Systematic transcriptome analysis reveals tumor-specific isoforms for ovarian cancer diagnosis and therapy

Giant Magnetoresistance Based Biosensors for Cancer Screening and Detection

Early-stage screening of cancer is critical in preventing its development and therefore can improve the prognosis of the disease. One accurate and effective method of cancer screening is using high sensitivity biosensors to detect optically, chemically, or magnetically labeled cancer biomarkers. Among a wide range of biosensors, giant magnetoresistance (GMR) based devices offer high sensitivity, low background noise, robustness, and low cost. With state-of-the-art micro- and nanofabrication techniques, tens to hundreds of independently working GMR biosensors can be integrated into fingernail-sized chips for the simultaneous detection of multiple cancer biomarkers (i.e., multiplexed assay). Meanwhile, the miniaturization of GMR chips makes them able to be integrated into point-of-care (POC) devices. In this review, we first introduce three types of GMR biosensors in terms of their structures and physics, followed by a discussion on fabrication techniques for those sensors. In order to achieve target cancer biomarker detection, the GMR biosensor surface needs to be subjected to biological decoration. Thus, commonly used methods for surface functionalization are also reviewed. The robustness of GMR-based biosensors in cancer detection has been demonstrated by multiple research groups worldwide and we review some representative examples. At the end of this review, the challenges and future development prospects of GMR biosensor platforms are commented on. With all their benefits and opportunities, it can be foreseen that GMR biosensor platforms will transition from a promising candidate to a robust product for cancer screening in the near future.

CanISO: a database of genomic and transcriptomic variations in domestic dog (Canis lupus familiaris)

BACKGROUND

The domestic dog, Canis lupus familiaris, is a companion animal for humans as well as an animal model in cancer research due to similar spontaneous occurrence of cancers as humans. Despite the social and biological importance of dogs, the catalogue of genomic variations and transcripts for dogs is relatively incomplete.

RESULTS

We developed CanISO, a new database to hold a large collection of transcriptome profiles and genomic variations for domestic dogs. CanISO provides 87,692 novel transcript isoforms and 60,992 known isoforms from whole transcriptome sequencing of canine tumors (N = 157) and their matched normal tissues (N = 64). CanISO also provides genomic variation information for 210,444 unique germline single nucleotide polymorphisms (SNPs) from the whole exome sequencing of 183 dogs, with a query system that searches gene- and transcript-level information as well as covered SNPs. Transcriptome profiles can be compared with corresponding human transcript isoforms at a tissue level, or between sample groups to identify tumor-specific gene expression and alternative splicing patterns.

CONCLUSIONS

CanISO is expected to increase understanding of the dog genome and transcriptome, as well as its functional associations with humans, such as shared/distinct mechanisms of cancer. CanISO is publicly available at https://www.kobic.re.kr/caniso/ .

RNA-Based Strategies for Cancer Therapy: In Silico Design and Evaluation of ASOs for Targeted Exon Skipping

Precision medicine in oncology has made significant progress in recent years by approving drugs that target specific genetic mutations. However, many cancer driver genes remain challenging to pharmacologically target ("undruggable"). To tackle this issue, RNA-based methods like antisense oligonucleotides (ASOs) that induce targeted exon skipping (ES) could provide a promising alternative. In this work, a comprehensive computational procedure is presented, focused on the development of ES-based cancer treatments. The procedure aims to produce specific protein variants, including inactive oncogenes and partially restored tumor suppressors. This novel computational procedure encompasses target-exon selection, in silico prediction of ES products, and identification of the best candidate ASOs for further experimental validation. The method was effectively employed on extensively mutated cancer genes, prioritized according to their suitability for ES-based interventions. Notable genes, such as NRAS and VHL, exhibited potential for this therapeutic approach, as specific target exons were identified and optimal ASO sequences were devised to induce their skipping. To the best of our knowledge, this is the first computational procedure that encompasses all necessary steps for designing ASO sequences tailored for targeted ES, contributing with a versatile and innovative approach to addressing the challenges posed by undruggable cancer driver genes and beyond.

Integrative analysis of histone acetyltransferase KAT2A in human cancer

The high incidence of mutations and the crucial roles of KAT2A in cancer development have received increased attention. Nevertheless, a systematic comparison of the heterogeneity and dynamics across different cancer types has not been conducted. Hence, a deep analysis using public databases was performed to clarify the contributions of KAT2A and its correlation with tumorigenesis. The raw data regarding KAT2A expression in cancer patients and healthy controls were obtained from The Cancer Genome Atlas (TCGA). Sexually dimorphic manner, genomic alterations, and expression pattern of KAT2A, as well as the association of the KAT2A with survival, were retrieved from UALCAN, cBioportal, and TISIDB databases. Additionally, the Protein-Protein Interaction (PPI) analysis was conducted using the STRING database. The human protein atlas was used to obtain the staining results of protein levels in cancer and normal samples. The correlation between KAT2A and its potential target drugs was determined using TISIDB and HISTome2. Compared to the normal tissues, CHOL and TGCT tumors presented significantly high KAT2A expression, which was positively correlated with BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, KICH, KIRP, LIHC, LUAD, LUSC, READ, STAD, and THCA. However, no significant difference was detected between normal and tumor tissues for the sex difference pattern of KAT2A expression. The PPI analysis indicated that TADA3, CCDC101, TRRAP, SUPT3H, MYC, TADA2A, and USP22 levels were positively correlated with KAT2A expression, while TADA2B and ATXN7 were negatively correlated. A positive link of KAT2A with cancer isotypes and significant connections of the KAT2A expression to poor overall and disease-free survival were also observed. Further validation was conducted using immunohistochemistry (IHC) staining, qPCR, and Western blot. Some potential HAT inhibitory drugs of KAT2A were also determined, but more work and clinical trials are required before their application.

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data

The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

Comprehensive characterization of the alternative splicing landscape in ovarian cancer reveals novel events associated with tumor-immune microenvironment

Background: Ovarian cancer (OV) is a serious threat to women’s health. Immunotherapy is a new approach. Alternative splicing (AS) of messenger RNA (mRNA) and its regulation are highly relevant for understanding every cancer hallmark and may offer a broadened target space.

Methods: We downloaded the clinical information and mRNA expression profiles of 587 tumor tissues from The Cancer Genome Atlas (TCGA) database. We constructed a risk score model to predict the prognosis of OV patients. The association between AS-based clusters and tumor-immune microenvironment features was further explored. The ESTIMATE algorithm was also carried out on each OV sample depending on the risk score groups. A total of three immune checkpoint genes that have a significant correlation with risk scores were screened.

Results: The AS-events were a reliable and stable independent risk predictor in the OV cohort. Patients in the high-risk score group had a poor prognosis (P<0.001). Mast cells activated, NK cells resting, and Neutrophils positively correlated with the risk score. The number of Macrophages M1 was also more numerous in the low-risk score group (P<0.05). Checkpoint genes CD274, CTLA-4, and PDCD1LG2, showed a negative correlation with the risk score of AS in OV.

Conclusions: The proposed AS signature is a promising biomarker for estimating overall survival (OS) in OV. The AS-events signature combined with tumor-immune microenvironment enabled a deeper understanding of the immune status of OV patients, and also provided new insights for exploring novel prognostic predictors and precise therapy methods.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer’s disease

Alzheimer’s disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer’s brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer’s Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer’s brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.

Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. There is an increasing interest in personalized cancer immunotherapy using activated natural killer (NK) cells. NK cells account for approximately 15% of circulating white blood cells. They are also an important element of the tumor microenvironment (TME) and the body's immune response to cancers. In the present study, DeepNEU-C2Rx, a machine learning platform, was first used to create validated artificially induced pluripotent stem cell simulations. These simulations were then used to generate wild-type artificially induced NK cells (aiNK-WT) and TME simulations. Once validated, the aiNK-WT simulations were exposed to artificially induced high-grade serous ovarian cancer represented by aiOVCAR3. Cytolytic activity of aiNK was evaluated in presence and absence of aiOVCAR3 and data were compared with the literature for validation. The TME simulations suggested 26 factors that could be evaluated based on their ability to enhance aiNK-WT cytolytic activity in the presence of aiOVCAR3. The addition of programmed cell death-1 inhibitor leads to significant reinvigoration of aiNK cytolytic activity. The combination of programmed cell death-1 and glycogen synthase kinase 3 inhibitors showed further improvement. Further addition of ascitic fluid factor inhibitors leads to optimal aiNK activation. Our data showed that NK cell simulations could be used not only to pinpoint novel immunotherapeutic targets to reinvigorate the activity of NK cells against cancers, but also to predict the outcome of targeting tumors with specific genetic expression and mutation profiles.

Characterization of the Potential Role of NTPCR in Epithelial Ovarian Cancer by Integrating Transcriptomic and Metabolomic Analysis

Background

Epithelial ovarian carcinoma (EOC) is a malignant tumor with high motility in women. Our previous study found that dysregulated nucleoside-triphosphatase cancer-related (NTPCR) was associated with the prognosis of EOC patients, and thus, this present study attempted to explore the potential roles of NTPCR in disease progression.

Methods

Expressed level of NTPCR was investigated in EOC tissues by RT-qPCR and Western blot analysis. NTPCR shRNA and overexpression vector were generated and transfected into OVCAR-3 or SKOV3 cells to detect the effect of NTPCR on cell proliferation, cell cycle, cell migration, and invasion. Transcriptomic sequencing and metabolite profiling analysis were performed in shNTPCR groups to identify transcriptome or metabolite alteration that might contribute to EOC. Finally, we searched the overlapped signaling pathways correlated with differential metabolites and differentially expressed genes (DEGs) by integrating analysis.

Results

Comparing para-cancerous tissues, we found that NTPCR is highly expressed in cancer tissues (p < 0.05). Overexpression of NTPCR inhibited cell proliferation, migration, and invasion and reduced the proportion of S- and G2/M-phase cells, while downregulation of NTPCR showed the opposite results. RNA sequencing analysis demonstrated cohorts of DEGs were identified in shNTPCR samples. Protein–protein interaction networks were constructed for DEGs. STAT1 (degree = 43) and OAS2 (degree = 36) were identified as hub genes in the network. Several miRNAs together with target genes were predicted to be crucial genes related to disease progression, including hsa-miR-124-3p, hsa-miR-30a-5p, hsa-miR-146a-5, EP300, GATA2, and STAT3. We also screened the differential metabolites from shNTPCR samples, including 22 upregulated and 22 downregulated metabolites. By integrating transcriptomics and metabolomics analysis, eight overlapped pathways were correlated with these DEGs and differential metabolites, such as primary bile acid biosynthesis, protein digestion, and absorption, pentose, and glucuronate interconversions.

Conclusion

NTPCR might serve as a tumor suppressor in EOC progression. Our results demonstrated that DEGs and differential metabolites were mainly related to several signaling pathways, which might be a crucial role in the progression of NTPCR regulation of EOC.

FOXM1: A Multifunctional Oncoprotein and Emerging Therapeutic Target in Ovarian Cancer

Forkhead box M1 (FOXM1) is a member of the conserved forkhead box (FOX) transcription factor family. Over the last two decades, FOXM1 has emerged as a multifunctional oncoprotein and a robust biomarker of poor prognosis in many human malignancies. In this review article, we address the current knowledge regarding the mechanisms of regulation and oncogenic functions of FOXM1, particularly in the context of ovarian cancer. FOXM1 and its associated oncogenic transcriptional signature are enriched in >85% of ovarian cancer cases and FOXM1 expression and activity can be enhanced by a plethora of genomic, transcriptional, post-transcriptional, and post-translational mechanisms. As a master transcriptional regulator, FOXM1 promotes critical oncogenic phenotypes in ovarian cancer, including: (1) cell proliferation, (2) invasion and metastasis, (3) chemotherapy resistance, (4) cancer stem cell (CSC) properties, (5) genomic instability, and (6) altered cellular metabolism. We additionally discuss the evidence for FOXM1 as a cancer biomarker, describe the rationale for FOXM1 as a cancer therapeutic target, and provide an overview of therapeutic strategies used to target FOXM1 for cancer treatment.

Multimodal detection of protein isoforms and nucleic acids from low starting cell numbers

Protein isoforms play a key role in disease progression and arise from mechanisms involving multiple molecular subtypes, including DNA, mRNA and protein. Recently introduced multimodal assays successfully link genomes and transcriptomes to protein expression landscapes. However, the specificity of the protein measurement relies on antibodies alone, leading to major challenges when measuring different isoforms of the same protein. Here we utilize microfluidic design to perform same-cell profiling of DNA, mRNA and protein isoforms (triBlot) on low starting cell numbers (1-100 s of cells). After fractionation lysis, cytoplasmic proteins are resolved by molecular mass during polyacrylamide gel electrophoresis (PAGE), adding a degree of specificity to the protein measurement, while nuclei are excised from the device in sections termed "gel pallets" for subsequent off-chip nucleic acid analysis. By assaying TurboGFP-transduced glioblastoma cells, we observe a strong correlation between protein expression prior to lysis and immunoprobed protein. We measure both mRNA and DNA from retrieved nuclei, and find that mRNA levels correlate with protein abundance in TurboGFP-expressing cells. Furthermore, we detect the presence of TurboGFP isoforms differing by an estimated <1 kDa in molecular mass, demonstrating the ability to discern different proteoforms with the same antibody probe. By directly relating nucleic acid modifications to protein isoform expression in 1-100 s of cells, the triBlot assay holds potential as a screening tool for novel biomarkers in diseases driven by protein isoform expression.

Tubulin Beta 2C Chain (TBB2C), a Potential Marker of Ovarian Cancer, an Insight from Ovarian Cancer Proteome Profile

Ovarian cancer (OC) is the most lethal among female reproductive system malignancies. Depending upon the stage at presentation, the five year survival ratio varies from ∼92 to ∼30%. The role of biomarkers in early cancer diagnosis, including OC, is well understood. In our previous study, through an initial screening, we have analyzed eleven proteins that exhibited differential expression in OC using two-dimensional gel electrophoresis (2D-GE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometric (MALDI-TOF MS) analysis. In continuation of our previous study, the present work describes analysis of twenty more proteins that showed aberrant expression in OC. Among these, six showed consistent significant deregulation in the OC false discovery rate [FDR ≤ 0.05]. Upon MS analysis, they were identified as vimentin, tubulin beta 2C chain, tubulin alpha 1C chain, actin cytoplasmic 2, apolipoprotein A-I, and collagen alpha 2(VI) chain [peptide mass fingerprint (PMF) score ≥ 79]. One of the differentially regulated proteins, tubulin beta 2C chain, was found to be significantly (fold change, 2.5) enhanced in OC. Verification by western blot and enzyme-linked immunosorbent assay (ELISA) demonstrated that the tubulin beta 2C chain may serve as a valuable marker for OC (ANOVA p < 0.0001). The assessment of the likely association of TBB2C with OC in a larger population will not only help in developing clinically useful biomarkers in the future but also improve our understanding of the progression of OC disease.

Identification and Analysis of An Epigenetically Regulated Five-lncRNA Signature Associated With Outcome and Chemotherapy Response in Ovarian Cancer

The deregulation of long non-coding RNAs (lncRNAs) by epigenetic alterations has been implicated in cancer initiation and progression. However, the epigenetically regulated lncRNAs and their association with clinical outcome and therapeutic response in ovarian cancer (OV) remain poorly investigated. This study performed an integrative analysis of DNA methylation data and transcriptome data and identified 419 lncRNAs as potential epigenetically regulated lncRNAs. Using machine-learning and multivariate Cox regression analysis methods, we identified and developed an epigenetically regulated lncRNA expression signature (EpiLncRNASig) consisting of five lncRNAs from the list of 17 epigenetically regulated lncRNAs significantly associated with outcome. The EpiLncRNASig could stratify patients into high-risk groups and low-risk groups with significantly different survival and chemotherapy response in different patient cohorts. Multivariate Cox regression analyses, after adjusted by other clinical features and treatment response, demonstrated the independence of the DEpiLncSig in predicting survival. Functional analysis for relevant protein-coding genes of the DEpiLncSig indicated enrichment of known immune-related or cancer-related biological pathways. Taken together, our study not only provides a promising prognostic biomarker for predicting outcome and chemotherapy response but also will improve our understanding of lncRNA epigenetic regulation mechanisms in OV.

RNA splicing alteration in the response to platinum chemotherapy in ovarian cancer: A possible biomarker and therapeutic target

Since its discovery, alternative splicing has been recognized as a powerful way for a cell to amplify the genetic information and for a living organism to adapt, evolve, and survive. We now know that a very high number of genes are regulated by alternative splicing and that alterations of splicing have been observed in different types of human diseases, including cancer. Here, we review the accumulating knowledge that links the regulation of alternative splicing to the response to chemotherapy, focusing our attention on ovarian cancer and platinum-based treatments. Moreover, we discuss how expanding information could be exploited to identify new possible biomarkers of platinum response, to better select patients, and/or to design new therapies able to overcome platinum resistance.

An AFM-Based Nanomechanical Study of Ovarian Tissues with Pathological Conditions

BACKGROUND

Different diseases affect both mechanical and chemical features of the involved tissue, enhancing the symptoms.

METHODS

In this study, using atomic force microscopy, we mechanically characterized human ovarian tissues with four distinct pathological conditions: mucinous, serous, and mature teratoma tumors, and non-tumorous endometriosis. Mechanical elasticity profiles were quantified and the resultant data were categorized using K-means clustering method, as well as fuzzy C-means, to evaluate elastic moduli of cellular and non-cellular parts of diseased tissues and compare them among four disease conditions. Samples were stained by hematoxylin-eosin staining to further study the content of different locations of tissues.

RESULTS

Pathological state vastly influenced the mechanical properties of the ovarian tissues. Significant alterations among elastic moduli of both cellular and non-cellular parts were observed. Mature teratoma tumors commonly composed of multiple cell types and heterogeneous ECM structure showed the widest range of elasticity profile and the stiffest average elastic modulus of 14 kPa. Samples of serous tumors were the softest tissues with elastic modulus of only 400 Pa for the cellular part and 5 kPa for the ECM. Tissues of other two diseases were closer in mechanical properties as mucinous tumors were insignificantly stiffer than endometriosis in cellular part, 1300 Pa compared to 1000 Pa, with the ECM average elastic modulus of 8 kPa for both.

CONCLUSION

The higher incidence of carcinoma out of teratoma and serous tumors may be related to the intense alteration of mechanical features of the cellular and the ECM, serving as a potential risk factor which necessitates further investigation.

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.

A New Strategy for Identifying Mechanisms of Drug-drug Interaction Using Transcriptome Analysis: Compound Kushen Injection as a Proof of Principle

Drug-drug interactions (DDIs), especially with herbal medicines, are complex, making it difficult to identify potential molecular mechanisms and targets. We introduce a workflow to carry out DDI research using transcriptome analysis and interactions of a complex herbal mixture, Compound Kushen Injection (CKI), with cancer chemotherapy drugs, as a proof of principle. Using CKI combined with doxorubicin or 5-Fu on cancer cells as a model, we found that CKI enhanced the cytotoxic effects of doxorubicin on A431 cells while protecting MDA-MB-231 cells treated with 5-Fu. We generated and analysed transcriptome data from cells treated with single treatments or combined treatments and our analysis showed that opposite directions of regulation for pathways related to DNA synthesis and metabolism which appeared to be the main reason for different effects of CKI when used in combination with chemotherapy drugs. We also found that pathways related to organic biosynthetic and metabolic processes might be potential targets for CKI when interacting with doxorubicin and 5-Fu. Through co-expression analysis correlated with phenotype results, we selected the MYD88 gene as a candidate major regulator for validation as a proof of concept for our approach. Inhibition of MYD88 reduced antagonistic cytotoxic effects between CKI and 5-Fu, indicating that MYD88 is an important gene in the DDI mechanism between CKI and chemotherapy drugs. These findings demonstrate that our pipeline is effective for the application of transcriptome analysis to the study of DDIs in order to identify candidate mechanisms and potential targets.

Toward in silico Identification of Tumor Neoantigens in Immunotherapy

Cancer immunotherapy includes cancer vaccination, adoptive T cell transfer (ACT) with chimeric antigen receptor (CAR) T cells, and administration of tumor-infiltrating lymphocytes and immune-checkpoint blockade such as anti-CTLA4/anti-PD1 inhibitors that can directly or indirectly target tumor neoantigens and elicit a T cell response. Accurate, rapid, and cost-effective identification of neoantigens, however, is critical for successful immunotherapy. Here, we review computational issues for neoantigen identification by summarizing the various sources of neoantigens and their identification from high-throughput sequencing data. Several opinions are presented to inspire further discussions toward improving neoantigen identification. Continuing efforts are required to improve the sensitivity and specificity of bona fide neoantigens, taking advantage of the development of high-throughput sequencing techniques for effective and personalized cancer immunotherapy.

Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery.

Single-Molecule Real-Time (SMRT) Full-Length RNA-Sequencing Reveals Novel and Distinct mRNA Isoforms in Human Bone Marrow Cell Subpopulations

Hematopoietic cells are continuously replenished from progenitor cells that reside in the bone marrow. To evaluate molecular changes during this process, we analyzed the transcriptomes of freshly harvested human bone marrow progenitor (lineage-negative) and differentiated (lineage-positive) cells by single-molecule real-time (SMRT) full-length RNA-sequencing. This analysis revealed a ~5-fold higher number of transcript isoforms than previously detected and showed a distinct composition of individual transcript isoforms characteristic for bone marrow subpopulations. A detailed analysis of messenger RNA (mRNA) isoforms transcribed from the ANXA1 and EEF1A1 loci confirmed their distinct composition. The expression of proteins predicted from the transcriptome analysis was evaluated by mass spectrometry and validated previously unknown protein isoforms predicted e.g., for EEF1A1. These protein isoforms distinguished the lineage negative cell population from the lineage positive cell population. Finally, transcript isoforms expressed from paralogous gene loci (e.g., CFD, GATA2, HLA-A, B, and C) also distinguished cell subpopulations but were only detectable by full-length RNA sequencing. Thus, qualitatively distinct transcript isoforms from individual genomic loci separate bone marrow cell subpopulations indicating complex transcriptional regulation and protein isoform generation during hematopoiesis.

Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer

FOXM1 is frequently overexpressed in cancer, but this has not been studied in a comprehensive manner. We utilized genotype-tissue expression (GTEx) normal and The Cancer Genome Atlas (TCGA) tumor data to define FOXM1 expression, including its isoforms, and to determine the genetic alterations that promote FOXM1 expression in cancer. Additionally, we used human fallopian tube epithelial (FTE) cells to dissect the role of Retinoblastoma (Rb)-E2F and Cyclin E1 in FOXM1 regulation, and a novel human embryonic kidney cell (HEK293T) CRISPR FOXM1 knockout model to define isoform-specific transcriptional programs. FOXM1 expression, at the mRNA and protein level, was significantly elevated in tumors with FOXM1 amplification, p53 inactivation, and Rb-E2F deregulation. FOXM1 expression was remarkably high in testicular germ cell tumors (TGCT), high-grade serous ovarian cancer (HGSC), and basal breast cancer (BBC). FOXM1 expression in cancer was associated with genomic instability, as measured using aneuploidy signatures. FTE models confirmed a role for Rb-E2F signaling in FOXM1 regulation and in particular identified Cyclin E1 as a novel inducer of FOXM1 expression. Among the three FOXM1 isoforms, FOXM1c showed the highest expression in normal and tumor tissues and cancer cell lines. The CRISPR knockout model demonstrated that FOXM1b and FOXM1c are transcriptionally active, while FOXM1a is not. Finally, we were unable to confirm the existence of a FOXM1 auto-regulatory loop. This study provides significant and novel information regarding the frequency, causes, and consequences of elevated FOXM1 expression in human cancer.

Rab11FIP proteins link endocytic recycling vesicles for cytoskeletal transport and tethering

Regulated trafficking of internalised integrins and growth factor receptors enables polarisation of morphology and motility and enables lumen formation in multicellular structures. Recycling vesicles marked with Rab11 direct internalised cargo back to the plasma membrane to affect biological processes such as polarised trafficking and cancer cell invasion. A recent study by Ji and colleagues, provides insight into how the trafficking protein Rab11FIP2 links with the actin-based motor myo5b and the small GTPase Rab11 to regulate vesicle tethering and transport along actin filaments [1]. The authors used biochemical methods to demonstrate that Rab11a binds directly to the tail of myo5b and that Rab11FIP2 also forms direct interactions with both Rab11a and myo5b tails. These proteins essentially compete for binding to similar regions and thus can regulate the association and activity of each other. Ji and colleagues further demonstrate that Rab11a activates myo5b by binding to its globular tail and relieving a head-tail autoinhibition. Due to differing affinities between Rab11 and myo5b or Rab11FIP2, they propose that Rab11FIP2 mediates the association of myo5b with cargo vesicles, while Rab11a regulates the motor activity of myo5b. The present study thus elucidates how myo5b is regulated by its interactions with Rab11a and Rab11FIP2 and proposes a model for coordination of recycling vesicle tethering and motor activity. The present study has implications for how cells control polarity and motility in health and disease and suggests how Rab11FIP proteins might control motor protein activity and engagement for transport.

Middle-down approach: a choice to sequence and characterize proteins/proteomes by mass spectrometry

Owing to rapid growth in the elucidation of genome sequences of various organisms, deducing proteome sequences has become imperative, in order to have an improved understanding of biological processes. Since the traditional Edman method was unsuitable for high-throughput sequencing and also for N-terminus modified proteins, mass spectrometry (MS) based methods, mainly based on soft ionization modes: electrospray ionization and matrix-assisted laser desorption/ionization, began to gain significance. MS based methods were adaptable for high-throughput studies and applicable for sequencing N-terminus blocked proteins/peptides too. Consequently, over the last decade a new discipline called 'proteomics' has emerged, which encompasses the attributes necessary for high-throughput identification of proteins. 'Proteomics' may also be regarded as an offshoot of the classic field, 'biochemistry'. Many protein sequencing and proteomic investigations were successfully accomplished through MS dependent sequence elucidation of 'short proteolytic peptides (typically: 7-20 amino acid residues), which is called the 'shotgun' or 'bottom-up (BU)' approach. While the BU approach continues as a workhorse for proteomics/protein sequencing, attempts to sequence intact proteins without proteolysis, called the 'top-down (TD)' approach started, due to ambiguities in the BU approach, e.g., protein inference problem, identification of proteoforms and the discovery of posttranslational modifications (PTMs). The high-throughput TD approach (TD proteomics) is yet in its infancy. Nevertheless, TD characterization of purified intact proteins has been useful for detecting PTMs. With the hope to overcome the pitfalls of BU and TD strategies, another concept called the 'middle-down (MD)' approach was put forward. Similar to BU, the MD approach also involves proteolysis, but in a restricted manner, to produce 'longer' proteolytic peptides than the ones usually obtained in BU studies, thereby providing better sequence coverage. In this regard, special proteases (OmpT, Sap9, IdeS) have been used, which can cleave proteins to produce longer proteolytic peptides. By reviewing ample evidences currently existing in the literature that is predominantly on PTM characterization of histones and antibodies, herein we highlight salient features of the MD approach. Consequently, we are inclined to claim that the MD concept might have widespread applications in future for various research areas, such as clinical, biopharmaceuticals (including PTM analysis) and even for general/routine characterization of proteins including therapeutic proteins, but not just limited to analysis of histones or antibodies.

Genome-wide isoform-level analysis reveals tumor-specific isoforms for lung adenocarcinoma diagnosis and prognosis

Last decades have witnessed the great progress in exploration of tumor transcriptome. However, most researches were restricted in gene-level expression. mRNA isoforms, especially tumor-specific isoforms have not been fully explored in tumor. Here, by analyzing RNA-seq data derived from hundreds of samples in TCGA projects, we comprehensively characterized the expression variations of mRNA isoforms in adenocarcinoma of lung (LUAD), which is one of leading causes of cancer-related death. Our analysis found that a variety of mRNA isoforms showed differential expression in LUAD tumor samples. Some of them even showed distinct variations compared to their host genes. Further analysis of functional enrichment revealed that up- and down-regulated mRNA isoforms took part in different types of biological process. In addition, we also identified hundreds of isoforms that expressed exclusively in LUAD tumor samples. Furthermore, the expression level of several isoforms, such as uc001kuk.3 and uc003yls.2, could separate tumor patients by overall survival periods. Our study provided new candidates for the diagnosis and prognosis of lung cancer.

Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions ("neojunctions") in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders ("putative neoantigens").

3-O-acetyl-11-keto-β-boswellic acid exerts anti-tumor effects in glioblastoma by arresting cell cycle at G2/M phase

Background

Glioblastoma (GBM) is the most common, malignant, and lethal primary brain tumor in adults accounting for about 50% of all gliomas. Up to now, the chemotherapy approaches for GBM were limited. 3-O-acetyl-11-keto-β-boswellic acid (AKBA), the major active ingredient of the gum resin from Boswellia serrata and Boswellia carteri Birdw., was reported to inhibit the growth of many types of cancer cells; however, the underlying mechanism of its anticancer effects are still unclear.

Methods

The effects of AKBA on cell viability and its cytotoxicity were determined using CCK8 and LDH kits respectively. The EdU-DNA synthesis assay was used to evaluate inhibition of cell proliferation by AKBA. The role of AKBA in glioblastoma cell functions such as migration/invasion, and colony formation was evaluated using transwell chambers and soft agar, respectively. Flow cytometry and western blotting were used to detect AKBA-induced apoptosis. Potential mechanisms of AKBA action were explored by RNA sequencing and the identified hub genes were validated by real-time quantitative PCR and western blotting. Finally, the in vivo anti-tumor activity of AKBA was evaluated against a human glioblastoma cell line, U87-MG, in a xenograft mouse model.

Results

AKBA inhibited cell proliferation, caused the release of LDH, decreased DNA synthesis, and inhibited the migration, invasion, and colony formation of U251 and U87-MG human glioblastoma cell lines. AKBA increased apoptosis as well as the activity of caspase 3/7 and the protein expression of cleaved-caspase 3 and cleaved PARP, while decreasing mitochondrial membrane potential. RNA-sequencing analyses showed that AKBA suppressed the expression of pRB, FOXM1, Aurora A, PLK1, CDC25C, p-CDK1, cyclinB1, Aurora B, and TOP2A while increasing the expression of p21 and GADD45A. These findings were validated by qRT-PCR and western blotting. The data are consistent with a mechanism in which AKBA arrested the cell cycle in glioblastoma cells at the G2/M phase by regulating the p21/FOXM1/cyclin B1 pathway, inhibited mitosis by downregulating the Aurora B/TOP2A pathway, and induced mitochondrial-dependent apoptosis. Oral administration of AKBA (100 mg/kg) significantly suppressed the tumorigenicity of U87-MG cells in a xenograft mouse model.

Conclusions

Taken together, these results suggest that AKBA (molecular weight, 512.7 Da) might be a promising chemotherapy drug in the treatment of GBM.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0805-4) contains supplementary material, which is available to authorized users.

Ophiopogonin B suppresses the metastasis and angiogenesis of A549 cells in vitro and in vivo by inhibiting the EphA2/Akt signaling pathway

Lung adenocarcinoma is the most common metastatic cancer, and is associated with high patient mortality. Therefore, investigation of anti‑metastatic treatments for lung adenocarcinoma is crucial. Ophiopogonin B (OP‑B) is a bioactive component of Radix Ophiopogon Japonicus, which is often used in Chinese traditional medicine to treat pulmonary disease. Screening of transcriptome and digital gene expression (DGE) profiling data in NSCLC cell lines showed that OP‑B regulated the epithelial‑mesenchymal transition (EMT) pathway in A549 cells. Further results showed that 10 µmol/l OP‑B downregulated EphA2 expression and phosphorylation (Ser897) in A549 cells but upregulated them in NCI‑H460 cells. Meanwhile, the Ras/ERK pathway was unaffected in A549 cells and stimulated in NCI‑H460 cells. More importantly, detection of the EMT pathway showed that OP‑B treatment increased the epithelial markers ZO‑1 and E‑cadherin and decreased the expression of the mesenchymal marker N‑cadherin and the transcriptional repressors Snail, Slug and ZEB1. Furthermore, through Transwell migration and scratch wound healing assays, we found that 10 µmol/l OP‑B significantly reduced the invasion and migration of A549 cells. In vivo, we found that 75 mg/kg OP‑B inhibited A549 cell metastasis in a pulmonary metastasis nude mouse model. In addition, we also found that 10 µmol/l OP‑B significantly inhibited tube formation in EA.hy926 cells. The expression of VEGFR2 and Tie‑2, the phosphorylation of Akt (S473) and PLC (S1248), and the levels of EphA2 and phosphorylated EphA2 (S897) were all inhibited by OP‑B in this cell line. In vivo, using a Matrigel plug assay, we found that OP‑B inhibited angiogenesis and the hemoglobin content of A549 transplanted tumors. Taken together, OP‑B inhibited the metastasis and angiogenesis of A549 cells by inhibiting EphA2/Akt and the corresponding pathway. The investigation gives new recognition to the anticancer mechanism of OP‑B in NSCLC and this compound is a promising inhibitor of metastasis and angiogenesis of lung adenocarcinoma cells.

Natural killer cells inhibit metastasis of ovarian carcinoma cells and show therapeutic effects in a murine model of ovarian cancer

Ovarian cancer has the highest mortality rate and is the most common of all gynecologic malignancies. Novel treatments for ovarian cancer are urgently required to improve outcomes and the overall survival of patients. The present study investigated whether immunotherapy with natural killer (NK) cells affected the survival of mice with ovarian cancer. Results analysis identified adjunctive NK cells as a potential therapeutic method in ovarian cancer. Patient-derived ovarian cells were isolated, cultured and subsequently injected subcutaneously into immune deficient BALB/c-nude mice. Human NK cells were isolated from peripheral blood mononuclear cells and cultured for expansion in vitro. The present results demonstrated that ovarian cells in BALB/c-nude mice did not induce spontaneous ovarian cancer cell metastasis in the NK-treated group. In addition, NK cells activated immune cells in the immune system, which resulted in inhibition of ovarian tumor growth in vitro and in a murine xenograft model of ovarian cancer. The data also indicated that cytotoxic activity of NK cells prevented migration and invasion of ovarian cancer cells, which contributed to prevention of systemic metastasis and suggested that NK cells could be effective cells for therapy against ovarian cancer. Furthermore, NK cells induced apoptosis and increased the number of cluster of differentiation (CD)4⁺, CD8⁺ as well as cytotoxic T lymphocyte responses by intravenous injection in a murine xenograft model of ovarian cancer. These results suggested that NK cells inhibited the systemic metastasis for ovarian cancer cells. In conclusion, the present study suggested that NK cell immunotherapy inhibited systemic metastasis of ovarian cancer cells and improved the survival rate of mice. Sufficient supplementation of NK cells may serve as a promising immunotherapeutic strategy for ovarian cancer.

Comprehensive Analyses of Tissue-Specific Networks with Implications to Psychiatric Diseases

Recent advances in genome sequencing and "omics" technologies are opening new opportunities for improving diagnosis and treatment of human diseases. The precision medicine initiative in particular aims at developing individualized treatment options that take into account individual variability in genes and environment of each person. Systems biology approaches that group genes, transcripts and proteins into functionally meaningful networks will play crucial role in the future of personalized medicine. They will allow comparison of healthy and disease-affected tissues and organs from the same individual, as well as between healthy and disease-afflicted individuals. However, the field faces a multitude of challenges ranging from data integration to statistical and combinatorial issues in data analyses. This chapter describes computational approaches developed by us and the others to tackle challenges in tissue-specific network analyses, with the main focus on psychiatric diseases.

Human embryonic stem cells extracellular vesicles and their effects on immortalized human retinal Müller cells

Extracellular vesicles (EVs) released by virtually every cell of all organisms are involved in processes of intercellular communication through the delivery of their functional mRNAs, proteins and bioactive lipids. We previously demonstrated that mouse embryonic stem cell-released EVs (mESEVs) are able to transfer their content to different target retinal cells, inducing morphological and biochemical changes in them. The main objective of this paper is to characterize EVs derived from human embryonic stem cells (hESEVs) and investigate the effects that they have on cultured retinal glial, progenitor Müller cells, which are known to give rise to retinal neurons under specific conditions. This would allow us to establish if hESEVs have a pro-regenerative potential not yet described that could be used in the future for treatment of human retinal degenerative diseases. Initially, we showed that hESEVs are heterogeneous in size, contain mRNAs and proteins involved in the induction and maintenance of stem cell pluripotency and can be internalized by cultured Müller cells. After a single exposure to hESEVs these cells display changes in their gene expression profile, and with multiple exposures they de-differentiate and trans-differentiate into retinal neuronal precursors. hESEVs were then fractionated into microvesicles (MVs) and exosomes (EXOs), which were characterized by size, specific surface proteins and biochemical/molecular components. We demonstrate that despite the similar internalization of non-fractionated hESEVs, MVs and EXOs by Müller progenitor cells, in vitro, only the release of MVs’ cargo into the cells’ cytoplasm induces specific changes in their levels of pluripotency mRNAs and early retinal proteins. EXOs do not produce any detectable effect. Thus, we conclude that MVs and MVs-containing hESEVs are promising agents that possibly could promote the regeneration of diseased or damaged retinas in vivo through inducing glial Müller cells to become replacement neurons.

Identification of candidate anti-cancer molecular mechanisms of Compound Kushen Injection using functional genomics

Compound Kushen Injection (CKI) has been clinically used in China for over 15 years to treat various types of solid tumours. However, because such Traditional Chinese Medicine (TCM) preparations are complex mixtures of plant secondary metabolites, it is essential to explore their underlying molecular mechanisms in a systematic fashion. We have used the MCF-7 human breast cancer cell line as an initial in vitro model to identify CKI induced changes in gene expression. Cells were treated with CKI for 24 and 48 hours at two concentrations (1 and 2 mg/mL total alkaloids), and the effect of CKI on cell proliferation and apoptosis were measured using XTT and Annexin V/Propidium Iodide staining assays respectively. Transcriptome data of cells treated with CKI or 5-Fluorouracil (5-FU) for 24 and 48 hours were subsequently acquired using high-throughput Illumina RNA-seq technology. In this report we show that CKI inhibited MCF-7 cell proliferation and induced apoptosis in a dose-dependent fashion. We integrated and applied a series of transcriptome analysis methods, including gene differential expression analysis, pathway over-representation analysis, de novo identification of long non-coding RNAs (lncRNA) as well as co-expression network reconstruction, to identify candidate anti-cancer molecular mechanisms of CKI. Multiple pathways were perturbed and the cell cycle was identified as the potential primary target pathway of CKI in MCF-7 cells. CKI may also induce apoptosis in MCF-7 cells via a p53 independent mechanism. In addition, we identified novel lncRNAs and showed that many of them might be expressed as a response to CKI treatment.

Human PDE4D isoform composition is deregulated in primary prostate cancer and indicative for disease progression and development of distant metastases

Phosphodiesterase 4D7 was recently shown to be specifically over-expressed in localized prostate cancer, raising the question as to which regulatory mechanisms are involved and whether other isoforms of this gene family (PDE4D) are affected under the same conditions.We investigated PDE4D isoform composition in prostatic tissues using a total of seven independent expression datasets and also included data on DNA methylation, copy number and AR and ERG binding in PDE4D promoters to gain insight into their effect on PDE4D transcription.We show that expression of PDE4D isoforms is consistently altered in primary human prostate cancer compared to benign tissue, with PDE4D7 being up-regulated while PDE4D5 and PDE4D9 are down-regulated. Disease progression is marked by an overall down-regulation of long PDE4D isoforms, while short isoforms (PDE4D1/2) appear to be relatively unaffected. While these alterations seem to be independent of copy number alterations in the PDE4D locus and driven by AR and ERG binding, we also observed increased DNA methylation in the promoter region of PDE4D5, indicating a long lasting alteration of the isoform composition in prostate cancer tissues.We propose two independent metrics that may serve as diagnostic and prognostic markers for prostate disease: (PDE4D7 - PDE4D5) provides an effective means for distinguishing PCa from normal adjacent prostate, whereas PDE4D1/2 - (PDE4D5 + PDE4D7 + PDE4D9) offers strong prognostic potential to detect aggressive forms of PCa and is associated with metastasis free survival. Overall, our findings highlight the relevance of PDE4D as prostate cancer biomarker and potential drug target.

FGFRL1 Promotes Ovarian Cancer Progression by Crosstalk with Hedgehog Signaling

Fibroblast growth factor receptor-like-1 (FGFRL1) has been identified as the fifth fibroblast growth factor receptor. So far, little is known about its biological functions, particularly in cancer development. Here, for the first time, we demonstrated the roles of FGFRL1 in ovarian carcinoma (OC). An array and existing databases were used to investigate the expression profile of FGFRL1 and the relationship between FGFRL1 expression and clinicopathological parameters. FGFRL1 was significantly upregulated in OC patients, and high FGFRL1 expression was correlated with poor prognosis. In vitro cell proliferation, apoptosis and migration assays, and in vivo subcutaneous xenograft tumor models were used to determine the role of FGFRL1. Loss of function of FGFRL1 significantly influenced cell proliferation, apoptosis, and migration of OC cells in vitro and tumor growth in vivo. Chromatin immunoprecipitation PCR analysis and microarray hybridization were performed to uncover the mechanism. FGFRL1 expression could be induced by hypoxia through hypoxia-inducible factor 1α, which directly binds to the promoter elements of FGFRL1. FGFRL1 promoted tumor progression by crosstalk with Hedgehog (Hh) signaling. Taken together, FGFRL1 is a potential predictor and plays an important role in tumor growth and Hh signaling which could serve as potential therapeutic targets for the treatment of OC.

Isoform specific gene expression analysis of KRAS in the prognosis of lung adenocarcinoma patients

Background

Aberrant mutations in KRAS play a critical role in tumor initiation and progression, and are a negative prognosis factor in lung adenocarcinoma (LUAD).

Results

Using genomic analysis for K-Ras isoforms (K-Ras4A and K-Ras4B) and large-scale multi-omics data, we inspected the overall survival (OS) and disease-free survival (DFS) of LUAD patients based on the abundance of transcript variants by analyzing RNA expression and somatic mutation data from The Cancer Genome Atlas (n = 516). The expression of the minor transcript K-Ras4A and its proportion were positively correlated with the presence of KRAS mutations in LUAD. We found that both K-Ras4A abundance measures (expression and proportion) have a strong association with poor OS (p = 0.0149 and p = 3.18E-3, respectively) and DFS (p = 3.03E-4 and p = 0.0237, respectively), but only in patients harboring KRAS mutations. A Cox regression analysis showed significant results in groups with low expression (hazard ratio (HR) = 2.533, 95% confidence interval (CI) = 1.380−4.651, p = 2.72E-3) and low proportion (HR = 2.549, 95% CI = 1.387−4.684, p = 2.58E-3) of K-Ras4A.

Conclusions

Based on the above results, we report the possible use of abundance measures for K-Ras4A for predicting the survival of LUAD patients with KRAS mutations.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2011-y) contains supplementary material, which is available to authorized users.

Activation of the LKB1‑SIK1 signaling pathway inhibits the TGF‑β‑mediated epithelial‑mesenchymal transition and apoptosis resistance of ovarian carcinoma cells

Ovarian cancer is the most common and lethal type of gynecological malignancy, due to its invasiveness. The present study aimed to analyze the molecular mechanism underlying chemoresistance in ovarian carcinoma cells, which may lead to local migration toward adjacent tissues and long‑distance metastasis to other organs. A total of 12 patients with ovarian fibroma were used to evaluate chemoresistance and chemosensitivity. The sensitivity and resistance of ovarian carcinoma cells was measured using apoptosis analysis, morphological observation, survival rate analysis, immunohistochemistry and immunostaining. The mechanism underlying the interaction between the epithelial‑mesenchymal transition (EMT) and liver kinase B1 (LKB1)‑salt‑inducible kinase 1 (SIK1) signaling pathways was additionally investigated in ovarian carcinoma. The results of the present study demonstrated that ovarian carcinoma cells isolated from patients exhibited apoptosis resistance. Inhibition of TGF‑β expression led to an inhibition of growth, migration and invasion, in addition to a promotion of apoptosis, in ovarian carcinoma cells treated with paclitaxel. Studies have indicated that the LKB1‑SIK1 signaling pathway may be suppressed in ovarian carcinoma cells compared with normal ovarian cells, leading to activation of the EMT signaling pathway. The results of the present study demonstrated that upregulation of LKB1 promoted SIK1 expression and markedly suppressed the growth and aggressiveness of ovarian cancer cells. Upregulation of LKB1 additionally promoted apoptosis in ovarian carcinoma cells. In addition, the results of the present study demonstrated that the knockdown of LKB1 further promoted the expression of transforming growth factor‑β and EMT, which downregulated the chemosensitivity of ovarian carcinoma cells. Additionally, overexpression of LKB1 in ovarian carcinoma cells increased chemosensitivity, resulting in a significant inhibition of migration and invasion. The present findings indicated that the enhancement of LKB1‑SIK1 suppressed the growth and aggressiveness of ovarian carcinoma cells isolated from clinical patients, which subsequently contributed to an inhibition of metastatic potential. In conclusion, targeting the LKB1‑SIK1 signaling pathway to inhibit EMT may provide potential therapeutic benefits in ovarian carcinoma.

RNA-Seq of Kaposi's sarcoma reveals alterations in glucose and lipid metabolism

Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi’s sarcoma (KS). It is endemic in a number of sub-Saharan African countries with infection rate of >50%. The high prevalence of HIV-1 coupled with late presentation of advanced cancer staging make KS the leading cancer in the region with poor prognosis and high mortality. Disease markers and cellular functions associated with KS tumorigenesis remain ill-defined. Several studies have attempted to investigate changes of the gene profile with in vitro infection of monoculture models, which are not likely to reflect the cellular complexity of the in vivo lesion environment. Our approach is to characterize and compare the gene expression profile in KS lesions versus non-cancer tissues from the same individual. Such comparisons could identify pathways critical for KS formation and maintenance. This is the first study that utilized high throughput RNA-seq to characterize the viral and cellular transcriptome in tumor and non-cancer biopsies of African epidemic KS patients. These patients were treated anti-retroviral therapy with undetectable HIV-1 plasma viral load. We found remarkable variability in the viral transcriptome among these patients, with viral latency and immune modulation genes most abundantly expressed. The presence of KSHV also significantly affected the cellular transcriptome profile. Specifically, genes involved in lipid and glucose metabolism disorder pathways were substantially affected. Moreover, infiltration of immune cells into the tumor did not prevent KS formation, suggesting some functional deficits of these cells. Lastly, we found only minimal overlaps between our in vivo cellular transcriptome dataset with those from in vitro studies, reflecting the limitation of in vitro models in representing tumor lesions. These findings could lead to the identification of diagnostic and therapeutic markers for KS, and will provide bases for further mechanistic studies on the functions of both viral and cellular genes that are involved.

Kaposi’s sarcoma-associated herpesvirus (KSHV) is endemic in sub-Saharan Africa and cause Kaposi’s sarcoma (KS). KS is one of the most common cancer among HIV-1 patients in this region. Despite anti-retroviral treatment, prognosis for KS is poor with high mortality often due to presentation of late cancer stage. In order to identify biomarkers or therapeutic targets against KS, a better understanding of the viral and cellular genes expression/transcriptome in the tumor will be necessary. We used RNA-seq, a highly efficient method to sequence transcriptome, to characterize and compare the viral and cellular transcriptome in tumor and non-cancer tissues from KS patients. We found that viral genes involved in latency and immune modulation are most commonly expressed among KS patients. Additionally, cellular genes involved in lipid and glucose metabolism disorder pathways are significantly affected by the presence of KSHV. Despite the detection of immune cells in the tumor, it did not prevent the tumor progression, suggesting some level of immune cell dysfunctions in KS patients. Lastly, we found limited overlap of our data, derived from actual KS biopsy, with other cell culture models, suggesting that the complexity of tumor is difficult to be reflected in cell line models.

More than just antibodies: Protective mechanisms of a mucosal vaccine against fish pathogen Flavobacterium columnare

A recently developed attenuated vaccine for Flavobacterium columnare has been demonstrated to provide superior protection for channel catfish, Ictalurus punctatus, against genetically diverse columnaris isolates. We were interested in examining the mechanisms of this protection by comparing transcriptional responses to F. columnare challenge in vaccinated and unvaccinated juvenile catfish. Accordingly, 58 day old fingerling catfish (28 days post-vaccination or unvaccinated control) were challenged with a highly virulent F. columnare isolate (BGSF-27) and gill tissues collected pre-challenge (0 h), and 1 h and 2 h post infection, time points previously demonstrated to be critical in early host-pathogen interactions. Following RNA-sequencing and transcriptome assembly, differential expression (DE) analysis within and between treatments revealed several patterns and pathways potentially underlying improved survival of vaccinated fish. Most striking was a pattern of dramatically higher basal expression of an array of neuropeptides (e.g. somatostatin), hormones, complement factors, and proteases at 0 h in vaccinated fish. Previous studies indicate these are likely the preformed mediators of neuroendocrine cells and/or eosinophilic granular (mast-like) cells within the fish gill. Following challenge, these elements fell to almost undetectable levels (>100-fold downregulated) by 1 h in vaccinated fish, suggesting their rapid release and/or cessation of synthesis following degranulation. Concomitantly, levels of pro-inflammatory cytokines (IL-1b, IL-8, IL-17) were induced in unvaccinated fish. In contrast, in vaccinated catfish, we observed widespread induction of genes needed for collagen deposition and tissue remodeling. Taken together, our results indicate an important component of vaccine protection in fish mucosal tissues may be the sensitization, proliferation and arming of resident secretory cells in the period between primary and secondary challenge.

Systematic profiling of alternative splicing signature reveals prognostic predictor for ovarian cancer

OBJECTIVE

The majority of genes are alternatively spliced and growing evidence suggests that alternative splicing is modified in cancer and is associated with cancer progression. Systematic analysis of alternative splicing signature in ovarian cancer is lacking and greatly needed.

METHODS

We profiled genome-wide alternative splicing events in 408 ovarian serous cystadenocarcinoma (OV) patients in TCGA. Seven types of alternative splicing events were curated and prognostic analyses were performed with predictive models and splicing network built for OV patients.

RESULTS

Among 48,049 mRNA splicing events in 10,582 genes, we detected 2,611 alternative splicing events in 2,036 genes which were significant associated with overall survival of OV patients. Exon skip events were the most powerful prognostic factors among the seven types. The area under the curve of the receiver-operator characteristic curve for prognostic predictor, which was built with top significant alternative splicing events, was 0.937 at 2,000 days of overall survival, indicating powerful efficiency in distinguishing patient outcome. Interestingly, splicing correlation network suggested obvious trends in the role of splicing factors in OV.

CONCLUSIONS

In summary, we built powerful prognostic predictors for OV patients and uncovered interesting splicing networks which could be underlying mechanisms.

FoxM1: Repurposing an oncogene as a biomarker

The past few decades have witnessed a tremendous progress in understanding the biology of cancer, which has led to more comprehensive approaches for global gene expression profiling and genome-wide analysis. This has helped to determine more sophisticated prognostic and predictive signature markers for the prompt diagnosis and precise screening of cancer patients. In the search for novel biomarkers, there has been increased interest in FoxM1, an extensively studied transcription factor that encompasses most of the hallmarks of malignancy. Considering the attractive potential of this multifarious oncogene, FoxM1 has emerged as an important molecule implicated in initiation, development and progression of cancer. Bolstered with the skill to maneuver the proliferation signals, FoxM1 bestows resistance to contemporary anti-cancer therapy as well. This review sheds light on the large body of literature that has accumulated in recent years that implies that FoxM1 neoplastic functions can be used as a novel predictive, prognostic and therapeutic marker for different cancers. This assessment also highlights the key features of FoxM1 that can be effectively harnessed to establish FoxM1 as a strong biomarker in diagnosis and treatment of cancer.

Analysis of differential splicing suggests different modes of short-term splicing regulation

Motivation: Alternative splicing is an important mechanism in which the regions of pre-mRNAs are differentially joined in order to form different transcript isoforms. Alternative splicing is involved in the regulation of normal physiological functions but also linked to the development of diseases such as cancer. We analyse differential expression and splicing using RNA-sequencing time series in three different settings: overall gene expression levels, absolute transcript expression levels and relative transcript expression levels.

Results: Using estrogen receptor α signaling response as a model system, our Gaussian process-based test identifies genes with differential splicing and/or differentially expressed transcripts. We discover genes with consistent changes in alternative splicing independent of changes in absolute expression and genes where some transcripts change whereas others stay constant in absolute level. The results suggest classes of genes with different modes of alternative splicing regulation during the experiment.

Availability and Implementation: R and Matlab codes implementing the method are available at https://github.com/PROBIC/diffsplicing. An interactive browser for viewing all model fits is available at http://users.ics.aalto.fi/hande/splicingGP/

Contact:hande.topa@helsinki.fi or antti.honkela@helsinki.fi

Supplementary information:Supplementary data are available at Bioinformatics online.

RNA editing-dependent epitranscriptome diversity in cancer stem cells

Cancer stem cells (CSCs) can regenerate all facets of a tumour as a result of their stem cell-like capacity to self-renew, survive and become dormant in protective microenvironments. CSCs evolve during tumour progression in a manner that conforms to Charles Darwin's principle of natural selection. Although somatic DNA mutations and epigenetic alterations promote evolution, post-transcriptional RNA modifications together with RNA binding protein activity (the 'epitranscriptome') might also contribute to clonal evolution through dynamic determination of RNA function and gene expression diversity in response to environmental stimuli. Deregulation of these epitranscriptomic events contributes to CSC generation and maintenance, which governs cancer progression and drug resistance. In this Review, we discuss the role of malignant RNA processing in CSC generation and maintenance, including mechanisms of RNA methylation, RNA editing and RNA splicing, and the functional consequences of their aberrant regulation in human malignancies. Finally, we highlight the potential of these events as novel CSC biomarkers as well as therapeutic targets.

Antigen Discovery and Therapeutic Targeting in Hematologic Malignancies

Historically, immune-based therapies have played a leading role in the treatment of hematologic malignancies, with the efficacy of stem cell transplantation largely attributable to donor immunity against malignant cells. As new and more targeted immunotherapies have developed, their role in the treatment of hematologic malignancies is evolving and expanding. Herein, we discuss approaches for antigen discovery and review known and novel tumor antigens in hematologic malignancies. We further explore the role of established and investigational immunotherapies in hematologic malignancies, with a focus on personalization of treatment modalities such as cancer vaccines and adoptive cell therapy. Finally, we identify areas of active investigation and development. Immunotherapy is at an exciting crossroads for the treatment of hematologic malignancies, with further investigation aimed at producing effective, targeted immune therapies that maximize antitumor effects while minimizing toxicity.

A saga of cancer epigenetics: linking epigenetics to alternative splicing

The discovery of an increasing number of alternative splicing events in the human genome highlighted that ∼94% of genes generate alternatively spliced transcripts that may produce different protein isoforms with diverse functions. It is now well known that several diseases are a direct and indirect consequence of aberrant splicing events in humans. In addition to the conventional mode of alternative splicing regulation by 'cis' RNA-binding sites and 'trans' RNA-binding proteins, recent literature provides enormous evidence for epigenetic regulation of alternative splicing. The epigenetic modifications may regulate alternative splicing by either influencing the transcription elongation rate of RNA polymerase II or by recruiting a specific splicing regulator via different chromatin adaptors. The epigenetic alterations and aberrant alternative splicing are known to be associated with various diseases individually, but this review discusses/highlights the latest literature on the role of epigenetic alterations in the regulation of alternative splicing and thereby cancer progression. This review also points out the need for further studies to understand the interplay between epigenetic modifications and aberrant alternative splicing in cancer progression.

Manananggal - a novel viewer for alternative splicing events

BACKGROUND

Alternative splicing is an important cellular mechanism that can be analyzed by RNA sequencing. However, identification of splicing events in an automated fashion is error-prone. Thus, further validation is required to select reliable instances of alternative splicing events (ASEs). There are only few tools specifically designed for interactive inspection of ASEs and available visualization approaches can be significantly improved.

RESULTS

Here, we present Manananggal, an application specifically designed for the identification of splicing events in next generation sequencing data. Manananggal includes a web application for visual inspection and a command line tool that allows for ASE detection. We compare the sashimi plots available in the IGV Viewer, the DEXSeq splicing plots and SpliceSeq to the Manananggal interface and discuss the advantages and drawbacks of these tools. We show that sashimi plots (such as those used by the IGV Viewer and SpliceSeq) offer a practical solution for simple ASEs, but also indicate short-comings for highly complex genes.

CONCLUSION

Manananggal is an interactive web application that offers functions specifically tailored to the identification of alternative splicing events that other tools are lacking. The ability to select a subset of isoforms allows an easier interpretation of complex alternative splicing events. In contrast to SpliceSeq and the DEXSeq splicing plot, Manananggal does not obscure the gene structure by showing full transcript models that makes it easier to determine which isoforms are expressed and which are not.

Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment

Epithelial ovarian cancer is a highly heterogeneous disease characterized by multiple histological subtypes. Molecular diversity has been shown to occur within specific histological subtypes of epithelial ovarian cancer, between different tumors of an individual patient, as well as within individual tumors. Recent advances in the molecular characterization of epithelial ovarian cancer tumors have provided the basis for a simplified classification scheme in which these cancers are classified as either type I or type II tumors, and these two categories have implications regarding disease pathogenesis and prognosis. Molecular analyses, primarily based on next-generation sequencing, otherwise known as high-throughput sequencing, are allowing for further refinement of ovarian cancer classification, facilitating the elucidation of the site(s) of precursor lesions of high-grade serous ovarian cancer, and providing insight into the processes of clonal selection and evolution that may be associated with development of chemoresistance. Potential therapeutic targets have been identified from recent molecular profiling studies of these tumors, and the effectiveness and safety of a number of specific targeted therapies have been evaluated or are currently being studied for the treatment of women with this disease.

Receptor FGFRL1 acts as a tumor suppressor in nude mice when overexpressed in HEK 293 Tet-On cells

Fibroblast growth factor receptor-like 1 (FGFRL1) is a transmembrane receptor that interacts with heparin and FGF ligands. In contrast to the classical FGF receptors, FGFR1 to FGFR4, it does not appear to affect cell growth and proliferation. In the present study, an inducible gene expression system was utilized in combination with a xenograft tumor model to investigate the effects of FGFRL1 on cell adhesion and tumor formation. It was determined that recombinant FGFRL1 promotes the adhesion of HEK 293 Tet-On® cells in vitro. Moreover, when such cells are induced to express FGFRL1ΔC they aggregate into huge clusters. If injected into nude mice, the cells form large tumors. Notably, this tumor growth is completely inhibited when the expression of FGFRL1 is induced. The forced expression of FGFRL1 in the tumor tissue may restore contact inhibition, thereby preventing growth of the cells in nude mice. The results of the present study demonstrate that FGFRL1 acts as a tumor suppressor similar to numerous other cell adhesion proteins. It is therefore likely that FGFRL1 functions as a regular cell-cell adhesion protein.

Genetic determinants of FOXM1 overexpression in epithelial ovarian cancer and functional contribution to cell cycle progression

The FOXM1 transcription factor network is frequently activated in high-grade serous ovarian cancer (HGSOC), the most common and lethal subtype of epithelial ovarian cancer (EOC). We used primary human EOC tissues, HGSOC cell lines, mouse and human ovarian surface epithelial (OSE) cells, and a murine transgenic ovarian cancer model to investigate genetic determinants of FOXM1 overexpression in EOC, and to begin to define its functional contribution to disease pathology. The Cancer Genome Atlas (TCGA) data indicated that the FOXM1 locus is amplified in ~12% of HGSOC, greater than any other tumor type examined, and that FOXM1 amplification correlates with increased expression and poor survival. In an independent set of primary EOC tissues, FOXM1 expression correlated with advanced stage and grade. Of the three known FOXM1 isoforms, FOXM1c showed highest expression in EOC. In murine OSE cells, combined knockout of Rb1 and Trp53 synergistically induced FOXM1. Consistently, human OSE cells immortalized with SV40 Large T antigen (IOSE-SV) had significantly higher FOXM1 expression than OSE immortalized with hTERT (IOSE-T). FOXM1 was overexpressed in murine ovarian tumors driven by combined Rb1/Trp53 disruption. FOXM1 induction in IOSE-SV cells was partially dependent on E2F1, and FOXM1 expression correlated with E2F1 expression in human EOC tissues. Finally, FOXM1 functionally contributed to cell cycle progression and relevant target gene expression in human OSE and HGSOC cell models. In summary, gene amplification, p53 and Rb disruption, and E2F1 activation drive FOXM1 expression in EOC, and FOXM1 promotes cell cycle progression in EOC cell models.