic strategies have been successfully utilized to identify novel genes and redefine annotation of existing gene models in various genomes. In recent years, this approach has been extended to the field of cancer biology to unravel complexities in the tumor genomes and proteomes. Standard proteomics workflows employing translated cancer genomes and transcriptomes can potentially identify peptides from mutant proteins, splice variants and fusion proteins in the tumor proteome, which in addition to the currently available biomarker panels can serve as potential diagnostic and prognostic biomarkers, besides having therapeutic utility. This review focuses on the role of proteogenomics to understand cancer biology.

The LINK-A lncRNA activates normoxic HIF1α signalling in triple-negative breast cancer

Although long noncoding RNAs (lncRNAs) predominately reside in nuclear and exert their functions in many biological processes, their potential involvement in cytoplasmic signal transduction remains unexplored. Here, we identified a cytoplasmic lncRNA, Long-Intergenic Noncoding RNA for Kinase Activation (LINK-A), which mediates HB-EGF triggered, EGFR:GPNMB heterodimer-dependent HIF1α phosphorylation at Tyr565 and Ser797 by BRK and LRRK2 respectively. These events cause HIF1α stabilization, HIF1α-p300 interaction, and activation of HIF1α transcriptional programs under normoxic conditions. Mechanistically, LINK-A facilitates the recruitment of BRK to EGFR:GPNMB complex and BRK kinase activation. The BRK-dependent HIF1α Tyr565 phosphorylation interferes with Pro564 hydroxylation, leading to normoxic HIF1α stabilization. Both LINK-A and LINK-A-dependent signaling pathway activation correlate with TNBC, promoting breast cancer glycolysis reprogramming and tumorigenesis. Our findings illustrate the magnitude and diversity of cytoplasmic lncRNAs in signal transduction and highlight the important roles of lncRNAs in cancer.

Many lncRNAs, 5'UTRs, and pseudogenes are translated and some are likely to express functional proteins

Using a new bioinformatic method to analyze ribosome profiling data, we show that 40% of lncRNAs and pseudogene RNAs expressed in human cells are translated. In addition, ~35% of mRNA coding genes are translated upstream of the primary protein-coding region (uORFs) and 4% are translated downstream (dORFs). Translated lncRNAs preferentially localize in the cytoplasm, whereas untranslated lncRNAs preferentially localize in the nucleus. The translation efficiency of cytoplasmic lncRNAs is nearly comparable to that of mRNAs, suggesting that cytoplasmic lncRNAs are engaged by the ribosome and translated. While most peptides generated from lncRNAs may be highly unstable byproducts without function, ~9% of the peptides are conserved in ORFs in mouse transcripts, as are 74% of pseudogene peptides, 24% of uORF peptides and 32% of dORF peptides. Analyses of synonymous and nonsynonymous substitution rates of these conserved peptides show that some are under stabilizing selection, suggesting potential functional importance.

DOI:http://dx.doi.org/10.7554/eLife.08890.001

Our genes encode the instructions needed to make proteins. When a gene is switched on, it’s DNA is used as a template to make molecules of messenger ribonucleic acid (RNA). These RNAs are then “translated” into proteins by large cell machines called ribosomes. Within the messenger RNA, a long region called an “open reading frame” is the section that encodes the protein.

The human genome also contains a vast amount of DNA that is not part of any gene. Cells can produce molecules of RNA from this DNA (so-called “non-coding RNAs”), but these RNAs are not thought to code for proteins because they lack long open reading frames. Non-coding RNAs can also be made from sections of DNA called “pseudogenes”, which have lost their ability to code for proteins over the course of evolution. Furthermore, messenger RNAs also contain short open reading frames in the “untranslated” regions that flank the protein-coding region.

The extent to which cells translate non-coding RNAs to produce small proteins (or peptides) is not known. “Ribosome profiling” is a powerful method to determine which RNAs are translated, but it is not always possible to distinguish between the RNAs that are genuinely translated and those that just happen to be bound to ribosomes. Ji et al. overcome these limitations by developing a new computational method to analyse data from ribosome profiling.

The experiments show that thousands of non-coding RNAs in the human genome are, in fact, translated. This is many more than anticipated and represents approximately 40% of the lncRNAs and pseudogene RNAs, and 35% of untranslated regions in messenger RNAs. Ji et al. also found that a small group of all the lncRNA peptides in the human genome appear to have changed little over the course of evolution, which strongly suggests that they have specific roles in cells. The next challenge is to find out what roles the peptides encoded by these lncRNAs play in cells.

DOI:http://dx.doi.org/10.7554/eLife.08890.002

RNA editing in cancer: Mechanistic, prognostic, and therapeutic implications

We have recently provided a comprehensive analysis of A-to-I RNA editing events in various cancer types, revealing many clinically relevant RNA editing sites and demonstrating that RNA editing can selectively affect cancer drug sensitivity. Our results unveil mechanistic, prognostic, and therapeutic implications for RNA editing in cancer.

Pseudogene-expressed RNAs: a new frontier in cancers

Over the past decade, the importance of non-protein-coding functional elements in the human genome has emerged from the water and been identified as a key revelation in post-genomic biology. Since the completion of the ENCODE (Encyclopedia of DNA Elements) and FANTOM (Functional Annotation of Mammals) project, tens of thousands of pseudogenes as well as numerous long non-coding RNA (lncRNA) genes were identified. However, while pseudogenes were initially regarded as non-functional relics littering the human genome during evolution, recent studies have revealed that they play critical roles at multiple levels in diverse physiological and pathological processes, especially in cancer through parental-gene-dependent or parental-gene-independent regulation. Herein, we review the current knowledge of pseudogenes and synthesize the nascent evidence for functional properties and regulatory modalities exerted by pseudogene-transcribed RNAs in human cancers and prospect the potential as molecular signatures in cancer reclassification and tailored therapy.

RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics

Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and local responses to tumor growth, thereby altering their RNA profile. We determined the diagnostic potential of TEPs by mRNA sequencing of 283 platelet samples. We distinguished 228 patients with localized and metastasized tumors from 55 healthy individuals with 96% accuracy. Across six different tumor types, the location of the primary tumor was correctly identified with 71% accuracy. Also, MET or HER2-positive, and mutant KRAS, EGFR, or PIK3CA tumors were accurately distinguished using surrogate TEP mRNA profiles. Our results indicate that blood platelets provide a valuable platform for pan-cancer, multiclass cancer, and companion diagnostics, possibly enabling clinical advances in blood-based "liquid biopsies".

Differentially-Expressed Pseudogenes in HIV-1 Infection

Not all pseudogenes are transcriptionally silent as previously thought. Pseudogene transcripts, although not translated, contribute to the non-coding RNA pool of the cell that regulates the expression of other genes. Pseudogene transcripts can also directly compete with the parent gene transcripts for mRNA stability and other cell factors, modulating their expression levels. Tissue-specific and cancer-specific differential expression of these “functional” pseudogenes has been reported. To ascertain potential pseudogene:gene interactions in HIV-1 infection, we analyzed transcriptomes from infected and uninfected T-cells and found that 21 pseudogenes are differentially expressed in HIV-1 infection. This is interesting because parent genes of one-third of these differentially-expressed pseudogenes are implicated in HIV-1 life cycle, and parent genes of half of these pseudogenes are involved in different viral infections. Our bioinformatics analysis identifies candidate pseudogene:gene interactions that may be of significance in HIV-1 infection. Experimental validation of these interactions would establish that retroviruses exploit this newly-discovered layer of host gene expression regulation for their own benefit.

Pseudogenes in Human Cancer

Recent advances in the analysis of RNA sequencing data have shown that pseudogenes are highly specific markers of cell identity and can be used as diagnostic and prognostic markers. Furthermore, genetically engineered mouse models have recently provided compelling support for a causal link between altered pseudogene expression and cancer. In this review, we discuss the most recent milestones reached in the pseudogene field and the use of pseudogenes as cancer classifiers.