scholarly journals A Pan-Cancer Analysis of Alternative Splicing Events Reveals Novel Tumor-Associated Splice Variants of Matriptase

2014 ◽  
Vol 13 ◽  
pp. CIN.S19435 ◽  
Author(s):  
Daryanaz Dargahi ◽  
Richard D. Swayze ◽  
Leanna Yee ◽  
Peter J. Bergqvist ◽  
Bradley J. Hedberg ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
High Throughput ◽  
Messenger Rna ◽  
Transcriptome Sequencing ◽  
Splice Variants ◽  
Flow Cytometric Analysis ◽  
Chinese Hamster ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Sequencing Data

High-throughput transcriptome sequencing allows identification of cancer-related changes that occur at the stages of transcription, pre-messenger RNA (mRNA), and splicing. In the current study, we devised a pipeline to predict novel alternative splicing (AS) variants from high-throughput transcriptome sequencing data and applied it to large sets of tumor transcriptomes from The Cancer Genome Atlas (TCGA). We identified two novel tumor-associated splice variants of matriptase, a known cancer-associated gene, in the transcriptome data from epithelial-derived tumors but not normal tissue. Most notably, these variants were found in 69% of lung squamous cell carcinoma (LUSC) samples studied. We confirmed the expression of matriptase AS transcripts using quantitative reverse transcription PCR (qRT-PCR) in an orthogonal panel of tumor tissues and cell lines. Furthermore, flow cytometric analysis confirmed surface expression of matriptase splice variants in chinese hamster ovary (CHO) cells transiently transfected with cDNA encoding the novel transcripts. Our findings further implicate matriptase in contributing to oncogenic processes and suggest potential novel therapeutic uses for matriptase splice variants.

scholarly journals HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 120
Author(s):  
Yiyun Sun ◽  
Dandan Xu ◽  
Chundong Zhang ◽  
Yitao Wang ◽  
Lian Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Squamous Cell ◽  
Gene Pair ◽  
Gene List ◽  
Ectopic Expression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

scholarly journals rMAPS2: an update of the RNA map analysis and plotting server for alternative splicing regulation

2020 ◽  
Vol 48 (W1) ◽  
pp. W300-W306 ◽  
Author(s):  
Jae Y Hwang ◽  
Sungbo Jung ◽  
Tae L Kook ◽  
Eric C Rouchka ◽  
Jinwoong Bok ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
High Throughput ◽  
Splice Site ◽  
High Throughput Sequencing ◽  
Rna Binding ◽  
Rna Binding Protein ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Retained Intron ◽  
Map Analysis

Abstract The rMAPS2 (RNA Map Analysis and Plotting Server 2) web server, freely available at http://rmaps.cecsresearch.org/, has provided the high-throughput sequencing data research community with curated tools for the identification of RNA binding protein sites. rMAPS2 analyzes differential alternative splicing or CLIP peak data obtained from high-throughput sequencing data analysis tools like MISO, rMATS, Piranha, PIPE-CLIP and PARalyzer, and then, graphically displays enriched RNA-binding protein target sites. The initial release of rMAPS focused only on the most common alternative splicing event, skipped exon or exon skipping. However, there was a high demand for the analysis of other major types of alternative splicing events, especially for retained intron events since this is the most common type of alternative splicing in plants, such as Arabidopsis thaliana. Here, we expanded the implementation of rMAPS2 to facilitate analyses for all five major types of alternative splicing events: skipped exon, mutually exclusive exons, alternative 5′ splice site, alternative 3′ splice site and retained intron. In addition, by employing multi-threading, rMAPS2 has vastly improved the user experience with significant reductions in running time, ∼3.5 min for the analysis of all five major alternative splicing types at once.

scholarly journals Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qidong Cai ◽  
Boxue He ◽  
Pengfei Zhang ◽  
Zhenyu Zhao ◽  
Xiong Peng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Machine Learning Algorithms ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

scholarly journals Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits

Genetics ◽  
2019 ◽  
Vol 213 (4) ◽  
pp. 1209-1224 ◽  
Author(s):  
Juho A. J. Kontio ◽  
Mikko J. Sillanpää
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Real Data ◽  
Higher Order ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Automatic Relevance Determination ◽  
Cancer Genome Atlas ◽  
Low Dimensional

Gaussian process (GP)-based automatic relevance determination (ARD) is known to be an efficient technique for identifying determinants of gene-by-gene interactions important to trait variation. However, the estimation of GP models is feasible only for low-dimensional datasets (∼200 variables), which severely limits application of the GP-based ARD method for high-throughput sequencing data. In this paper, we provide a nonparametric prescreening method that preserves virtually all the major benefits of the GP-based ARD method and extends its scalability to the typical high-dimensional datasets used in practice. In several simulated test scenarios, the proposed method compared favorably with existing nonparametric dimension reduction/prescreening methods suitable for higher-order interaction searches. As a real-data example, the proposed method was applied to a high-throughput dataset downloaded from the cancer genome atlas (TCGA) with measured expression levels of 16,976 genes (after preprocessing) from patients diagnosed with acute myeloid leukemia.

scholarly journals Identification of prognostic factors for intrahepatic cholangiocarcinoma using long non-coding RNAs-associated ceRNA network

2020 ◽  
Author(s):  
Zhichen Kang ◽  
Lixin Guo ◽  
Zhuo Zhu ◽  
Rongfeng Qu
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Signaling Pathway ◽  
Intrahepatic Cholangiocarcinoma ◽  
Messenger Rna ◽  
Transcriptome Sequencing ◽  
Pathway Enrichment Analysis ◽  
Sequencing Data ◽  
Cerna Network ◽  
Non Coding Rnas

Abstract Background: Accumulating amount of evidence has highlighted the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in tumor pathogenesis. However, the roles of long non coding RNAs (lncRNAs) in the lncRNA-related ceRNA network of intrahepatic cholangiocarcinoma (ICC) still remain enigmatic. The current study aims to identify prognostic factors in the lncRNA-related ceRNA network of ICC.Methods: The transcriptome sequencing data of lncRNAs, messenger RNA (mRNA) and microRNA (miR) were downloaded from the SRA and TCGA databases. Differentially expressed lncRNAs (DElncRNAs), DEmiRs and DEmRNAs were identified and adopted to construct an lncRNA-miR-mRNA ceRNA network. ICC-associated DEmRNAs were adopted to construct the protein-protein interaction (PPI) network. The expression of the top 6 genes in the hub module was validated with mRNA transcriptome sequencing data and ICC-related gene expression dataset GSE45001, followed by GO and KEGG pathway enrichment analysis. The relationship between the hub gene-associated ceRNA network and the overall survival of patients with ICC was predicted by conducting a Kaplan-Meier survival analysis. Results: Sixty co-expressed DEmRNAs were identified in the ceRNA network. The top 6 hub genes consisted of downregulated FOS, IGF2, FOXO1 and NTF3, upregulated IGF1R, and insignificantly downregulated HGF in ICC tissues, when compared to that of normal adjacent tissues, followed by the successful construction of lncRNA-miR-hub network consisting of 86 ceRNA modules. MME-AS1 and hsa-miR-182 were associated with overall survival in ICC patients. FOS, IGF1R, IGF2, FOXO1, and NTF3 might target “TGF-β signaling pathway”, “the hedgehog signaling pathway”, “retinol metabolism”, or “type II diabetes mellitus” pathways respectively. Conclusion: These results indicate that FOS, IGF1R, IGF2, FOXO1, and NTF3 were useful prognostic factors in determining the prognosis of patients with ICC.

Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq data

Epigenomics ◽  
2019 ◽  
Vol 11 (15) ◽  
pp. 1679-1692
Author(s):  
Jiang Zhu ◽  
Mu Su ◽  
Yue Gu ◽  
Xingda Zhang ◽  
Wenhua Lv ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Throughput ◽  
Bisulfite Sequencing ◽  
The Cancer Genome Atlas ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Allele Specific ◽  
New Perspective

Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

Exosomal hsa-miR-129-2 and hsa-miR-889 from a 6-microRNA signature might be a potential biomarker for predicting prognosis of papillary thyroid carcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Ying Xin ◽  
Kexin Meng ◽  
Haiwei Guo ◽  
Bin Chen ◽  
Chuanming Zheng ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
High Throughput ◽  
Messenger Rna ◽  
High Throughput Sequencing ◽  
The Cancer Genome Atlas ◽  
Papillary Thyroid ◽  
Control Groups ◽  
Potential Biomarker ◽  
And Control

Background: Papillary thyroid carcinoma (PTC) is a subtype of thyroid cancer with increasing incidence over time. Objective: This study aimed to build a risk score (RS) system for PTC patients. Methods: PTC microRNA (miRNA) and messenger RNA (mRNA) expression data were extracted from The Cancer Genome Atlas (TCGA) database. The 491 PTC samples were randomly divided into training and validation sets. Using the limma software package, differentially expressed mRNAs (DEGs) and miRNAs (DEMs) between the tumor and control groups were screened. In order to construct an RS system, a survival package was used to select independent miRNAs related to prognosis. Enrichment analysis was performed, and a miRNA-mRNA co-expression network was constructed. High-throughput sequencing was also used to verify the prognostic miRNAs in exosomes. Results: We found 1363 DEGs and 171 DEMs between the tumor and control groups. After identifying 26 DEMs that were significantly related to prognosis, 6 independent prognosis-associated miRNAs were selected to build an RS system. The areas under the curves of the overall survival rates of the training, validation, and entire sets were 0.847, 0.772, and 0.819, respectively. By conducting pathway analysis using the miRNA-mRNA co-expression network, one overlapping factor and five overlapping pathways were obtained. In addition, high-throughput sequencing revealed that the hsa-miR-129-2, hsa-miR-548j, hsa-miR-6734, and hsa-miR-889 expression levels in TCGA tumor tissues and exosomes were consistent, and those of hsa-miR-129-2 and hsa-miR-889 between patients and controls were significantly different in exosomes. Conclusion: The six-miRNA RS system in exosomes may comprise independent signatures for predicting PTC patient prognosis.

MLL-Rearranged Acute Myeloid Leukemias Drive Expression Of Mir-9, a Critical Oncogene In Leukemogenesis

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3740-3740
Author(s):  
Colles Price ◽  
Ping Chen ◽  
Zejuan Li ◽  
Yuanyuan Li ◽  
Anissa Wiley ◽  
...  
Keyword(s):  
Messenger Rna ◽  
Cancer Biology ◽  
Conflicts Of Interest ◽  
Univariate Analysis ◽  
The United States ◽  
The Cancer Genome Atlas ◽  
Leukemic Cells ◽  
Sequencing Data ◽  
Significant Difference ◽  
Acute Myeloid

Abstract A critical area of cancer biology is the study of the deregulation of noncoding RNAs called microRNAs (miRNAs). Acute leukemia represents one of the most deadly cancers in the United States. One subset of leukemia with a poor to intermediate clinical outcome are chromosomal translocations involving Mixed Lineage Leukemia (MLL). As MLL-translocations are sufficient to drive leukemogenesis, and few additional mutations are observed in patients, it is imperative to understand the biology driving leukemogenesis. Previously, we and others have shown that several miRNAs are deregulated in MLL-rearranged Acute Myeloid Leukemia (AML). To identify miRNAs that are driving leukemogenesis we performed messenger RNA and miRNA expression profiling on primary patient samples and identified microRNA-9 (miR-9) as specifically overexpressed in MLL-rearranged AML. We further confirmed this observation using publically available microRNA sequencing data from the Cancer Genome Atlas (TCGA) and several AML cell lines. After showing that MLL directly binds and regulates miR-9 we show that depletion of MLL-fusion expression leads to the loss of miR-9 expression. Using publically available Illumina 450K methylation data from TCGA, we show that there is no significant difference in modified cytosine between miR-9 high and miR-9-low patients, suggesting that expression of miR-9 is likely not be regulated by DNA methylation machinery in AML patients. We show that miR-9 in the presence of MLL-AF9 (a common MLL-fusion) promotes colony growth over multiple passages while blocking miR-9 using a miR-9 sponge remarkably inhibits MLL-fusion-mediated cell transformation. Furthermore, we show that miR-9 increases proliferation and reduces apoptosis of human MLL-rearranged leukemic cells in vitro using MTT and Caspase 3/7 assays. We then show that co-transfection of miR-9 with MLL-AF9 in a bone marrow transplantation assay results in a higher leukemia burden in vivo compared to MLL-AF9 alone and promotes an immature cellular phenotype. Using microarray data we found several putative miR-9 targets by identifying genes that had an inverse correlation to miR-9. Next, we verified several genes were being inhibited by miR-9 such as Ras homology gene family member H (RHOH) and Ring1- and YY1-binding protein (RYBP). To understand the role of miR-9 in context with other miRNAs we did an association analysis of the top 300 differentially expressed miRNAs in the TCGA dataset. We found interestingly, that two of the miR-9 genes, miR-9-1 and miR-9-2, are highly correlated with each other across all the patients although they are located on distinct chromosomes. We also found that several other miRs were either negatively (e.g., miR-130a and miR-221) or positively (e.g., miR-191 and miR-642) associated with miR-9 expression, suggesting that these miRs might be operating either cooperatively or antagonistically in a complex circuitry. To support this hypothesis we found in univariate analysis that miR-9 itself was not a good predictor of patient survival but was a better predictor when combined with other miRs including the miR-181 family. Together this suggests that miR-9 is an important and critical regulator of MLL-rearranged AML and is a very good candidate for potential therapeutic targeting. Disclosures: No relevant conflicts of interest to declare.

scholarly journals neoantigenR: An annotation based pipeline for tumor neoantigen identification from sequencing data

2017 ◽  
Author(s):  
Shaojun Tang ◽  
Subha Madhavan
Keyword(s):  
Alternative Splicing ◽  
Cancer Immunotherapy ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Peptide Epitopes ◽  
Cancer Antigens ◽  
Long Read ◽  
Personalized Cancer ◽  
Specific Peptide

AbstractStudies indicate that more than 90% of human genes are alternatively spliced, suggesting the complexity of the transcriptome assembly and analysis. The splicing process is often disrupted, resulting in both functional and non-functional end-products (Sveen et al. 2016) in many cancers. Harnessing the immune system to fight against malignant cancers carrying aberrantly mutated or spliced products is becoming a promising approach to cancer therapy. Advances in immune checkpoint blockade have elicited adaptive immune responses with promising clinical responses to treatments against human malignancies (Tumor Neoantigens in Personalized Cancer Immunotherapy 2017). Emerging data suggest that recognition of patient-specific mutation-associated cancer antigens (i.e. from alternative splicing isoforms) may allow scientists to dissect the immune response in the activity of clinical immunotherapies (Schumacher and Schreiber 2015). The advent of high-throughput sequencing technology has provided a comprehensive view of both splicing aberrations and somatic mutations across a range of human malignancies, allowing for a deeper understanding of the interplay of various disease mechanisms.Meanwhile, studies show that the number of transcript isoforms reported to date may be limited by the short-read sequencing due to the inherit limitation of transcriptome reconstruction algorithms, whereas long-read sequencing is able to significantly improve the detection of alternative splicing variants since there is no need to assemble full-length transcripts from short reads. The analysis of these high-throughput long-read sequencing data may permit a systematic view of tumor specific peptide epitopes (also known as neoantigens) that could serve as targets for immunotherapy (Tumor Neoantigens in Personalized Cancer Immunotherapy 2017).Currently, there is no software pipeline available that can efficiently produce mutation-associated cancer antigens from raw high-throughput sequencing data on patient tumor DNA (The Problem with Neoantigen Prediction 2017). In addressing this issue, we introduce a R package that allows the discoveries of peptide epitope candidates, which are the tumor-specific peptide fragments containing potential functional neoantigens. These peptide epitopes consist of structure variants including insertion, deletions, alternative sequences, and peptides from nonsynonymous mutations. Analysis of these precursor candidates with widely used tools such as netMHC allows for the accurate in-silico prediction of neoantigens. The pipeline named neoantigeR is currently hosted in https://github.com/ICBI/neoantigeR.

scholarly journals Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

2020 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Qian Niu ◽  
Yun Han ◽  
Xingyu Liu ◽  
Jie Jiang ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
High Efficiency ◽  
Cox Regression ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Regulation Mechanism ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Molecular Events ◽  
Stomach Adenocarcinoma

Abstract Background: Alternative splicing (AS) offers a main mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing disorders and carcinoma. Nevertheless, an overall analysis of AS signatures in stomach adenocarcinoma (STAD) is absent and urgently needed.Methods: Within this work, genetic expression and clinical data of STAD were queried from The Cancer Genome Atlas (TCGA), and profiles of AS events were searched from the SpliceSeq database. Cox regression analysis found survival associated AS events. Finally, the splicing network was constructed to reflect the correlation between survival associated AS events and splicing factors (SF).Results: 2042 splicing events were confirmed as prognostic molecular events. Furthermore, the final prognostic signature constructed by 10 AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.902 for 5 years, showing high potency in predicting patient outcome. We built the splicing regulatory network to show the internal regulation mechanism of splicing events in STAD. QKI may play a significant part in the prognosis induced by splicing events.Conclusions: In our study, a high-efficiency prognostic prediction model was built for STAD patients, and the results showed that AS events could become potential prognostic biomarkers for STAD. Meanwhile, QKI may become an important target for drug design in the future.

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