Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data

Abstract

Cigarette smoking is the best established modifiable risk factor for pancreatic cancer. Genetic factors that underlie smoking-related pancreatic cancer have previously not been examined at the genome-wide level. Taking advantage of the existing Genome-wide association study (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study in 2028 cases and 2109 controls to examine gene-smoking interactions at pathway/gene/single nucleotide polymorphism (SNP) level. Using the likelihood ratio test nested in logistic regression models and ingenuity pathway analysis (IPA), we examined 172 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, 3 manually curated gene sets, 3 nicotine dependency gene ontology pathways, 17 912 genes and 468 114 SNPs. None of the individual pathway/gene/SNP showed significant interaction with smoking after adjusting for multiple comparisons. Six KEGG pathways showed nominal interactions (P < 0.05) with smoking, and the top two are the pancreatic secretion and salivary secretion pathways (major contributing genes: RAB8A, PLCB and CTRB1). Nine genes, i.e. ZBED2, EXO1, PSG2, SLC36A1, CLSTN1, MTHFSD, FAT2, IL10RB and ATXN2 had P interaction < 0.0005. Five intergenic region SNPs and two SNPs of the EVC and KCNIP4 genes had P interaction < 0.00003. In IPA analysis of genes with nominal interactions with smoking, axonal guidance signaling $$\left(P=2.12\times 1{0}^{-7}\right)$$ and α-adrenergic signaling $$\left(P=2.52\times 1{0}^{-5}\right)$$ genes were significantly overrepresented canonical pathways. Genes contributing to the axon guidance signaling pathway included the SLIT/ROBO signaling genes that were frequently altered in pancreatic cancer. These observations need to be confirmed in additional data set. Once confirmed, it will open a new avenue to unveiling the etiology of smoking-associated pancreatic cancer.

Mithramycin Depletes Specificity Protein 1 and Activates p53 to Mediate Senescence and Apoptosis of Malignant Pleural Mesothelioma Cells

“…Additionally, the oncofetal receptor tyrosine kinase ROR1, which is over-expressed in a variety of human malignancies but to date has not been studied in pleural mesotheliomas, was significantly down-regulated in MPM xenografts from MM treated mice. Top canonical pathways included G-alpha 12/13 signaling, which mediates cytoplasmic beta catenin levels {Meigs, 2001 2944 /id}, Wnt/Ca++ signaling, axonal guidance signaling, which has recently been implicated recently in pluripotency and metastatic potential of cancer cells {Tang, 2014 2875 /id}, p53 signaling, as well as pathways associated with bladder, pancreas, ovarian, and brain cancer (Figure 2D; right panel).…”

The Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer

“…These efforts are fueled by a growing number of bioinformatic approaches for integrating genetic data with molecular interaction networks or pathway databases (Bandyopadhyay et al, 2008; Collins et al, 2007; Hannum et al, 2009; Jia et al, 2011; Kelley and Ideker, 2005; Roguev et al, 2008; Wang et al, 2010). Pathway and network-guided GWAS in the cancer field are in their infancy (Fehringer et al, 2012; Koster et al, 2014; Tang et al, 2014), although highly related progress is being made in identifying new cancer genes based on integration of somatic mutations and networks (Ciriello et al, 2012; Leiserson et al, 2014). Regardless, the concepts and methodology are general and we anticipate that, as with other diseases, future cancer genomic studies will rely increasingly on network knowledge.…”

Maximizing Effectiveness Trials in PTSD and SUD Through Secondary Analysis: Benefits and Limitations Using the National Institute on Drug Abuse Clinical Trials Network "Women and Trauma" Study as a Case Example

“…In turn, this is likely to broaden the adoption and implementation of evidence-based treatments. Moving forward, planning for and description of secondary analyses in large efficacy and effectiveness RCTs, which encumber extensive economic resources and time, should be given equal consideration and care to the primary outcome analysis (Benjamini & Hochberg, 1995; Chambless & Ollendick, 2001; Fairburn & Wilson, 2013; Kar et al, 2013; Tang et al, 2014; White, Horton, & Carpenter, 2011; Xi et al, 2013). …”