Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis

LncRNA-mediated orchestrations of alternative splicing in the landscape of breast cancer

Alternative splicing (AS) is a fundamental post-transcriptional process in eukaryotes, enabling a single gene to generate diverse mRNA transcripts, thereby enhancing protein variability. This process involves the excision of introns and the joining of exons in pre-mRNA(s) to form mature mRNA. The resulting mature mRNAs exhibit various combinations of exons, contributing to functional diversity. Dysregulation of AS can substantially modulate protein functions, impacting the onset and progression of numerous diseases, including cancer. Non-coding RNAs (ncRNAs) are distinct from protein-coding RNAs and consist of short and long types. Long non-coding RNAs (lncRNAs) play an important role in regulating several cellular processes, particularly alternative splicing, according to new research. This review provides insight into the latest discoveries concerning how lncRNAs influence alternative splicing within the realm of breast cancer. Additionally, it explores potential therapeutic strategies focused on targeting lncRNAs.

AS-Quant: Detection and Visualization of Alternative Splicing Events with RNA-seq Data

(1) Background: A simplistic understanding of the central dogma falls short in correlating the number of genes in the genome to the number of proteins in the proteome. Post-transcriptional alternative splicing contributes to the complexity of the proteome and is critical in understanding gene expression. mRNA-sequencing (RNA-seq) has been widely used to study the transcriptome and provides opportunity to detect alternative splicing events among different biological conditions. Despite the popularity of studying transcriptome variants with RNA-seq, few efficient and user-friendly bioinformatics tools have been developed for the genome-wide detection and visualization of alternative splicing events. (2) Results: We propose AS-Quant, (Alternative Splicing Quantitation), a robust program to identify alternative splicing events from RNA-seq data. We then extended AS-Quant to visualize the splicing events with short-read coverage plots along with complete gene annotation. The tool works in three major steps: (i) calculate the read coverage of the potential spliced exons and the corresponding gene; (ii) categorize the events into five different categories according to the annotation, and assess the significance of the events between two biological conditions; (iii) generate the short reads coverage plot for user specified splicing events. Our extensive experiments on simulated and real datasets demonstrate that AS-Quant outperforms the other three widely used baselines, SUPPA2, rMATS, and diffSplice for detecting alternative splicing events. Moreover, the significant alternative splicing events identified by AS-Quant between two biological contexts were validated by RT-PCR experiment. (3) Availability: AS-Quant is implemented in Python 3.0. Source code and a comprehensive user's manual are freely available online.

CXCL12 and IL7R as Novel Therapeutic Targets for Liver Hepatocellular Carcinoma Are Correlated With Somatic Mutations and the Tumor Immunological Microenvironment

The mechanism of liver hepatocellular carcinoma (LIHC) development in correlation with tumor microenvironments and somatic mutations is still being elucidated. This study aims to identify the potential molecular mechanisms and candidate biomarkers in response to tumor microenvironments and somatic mutations. Multiple bioinformatics analysis methods were applied to assess the tumor immunological microenvironment, differentially expressed genes, genetic function enrichment, immunocyte infiltration, regulatory network construction, and tumor mutational burden, and to identify DNA methylation sites. The immunological microenvironment features of ESTIMATE score (OS: p = 0.017, HR = 0.64; RFS: HR = 0.43, p < 0.001) have an important impact on the prognosis of LIHC patients. Cut-off by ESTIMATE score and prognostic information identified 666 DEGs (45 downregulated and 621 upregulated) that were linked with leukocyte migration and lymphocyte activation. In immunocyte infiltration analysis, NK cells (resting), M1 macrophages, CD8+ T cells, and regulatory T cells (Tregs), which are considered core immunoregulatory cells, exhibited significant differences between higher and lower ESTIMATE scores (overall survival and recurrence-free survival p-values < 0.01). Subsequently, further analysis of immunocyte-hub gene identification illustrated that the expression levels of CXCL12 and IL7R significantly correlated with core immunoregulatory cells and somatic mutations (CXCL12: p = 2.1E-06; IL7R: p = 0.001). This study provides new insight into our understanding of the mechanisms of immunocyte regulation and microenvironment involved in LIHC development as well as the effective biomarkers of CXCL12 and IL7R and core immunoregulatory cells, which may emerge as novel therapies for LIHC patients.

tRNA Fragments Populations Analysis in Mutants Affecting tRNAs Processing and tRNA Methylation

tRNA fragments (tRFs) are a class of small non-coding RNAs (sncRNAs) derived from tRNAs. tRFs are highly abundant in many cell types including stem cells and cancer cells, and are found in all domains of life. Beyond translation control, tRFs have several functions ranging from transposon silencing to cell proliferation control. However, the analysis of tRFs presents specific challenges and their biogenesis is not well understood. They are very heterogeneous and highly modified by numerous post-transcriptional modifications. Here we describe a bioinformatic pipeline (tRFs-Galaxy) to study tRFs populations and shed light onto tRNA fragments biogenesis in Drosophila melanogaster. Indeed, we used small RNAs Illumina sequencing datasets extracted from wild type and mutant ovaries affecting two different highly conserved steps of tRNA biogenesis: 5'pre-tRNA processing (RNase-P subunit Rpp30) and tRNA 2'-O-methylation (dTrm7_34 and dTrm7_32). Using our pipeline, we show how defects in tRNA biogenesis affect nuclear and mitochondrial tRFs populations and other small non-coding RNAs biogenesis, such as small nucleolar RNAs (snoRNAs). This tRF analysis workflow will advance the current understanding of tRFs biogenesis, which is crucial to better comprehend tRFs roles and their implication in human pathology.

Platform-integrated mRNA isoform quantification

MOTIVATION

Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction.

RESULTS

In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString's nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available.

AVAILABILITY AND IMPLEMENTATION

Source code is available at: https://github.com/CompbioLabUcf/IntMTQ.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A large-scale comparative study of isoform expressions measured on four platforms

BACKGROUND

Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains.

RESULTS

In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis.

CONCLUSION

In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis

BACKGROUND

Alternative polyadenylation (APA) has emerged as a pervasive mechanism that contributes to the transcriptome complexity and dynamics of gene regulation. The current tsunami of whole genome poly(A) site data from various conditions generated by 3' end sequencing provides a valuable data source for the study of APA-related gene expression. Cluster analysis is a powerful technique for investigating the association structure among genes, however, conventional gene clustering methods are not suitable for APA-related data as they fail to consider the information of poly(A) sites (e.g., location, abundance, number, etc.) within each gene or measure the association among poly(A) sites between two genes.

RESULTS

Here we proposed a computational framework, named PASCCA, for clustering genes from replicated or unreplicated poly(A) site data using canonical correlation analysis (CCA). PASCCA incorporates multiple layers of gene expression data from both the poly(A) site level and gene level and takes into account the number of replicates and the variability within each experimental group. Moreover, PASCCA characterizes poly(A) sites in various ways including the abundance and relative usage, which can exploit the advantages of 3' end deep sequencing in quantifying APA sites. Using both real and synthetic poly(A) site data sets, the cluster analysis demonstrates that PASCCA outperforms other widely-used distance measures under five performance metrics including connectivity, the Dunn index, average distance, average distance between means, and the biological homogeneity index. We also used PASCCA to infer APA-specific gene modules from recently published poly(A) site data of rice and discovered some distinct functional gene modules. We have made PASCCA an easy-to-use R package for APA-related gene expression analyses, including the characterization of poly(A) sites, quantification of association between genes, and clustering of genes.

CONCLUSIONS

By providing a better treatment of the noise inherent in repeated measurements and taking into account multiple layers of poly(A) site data, PASCCA could be a general tool for clustering and analyzing APA-specific gene expression data. PASCCA could be used to elucidate the dynamic interplay of genes and their APA sites among various biological conditions from emerging 3' end sequencing data to address the complex biological phenomenon.

An integrative model for alternative polyadenylation, IntMAP, delineates mTOR-modulated endoplasmic reticulum stress response

3'-untranslated regions (UTRs) can vary through the use of alternative polyadenylation sites during pre-mRNA processing. Multiple publically available pipelines combining high profiling technologies and bioinformatics tools have been developed to catalog changes in 3'-UTR lengths. In our recent RNA-seq experiments using cells with hyper-activated mammalian target of rapamycin (mTOR), we found that cellular mTOR activation leads to transcriptome-wide alternative polyadenylation (APA), resulting in the activation of multiple cellular pathways. Here, we developed a novel bioinformatics algorithm, IntMAP, which integrates RNA-Seq and PolyA Site (PAS)-Seq data for a comprehensive characterization of APA events. By applying IntMAP to the datasets from cells with hyper-activated mTOR, we identified novel APA events that could otherwise not be identified by either profiling method alone. Several transcription factors including Cebpg (CCAAT/enhancer binding protein gamma) were among the newly discovered APA transcripts, indicating that diverse transcriptional networks may be regulated by mTOR-coordinated APA. The prevention of APA in Cebpg using the CRISPR/cas9-mediated genome editing tool showed that mTOR-driven 3'-UTR shortening in Cebpg is critical in protecting cells from endoplasmic reticulum (ER) stress. Taken together, we present IntMAP as a new bioinformatics algorithm for APA analysis by which we expand our understanding of the physiological role of mTOR-coordinated APA events to ER stress response. IntMAP toolbox is available at http://compbio.cs.umn.edu/IntMAP/.

Vasodilator-stimulated phosphoprotein promotes liver metastasis of gastrointestinal cancer by activating a β1-integrin-FAK-YAP1/TAZ signaling pathway

Extracellular matrix (ECM)-induced β1-integrin-FAK signaling promotes cell attachment, survival, and migration of cancer cells in a distant organ so as to enable cancer metastasis. However, mechanisms governing activation of the β1-integrin-FAK signaling remain incompletely understood. Here, we report that vasodilator-stimulated phosphoprotein (VASP), an actin binding protein, is required for ECM–mediated β1-integrin-FAK-YAP1/TAZ signaling in gastrointestinal (GI) cancer cells and their liver metastasis. In patient-derived samples, VASP is upregulated in 53 of 63 colorectal cancers and 43 of 53 pancreatic ductal adenocarcinomas and high VASP levels correlate with liver metastasis and reduced patient survival. In a Matrigel-based 3-dimensional (3D) culture model, short hairpin RNA (shRNA)–mediated VASP knockdown in colorectal cancer cells (KM12L4, HCT116, and HT29) and pancreatic cancer cells (L3.6 and MIA PaCa-1) suppresses the growth of 3D cancer spheroids. Mechanistic studies reveal that VASP knockdown suppresses FAK phosphorylation and YAP1/TAZ protein levels, but not Akt or Erk-related pathways and that YAP1/TAZ proteins are enhanced by the β1-integrin-FAK signaling. Additionally, VASP regulates the β1-integrin-FAK-YAP1/TAZ signaling by at least two mechanisms: (1) promoting ECM-mediated β1-integrin activation and (2) regulating YAP1/TAZ dephosphorylation at downstream of RhoA to enhance the stability of YAP1/TAZ proteins. In agreement with these, preclinical studies with two experimental liver metastasis mouse models demonstrate that VASP knockdown suppresses GI cancer liver metastasis, β1-integrin activation, and YAP1/TAZ levels of metastatic cancer cells. Together, our data support VASP as a treatment target for liver metastasis of colorectal and pancreatic cancers.

A protein involved in cytoskeleton regulation and cell motility control offers a new drug target for cancer spreading to the liver. Ningling Kang Ph.D. from the Hormel Institute in Austin, Minnesota, USA, and colleagues showed that levels of this actin-binding protein, known as vasodilator-stimulated phosphoprotein (VASP), are elevated in most patients with advanced colon and pancreatic cancers and that higher VASP expression levels are linked to liver metastasis and poorer patients’ outcomes. To explore the reasons why, the researchers studied three-dimensional tumor spheroids and mouse metastasis models of these cancers, and identified the signaling pathway by which VASP promotes the survival of cancer cells in distant organs, such as the liver. What’s more, they showed that knocking down VASP of cancer cells in metastasis mouse models suppressed cancer metastatic growth in the liver, suggesting that the same might be true in patients as well.

Network-based machine learning and graph theory algorithms for precision oncology

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process

This paper introduces an approach to classification of RNA-seq read counts using grey relational analysis (GRA) and Bayesian Gaussian process (GP) models. Read counts are transformed to microarray-like data to facilitate normal-based statistical methods. GRA is designed to select differentially expressed genes by integrating outcomes of five individual feature selection methods including two-sample t-test, entropy test, Bhattacharyya distance, Wilcoxon test and receiver operating characteristic curve. GRA performs as an aggregate filter method through combining advantages of the individual methods to produce significant feature subsets that are then fed into a nonparametric GP model for classification. The proposed approach is verified by using two benchmark real datasets and the five-fold cross-validation method. Experimental results show the performance dominance of the GRA-based feature selection method as well as GP classifier against their competing methods. Moreover, the results demonstrate that GRA-GP considerably dominates the sparse Poisson linear discriminant analysis classifiers, which were introduced specifically for read counts, on different number of features. The proposed approach therefore can be implemented effectively in real practice for read count data analysis, which is useful in many applications including understanding disease pathogenesis, diagnosis and treatment monitoring at the molecular level.