DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity.

SUMMARY: Circadian oscillations of gene expression regulate daily physiological processes, and their disruption is linked to many diseases. Circadian rhythms can be disrupted in a variety of ways, including differential phase, amplitude and rhythm fitness. Although many differential circadian biomarker detection methods have been proposed, a workflow for systematic detection of multifaceted differential circadian characteristics with accurate false positive control is not currently available. We propose a comprehensive and interactive pipeline to capture the multifaceted characteristics of differentially rhythmic biomarkers. Analysis outputs are accompanied by informative visualization and interactive exploration. The workflow is demonstrated in multiple case studies and is extensible to general omics applications. AVAILABILITY AND IMPLEMENTATION: R package, Shiny app and source code are available in GitHub (https://github.com/DiffCircaPipeline) and Zenodo (https://doi.org/10.5281/zenodo.7507989). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NF-κB modifies the mammalian circadian clock through interaction with the core clock protein BMAL1.

In mammals, the circadian clock coordinates cell physiological processes including inflammation. Recent studies suggested a crosstalk between these two pathways. However, the mechanism of how inflammation affects the clock is not well understood. Here, we investigated the role of the proinflammatory transcription factor NF-κB in regulating clock function. Using a combination of genetic and pharmacological approaches, we show that perturbation of the canonical NF-κB subunit RELA in the human U2OS cellular model altered core clock gene expression. While RELA activation shortened period length and dampened amplitude, its inhibition lengthened period length and caused amplitude phenotypes. NF-κB perturbation also altered circadian rhythms in the master suprachiasmatic nucleus (SCN) clock and locomotor activity behavior under different light/dark conditions. We show that RELA, like the clock repressor CRY1, repressed the transcriptional activity of BMAL1/CLOCK at the circadian E-box cis-element. Biochemical and biophysical analysis showed that RELA binds to the transactivation domain of BMAL1. These data support a model in which NF-kB competes with CRY1 and coactivator CBP/p300 for BMAL1 binding to affect circadian transcription. This is further supported by chromatin immunoprecipitation analysis showing that binding of RELA, BMAL1 and CLOCK converges on the E-boxes of clock genes. Taken together, these data support a significant role for NF-κB in directly regulating the circadian clock and highlight mutual regulation between the circadian and inflammatory pathways.

Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications.

Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been found to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR_rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR_diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR_rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR_diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR_rhythmicity and LR_diff was demonstrated in four real data applications, including a brain aging data (gene expression microarray data of human postmortem brain), a time-restricted feeding data (RNA sequencing data of human skeletal muscles) and a scRNAseq data (single cell RNA sequencing data of mouse suprachiasmatic nucleus). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model.

Circadian clocks are 24-h endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub ( https://github.com/circaPower/circaPower).

Fine-mapping and replication of EWAS loci harboring putative epigenetic alterations associated with AD neuropathology in a large collection of human brain tissue samples.

INTRODUCTION: Our previous epigenome-wide association study (EWAS) of Alzheimer's disease (AD) in human brain identified 71 CpGs associated with AD pathology. However, due to low coverage of the Illumina platform, many important CpGs might have been missed. METHODS: In a large collection of human brain tissue samples (N = 864), we fine-mapped previous EWAS loci by targeted bisulfite sequencing and examined their associations with AD neuropathology. DNA methylation was also linked to gene expression of the same brain cortex. RESULTS: Our targeted sequencing captured 130 CpGs (∼1.2 kb), 93 of which are novel. Of the 130 CpGs, 57 sites (only 17 included in previous EWAS) and 12 gene regions (e.g., ANK1, BIN1, RHBDF2, SPG7, PODXL) were significantly associated with amyloid load. DNA methylation in some regions was associated with expression of nearby genes. DISCUSSION: Targeted methylation sequencing can validate previous EWAS loci and discover novel CpGs associated with AD pathology.

Opportunities and Challenges of Kava in Lung Cancer Prevention.

Lung cancer is the leading cause of cancer-related deaths due to its high incidence, late diagnosis, and limited success in clinical treatment. Prevention therefore is critical to help improve lung cancer management. Although tobacco control and tobacco cessation are effective strategies for lung cancer prevention, the numbers of current and former smokers in the USA and globally are not expected to decrease significantly in the near future. Chemoprevention and interception are needed to help high-risk individuals reduce their lung cancer risk or delay lung cancer development. This article will review the epidemiological data, pre-clinical animal data, and limited clinical data that support the potential of kava in reducing human lung cancer risk via its holistic polypharmacological effects. To facilitate its future clinical translation, advanced knowledge is needed with respect to its mechanisms of action and the development of mechanism-based non-invasive biomarkers in addition to safety and efficacy in more clinically relevant animal models.

Brain predicted age difference mediates pain impact on physical performance in community dwelling middle to older aged adults.

The purpose of the study was to examine associations between physical performance and brain aging in individuals with knee pain and whether the association between pain and physical performance is mediated by brain aging. Participants (n=202) with low impact knee pain (n=111), high impact knee pain (n=60) and pain-free controls (n=31) completed self-reported pain, magnetic resonance imaging (MRI), and a Short Physical Performance Battery (SPPB) that included balance, walking, and sit to stand tasks. Brain predicted age difference, calculated using machine learning from MRI images, significantly mediated the relationships between walking and knee pain impact (CI: -0.124; -0.013), walking and pain-severity (CI: -0.008; -0.001), total SPPB score and knee pain impact (CI: -0.232; -0.025), and total SPPB scores and pain-severity (CI: -0.019; -0.001). Brain-aging begins to explain the association between pain and physical performance, especially walking. This study supports the idea that a brain aging prediction can be calculated from shorter duration MRI sequences and possibly implemented in a clinical setting to be used to identify individuals with pain who are at risk for accelerated brain atrophy and increased likelihood of disability.

Characterization of adductomic totality of NNK, (R)-NNAL and (S)-NNAL in A/J mice, and their correlations with distinct lung carcinogenicity.

Lung cancer is the leading cause of cancer-related deaths. While tobacco use is the main cause, only 10-20% of smokers eventually develop clinical lung cancer. Thus, the ability of lung cancer risk prediction among smokers could transform lung cancer management with early preventive interventions. Given that DNA damage by tobacco carcinogens is the potential root cause of lung carcinogenesis, we characterized the adductomic totality of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (a potent lung carcinogen in tobacco, commonly known as NNK) in the target lung tissues, the liver tissues and the peripheral serum samples in a single-dose NNK-induced lung carcinogenesis A/J mouse model. We also characterized these adductomic totalities from the two enantiomers of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL, the major in vivo metabolite of NNK) given their distinct carcinogenicity in A/J mice. With these adductomic data, we demonstrated that tissue protein adductomics have the highest abundance. We also identified that the adductomic levels at the 8 h time point after carcinogen exposure were among the highest. More importantly, the relationships among these adductomics were characterized with overall strong positive linear correlations, demonstrating the potential of using peripheral serum protein adductomics to reflect DNA adductomics in the target lung tissues. Lastly, we explored the relationships of these adductomics with lung tumor status in A/J mice, providing preliminary but promising evidence of the feasibility of lung cancer risk prediction using peripheral adductomic profiling.

Suppressing the activation of protein kinase A as a DNA damage-independent mechanistic lead for dihydromethysticin prophylaxis of NNK-induced lung carcinogenesis.

Our earlier work demonstrated varying potency of dihydromethysticin (DHM) as the active kava phytochemical for prophylaxis of tobacco carcinogen nicotine-derived nitrosamine ketone (NNK)-induced mouse lung carcinogenesis. Efficacy was dependent on timing of DHM gavage ahead of NNK insult. In addition to DNA adducts in the lung tissues mitigated by DHM in a time-dependent manner, our in vivo data strongly implicated the existence of DNA damage-independent mechanism(s) in NNK-induced lung carcinogenesis targeted by DHM to fully exert its anti-initiation efficacy. In the present work, RNA seq transcriptomic profiling of NNK-exposed (2 h) lung tissues with/without a DHM (8 h) pretreatment revealed a snap shot of canonical acute phase tissue damage and stress response signaling pathways as well as an activation of protein kinase A (PKA) pathway induced by NNK and the restraining effects of DHM. The activation of the PKA pathway by NNK active metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) at a concentration incapable of promoting DNA adduct was confirmed in a lung cancer cell culture model, potentially through NNAL binding to and activation of the ß-adrenergic receptor. Our in vitro and in vivo data overall support the hypothesis that DHM suppresses PKA activation as a key DNA damage-independent mechanistic lead, contributing to its effective prophylaxis of NNK-induced lung carcinogenesis. Systems biology approaches with a detailed temporal dissection of timing of DHM intake versus NNK exposure are warranted to fill the knowledge gaps concerning the DNA damage-driven mechanisms and DNA damage-independent mechanisms to optimize the implementation strategy for DHM to achieve maximal lung cancer chemoprevention.

Integrated multiomics analysis identifies molecular landscape perturbations during hyperammonemia in skeletal muscle and myotubes.

Ammonia is a cytotoxic molecule generated during normal cellular functions. Dysregulated ammonia metabolism, which is evident in many chronic diseases such as liver cirrhosis, heart failure, and chronic obstructive pulmonary disease, initiates a hyperammonemic stress response in tissues including skeletal muscle and in myotubes. Perturbations in levels of specific regulatory molecules have been reported, but the global responses to hyperammonemia are unclear. In this study, we used a multiomics approach to vertically integrate unbiased data generated using an assay for transposase-accessible chromatin with high-throughput sequencing, RNA-Seq, and proteomics. We then horizontally integrated these data across different models of hyperammonemia, including myotubes and mouse and human muscle tissues. Changes in chromatin accessibility and/or expression of genes resulted in distinct clusters of temporal molecular changes including transient, persistent, and delayed responses during hyperammonemia in myotubes. Known responses to hyperammonemia, including mitochondrial and oxidative dysfunction, protein homeostasis disruption, and oxidative stress pathway activation, were enriched in our datasets. During hyperammonemia, pathways that impact skeletal muscle structure and function that were consistently enriched were those that contribute to mitochondrial dysfunction, oxidative stress, and senescence. We made several novel observations, including an enrichment in antiapoptotic B-cell leukemia/lymphoma 2 family protein expression, increased calcium flux, and increased protein glycosylation in myotubes and muscle tissue upon hyperammonemia. Critical molecules in these pathways were validated experimentally. Human skeletal muscle from patients with cirrhosis displayed similar responses, establishing translational relevance. These data demonstrate complex molecular interactions during adaptive and maladaptive responses during the cellular stress response to hyperammonemia.

Epigenetic age predictors in community-dwelling adults with high impact knee pain.

Gerontological research reveals considerable interindividual variability in aging phenotypes, and emerging evidence suggests that high impact chronic pain may be associated with various accelerated biological aging processes. In particular, epigenetic aging is a robust predictor of health-span and disability compared to chronological age alone. The current study aimed to determine whether several epigenetic aging biomarkers were associated with high impact chronic pain in middle to older age adults (44-78 years old). Participants (n = 213) underwent a blood draw, demographic, psychosocial, pain and functional assessments. We estimated five epigenetic clocks and calculated the difference between epigenetic age and chronological age, which has been previously reported to predict overall mortality risk, as well as included additional derived variables of epigenetic age previously associated with pain. There were significant differences across Pain Impact groups in three out of the five epigenetic clocks examined (DNAmAge, DNAmPhenoAge and DNAmGrimAge), indicating that pain-related disability during the past 6 months was associated with markers of epigenetic aging. Only DNAmPhenoAge and DNAmGrimAge were associated with higher knee pain intensity during the past 48 h. Finally, pain catastrophizing, depressive symptomatology and more neuropathic pain symptoms were significantly associated with an older epigenome in only one of the five epigenetic clocks (i.e. DNAmGrimAge) after correcting for multiple comparisons (corrected p's < 0.05). Given the scant literature in relation to epigenetic aging and the complex experience of pain, additional research is needed to understand whether epigenetic aging may help identify people with chronic pain at greater risk of functional decline and poorer health outcomes.

HCMMCNVs: hierarchical clustering mixture model of copy number variants detection using whole exome sequencing technology.

SUMMARY: In this article, we introduce a hierarchical clustering and Gaussian mixture model with expectation-maximization (EM) algorithm for detecting copy number variants (CNVs) using whole exome sequencing (WES) data. The R shiny package 'HCMMCNVs' is also developed for processing user-provided bam files, running CNVs detection algorithm and conducting visualization. Through applying our approach to 325 cancer cell lines in 22 tumor types from Cancer Cell Line Encyclopedia (CCLE), we show that our algorithm is competitive with other existing methods and feasible in using multiple cancer cell lines for CNVs estimation. In addition, by applying our approach to WES data of 120 oral squamous cell carcinoma (OSCC) samples, our algorithm, using the tumor sample only, exhibits more power in detecting CNVs as compared with the methods using both tumors and matched normal counterparts. AVAILABILITY AND IMPLEMENTATION: HCMMCNVs R shiny software is freely available at github repository https://github.com/lunching/HCMM_CNVs.and Zenodo https://doi.org/10.5281/zenodo.4593371. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Valproate reverses mania-like behaviors in mice via preferential targeting of HDAC2.

Valproate (VPA) has been used in the treatment of bipolar disorder since the 1990s. However, the therapeutic targets of VPA have remained elusive. Here we employ a preclinical model to identify the therapeutic targets of VPA. We find compounds that inhibit histone deacetylase proteins (HDACs) are effective in normalizing manic-like behavior, and that class I HDACs (e.g., HDAC1 and HDAC2) are most important in this response. Using an RNAi approach, we find that HDAC2, but not HDAC1, inhibition in the ventral tegmental area (VTA) is sufficient to normalize behavior. Furthermore, HDAC2 overexpression in the VTA prevents the actions of VPA. We used RNA sequencing in both mice and human induced pluripotent stem cells (iPSCs) derived from bipolar patients to further identify important molecular targets. Together, these studies identify HDAC2 and downstream targets for the development of novel therapeutics for bipolar mania.

P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications.

MOTIVATION: Meta-analysis methods have been widely used to combine results from multiple clinical or genomic studies to increase statistical powers and ensure robust and accurate conclusions. The adaptively weighted Fisher's method (AW-Fisher), initially developed for omics applications but applicable for general meta-analysis, is an effective approach to combine P-values from K independent studies and to provide better biological interpretability by characterizing which studies contribute to the meta-analysis. Currently, AW-Fisher suffers from the lack of fast P-value computation and variability estimate of AW weights. When the number of studies K is large, the 3K - 1 possible differential expression pattern categories generated by AW-Fisher can become intractable. In this paper, we develop an importance sampling scheme with spline interpolation to increase the accuracy and speed of the P-value calculation. We also apply bootstrapping to construct a variability index for the AW-Fisher weight estimator and a co-membership matrix to categorize (cluster) differentially expressed genes based on their meta-patterns for intuitive biological investigations. RESULTS: The superior performance of the proposed methods is shown in simulations as well as two real omics meta-analysis applications to demonstrate its insightful biological findings. AVAILABILITY AND IMPLEMENTATION: An R package AWFisher (calling C++) is available at Bioconductor and GitHub (https://github.com/Caleb-Huo/AWFisher), and all datasets and programing codes for this paper are available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ESR1 ChIP-Seq Identifies Distinct Ligand-Free ESR1 Genomic Binding Sites in Human Hepatocytes and Liver Tissue.

The estrogen receptor alpha (ESR1) is an important gene transcriptional regulator, known to mediate the effects of estrogen. Canonically, ESR1 is activated by its ligand estrogen. However, the role of unliganded ESR1 in transcriptional regulation has been gaining attention. We have recently shown that ligand-free ESR1 is a key regulator of several cytochrome P450 (CYP) genes in the liver, however ligand-free ESR1 has not been characterized genome-wide in the human liver. To address this, ESR1 ChIP-Seq was conducted in human liver samples and in hepatocytes with or without 17beta-estradiol (E2) treatment. We identified both ligand-dependent and ligand-independent binding sites throughout the genome. These two ESR1 binding categories showed different genomic localization, pathway enrichment, and cofactor colocalization, indicating different ESR1 regulatory function depending on ligand availability. By analyzing existing ESR1 data from additional human cell lines, we uncovered a potential ligand-independent ESR1 activity, namely its co-enrichment with the zinc finger protein 143 (ZNF143). Furthermore, we identified ESR1 binding sites near many gene loci related to drug therapy, including the CYPs. Overall, this study shows distinct ligand-free and ligand-bound ESR1 chromatin binding profiles in the liver and suggests the potential broad influence of ESR1 in drug metabolism and drug therapy.

Enrichment of genomic pathways based on differential DNA methylation profiles associated with chronic musculoskeletal pain in older adults: An exploratory study.

Our study aimed to identify differentially methylated CpGs/regions and their enriched genomic pathways associated with underlying chronic musculoskeletal pain in older individuals. We recruited cognitively healthy older adults with (n = 20) and without (n = 9) self-reported musculoskeletal pain and collected DNA from peripheral blood that was analyzed using MethylationEPIC arrays. We identified 31,739 hypermethylated CpG and 10,811 hypomethylated CpG probes (ps ≤ 0.05). All CpG probes were clustered into 5966 regions, among which 600 regions were differentially methylated at p ≤ 0.05 level, including 294 hypermethylated regions and 306 hypomethylated regions (differentially methylated regions). Ingenuity pathway enrichment analysis revealed that the pain-related differentially methylated regions were enriched across multiple pathways. The top 10 canonical pathways were linked to cellular signaling processes related to immune responses (i.e. antigen presentation, programed cell death 1 receptor/PD-1 ligand 1, interleukin-4, OX40 signaling, T cell exhaustion, and apoptosis) and gamma-aminobutyric acid receptor signaling. Further, Weighted Gene Correlation Network Analysis revealed a comethylation network module in the pain group that was not preserved in the control group, where the hub gene was the cyclic adenosine monophosphate-dependent transcription factor ATF-2. Our preliminary findings provide new epigenetic insights into the role of aberrant immune signaling in musculoskeletal pain in older adults while further supporting involvement of dysfunctional GABAergic signaling mechanisms in chronic pain. Our findings need to be urgently replicated in larger cohorts as they may serve as a basis for developing and targeting future interventions.

MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis.

SUMMARY: The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions. AVAILABILITY AND IMPLEMENTATION: MetaOmics is freely available at https://github.com/metaOmics/metaOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?

As more and more health systems have converted to the use of electronic health records, the amount of searchable and analyzable data is exploding. This includes not just provider or laboratory created data but also data collected by instruments, personal devices, and patients themselves, among others. This has led to more attention being paid to the analysis of these data to answer previously unaddressed questions. This is especially important given the number of therapies previously found to be beneficial in clinical trials that are currently being re-scrutinized. Because there are orders of magnitude more information contained in these data sets, a fundamentally different approach needs to be taken to their processing and analysis and the generation of knowledge. Health care and medicine are drivers of this phenomenon and will ultimately be the main beneficiaries. Concurrently, many different types of questions can now be asked using these data sets. Research groups have become increasingly active in mining large data sets, including nationwide health care databases, to learn about associations of medication use and various unrelated diseases such as cancer. Given the recent increase in research activity in this area, its promise to radically change clinical research, and the relative lack of widespread knowledge about its potential and advances, we surveyed the available literature to understand the strengths and limitations of these new tools. We also outline new databases and techniques that are available to researchers worldwide, with special focus on work pertaining to the broad and rapid monitoring of drug safety and secondary effects.

Biomarkers that differentiate false positive urinalyses from true urinary tract infection.

BACKGROUND: The specificity of the leukocyte esterase test (87%) is suboptimal. The objective of this study was to identify more specific screening tests that could reduce the number of children who unnecessarily receive antimicrobials to treat a presumed urinary tract infection (UTI). METHODS: Prospective cross-sectional study to compare inflammatory proteins in blood and urine samples collected at the time of a presumptive diagnosis of UTI. We also evaluated serum RNA expression in a subset. RESULTS: We enrolled 200 children; of these, 89 were later demonstrated not to have a UTI based on the results of the urine culture obtained. Urinary proteins that best discriminated between children with UTI and no UTI were involved in T cell response proliferation (IL-9, IL-2), chemoattractants (CXCL12, CXCL1, CXCL8), the cytokine/interferon pathway (IL-13, IL-2, INFγ), or involved in innate immunity (NGAL). The predictive power (as measured by the area under the curve) of a combination of four urinary markers (IL-2, IL-9, IL-8, and NGAL) was 0.94. Genes in the pathways related to inflammation were also upregulated in serum of children with UTI. CONCLUSIONS: Urinary proteins involved in the inflammatory response may be useful in identifying children with false positive results with current screening tests for UTI; this may reduce unnecessary treatment.

The Proteome of Pancreatic Cancer-Derived Exosomes Reveals Signatures Rich in Key Signaling Pathways.

Exosomes are membrane-bound vesicles that traffic small molecular cargos. These cargos participate in cell-cell communication and contribute to the pathogenesis of many disease including cancer. How these mechanisms contribute to communication within the pancreatic adenocarcinoma (PDAC) microenvironment and how they contribute to PDAC biology are poorly understood. Performed in this study are comprehensive, quantitative comparisons of the proteomes of three PDAC cell lines to those of the exosomes they produce. Approximately 35% of whole cell proteins sort into exosomes. Analysis of composition of microbiomes (ANCOM) determined a cluster of 98 enriched pancreatic cancer exosome core proteins (ePC-ECPs). Further, these proteins are predicted by ingenuity pathway analysis (IPA) as actively involved in signaling pathways regulating cell death and survival, cellular movement, and cell-to-cell signaling and interaction in particular (top three p-value significant pathways). Significant enrichment of canonical pathways of acute phase response signaling (inflammatory response signaling pathways) and FXR and RXR activation in biosynthetic pathways are also predicted; 97 ePC-ECPs are associated with cancer and among them, 34 are specifically associated with PDAC. In conclusion, exosomes from PDAC are enriched with cancer-associated signaling proteins. Further assessment of these proteins as PDAC biomarkers or therapeutic targets is warranted.