Transcriptomic analysis of serum in patients with pancreatic cysts reveals novel biomarkers of pancreatic cancer risk
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PROVIDER: PRJNA1180499 | ENA |
REPOSITORIES: ENA
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Project description:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
2025-01-06 | GSE280768 | GEO
Project description:Transcriptomic analysis of pancreatic cyst fluid in patients with pancreatic cysts reveals novel biomarkers of pancreatic cancer risk
| PRJNA1180512 | ENA
Project description:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
2025-01-06 | GSE280772 | GEO
Project description:Pancreatic Cancer (PC) has the worst 5-year survival rate of any cancer as of 2024, at just 13%. The late-stage diagnosis of these patients limits their treatment options, further compounding the problem. Early detection of PC, therefore, is the primary concern of most PC research, as it has the potential to make a substantial difference to the treatment and survival of these patients. Pancreatic cystic lesions (PCLs) are fluid-filled sacs, on or inside the pancreas, that have the potential to become premalignant. While some PCLs are completely benign, others have been shown to have malignant potential and could therefore play a role in the progression to PC. Using the 2018 European evidence-based guidelines for pancreatic cystic neoplasms, patients were classified as being either at a low- or high-risk of PC development. In this study, we profile the proteome of serum from low-risk (n=45) and high-risk (n=23) patients with PCLs and identify differentially expressed proteins between these two risk classifications. We show that these serum-based differentially expressed proteins have potential utility as biomarkers of risk stratification in this setting.
2025-01-06 | PXD057661 | Pride
Project description:Pancreatic Cancer (PC) has the worst 5-year survival rate of any cancer as of 2024, at just 13%. The late-stage diagnosis of these patients limits their treatment options, further compounding the problem. Early detection of PC, therefore, is the primary concern of most PC research, as it has the potential to make a substantial difference to the treatment and survival of these patients. Pancreatic cystic lesions (PCLs) are fluid-filled sacs, on or inside the pancreas, that have the potential to become premalignant. While some PCLs are completely benign, others have been shown to have malignant potential and could therefore play a role in the progression to PC. Using the 2018 European evidence-based guidelines for pancreatic cystic neoplasms, patients were classified as being either at a low- or high-risk of PC development. In this study, we profile the proteome of pancreatic cyst fluid from low-risk (n=15) and high-risk (n=17) patients with PCLs and identify differentially expressed proteins between these two risk classifications. We show that these PCF-based differentially expressed proteins have potential utility as biomarkers of risk stratification in this setting.
2025-01-06 | PXD057299 | Pride
Project description:Circulating microRNAs (c-miRNAs) have emerged as measurable biomarkers (liquid biopsies) for cancer detection. The goal of our study was to identify novel biomarkers to predict long-term breast cancer risk in cancer-free women. We evaluated the ability of c-miRNAs to identify women most likely to develop breast cancer by profiling miRNA from serum obtained long before diagnosis. 24 breast cancer cases and controls (matched for risk and age) were identified from women enrolled in the High-Risk Breast Program at the UVM Cancer Center. We used Affymetrix miRNA v4 microarrays to interrogate miRNAs (miRBase v20) in the serum of cancer-free women at high-risk for breast cancer. The 24 cases developed breast cancer at least 6 months (average of 3.2 years) and the 24 controls remain cancer-free.
2018-01-22 | GSE98181 | GEO
Project description:This is a pilot study. We are trying to detect potential salivary biomarkers in mice with a pancreatic tumor. Global gene expression profiling has shown great promise in high-throughput biomarker discovery for early disease detection in body fluids such as saliva, which is accessible, cost-effective, and non-invasive. However, this goal has not been fully realized because saliva, like many clinical samples, contains partially fragmented and degraded RNAs that are difficult to amplify and detect with prevailing technologies. Here, using nanogram scale salivary RNA as a proof-of-principle example, we describe our progress with a novel poly-A tail independent mRNA amplification strategy combined with the Affymetrix GeneChip Exon arrays. We defined a Salivary Exon Core Transcriptome (SECT) with highly similar expression profiles in healthy individuals verified by quantitative PCR. Informatics analysis of SECT provided important mechanistic insight to their potential origin and function. Finally we demonstrated the diagnostic potential of true exon level expression profiling approach with salivary exon biomarkers that accurately discriminated gender in healthy individuals. Recent studies have demonstrated that discriminatory salivary biomarkers can be readily detected upon the development of systemic diseases such as pancreatic cancer, breast cancer, lung cancer and ovarian cancer. However, the utility of salivary biomarkers for the detection of systemic diseases has been undermined due to the absence of biological and mechanistic rationale why distal diseases from the oral cavity would lead to the development of discriminatory biomarkers in saliva. Here, we examine the hypothesis that pancreatic tumor-derived exosomes are mechanistically involved in the development of pancreatic cancer-discriminatory salivary transcriptomic biomarkers. We first developed a pancreatic cancer mouse model that yielded discriminatory salivary biomarkers by implanting the mouse pancreatic cancer cell line Pan02 into the pancreas of the syngeneic host C57BL/6. The role of pancreatic cancer-derived exosomes in the development of discriminatory salivary biomarkers was then tested by engineered a Pan02 cell line that is suppressed for exosome biogenesis, implanted into the C56BL/6 mouse and examine if the discriminatory salivary biomarker profile was ablated or disrupted. Suppression of exosome biogenesis results in the ablation of discriminatory salivary biomarker development. This study supports that tumor-derived exosomes provide a mechanism in the development of discriminatory biomarkers in saliva and distal systemic diseases. We analyzed saliva from 6 healthy mice and 6 mice with a pancreatic tumor using the Affymetrix Mouse Exon Genome 430 2.0 platform. Array data was processed by dChip. No techinical replicates were performed.
2013-06-12 | E-GEOD-47811 | biostudies-arrayexpress
Project description:Serum miRNA-based risk prediction for stroke
| PRJNA480974 | ENA
Project description:We profiled expression of serum miRNAs derived from pancreatic and biliary tract cancer patients and detected biomarker candidates using high-throughput sequencing
2019-04-24 | GSE109319 | GEO
Project description:Pancreatic cancer is the fourth leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. Oral fluid (saliva) meets the demand for non-invasive, accessible, and highly efficient diagnostic medium. The level of salivary analytes, such as mRNA and microflora, vary upon disease onset; thus possess valuable signatures for early detection and screening. In this study, we evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for non-invasive detection of early pancreatic cancer. Two biomarker discovery technologies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133 Plus 2.0 Array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 early pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. Importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This study comprehensively compared the salivary transcriptome and microflora between pancreatic cancer and control subjects. We have discovered and validated eleven mRNA biomarkers and two microbial biomarkers for early detection of pancreatic cancer in saliva. The logistic regression model with four salivary biomarkers can detect pancreatic cancer specifically without the complication of chronic pancreatitis. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer. Keywords: Salivary biomarker, pancreatic cancer, early detection, salivary transcriptome, salivary microflora This study, which was approved by the UCLA Institutional Review Board, started sample collection in February 2006 and ended in April 2008. It had a discovery and verification phase, followed by an independent validation phase. All patients with clinically diagnosed early or locally advanced pancreatic cancer, chronic pancreatitis and matched healthy control were recruited from the UCLA Medical Center. The controls were matched for gender, age, ethnicity and smoking history. We chose early and locally advanced pancreatic cancers because they were considered early stage diseases. All patients were recently diagnosed with primary disease, and had not received any prior treatment in the form of chemotherapy, radiotherapy, surgery, or alternative remedies. No subjects had a history of prior malignancy, immunodeficiency, autoimmune disorders, hepatitis, or HIV infection. Written informed consent was obtained from all patients who agreed to serve as saliva donors. Unstimulated saliva samples were collected and processed as previously described. The saliva bank of pancreatic disease at the UCLA Dental Research Institute has collected 283 saliva samples since 2006. Of these, 114 samples, from 42 pancreatic cancer patients, 30 chronic pancreatitis patients and 42 healthy control subjects, met the following eligibility criteria: the disease subjectsâ?? samples were obtained immediately after primary diagnosis from patients without evidence of metastasis; the saliva sample was free of blood (Table 1 in accompanying manuscript). Of the 114 samples, 12 pancreatic cancer samples and 12 healthy control samples were chosen for the discovery and verification phase. The transcriptomic approach profiled the saliva supernatant samples from 12 pancreatic cancer patients and 12 healthy control subjects using the Affymetrix Human Genome U133 Plus 2.0 Array platform.
2008-12-30 | E-GEOD-14245 | biostudies-arrayexpress