Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/.

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that PGRMC1 and HADH genes were significant over diabetes studies, while IRS1 and MPST genes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.

Colorectal cancer (CRC) is the third most common cancer diagnosed and the second leading cause of cancer-related deaths in the United States. About 50% of CRC patients relapsed after surgical resection and ultimately died of metastatic disease. Cancer stem cells (CSCs) are believed to be the primary reason for the recurrence of CRC. Specific stem cell marker, doublecortin-like kinase 1 (DCLK1) plays critical roles in initiating tumorigenesis, facilitating tumor progression, and promoting metastasis of CRC. It is up-regulated in CRC and upregulation of DCLK1 indicates poor prognosis. Whether DCLK1 is correlated with enhanced chemoresistance of CRC cells is unclear. Our research aims to reveal association of DCLK1 with chemoresistance of CRC cells and the underlying molecular mechanisms. In order to achieve our goal, we established stable DCLK1 over-expression cells (DCLK1+) using the HCT116 cells (WT). DCLK1+ and WT cells were treated with 5-Fluorouracil (5-Fu) at different doses for 24 or 48 hours. MTT assay was used to evaluate cell viability and IC 50 of 5-Fu was determined. Quantitative real time PCR was applied to determine gene expression of caspase-3 (casp-3), caspase-4 (casp-4), and caspase-10 (casp-10). Cleaved casp-3 expression was investigated using Western blot and immunofluorescence. Our results demonstrated that IC 50 of 5-Fu for the DCLK1+ cells was significantly higher than that of the WT cells for both 24 and 48hour treatment (P=0.002 and 0.048 respectively), indicating increased chemoresistance of the DCLK1+ cells. Gene expression of casp-3, casp-4, and casp-10 were significantly inhibited in the DCLK1+ cells after 5-Fu treatment compared to the WT cells (P=7.616e-08, 1.575e-05 and 5.307e-08, respectively). Cleaved casp-3 amount and casp-3 positive cells were significantly decreased in the DCLK1+ cells after 5-Fu treatment compared to the WT cells (P=0.015). In conclusion, our results demonstrated that DCLK1 overexpression enhanced the chemoresistance of CRC cells to 5-Fu treatment by suppressing gene expression of key caspases in the apoptosis pathway and activation of apoptosis pathway. DCLK1 can be an intriguing therapeutic target for the effective treatment of CRC patients.

BackgroundGenetic heritability and expression study have shown that different diabetes traits have common genetic components and pathways. A computationally efficient pathway analysis of GWAS results will benefit post-GWAS study of SNP associations and identification of common genetic pathways from diabetes GWAS can help to improve understanding of the disease pathogenesis.ResultsWe proposed a uniform-score gene-set analysis (USGSA) with implemented package to unify different gene measures by a uniform score for identifying pathways from GWAS data, and use a pre-generated permutation distribution table to quickly obtain multiple-testing adjusted p-value. Simulation studies of uniform score for four gene measures (minP, 2ndP, simP and fishP) have shown that USGSA has strictly controlled family-wise error rate. The power depends on types of gene measure. USGSA with a two-stage study strategy was applied to identify common pathways associated with diabetes traits based on public dbGaP GWAS results. The study identified 7 gene sets that contain binding motifs at promoter region of component genes for 5 transcription factors (TFs) of FOXO4, TCF3, NFAT, VSX1 and POU2F1, and 1 microRNA of mir-218. These gene sets include 25 common genes that are among top 5% of the gene associations over genome for all GWAS. Previous evidences showed that nearly all of these genes are mainly expressed in the brain.ConclusionsUSGSA is a computationally efficient approach for pathway analysis of GWAS data with promoted interpretability and comparability. The pathway analysis suggested that different diabetes traits share common pathways and component genes are potentially regulated by common TFs and microRNA. The result also indicated that the central nervous system has a critical role in diabetes pathogenesis. The findings will be important in formulating novel hypotheses for guiding follow-up studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1515-3) contains supplementary material, which is available to authorized users.

Objective. Triple-negative breast cancer (TNBC) is an aggressive disease with highly invasive nature and poor outcomes. Due to the absence of specific treatment strategies for this tumor subgroup, patients with TNBC are treated with conventional therapeutics, frequently leading to systemic relapse. In this study, we sought to investigate apatinib combined with conventional chemotherapy regimens in treating patients with advanced TNBC concerning the efficacy, safety, expressions of tumor markers, and patient survival. Methods. This is a prospective study including 150 cases of advanced TNBC who were randomly arranged into a conventional group and combined group, with 75 cases per group. The patients in the conventional group were treated with conventional chemotherapy, and those in the combined group were treated with apatinib combined with conventional chemotherapy. The peripheral blood was collected from each patient, and carcinoembryonic antigen (CEA), carbohydrate antigen 153 (CA153), and carbohydrate antigen 125 (CA125) were determined. The expressions of nuclear proliferation antigen marker (Ki67), β-catenin, and E-cadherin were determined in the biopsy collected from each patient. Results. The objective remission rate (ORR) and disease control rate (DCR) (41.33% and 81.33%) in the combined group were notably higher than those in the conventional group (29.33% and 68.00%) ( P < 0.05 ). After treatment, the serum levels of CEA, CA153, and CA125 and the expressions of Ki67 and β-catenin were declined, but the expression of E-cadherin was increased in both groups; the combined group exhibited lower serum levels of CEA, CA153, and CA125, and the expressions of Ki67 and β-catenin were concurrent with a higher expression of E-cadherin than the conventional group ( P < 0.05 ). No significant difference was noted between the two groups regarding the occurrence of adverse reactions ( P > 0.05 ). Improved progression-free survival (PFS) was observed in the combined group compared to the conventional group ( P < 0.05 . Conclusion. These findings suggest that apatinib combined with conventional chemotherapy regimens confers a prolonged PFS for treating patients with advanced TNBC.