Salivary microRNAs May Be A Valuable Biomarker To Detect Resectable Pancreatic Cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE53325 | GEO | 2013/12/15
SECONDARY ACCESSION(S): PRJNA231719
REPOSITORIES: GEO
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Project description:Early surgery is vital in the treatment of highly fatal pancreatic cancer (PC). But there is no valuable and non-invasive biomarker to screen PC currently. Studies showed many salivary molecules can detect several systemic diseases. We aimed to investigate whether salivary microRNAs (miRNAs) can act as a biomarker to detect resectable PC. By Agilent microarray salivary miRNAs were profiled from saliva samples from 8 patients with resectable PC and 8 healthy controls. Candidate biomarkers discovered from the profile were subjected to validation by qPCR.
2013-12-15 | E-GEOD-53325 | biostudies-arrayexpress
Project description: Not available
| S-ECPF-GEOD-53325 | biostudies-other
Project description:Saliva (oral fluids) is an emerging biofluid poised for clinical diseases detection. Although the rationale for oral diseases applications (e.g. oral cancer) is clear, the rationale and relationship between systemic diseases and saliva biomarkers are unknown. In this study, we used mouse models of melanoma and non-small cell lung cancer and compared the transcriptome biomarker profiles of tumor-bearing mice to those of control mice. Microarray analysis showed that salivary transcriptomes were significantly altered in tumor-bearing mice vs. controls. Analysis of the transcriptomes in the mouse tumors, serum, salivary glands and saliva revealed that salivary biomarkers have multiple origins. Furthermore, we identified that the expression of two groups of significantly altered transcription factors Runx1, Mlxipl, Trim30 and Egr1, Tbx1, Nr1d1 in melanoma-bearing mice that can potentially be responsible for 82.6% of the up-regulated genes expression and 62.5% of the down-regulated gene expression in the mice saliva, respectively. We also confirmed that the ectopic production of nerve growth factor (NGF) in the melanoma tumor tissue as a tumor-released mediator that can induce expression of the transcription factor Egr-1 in the salivary gland. Taken together, our data support the conclusion that upon systemic disease development, a disease-specific change occurs in the salivary biomarker profile. Although the origins of the disease-specific salivary biomarkers are both systemic and local, stimulation of salivary gland by mediators released from remote tumors play an important role in regulating the salivary surrogate biomarker profiles.
2008-11-11 | GSE13443 | GEO
Project description:Salivary microRNAs May Be A Valuable Biomarker To Detect Resectable Pancreatic Cancer
| PRJNA231719 | ENA
Project description:Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA) was used to profile transcriptomes and discover altered gene expression in saliva supernatant. Salivary transcriptomic biomarker discovery was performed on 10 lung cancer patients and 10 matched controls. Seven messenger RNA biomarkers were discovered and pre-validated
2013-01-11 | GSE32175 | GEO
Project description:Saliva is rich in proteins, DNA, RNA and microorganisms, and can be regarded as a biomarker library. In order to explore a noninvasive and simple means of early screening for liver cancer, proteomics was used to screen salivary markers of hepatitis B associated liver cancer. We used mass spectrometry coupled isobaric tags for relative and absolute quantitation (iTRAQ)-technology to identify differentially expressed proteins (DEPs). Western blot, immunohistochemistry and enzyme linked immunosorbent assay were used to detect marker expression of in tissues and saliva. Statistical analysis was used to analyze the diagnostic efficacy of the markers was analyzed through statistical analyses. By comparing the hepatocellular carcinoma (HCC) group with non-HCC groups, we screened out 152 salivary DEPs. We found orosomucoid 1(ORM1) had significantly higher expression in saliva of HCC patients compared with non-HCC groups (p<0.001) and the expression of ORM1 in liver cancer tissues was significantly higher than that in adjacent normal tissues(p<0.001). The combination of salivary ORM1 and alpha-fetoprotein (AFP) showed reasonable specificities and sensitivities for detecting HCC. In a word, salivary ORM1 as a new biomarker of hepatitis B associated hepatocellular carcinoma, combination of salivary ORM1 and AFP as an improved diagnostic tool for hepatocellular carcinoma.
2022-07-13 | PXD033752 | Pride
Project description:Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA) was used to profile transcriptomes and discover altered gene expression in saliva supernatant. Salivary transcriptomic biomarker discovery was performed on 10 lung cancer patients and 10 matched controls. Seven messenger RNA biomarkers were discovered and pre-validated This study consisted of two phases, including a discovery phase, followed by a pre-validation phase. 10 lung cancer samples and 10 matched control samples were chosen for the biomarker discovery phase. The transscriptomic approach profiled the saliva supernatant samples from 10 lung cancer patients and 10 healthy control subjects using the Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA). Biomarkers identified from the microarray study were first verified using the discovery sample set (10 lung cancer and 10 healthy control).
2013-01-11 | E-GEOD-32175 | biostudies-arrayexpress
Project description:A sensitive assay to identify biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. In this study, we have conducted a prospective sample collection and retrospective blinded validation (PRoBE design) to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. The Affymetrix HG U133 Plus 2.0 Array and 2D-DIGE were used to profile transcriptomes and proteomes in saliva supernatants respectively. Significant variations of salivary transcriptomic and proteomic profiles were observed between breast cancer patients and healthy controls. Eleven transcriptomic biomarker candidates and two proteomic biomarker candidates were selected for a preclinical validation using an independent sample set. Transcriptomic biomarkers were validated by RT-qPCR and proteomic biomarkers were validated by quantitative protein immunoblot. Eight mRNA biomarkers and one protein biomarker have been validated for breast cancer detection, yielding ROC-plot AUC values between 0.665 and 0.959. This report provides proof of concept of salivary biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers’ discriminatory power paves the way for a PRoBE-design definitive validation study. Keywords: Salivary biomarker, Breast cancer, Early detection, Salivary transcriptome, Salivary proteome
2010-02-11 | GSE20266 | GEO
Project description:Ovarian cancer is the leading cause of death in gynecological diseases, and has been considered as one of the most fatal cancers due to lack of reliable detection strategy in the early stage. Therefore the capability to detect the morbidity initiation with an sensitive and effective approach is one of the most desirable goals for curing ovarian cancer. In this study, we used microarray technology for salivary mRNA biomarkers discovery, and evaluated the performance and translational utilities of discovered markers from a clinical study using an independent sample cohort . We used microarrays to profile and compare the gene expressions between ovairan cancer patient and matched controls, and identified seven down-regulated genes after the validation study. To find salivary transcriptomic biomarkers for ovarian cancer, salivary transcriptomes in 11 ovarian cancer patients and 11 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 array, and followed by t-test and fold-change analyses. The biomarker candidates selected from the microarray results were subjected to clinical validation using an independent sample cohort by RT-qPCR. The prediction power of biomarkers was analyzed by logistic regression approach
2011-05-12 | E-GEOD-29220 | biostudies-arrayexpress
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
2008-12-31 | GSE14245 | GEO