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Colorectal cancer (CRC) is often curable and preventable using current screening modalities. Unfortunately, screening compliance remains low, partly due to patient dissatisfaction with faecal/endoscopic testing. Recent guidelines advise CRC screening should begin with risk stratification. A blood-based test providing clinically actionable CRC risk information would likely improve screening compliance and enhance clinical decision making. We analyzed 196 gene expression profiles to select candidate CRC biomarkers. qRT-PCR was performed on 642 samples to develop a 7-gene biomarker panel using 112 CRC/120 controls (training set) and 202 CRC/208 controls (independent, blind test set). Panel performance characteristics and disease prevalence (0.7%) were then used to develop a scale assessing an individual's current risk of having CRC based on his/her gene signature. A 7-gene panel (ANXA3, CLEC4D, LMNB1, PRRG4, TNFAIP6, VNN1 and IL2RB) discriminated CRC in the training set (area under the receiver-operating-characteristic curve (ROC AUC), 0.80; accuracy, 73%; sensitivity, 82%; specificity 64%). The independent blind test set confirmed performance (ROC AUC, 0.80; accuracy, 71%; sensitivity, 72%; specificity, 70%). Individual gene profiles were compared against the population results and used to calculate the current relative risk for CRC. We have developed a 7-gene, blood-based biomarker panel that can stratify subjects according to their current relative risk across a broad range in an average-risk population. Across the continuous spectrum of risk as defined by the current relative risk scale, it is possible to identify clinically meaningful reference points that can assist patients and physicians in CRC screening decision making.Colorectal cancer (CRC), the third most frequently diagnosed cancer in men and women in the United States and the United Kingdom, carries an overall population lifetime risk of about 5%. 1,2 Despite being among the most preventable of neoplasms and surgically curable in early stages, cancer of the colon and rectum remains the second leading cause of cancer death in the western world. In the United States, $150,000 people will be diagnosed with CRC in 2008 and some 50,000 will die of their disease. 1 Each year in the United Kingdom, about 36,500 people receive a diagnosis of CRC and some 16,000 die of it. 2 Most CRC arises from precursor adenomatous polyps, developing over many years. 3 Stage at detection is critically related to patient survival. Localized cancers (tumor-nodemetastasis [TNM] Stages IÀII) have an excellent 5-year survival prognosis (93% and 83%); regional stage (TNM Stage III) patients have a 5-year survival rate about 60%; only 8% of patients with late stage (TNM Stage IV) disease will survive 5 years. 4 These features make CRC eminently suitable for a screening program, and health authorities have long promoted screening for CRC in average-risk adults, beginning at the age of 50 years. 1,5,6 Despite repeated recommendations and awareness campaigns, however, populations have...

BackgroundGene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have been developed. Despite this popularity, systematic comparative studies have been limited in scope.MethodsIn this study we present a semi-synthetic simulation study using real datasets in order to test and compare commonly used methods.ResultsA software pipeline, Flexible Algorithm for Novel Gene set Simulation (FANGS) develops simulated data based on a prostate cancer dataset where the KRAS and TGF-β pathways were differentially expressed. The FANGS software is compatible with other datasets and pathways. Comparisons of gene set analysis methods are presented for Gene Set Enrichment Analysis (GSEA), Significance Analysis of Function and Expression (SAFE), sigPathway, and Correlation Adjusted Mean RAnk (CAMERA) methods. All gene set analysis methods are tested using gene sets from the MSigDB knowledge base. The false positive rate and power are estimated and presented for comparison. Recommendations are made for the utility of the default settings of methods and each method’s sensitivity towards various effect sizes.ConclusionsThe results of this study provide empirical guidance to users of gene set analysis methods. The FANGS software is available for researchers for continued methods comparisons.Electronic supplementary materialThe online version of this article (10.1186/s13040-018-0166-8) contains supplementary material, which is available to authorized users.

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BackgroundSunflower is recognized as one of the most important oil plants with strong tolerance to drought in the world. In order to study the response mechanisms of sunflower plants to drought stress, gene expression profiling using high throughput sequencing was performed for seedling leaves and roots (sunflower inbred line R5) after 24 h of drought stress (15% PEG 6000). The transcriptome assembled using sequences of 12 samples was used as a reference.Results805 and 198 genes were identified that were differentially expressed in leaves and roots, respectively. Another 71 genes were differentially expressed in both organs, in which more genes were up-regulated than down-regulated. In agreement with results obtained for other crops or from previous sunflower studies, we also observed that nine genes may be associated with the response of sunflower to drought.ConclusionsThe results of this study may provide new information regarding the sunflower drought response, as well as add to the number of known genes associated with drought tolerance.Electronic supplementary materialThe online version of this article (doi:10.1186/s40529-017-0197-3) contains supplementary material, which is available to authorized users.

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Aging is closely related to the occurrence of human diseases; however, its exact biological mechanism is unclear. Advancements in high-throughput technology provide new opportunities for omics research to understand the pathological process of various complex human diseases. However, single-omics technologies only provide limited insights into the biological mechanisms of diseases. DNA, RNA, protein, metabolites, and microorganisms usually play complementary roles and perform certain biological functions together. In this review, we summarize multi-omics methods based on the most relevant biomarkers in single-omics to better understand molecular functions and disease causes. The integration of multi-omics technologies can systematically reveal the interactions among aging molecules from a multidimensional perspective. Our review provides new insights regarding the discovery of aging biomarkers, mechanism of aging, and identification of novel antiaging targets. Overall, data from genomics, transcriptomics, proteomics, metabolomics, integromics, microbiomics, and systems biology contribute to the identification of new candidate biomarkers for aging and novel targets for antiaging interventions.

was applied to analyze the association of ER signal pathway with the 10 DEGs. 3 significant genes (GFRA3, NPY1R and PTPRN2) were closely related to ER related pathway. These significant DEGs regulated many biological activities such as cell proliferation and survival, motility and migration, and tumor cell invasion. The interactions between these DEGs and drug resistance phenomenon need to be further elucidated at a functional level in further studies. Based on our findings, we believed that these DEGs could be therapeutic targets, which can be explored to develop new treatment options.