BioXpress: an integrated RNA-seq-derived gene expression database for pan-cancer analysis

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

The RNA world: from experimental laboratory to "in silico" approach. Part 1: User friendly RNA expression databases portals

Cellular information about "life instruction" is stored, transferred, and modified using different types of RNA molecules. During the last decades, a growing number of RNA data has been generated thanks to the development of microarray chips and next-generation sequencing (NGS) methods. Improvement of bioinformatics contributed to the discovery of many types of new non-coding RNAs (ncRNAs), mostly with regulatory functions that supplemented the knowledge about the world of RNA. All of it, as well as the Human Genome Project (HGP) and the Cancer Genome Atlas (TCGA) project, has resulted in the formation of data storage and analysis portals which are widely used in cancer research and moved science from in vitro to in silico research. In this review we presented and discussed the data storage and analysis portals used by us, such as cBioPortal, UALCAN, ENCORI, and others. During the revision of these sites, we paid attention to data integration, simplicity of analysis, and results visualization, which are important for users without bioinformatic or statistical skills. In our opinion, the RNA analysis online tools will rapidly develop during the next decade and it seems to be a way for personalization of cancer treatment.

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

Unearthing FLVCR1a: tracing the path to a vital cellular transporter

The Feline Leukemia Virus Subgroup C Receptor 1a (FLVCR1a) is a member of the SLC49 Major Facilitator Superfamily of transporters. Initially recognized as the receptor for the retrovirus responsible of pure red cell aplasia in cats, nearly two decades since its discovery, FLVCR1a remains a puzzling transporter, with ongoing discussions regarding what it transports and how its expression is regulated. Nonetheless, despite this, the substantial body of evidence accumulated over the years has provided insights into several critical processes in which this transporter plays a complex role, and the health implications stemming from its malfunction. The present review intends to offer a comprehensive overview and a critical analysis of the existing literature on FLVCR1a, with the goal of emphasising the vital importance of this transporter for the organism and elucidating the interconnections among the various functions attributed to this transporter.

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.

Elucidating the molecular role of MUC5B in progressive lung adenocarcinoma: Prospects for early diagnosis

Gel-forming mucin MUC5B is significantly deregulated in lung adenocarcinoma (LUAD), however, its role in tumor progression is not yet clearly understood. Here, we used an integrated computational-pipeline-initiated with gene expression analysis followed by network, functional-enrichment, O-linked glycosylation analyses, mutational profiling, and immune cell infiltration estimation to functionally characterize MUC5B gene in LUAD. Thereafter, clinical biomarker validation was supported by the overall survival (OA) and comparative expression profiling across clinical stages using computational algorithms. The gene expression profile of LUAD identified MUC5B to be significantly up-regulated (logFC: 2.36; p-value: 0.01). Network analysis on LUAD interactome screened MUC5B-related genes, having key enrichment in immune suppression and O-linked glycosylation with serine-threonine-rich tandem repeats being highly glycosylated. Furthermore, positive correlation of mutant MUC5B with immune cells in tumor microenvironment (TME) such as cancer-associated fibroblasts and myeloid-derived suppressor cells indicates TME-mediated tumor progression. The positive correlation with immune inhibitors suggested the enhanced tumor proliferation mediated by MUC5B. Structural stability due to genetic alterations identified overall rigid N-H-backbone dynamics (S2 : 0.756), indicating an overall stable mutant protein. Moreover, the low median OA (<50 months) with a hazard ratio of 1.4 and clinical profile of MUC5B gene showed high median expression corresponding to lymph node (N2) and tumor (T3) stages. Our study concludes by highlighting the functional role of O-glycosylated and mutant MUC5B in promoting LUAD by immune suppression. Further, clinical gene expression validation of MUC5B suggests its potential role as a diagnostic biomarker for LUAD metastasis.

Detection of single nucleotide polymorphism in HTR4 and its relationship with growth traits in sheep

In this study, a single nucleotide polymorphism of HTR4 (hydroxytryptamine receptor 4) was detected using DNA sequencing and KASPar (Kompetitive Allele-Specific PCR) technique with the aim of analyzing its effect on growth traits in 1102 sheep. A synonymous mutation g.101220 C > T located on the fifth intron of the ovis HTR4 gene was detected, and association analysis showed that this mutation was significantly associated with growth traits in sheep (p ＜.05), with TT genotypes having significantly lower body weight, height, length and chest circumference than TC and CC genotypes. It showed that the polymorphism of this locus was significantly associated with growth traits in sheep. In addition, qRT-PCR results showed that HTR4 was expressed in different tissues of sheep. It is highly expressed in the liver, spleen and duodenum. As important metabolic, immune and digestive absorption organs in animals, the above tissues can regulate the excitability of intestinal smooth muscle by participating in the body metabolism and nutrient metabolism of sheep, so that sheep can show better growth characteristics. In conclusion, the polymorphic locus identified in HTR4 gene can be used as candidate molecular marker in sheep breeding.

Unveiling the Biomarkers of Cancer and COVID-19 and Their Regulations in Different Organs by Integrating RNA-Seq Expression and Protein-Protein Interactions

Cancer and COVID-19 have killed millions of people worldwide. COVID-19 is even more dangerous to people with comorbidities such as cancer. Thus, it is imperative to identify the key human genes or biomarkers that can be targeted to develop novel prognosis and therapeutic strategies. The transcriptomic data provided by the next-generation sequencing technique makes this identification very convenient. Hence, mRNA (messenger ribonucleic acid) expression data of 2265 cancer and 282 normal patients were considered, while for COVID-19 assessment, 784 and 425 COVID-19 and normal patients were taken, respectively. Initially, volcano plots were used to identify the up- and down-regulated genes for both cancer and COVID-19. Thereafter, protein-protein interaction (PPI) networks were prepared by combining all the up- and down-regulated genes for each of cancer and COVID-19. Subsequently, such networks were analyzed to identify the top 10 genes with the highest degree of connection to provide the biomarkers. Interestingly, these genes were all up-regulated for cancer, while they were down-regulated for COVID-19. This study had also identified common genes between cancer and COVID-19, all of which were up-regulated in both the diseases. This analysis revealed that FN1 was highly up-regulated in different organs for cancer, while EEF2 was dysregulated in most organs affected by COVID-19. Then, functional enrichment analysis was performed to identify significant biological processes. Finally, the drugs for cancer and COVID-19 biomarkers and the common genes between them were identified using the Enrichr online web tool. These drugs include lucanthone, etoposide, and methotrexate, targeting the biomarkers for cancer, while paclitaxel is an important drug for COVID-19.

Network-based cancer genomic data integration for pattern discovery

BACKGROUND

Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted.

RESULTS

In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered.

CONCLUSIONS

All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

Non-Coding RNA-Based Biosensors for Early Detection of Liver Cancer

Primary liver cancer is an aggressive, lethal malignancy that ranks as the fourth leading cause of cancer-related death worldwide. Its 5-year mortality rate is estimated to be more than 95%. This significant low survival rate is due to poor diagnosis, which can be referred to as the lack of sufficient and early-stage detection methods. Many liver cancer-associated non-coding RNAs (ncRNAs) have been extensively examined to serve as promising biomarkers for precise diagnostics, prognostics, and the evaluation of the therapeutic progress. For the simple, rapid, and selective ncRNA detection, various nanomaterial-enhanced biosensors have been developed based on electrochemical, optical, and electromechanical detection methods. This review presents ncRNAs as the potential biomarkers for the early-stage diagnosis of liver cancer. Moreover, a comprehensive overview of recent developments in nanobiosensors for liver cancer-related ncRNA detection is provided.

Molecular basis for RASSF10/NPM/RNF2 feedback cascade-mediated regulation of gastric cancer cell proliferation

Ras-association domain family (RASSF) proteins are encoded by numerous tumor suppressor genes that frequently become silenced in human cancers. RASSF10 is downregulated by promoter hypermethylation in cancers and has been shown to inhibit cell proliferation; however, the molecular mechanism(s) remains poorly understood. Here, we demonstrate for the first time that RASSF10 inhibits Cdk1/cyclin-B kinase complex formation to maintain stable levels of cyclin-B for inducing mitotic arrest during cell cycle. Using LC-MS/MS, live cell imaging, and biochemical approaches, we identify Nucleophosmin (NPM) as a novel functional target of RASSF10 and revealed that RASSF10 expression promoted the nuclear accumulation of GADD45a and knockdown of either NPM or GADD45a, resulting in impairment of RASSF10-mediated G2/M phase arrest. Furthermore, we demonstrate that RASSF10 is a substrate for the E3 ligase ring finger protein 2 (RNF2) and show that an NPM-dependent downregulation of RNF2 expression is critical to maintain stable RASSF10 levels in cells for efficient mitotic arrest. Interestingly, the Kaplan-Meier plot analysis shows a positive correlation of RASSF10 and NPM expression with greater gastric cancer patient survival and the reverse with expression of RNF2, suggesting that they may have a role in cancer progression. Finally, our findings provide insights into the mode of action of the RASSF10/NPM/RNF2 signaling cascade on controlling cell proliferation and may represent a novel therapeutic avenue for the prevention of gastric cancer metastasis.

The heme synthesis-export system regulates the tricarboxylic acid cycle flux and oxidative phosphorylation

Heme is an iron-containing porphyrin of vital importance for cell energetic metabolism. High rates of heme synthesis are commonly observed in proliferating cells. Moreover, the cell-surface heme exporter feline leukemia virus subgroup C receptor 1a (FLVCR1a) is overexpressed in several tumor types. However, the reasons why heme synthesis and export are enhanced in highly proliferating cells remain unknown. Here, we illustrate a functional axis between heme synthesis and heme export: heme efflux through the plasma membrane sustains heme synthesis, and implementation of the two processes down-modulates the tricarboxylic acid (TCA) cycle flux and oxidative phosphorylation. Conversely, inhibition of heme export reduces heme synthesis and promotes the TCA cycle fueling and flux as well as oxidative phosphorylation. These data indicate that the heme synthesis-export system modulates the TCA cycle and oxidative metabolism and provide a mechanistic basis for the observation that both processes are enhanced in cells with high-energy demand.

CancerLivER: a database of liver cancer gene expression resources and biomarkers

Liver cancer is the fourth major lethal malignancy worldwide. To understand the development and progression of liver cancer, biomedical research generated a tremendous amount of transcriptomics and disease-specific biomarker data. However, dispersed information poses pragmatic hurdles to delineate the significant markers for the disease. Hence, a dedicated resource for liver cancer is required that integrates scattered multiple formatted datasets and information regarding disease-specific biomarkers. Liver Cancer Expression Resource (CancerLivER) is a database that maintains gene expression datasets of liver cancer along with the putative biomarkers defined for the same in the literature. It manages 115 datasets that include gene-expression profiles of 9611 samples. Each of incorporated datasets was manually curated to remove any artefact; subsequently, a standard and uniform pipeline according to the specific technique is employed for their processing. Additionally, it contains comprehensive information on 594 liver cancer biomarkers which include mainly 315 gene biomarkers or signatures and 178 protein- and 46 miRNA-based biomarkers. To explore the full potential of data on liver cancer, a web-based interactive platform was developed to perform search, browsing and analyses. Analysis tools were also integrated to explore and visualize the expression patterns of desired genes among different types of samples based on individual gene, GO ontology and pathways. Furthermore, a dataset matrix download facility was provided to facilitate the users for their extensive analysis to elucidate more robust disease-specific signatures. Eventually, CancerLivER is a comprehensive resource which is highly useful for the scientific community working in the field of liver cancer.Availability: CancerLivER can be accessed on the web at https://webs.iiitd.edu.in/raghava/cancerliver.

Processing and Analysis of RNA-seq Data from Public Resources

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data

PURPOSE

The purpose of OncoMX¹ knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types.

METHODS

Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database.

RESULTS

OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs.

CONCLUSION

OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

Cancer classification of single-cell gene expression data by neural network

MOTIVATION

Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq).

RESULTS

We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples.

AVAILABILITY AND IMPLEMENTATION

Cancer classification by neural network.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Ubiquitin Gene Expression Pattern and Sensitivity to UBB and UBC Knockdown Differentiate Primary 23132/87 and Metastatic MKN45 Gastric Cancer Cells

Gastric cancer (GC) is one of the most common and lethal cancers. Alterations in the ubiquitin (Ub) system play key roles in the carcinogenetic process and in metastasis development. Overexpression of transcription factors YY1, HSF1 and SP1, known to regulate Ub gene expression, is a predictor of poor prognosis and shorter survival in several cancers. In this study, we compared a primary (23132/87) and a metastatic (MKN45) GC cell line. We found a statistically significant higher expression of three out of four Ub coding genes, UBC, UBB and RPS27A, in MKN45 compared to 23132/87. However, while the total Ub protein content and the distribution of Ub between the conjugated and free pools were similar in these two GC cell lines, the proteasome activity was higher in MKN45. Ub gene expression was not affected upon YY1, HSF1 or SP1 small interfering RNA (siRNA) transfection, in both 23132/87 and MKN45 cell lines. Interestingly, the simultaneous knockdown of UBB and UBC mRNAs reduced the Ub content in both cell lines, but was more critical in the primary GC cell line 23132/87, causing a reduction in cell viability due to apoptosis induction and a decrease in the oncoprotein and metastatization marker β-catenin levels. Our results identify UBB and UBC as pro-survival genes in primary gastric adenocarcinoma 23132/87 cells.

An enhanced chemopreventive effect of methyl donor S-adenosylmethionine in combination with 25-hydroxyvitamin D in blocking mammary tumor growth and metastasis

Therapeutic targeting of metastatic breast cancer still remains a challenge as the tumor cells are highly heterogenous and exploit multiple pathways for their growth and metastatic spread that cannot always be targeted by a single-agent monotherapy regimen. Therefore, a rational approach through simultaneous targeting of several pathways may provide a better anti-cancer therapeutic effect. We tested this hypothesis using a combination of two nutraceutical agents S-adenosylmethionine (SAM) and Vitamin D (Vit. D) prohormone [25-hydroxyvitamin D; '25(OH)D'] that are individually known to exert distinct changes in the expression of genes involved in tumor growth and metastasis. Our results show that both SAM and 25(OH)D monotherapy significantly reduced proliferation and clonogenic survival of a panel of breast cancer cell lines in vitro and inhibited tumor growth, lung metastasis, and breast tumor cell colonization to the skeleton in vivo. However, these effects were significantly more pronounced in the combination setting. RNA-Sequencing revealed that the transcriptomic footprint on key cancer-related signaling pathways is broader in the combination setting than any of the monotherapies. Furthermore, comparison of the differentially expressed genes from our transcriptome analyses with publicly available cancer-related dataset demonstrated that the combination treatment upregulates genes from immune-related pathways that are otherwise downregulated in bone metastasis in vivo. Since SAM and Vit. D are both approved nutraceuticals with known safety profiles, this combination treatment may serve as a novel strategy to reduce breast cancer-associated morbidity and mortality.

uPAR antibody (huATN-658) and Zometa reduce breast cancer growth and skeletal lesions

Urokinase plasminogen activator receptor (uPAR) is implicated in tumor growth and metastasis due to its ability to activate latent growth factors, proteases, and different oncogenic signaling pathways upon binding to different ligands. Elevated uPAR expression is correlated with the increased aggressiveness of cancer cells, which led to its credentialing as an attractive diagnostic and therapeutic target in advanced solid cancer. Here, we examine the antitumor effects of a humanized anti-uPAR antibody (huATN-658) alone and in combination with the approved bisphosphonate Zometa (Zoledronic acid) on skeletal lesion through a series of studies in vitro and in vivo. Treatment with huATN-658 or Zometa alone significantly decreased human MDA-MB-231 cell proliferation and invasion in vitro, effects which were more pronounced when huATN-658 was combined with Zometa. In vivo studies demonstrated that huATN-658 treatment significantly reduced MDA-MB-231 primary tumor growth compared with controls. In a model of breast tumor-induced bone disease, huATN-658 and Zometa were equally effective in reducing skeletal lesions. The skeletal lesions were significantly reduced in animals receiving the combination of huATN-658 + Zometa compared with monotherapy treatment. These effects were due to a significant decrease in osteoclastic activity and tumor cell proliferation in the combination treatment group. Transcriptome analysis revealed that combination treatment significantly changes the expression of genes from signaling pathways implicated in tumor progression and bone remodeling. Results from these studies provide a rationale for the continued development of huATN-658 as a monotherapy and in combination with currently approved agents such as Zometa in patients with metastatic breast cancer.

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations

Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein-protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.

LINC00319-Mediated miR-3127 Repression Enhances Bladder Cancer Progression Through Upregulation of RAP2A

Recent studies suggested that microRNA-3127 (miR-3127) was dysregulated in multiple tumor types and has important roles in tumorigenesis and cancer progression. However, its biological roles and the mechanisms that regulate its expression in bladder cancer (BCA) remain to be determined. The expression level of miR-3127 was measured in BCA tissues and its cellular functions were examined using both in vitro and in vivo experiments. The interaction between miR-3127 and long non-coding RNA (lncRNA) LINC00319 was explored using RNA immunoprecipitation assay and luciferase reporter assays. We showed that miR-3127 expression was significantly downregulated in human BCA tissues and BCA cell lines. Lower miR-3127 levels were associated with worse survival in BCA patients. The overexpression of miR-3127 impaired BCA cell proliferation and invasion, and the knockdown of miR-3127 enhanced BCA cell proliferation and invasion in vitro. Importantly, miR-3127 was able to suppress cell growth in vivo. We demonstrated that miR-3127 repressed the proliferation and invasion of BCA cells though directly targeted the 3′-UTR of RAP2A, which served as a novel oncogene in BCA cells. The suppression of cell proliferation and invasion caused by miR-3127 overexpression could be partially abrogated by ectopic expression of RAP2A. Furthermore, high expression of LINC00319 was correlated with adverse survival in BCA patients. LINC00319 could bind directly with miR-3127 and inhibited its expression, and the tumor-promoting effects of LINC00319 could be reversed by re-expression of miR-3127 in BCA cells. Our findings indicated that lncRNA LINC00319-mediated miR-3127 repression promotes BCA progression through the upregulation of RAP2A. The re-introduction of miR-3127 or inhibition of LINC00319 might represent a promising therapeutic strategy for BCA treatment.

Long noncoding RNA DLX6-AS1 promotes cell growth and invasiveness in bladder cancer via modulating the miR-223-HSP90B1 axis

Long noncoding RNA (lncRNA) regulate many biological processes ranging from tumorigenesis to cancer metastasis. MicroRNA-223 (miR-223) acts as a novel tumor suppressor in bladder cancer (BC), however its target genes involved in BC, the molecular mechanisms governing its expression remain largely unknown. Both gain-of-function and loss of function experiments were performed to investigate the role of miR-223 in BC cells. The effects of miR-223 on BC progression were assessed using in vivo subcutaneous xenografts. The luciferase reporter assays were utilized to confirm the putative miR-223-binding site in the 3'-UTR of oncogene HSP90B1. The luciferase reporter assays and RNA immunoprecipitation assays were used to analyze the association between miR-223 and lncRNA DXL6-AS1 in BC cells. The expression of miR-223 was remarkably decreased in BC samples and BC cells. High miR-223 expression was correlated with favorable patient survival. BC cell growth in vivo was delayed by miR-223 overexpression. HSP90B1 was a direct target of miR-223 in BC cells, and the suppression of BC cell growth and invasion induced by miR-223 could be rescued by overexpression of HSP90B1. Moreover, lncRNA DXL6-AS1 was upregulated in BC tissues and functioned as a sponge for miR-223 and reduced its expression in BC cells, thereby enhancing cell proliferation and invasion. Forced expression of miR-223 could reverse the oncogenic effects of DXL6-AS1 on BC cell proliferation and invasion. Our study suggested that DLX6-AS1-mediated silencing of miR-223 promotes BC progression through the upregulation of HSP90B1.

DNA methylation profiling to explore colorectal tumor differences according to menopausal hormone therapy use in women

Aim: Use of menopausal hormone therapy (MHT) has been associated with a reduced risk for colorectal cancer, but mechanisms underlying this relationship are not well understood. In the colon, MHT appears to act through estrogen receptor β (ERβ) which may influence DNA methylation by binding to DNA. Using genome-wide methylation profiling data, we aimed to identify genes that may be differentially methylated according to MHT use. Materials & methods: DNA methylation was measured using Illumina HumanMethylation450k arrays in two independent tumor sample sets of colorectal cancer patients. Differential methylation was determined using R/limma. Results: In the discovery analysis, two CpG sites showed differential DNA methylation according to MHT use, both were not replicated. In stratified analyses, 342 CpG sites were associated with current MHT use only in ERβ-positive tumors. Conclusion: The suggestive findings of differential methylation according to current MHT use in ERβ-positive tumors warrant further investigation.

Structure, regulation, and (patho-)physiological functions of the stress-induced protein kinase CK1 delta (CSNK1D)

Members of the highly conserved pleiotropic CK1 family of serine/threonine-specific kinases are tightly regulated in the cell and play crucial regulatory roles in multiple cellular processes from protozoa to human. Since their dysregulation as well as mutations within their coding regions contribute to the development of various different pathologies, including cancer and neurodegenerative diseases, they have become interesting new drug targets within the last decade. However, to develop optimized CK1 isoform-specific therapeutics in personalized therapy concepts, a detailed knowledge of the regulation and functions of the different CK1 isoforms, their various splice variants and orthologs is mandatory. In this review we will focus on the stress-induced CK1 isoform delta (CK1δ), thereby addressing its regulation, physiological functions, the consequences of its deregulation for the development and progression of diseases, and its potential as therapeutic drug target.

DDX49 is an RNA helicase that affects translation by regulating mRNA export and the levels of pre-ribosomal RNA

Among the proteins predicted to be a part of the DExD box RNA helicase family, the functions of DDX49 are unknown. Here, we characterize the enzymatic activities and functions of DDX49 by comparing its properties with the well-studied RNA helicase, DDX39B. We find that DDX49 exhibits a robust ATPase and RNA helicase activity, significantly higher than that of DDX39B. DDX49 is required for the efficient export of poly (A)+ RNA from nucleus in a splicing-independent manner. Furthermore, DDX49 is a resident protein of nucleolus and regulates the steady state levels of pre-ribosomal RNA by regulating its transcription and stability. These dual functions of regulating mRNA export and pre-ribosomal RNA levels enable DDX49 to modulate global translation. Phenotypically, DDX49 promotes proliferation and colony forming potential of cells. Strikingly, DDX49 is significantly elevated in diverse cancer types suggesting that the increased abundance of DDX49 has a role in oncogenic transformation of cells. Taken together, this study shows the physiological role of DDX49 in regulating distinct steps of mRNA and pre-ribosomal RNA metabolism and hence translation and potential pathological role of its dysregulation, especially in cancers.

GENT2: an updated gene expression database for normal and tumor tissues

BACKGROUND

Gene Expression database of Normal and Tumor tissues 2 (GENT2) is an updated version of GENT, which has provided a user-friendly search platform for gene expression patterns across different normal and tumor tissues compiled from public gene expression data sets.

RESULTS

We refactored GENT2 with recent technologies such as Apache Lucene indexing for fast search and Google Web Toolkit (GWT) framework for a user-friendly web interface. Now, GENT2 contains more than 68,000 samples and has several new useful functions. First, GENT2 now provides gene expression across 72 different tissues compared to 57 in GENT. Second, with increasing importance of tumor subtypes, GENT2 provides an option to study the differential expression and its prognostic significance based on tumor subtypes. Third, whenever available, GENT2 provides prognostic information of a gene of interest. Fourth, GENT2 provides a meta-analysis of survival information to provide users more reliable prognostic value of a gene of interest.

CONCLUSIONS

In conclusion, with these significant improvements, GENT2 will continue to be a useful tool to a wide range of researchers. GENT2 is freely available at http://gent2.appex.kr .

MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

A Primer for Access to Repositories of Cancer-Related Genomic Big Data

The use of large datasets has become ubiquitous in biomedical sciences. Researchers in the field of cancer genomics have, in recent years, generated large volumes of data from their experiments. Those responsible for production of this data often analyze a narrow subset of this data based on the research question they are trying to address: this is the case whether or not they are acting independently or in conjunction with a large-scale cancer genomics project. The reality of this situation creates the opportunity for other researchers to repurpose this data for different hypotheses if the data is made easily and freely available. New insights in biology resulting from more researchers having access to data they otherwise would be unable to generate on their own are a boon for the field. The following chapter reviews several cancer genomics-related databases and outlines the type of data they contain, as well as the methods required to access each database. While this list is not comprehensive, it should provide a basis for cancer researchers to begin exploring some of the many large datasets that are available to them.

Identification of ESM1 overexpressed in head and neck squamous cell carcinoma

Background

Endocan, also known as endothelial cell specific molecule-1 (ESM1), is a 50 kDa soluble proteoglycan which is frequently overexpressed in many cancer types. Whether it is dysregulated in head and neck squamous cell carcinoma (HNSCC) has not been investigated.

Methods

We analyzed the expression of ESM1 using bioinformatics analysis based on data from The Cancer Genome Atlas (TCGA), and then validated that ESM1 was significantly overexpressed in human HNSCC at the protein level using immunohistochemistry. We also analyzed the genes co-expressed with ESM1 in HNSCC.

Results

The most correlated gene was angiopoietin-2 (ANGPT2), a molecule which regulates physiological and pathological angiogenesis. Several transcription factor binding motifs including SMAD3, SMAD4, SOX3, SOX4, HIF2A and AP-1 components were significantly enriched in the promoter regions of the genes co-expressed with ESM1. Further analysis based on ChIP-seq data from the ENCODE (Encyclopedia of DNA Elements) project revealed that AP-1 is an important regulator of ESM1 expression.

Conclusions

Our results revealed a dysregulation of ESM1 and a potential regulatory mechanism for the co-expression network in HNSCC.

Investigation of somatic single nucleotide variations in human endogenous retrovirus elements and their potential association with cancer

Human endogenous retroviruses (HERVs) have been investigated for potential links with human cancer. However, the distribution of somatic nucleotide variations in HERV elements has not been explored in detail. This study aims to identify HERV elements with an over-representation of somatic mutations (hot spots) in cancer patients. Four HERV elements with mutation hotspots were identified that overlap with exons of four human protein coding genes. These hotspots were identified based on the significant over-representation (p<8.62e-4) of non-synonymous single-nucleotide variations (nsSNVs). These genes are TNN (HERV-9/LTR12), OR4K15 (HERV-IP10F/LTR10F), ZNF99 (HERV-W/HERV17/LTR17), and KIR2DL1 (MST/MaLR). In an effort to identify mutations that effect survival, all nsSNVs were further evaluated and it was found that kidney cancer patients with mutation C2270G in ZNF99 have a significantly lower survival rate (hazard ratio = 2.6) compared to those without it. Among HERV elements in the human non-protein coding regions, we found 788 HERVs with significantly elevated numbers of somatic single-nucleotide variations (SNVs) (p<1.60e-5). From this category the top three HERV elements with significantly over-represented SNVs are HERV-H/LTR7, HERV-9/LTR12 and HERV-L/MLT2. Majority of the SNVs in these 788 HERV elements are located in three DNA functional groups: long non-coding RNAs (lncRNAs) (60%), introns (22.2%) and transcriptional factor binding sites (TFBS) (14.8%). This study provides a list of mutational hotspots in HERVs, which could potentially be used as biomarkers and therapeutic targets.

Genome-wide DNA methylation differences according to oestrogen receptor beta status in colorectal cancer

Involvement of sex hormones in colorectal cancer (CRC) development has been linked to oestrogen receptor β (ERβ). Expression of ERβ is found reduced in tumour tissue and inversely related to mortality. However, mechanisms are not well understood. Our study aimed to detect differentially methylated genes associated with ERβ expression, which could point to mechanisms by which ERβ could influence risk and prognosis of CRC. Epigenome-wide DNA methylation profiling was performed using Illumina HumanMethylation450k BeadChip arrays in two independent tumour sample sets of CRC patients recruited in 2003-2010 by the German DACHS study (discovery cohort n = 917, replication cohort n = 907). ERβ expression was measured using immunohistochemistry and scored as negative, moderate and high. Differentially methylated CpG sites and genomic regions were determined using limma in the R-package RnBeads. For the comparison of tumours with moderate/high ERβ versus negative expression, differentially methylated CpG sites were identified but not confirmed by replication. Comparing tumours of high with tumours of negative ERβ expression revealed 2,904 differentially methylated CpG sites of which 403 were replicated (FDR adjusted p-value<0.05). Replicated CpGs were annotated to genes such as CD36, HK1 or LRP5. A survival analysis indicates that 30 of the replicated CpGs are also associated with overall survival (FDR-adjusted p-value<0.05). The regional analysis identified 60 differentially methylated promotor regions. The epigenome-wide analysis identified both novel genes as well as genes already implicated in CRC. Follow-up mechanistic studies to better understand the regulatory role of ERβ could inform potential targets for improving treatment or prevention of CRC.

Effective breast cancer combination therapy targeting BACH1 and mitochondrial metabolism

Mitochondrial metabolism is an attractive target for cancer therapy^1,2. Reprogramming metabolic pathways could improve the ability of metabolic inhibitors to suppress cancers with limited treatment options, such as triple-negative breast cancer (TNBC)^1,3. Here we show that BTB and CNC homology1 (BACH1)⁴, a haem-binding transcription factor that is increased in expression in tumours from patients with TNBC, targets mitochondrial metabolism. BACH1 decreases glucose utilization in the tricarboxylic acid cycle and negatively regulates transcription of electron transport chain (ETC) genes. BACH1 depletion by shRNA or degradation by hemin sensitizes cells to ETC inhibitors such as metformin^5,6, suppressing growth of both cell line and patient-derived tumour xenografts. Expression of a haem-resistant BACH1 mutant in cells that express a short hairpin RNA for BACH1 rescues the BACH1 phenotype and restores metformin resistance in hemin-treated cells and tumours⁷. Finally, BACH1 gene expression inversely correlates with ETC gene expression in tumours from patients with breast cancer and in other tumour types, which highlights the clinical relevance of our findings. This study demonstrates that mitochondrial metabolism can be exploited by targeting BACH1 to sensitize breast cancer and potentially other tumour tissues to mitochondrial inhibitors.

KM-express: an integrated online patient survival and gene expression analysis tool for the identification and functional characterization of prognostic markers in breast and prostate cancers

The identification and functional characterization of novel biomarkers in cancer requires survival analysis and gene expression analysis of both patient samples and cell line models. To help facilitate this process, we have developed KM-Express. KM-Express holds an extensive manually curated transcriptomic profile of 45 different datasets for prostate and breast cancer with phenotype and pathoclinical information, spanning from clinical samples to cell lines. KM-Express also contains The Cancer Genome Atlas datasets for 30 other cancer types with matching cell line expression data for 23 of them. We present KM-Express as a hypothesis generation tool for researchers to identify potential new prognostic RNA biomarkers as well as targets for further downstream functional cell-based studies. Specifically, KM-Express allows users to compare the expression level of genes in different groups of patients based on molecular, genetic, clinical and pathological status. Moreover, KM-Express aids the design of biological experiments based on the expression profile of the genes in different cell lines. Thus, KM-Express provides a one-stop analysis from bench work to clinical prospects. We have used this tool to successfully evaluate the prognostic potential of previously published biomarkers for prostate cancer and breast cancer. We believe KM-Express will accelerate the translation of biomedical research from bench to bed.

Database URL: http://ec2-52-201-246-161.compute-1.amazonaws.com/kmexpress/index.php

DDX39B promotes translation through regulation of pre-ribosomal RNA levels

DDX39B, a DExD RNA helicase, is known to be involved in various cellular processes such as mRNA export, splicing and translation. Previous studies showed that the overexpression of DDX39B promotes the global translation but inhibits the mRNA export in a dominant negative manner. This presents a conundrum as to how DDX39B overexpression would increase the global translation if it inhibits the nuclear export of mRNAs. We resolve this by showing that DDX39B affects the levels of pre-ribosomal RNA by regulating its stability as well as synthesis. Furthermore, DDX39B promotes proliferation and colony forming potential of cells and its levels are significantly elevated in diverse cancer types. Thus, increase in DDX39B enhances global translation and cell proliferation through upregulation of pre-ribosomal RNA. This highlights a possible mechanism by which dysregulation of DDX39B expression could lead to oncogenesis.

The non-enzymatic RAS effector RASSF7 inhibits oncogenic c-Myc function

c-Myc is a proto-oncogene controlling expression of multiple genes involved in cell growth and differentiation. Although the functional role of c-Myc as a transcriptional regulator has been intensively studied, targeting this protein in cancer remains a challenge. Here, we report a trimodal regulation of c-Myc function by the Ras effector, Ras-association domain family member 7 (RASSF7), a nonenzymatic protein modulating protein-protein interactions to regulate cell proliferation. Using HEK293T and HeLa cell lines, we provide evidence that RASSF7 destabilizes the c-Myc protein by promoting Cullin4B-mediated polyubiquitination and degradation. Furthermore, RASSF7 competed with MYC-associated factor X (MAX) in the formation of a heterodimeric complex with c-Myc and attenuated its occupancy on target gene promoters to regulate transcription. Consequently, RASSF7 inhibited c-Myc-mediated oncogenic transformation, and an inverse correlation between the expression levels of the RASSF7 and c-Myc genes was evident in human cancers. Furthermore, we found that RASSF7 interacts with c-Myc via its RA and leucine zipper (LZ) domains and LZ domain peptide is sufficient to inhibit c-Myc function, suggesting that this peptide might be used to target oncogenic c-Myc. These results unveil that RASSF7 and c-Myc are functionally linked in the control of tumorigenesis and open up potential therapeutic avenues for targeting the "undruggable" c-Myc protein in a subset of human cancers.

Interplay between human nucleolar GNL1 and RPS20 is critical to modulate cell proliferation

Human Guanine nucleotide binding protein like 1 (GNL1) belongs to HSR1_MMR1 subfamily of nucleolar GTPases. Here, we report for the first time that GNL1 promotes cell cycle and proliferation by inducing hyperphosphorylation of retinoblastoma protein. Using yeast two-hybrid screening, Ribosomal protein S20 (RPS20) was identified as a functional interacting partner of GNL1. Results from GST pull-down and co-immunoprecipitation assays confirmed that interaction between GNL1 and RPS20 was specific. Further, GNL1 induced cell proliferation was altered upon knockdown of RPS20 suggesting its critical role in GNL1 function. Interestingly, cell proliferation was significantly impaired upon expression of RPS20 interaction deficient GNL1 mutant suggest that GNL1 interaction with RPS20 is critical for cell growth. Finally, the inverse correlation of GNL1 and RPS20 expression in primary colon and gastric cancers with patient survival strengthen their critical importance during tumorigenesis. Collectively, our data provided evidence that cross-talk between GNL1 and RPS20 is critical to promote cell proliferation.

Review and Meta-Analyses of TAAR1 Expression in the Immune System and Cancers

Since its discovery in 2001, the major focus of TAAR1 research has been on its role in monoaminergic regulation, drug-induced reward and psychiatric conditions. More recently, TAAR1 expression and functionality in immune system regulation and immune cell activation has become a topic of emerging interest. Here, we review the immunologically-relevant TAAR1 literature and incorporate open-source expression and cancer survival data meta-analyses. We provide strong evidence for TAAR1 expression in the immune system and cancers revealed through NCBI GEO datamining and discuss its regulation in a spectrum of immune cell types as well as in numerous cancers. We discuss connections and logical directions for further study of TAAR1 in immunological function, and its potential role as a mediator or modulator of immune dysregulation, immunological effects of psychostimulant drugs of abuse, and cancer progression.

Characterization of the Olfactory Receptor OR10H1 in Human Urinary Bladder Cancer

Olfactory receptors (ORs) are a large group of G-protein coupled receptors predominantly found in the olfactory epithelium. Many ORs are, however, ectopically expressed in other tissues and involved in several diseases including cancer. In this study, we describe that one OR, OR10H1, is predominantly expressed in the human urinary bladder with a notably higher expression at mRNA and protein level in bladder cancer tissues. Interestingly, also significantly higher amounts of OR10H1 transcripts were detectable in the urine of bladder cancer patients than in the urine of control persons. We identified the sandalwood-related compound Sandranol as a specific agonist of OR10H1. This deorphanization allowed the functional characterization of OR10H1 in BFTC905 bladder cancer cells. The effect of receptor activation was morphologically apparent in cell rounding, accompanied by changes in the cytoskeleton detected by β-actin, T-cadherin and β-Catenin staining. In addition, Sandranol treatment significantly diminished cell viability, cell proliferation and migration and induced a limited degree of apoptosis. Cell cycle analysis revealed an increased G1 fraction. In a concentration-dependent manner, Sandranol application elevated cAMP levels, which was reduced by inhibition of adenylyl cyclase, and elicited intracellular Ca²⁺ concentration increase. Furthermore, activation of OR10H1 enhanced secretion of ATP and serotonin. Our results suggest OR10H1 as a potential biomarker and therapeutic target for bladder cancer.

Unifying cancer and normal RNA sequencing data from different sources

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data, such as the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA). While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources remains challenging, due to differences in sample and data processing. Here, we developed a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment, gene expression quantification, and batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from GTEx and TCGA and successfully corrected for study-specific biases, enabling comparative analysis between TCGA and GTEx. The normalized datasets are available for download on figshare.

E4BP4/NFIL3 modulates the epigenetically repressed RAS effector RASSF8 function through histone methyltransferases

RAS proteins are major human oncogenes, and most of the studies are focused on enzymatic RAS effectors. Recently, nonenzymatic RAS effectors (RASSF, RAS association domain family) have garnered special attention because of their tumor-suppressive properties in contrast to the oncogenic potential of the classical enzymatic RAS effectors. Whereas most members of RASSF family are deregulated by promoter hypermethylation, RASSF8 promoter remains unmethylated in many cancers but the mechanism(s) of its down-regulation remains unknown. Here, we unveil E4BP4 as a critical transcriptional modulator repressing RASSF8 expression through histone methyltransferases, G9a and SUV39H1. In line with these observations, we noticed a negative correlation of RASSF8 and E4BP4 expression in primary breast tumor samples. In addition, our data provide evidence that E4BP4 attenuates RASSF8-mediated anti-proliferation and apoptosis, shedding mechanistic insights into RASSF8 down-regulation in breast cancers. Collectively, our study provides a better understanding on the epigenetic regulation of RASSF8 function and implicates the development of better treatment strategies.

Global Quantitative Proteomics reveal Deregulation of Cytoskeletal and Apoptotic Signalling Proteins in Oral Tongue Squamous Cell Carcinoma

Oral malignancies remain to have higher morbidity and mortality rates owing to the poor understanding of the carcinogenesis and the lack of early detection and diagnosis. The lack of established biomarkers for oral tongue squamous cell carcinoma (OTSCC) resulted in aggressive multi-modality management less effective. Here, we report for the first time that a panel of potential markers identified from tongue tumor samples using two-dimensional-differential-in-gel-electrophoresis (2D-DIGE). Our approach of combining 2D-DIGE with tandem mass spectrometry identified 24 candidate proteins including cofilins, myosin light chain family members, annexins, serpins, HSPs and tropomyosins, with significant differential expression in tongue carcinomas as compared with their matched adjacent normal tissues. The expression levels of the identified proteins were further validated in larger cohort of Indian samples using qPCR. Most of the differentially regulated proteins are involved in actin cytoskeletal dynamics, drug resistance, immune system, inflammation and apoptotic signalling pathways and are known to play critical role in oral tumorigenesis. Taken together, the results from present investigation provide a valuable base for understanding the development and progression of OTSCC. The validated panel of proteins may be used as potential biomarkers for early detection as well as in predicting therapeutic outcome of OTSCC.

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery

Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited in scope and/or content. BioMuta, a database of cancer-associated single-nucleotide variations, and BioXpress, a database of cancer-associated differentially expressed genes and microRNAs, differ from other disease-associated variation and expression databases primarily through the aggregation of data across many studies into a single source with a unified representation and annotation of functional attributes. Early versions of these resources were initiated by pilot funding for specific research applications, but newly awarded funds have enabled hardening of these databases to production-level quality and will allow for sustained development of these resources for the next few years. Because both resources were developed using a similar methodology of integration, curation, unification, and annotation, we present BioMuta and BioXpress as allied databases that will facilitate a more comprehensive view of gene associations in cancer. BioMuta and BioXpress are hosted on the High-performance Integrated Virtual Environment (HIVE) server at the George Washington University at https://hive.biochemistry.gwu.edu/biomuta and https://hive.biochemistry.gwu.edu/bioxpress, respectively.

DEXTER: Disease-Expression Relation Extraction from Text

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.

Musashi-2, a novel oncoprotein promoting cervical cancer cell growth and invasion, is negatively regulated by p53-induced miR-143 and miR-107 activation

Background

Although previous studies have shown promise for targeting Musashi RNA-binding protein 2 (MSI-2) in diverse tumors, the role and mechanism of MSI-2 for cervical cancer (CC) progression and the regulation of MSI-2 expression remains unclear.

Methods

Using gene expression and bioinformatic analysis, together with gain- and loss-of-function assays, we identified MSI-2 as a novel oncogenic driver and a poor prognostic marker in CC. We explored the regulation of c-FOS by MSI-2 via RNA-immunoprecipitation and luciferase assay, and confirmed a direct inhibition of MSI-2 by miR-143/miR-107 using luciferase assay. We assessed the effect of a natural antibiotic Mithramycin A on p53, miR-143/miR-107 and MSI-2 expression in CC cells.

Results

MSI-2 mRNA is highly expressed in CC tissues and its overexpression correlates with lower overall survival. MSI-2 promotes CC cell growth, invasiveness and sphere formation through directly binding to c-FOS mRNA and by increasing c-FOS protein expression. Furthermore, miR-143/miR-107 are two tumor suppressor miRNAs that directly bind and inhibit MSI-2 expression in CC cells, and downregulation of miR-143/miR-107 associates with poor patient prognosis. Importantly, we found that p53 decreases the expression of MSI-2 through elevating miR-143/miR-107 levels, and treatment with a natural antibiotic Mithramycin A increased p53 and miR-143/miR-107 expression and reduced MSI-2 expression, resulting in the inhibition of CC cell proliferation, invasion and sphere formation.

Conclusions

These results suggest that MSI-2 plays a crucial role in promoting the aggressive phenotypes of CC cells, and restoration of miR-143/miR-107 by Mithramycin A via activation of p53 may represent a novel therapeutic approach for CC.

Statistical algorithms improve accuracy of gene fusion detection

Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors.

Transcriptional signature of lymphoblastoid cell lines of BRCA1, BRCA2 and non-BRCA1/2 high risk breast cancer families

Approximately 25% of hereditary breast cancer cases are associated with a strong familial history which can be explained by mutations in BRCA1 or BRCA2 and other lower penetrance genes. The remaining high-risk families could be classified as BRCAX (non-BRCA1/2) families.

Gene expression involving alternative splicing represents a well-known mechanism regulating the expression of multiple transcripts, which could be involved in cancer development. Thus using RNA-seq methodology, the analysis of transcriptome was undertaken to potentially reveal transcripts implicated in breast cancer susceptibility and development.

RNA was extracted from immortalized lymphoblastoid cell lines of 117 women (affected and unaffected) coming from BRCA1, BRCA2 and BRCAX families. Anova analysis revealed a total of 95 transcripts corresponding to 85 different genes differentially expressed (Bonferroni corrected p-value <0.01) between those groups. Hierarchical clustering allowed distinctive subgrouping of BRCA1/2 subgroups from BRCAX individuals. We found 67 transcripts, which could discriminate BRCAX from BRCA1/BRCA2 individuals while 28 transcripts discriminate affected from unaffected BRCAX individuals.

To our knowledge, this represents the first study identifying transcripts differentially expressed in lymphoblastoid cell lines from major classes of mutation-related breast cancer subgroups, namely BRCA1, BRCA2 and BRCAX. Moreover, some transcripts could discriminate affected from unaffected BRCAX individuals, which could represent potential therapeutic targets for breast cancer treatment.

Advancing Public Health Using Regulatory Science to Enhance Development and Regulation of Medical Products: Food and Drug Administration Research at the Center for Biologics Evaluation and Research

Center for Biologics Evaluation and Research enhances and supports regulatory decision-making and policy development. This work contributes to our regulatory mission, advances medical product development, and supports Food and Drug Administration’s regulatory response to public health crises. This review presents some examples of our diverse scientific work undertaken in recent years to support our regulatory and public health mission.