Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

Identifying Effect Modification of Latent Population Characteristics on Risk Factors with a Sparse Varying Coefficient Regression

Leveraging observational data to understand the associations between risk factors and disease outcomes and conduct disease risk prediction is a common task in epidemiology. While traditional linear regression and other machine learning models have been extensively implemented for this task, the associations between risk factors and disease outcomes are typically deemed fixed. In many cases, however, such associations may vary by some underlying features of the individuals, which may involve certain subpopulation characteristics and environmental factors. While data for these latent features may not be available, the observed data on risk factors may have captured some proportion of the variation in these features. Thus extracting latent factors from risk factors and incorporating this effect modification into the model may better capture the underlying data structure and improve inference. We develop a novel regression model with some coefficients varying as functions of latent features extracted from the risk factors. We have demonstrated the superiority of our approach in various data settings via simulation studies. An application on a dataset for lung cancer patients from The Cancer Genome Atlas (TCGA) Program showed that our approach led to a 6% - 118% increase in (AUC-0.5) for distinguishing between different lung cancer stages compared to the classic lasso and elastic net regressions and identified interesting latent effect modifications associated with certain gene pathways.

T2-DAG: a powerful test for differentially expressed gene pathways via graph-informed structural equation modeling

MOTIVATION

A major task in genetic studies is to identify genes related to human diseases and traits to understand functional characteristics of genetic mutations and enhance patient diagnosis. Compared with marginal analyses of individual genes, identification of gene pathways, i.e. a set of genes with known interactions that collectively contribute to specific biological functions, can provide more biologically meaningful results. Such gene pathway analysis can be formulated into a high-dimensional two-sample testing problem. Given the typically limited sample size of gene expression datasets, most existing two-sample tests tend to have compromised powers because they ignore or only inefficiently incorporate the auxiliary pathway information on gene interactions.

RESULTS

We propose T2-DAG, a Hotelling's T2-type test for detecting differentially expressed gene pathways, which efficiently leverages the auxiliary pathway information on gene interactions from existing pathway databases through a linear structural equation model. We further establish its asymptotic distribution under pertinent assumptions. Simulation studies under various scenarios show that T2-DAG outperforms several representative existing methods with well-controlled type-I error rates and substantially improved powers, even with incomplete or inaccurate pathway information or unadjusted confounding effects. We also illustrate the performance of T2-DAG in an application to detect differentially expressed KEGG pathways between different stages of lung cancer.

AVAILABILITY AND IMPLEMENTATION

The R (R Development Core Team, 2021) package T2DAG which implements the proposed T2-DAG test is available on Github at https://github.com/Jin93/T2DAG.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of Dusp6 Phosphatase as a Tumor Suppressor in Non-Small Cell Lung Cancer

DUSP6/MKP3 is a dual-specific phosphatase that regulates extracellular regulated kinase ERK1/2 and ERK5 activity, with an increasingly recognized role as tumor suppressor. In silico studies from Gene expression Omnibus (GEO) and Cancer Genome atlas (TCGA) databases reveal poor prognosis in those Non-small cell lung cancer (NSCLC) patients with low expression levels of DUSP6. In agreement with these data, here we show that DUSP6 plays a major role in the regulation of cell migration, motility and tumor growth. We have found upregulation in the expression of several genes involved in epithelial to mesenchymal transition (EMT) in NSCLC-DUSP6 depleted cells. Data obtained in RNA-seq studies carried out in DUSP6 depleted cells identified EGFR, TGF-β and WNT signaling pathways and several genes such as VAV3, RUNXR2, LEF1, FGFR2 whose expression is upregulated in these cells and therefore affecting cellular functions such as integrin mediated cell adhesion, focal adhesion and motility. Furthermore, EGF signaling pathway is activated via ERK5 and not ERK1/2 and TGF-β via SMAD2/3 in DUSP6 depleted cells. In summary DUSP6 is a tumor suppressor in NSCLC and re-establishment of its expression may be a potential strategy to revert poor outcome in NSCLC patients.

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

Internal driving factors leading to extrahepatic manifestation of the hepatitis C virus infection

The hepatitis C virus (HCV) infection is associated with various extrahepatic manifestations, which are correlated with poor outcomes, and thus increase the morbidity and mortality of chronic hepatitis C (CHC). Therefore, understanding the internal linkages between systemic manifestations and HCV infection is helpful for treatment of CHC. Yet, the mechanism by which the virus evokes the systemic diseases remains to be elucidated. In the present study, using gene set enrichment analysis (GSEA) and signaling pathway impact analysis (SPIA), a comprehensive analysis of microarray data of mRNAs was conducted in HCV-infected and -uninfected Huh7.5 cells, and signaling pathways (which are significantly activated or inhibited) and certain molecules (which are commonly important in those signaling pathways) were selected. Forty signaling pathways were selected using GSEA, and eight signaling pathways were selected with SPIA. These pathways are associated with cancer, metabolism, environmental information processing and organismal systems, which provide important information for further clarifying the intrinsic associations between syndromes of HCV infection, of which seven pathways were not previously reported, including basal transcription factors, pathogenic Escherichia coli infection, shigellosis, gastric acid secretion, dorso-ventral axis formation, amoebiasis and cholinergic synapse. Ten genes, SOS1, RAF1, IFNA2, IFNG, MTHFR, IGF1, CALM3, UBE2B, TP53 and BMP7 whose expression may be the key internal driving molecules, were selected using the online tool Anni 2.1. Furthermore, the present study demonstrated the internal linkages between systemic manifestations and HCV infection, and presented the potential molecules that are key to those linkages.

Contextualizing the Genes Altered in Bladder Neoplasms in Pediatric andTeen Patients Allows Identifying Two Main Classes of Biological ProcessesInvolved and New Potential Therapeutic Targets

Research on bladder neoplasms in pediatric and teen patients (BNPTP) has described 21 genes, which are variously involved in this disease and are mostly responsible for deregulated cell proliferation. However, due to the limited number of publications on this subject, it is still unclear what type of relationships there are among these genes and which are the chances that, while having different molecular functions, they i) act as downstream effector genes of well-known pro- or anti- proliferative stimuli and/or interplay with biochemical pathways having oncological relevance or ii) are specific and, possibly, early biomarkers of these pathologies. A Gene Ontology (GO)-based analysis showed that these 21 genes are involved in biological processes, which can be split into two main classes: cell regulation-based and differentiation/development-based. In order to understand the involvement/overlapping with main cancer-related pathways, we performed a meta-analysis dependent on the 189 oncogenic signatures of the Molecular Signatures Database (OSMSD) curated by the Broad Institute. We generated a binary matrix with 53 gene signatures having at least one hit; this analysis i) suggests that some genes of the original list show inconsistencies and might need to be experimentally re- assessed or evaluated as biomarkers (in particular, ACTA2) and ii) allows hypothesizing that important (proto)oncogenes (E2F3, ERBB2/HER2, CCND1, WNT1, and YAP1) and (putative) tumor suppressors (BRCA1, RBBP8/CTIP, and RB1-RBL2/p130) may participate in the onset of this disease or worsen the observed phenotype, thus expanding the list of possible molecular targets for the treatment of BNPTP.

The impact of the Cancer Genome Atlas on lung cancer

The Cancer Genome Atlas (TCGA) has profiled more than 10,000 samples derived from 33 types of cancer to date, with the goal of improving our understanding of the molecular basis of cancer and advancing our ability to diagnose, treat, and prevent cancer. This review focuses on lung cancer as it is the leading cause of cancer-related mortality worldwide in both men and women. Particularly, non-small cell lung cancers (including lung adenocarcinoma and lung squamous cell carcinoma) were evaluated. Our goal was to demonstrate the impact of TCGA on lung cancer research under 4 themes: diagnostic markers, disease progression markers, novel therapeutic targets, and novel tools. Examples are given related to DNA mutation, copy number variation, messenger RNA, and microRNA expression along with methylation profiling.