Salivary biomarkers for cancer diagnosis: a meta-analysis.
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ABSTRACT:
SUBMITTER: Rapado-Gonzalez O
PROVIDER: S-EPMC7877992 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSalivary diagnostics and their utility as a nonaggressive approach for breast cancer diagnosis have been extensively studied in recent years. This meta-analysis assesses the diagnostic value of salivary biomarkers in differentiating between patients with breast cancer and controls.MethodsWe conducted a meta-analysis and systematic review of studies related to salivary diagnostics published in PubMed, EMBASE, Scopus, Ovid, Science Direct, Web of Science (WOS), and Google Scholar. The articles were chosen utilizing inclusion and exclusion criteria, as well as assessing their quality. Specificity and sensitivity, along with negative and positive likelihood ratios (NLR and PLR) and diagnostic odds ratio (DOR), were calculated based on random- or fixed-effects model. Area under the curve (AUC) and summary receiver-operating characteristic (SROC) were plotted and evaluated, and Fagan's Nomogram was evaluated for clinical utility.ResultsOur systematic review and meta-analysis included 14 papers containing 121 study units with 8639 adult subjects (4149 breast cancer patients and 4490 controls without cancer). The pooled specificity and sensitivity were 0.727 (95% CI: 0.713-0.740) and 0.717 (95% CI: 0.703-0.730), respectively. The pooled NLR and PLR were 0.396 (95% CI: 0.364-0.432) and 2.597 (95% CI: 2.389-2.824), respectively. The pooled DOR was 7.837 (95% CI: 6.624-9.277), with the AUC equal to 0.801. The Fagan's nomogram showed post-test probabilities of 28% and 72% for negative and positive outcomes, respectively. We also conducted subgroup analyses to determine specificity, sensitivity, DOR, PLR, and NLR based on the mean age of patients (≤52 or >52 years old), saliva type (stimulated and unstimulated saliva), biomarker measurement method (mass spectrometry [MS] and non-MS measurement methods), sample size (≤55 or >55), biomarker type (proteomics, metabolomics, transcriptomics and proteomics, and reagent-free biophotonic), and nations.ConclusionSaliva, as a noninvasive biomarker, has the potential to accurately differentiate breast cancer patients from healthy controls.
| S-EPMC9249990 | biostudies-literature
Project description:Gastric cancer (GC) is still an extremely severe health issue with high mortality due to the lacking of effective biomarkers. In this study, we aimed to investigate the alterations of salivary protein glycosylation related to GC and assess the possibility of salivary glycopatterns as potential biomarkers for the diagnosis of GC. Firstly, 94 patients with GC (n = 64) and atrophic gastritis (AG) (n = 30), as well as 30 age- and sex-matched healthy volunteers (HV) were enrolled in the test group to probe the difference of salivary glycopatterns using lectin microarrays, the results were validated by saliva microarrays and lectin blotting analysis. Then, the diagnostic model of GC (Model GC) and AG (Model AG) were constructed based on 15 candidate lectins which exhibited significant alterations of salivary glycopattern by logistic stepwise regression. Finally, two diagnostic models were assessed in the validation group including HV (n = 30) and patients with GC (n = 23) and AG (n = 24) and achieved high diagnostic power (Model GC (AUC: 0.89, sensitivity: 0.96 and specificity: 0.80), Model AG (AUC: 0.83, sensitivity: 0.92 and specificity: 0.72)). This study provides pivotal information to distinguish HV, AG and GC based on precise alterations in salivary glycopatterns, which have great potential to be biomarkers for diagnosis of GC.
| S-EPMC5482611 | biostudies-literature
Project description:BackgroundAccumulating evidence has demonstrated that microRNAs (miRNAs) could serve as promising molecular biomarkers for cancer detection. This study aims to systematically assess the diagnostic performance of salivary miRNAs in detection of cancer through a comprehensive meta-analysis.MethodsEligible studies were identified using PubMed and other computerized databases up to October 31, 2015, supplemented by a manual search of references from retrieved articles. The pooled sensitivity, specificity, and other measurements of accuracy of salivary miRNAs in the diagnosis of cancer were analyzed using the bivariate binomial mixed model.ResultsSeventeen studies from 8 articles with 694 subjects were included in this meta-analysis. All studies have a relatively high score of quality assessment. The overall sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) of salivary miRNAs in detection of cancer were 0.77 (95% confidence intervals [CI]: 0.69-0.84), 0.77 (95%CI: 0.65-0.88), 3.37 (95%CI: 2.26-5.02), 0.29 (95%CI: 0.23-0.38), and 11.41 (95%CI: 7.35-17.73), respectively. The AUC was 0.84 (95%CI: 0.80-0.87). Moreover, both whole saliva and saliva supernatant could be used as sources of clinical specimens for miRNAs detection.ConclusionsOur meta-analysis demonstrated that salivary miRNAs may serve as potential noninvasive biomarkers for cancer detection. The findings need to be confirmed with further research before it can be applied in the clinic.
| S-EPMC5104484 | biostudies-literature
Project description:This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing. Reads with a minimum of 10× coverage were used to identify differentially methylated CpG sites (DMCs) and 100 bp regions (DMRs). The performance of eight ML models and three feature-selection methods (ANOVA, MRMR, and LASSO) were then compared to determine the optimal biomarker models based on DMCs and DMRs. A total of 1745 DMCs and 105 DMRs were identified for detecting OSCC. The proportion of hypomethylated and hypermethylated DMCs was similar (51% vs. 49%), while most DMRs were hypermethylated (62.9%). Furthermore, more DMRs than DMCs were annotated to promoter regions (36% vs. 16%) and more DMCs than DMRs were annotated to intergenic regions (50% vs. 36%). Of all the ML models compared, the linear SVM model based on 11 optimal DMRs selected by LASSO had a perfect AUC, recall, specificity, and calibration (1.00) for OSCC detection. Overall, genome-wide DNA methylation techniques can be applied directly to saliva samples for biomarker discovery and ML-based platforms may be useful in stratifying OSCC during disease screening and monitoring.
| S-EPMC9563273 | biostudies-literature
Project description:Current research efforts on neurological diseases are focused on identifying novel disease biomarkers to aid in diagnosis, provide accurate prognostic information and monitor disease progression. With advances in detection and quantification methods in genomics, proteomics and metabolomics, saliva has emerged as a good source of samples for detection of disease biomarkers. Obtaining a sample of saliva offers multiple advantages over the currently tested biological fluids as it is a non-invasive, painless and simple procedure that does not require expert training or harbour undesirable side effects for the patients. Here, we review the existing literature on salivary biomarkers and examine their validity in diagnosing and monitoring neurodegenerative and neuropsychiatric disorders such as autism and Alzheimer's, Parkinson's and Huntington's disease. Based on the available research, amyloid beta peptide, tau protein, lactoferrin, alpha-synuclein, DJ-1 protein, chromogranin A, huntingtin protein, DNA methylation disruptions, and micro-RNA profiles provide display a reliable degree of consistency and validity as disease biomarkers.
| S-EPMC6138769 | biostudies-literature
Project description:MicroRNAs (miRNAs/miRs) regulate the expression of target genes and are considered to be associated with human cancer. The aim of the present study was to screen novel miRNA biomarkers in esophageal cancer (EC). The miRNA expression profile GSE41268 was extracted from Gene Expression Omnibus database, and differentially expressed miRNAs between whole saliva samples from patients with EC and healthy controls were identified using the Linear Models for Microarray Data package. Then, the targets of these miRNAs were screened using the miRecords database and used to construct the regulatory network. Gene ontology and pathway enrichment analyses were performed for the target genes of differentially expressed miRNAs to predict their potential functions. A total of 18 differentially expressed miRNAs were identified in saliva samples from patients with EC, and 43 validated target genes corresponding to 7 upregulated miRNAs were identified. Then, 6 miRNAs (miR-144, miR-451, miR-98, miR-10b, miR-486-5p and miR-363) and their target genes were used to construct a regulatory network. Within the network, miR-144 may target Notch homolog 1, fibrinogen α chain and fibrinogen β chain; miR-451 may regulate murine thymoma viral oncogene homolog 1, matrix metalloproteinase (MMP)9 and MMP2; miR-98 may directly target E2F transcription factor (E2F) 1, E2F2 and v-myc avian myelocytomatosis viral oncogene homolog (MYC); miR-10b may modulate peroxisome proliferator-activated receptor α and Kruppel-like factor 4; miR-485-5p and miR-363 may regulate TNF receptor superfamily member 5 and cyclin-dependent kinase inhibitor 1A. In addition, E2F1, E2F2 and MYC were associated with the cell cycle, which was the most significantly enriched function and pathway in EC. The results of the present study suggested that miR-144, miR-451, miR-98, miR-10b and miR-363 may be involved in EC by regulating their target genes, and may be used as biomarkers for EC.
| S-EPMC5529882 | biostudies-other
Project description:Proteases are important molecules that are involved in many physiological and pathological processes of the human body, such as growth, apoptosis and metastasis cancer cells. They are potential targets in cancer diagnosis and biotherapy. In this study, we analyzed the salivary protease spectrum of patients with oral squamous cell carcinoma (OSCC), oral benign masses and chronic periodontitis, as well as that of health, using human protease array kits, enzyme-linked immunosorbent assay, western blot and immunofluorescence. The salivary protease spectrum was found to be associated with oral diseases. For example, the saliva of patients with OSCC contained increased numbers of proteases than those of other oral diseases and health. The levels of matrix metalloproteinase (MMP)-1, MMP-2, MMP-10, MMP-12, A disintegrin and metalloprotease (ADAM)9, A disintegrin and metalloprotease with thrombospondin type 13 motifs (ADAMST13), cathepsin V and kallikrein 5 in the saliva of patients with OSCC were significantly increased compared with those of other groups. Taking MMP-1, cathepsin V, kallikrein 5 and ADAM9 as biomarkers of OSCC, cutoff values were199, 11.34, 9.29 and 202.55 pg·mL-1, respectively. From the area under the curve, sensitivity and specificity, the combination of cathepsin V/kallikrein5/ADAM9 was an optimal biomarker for diagnosing OSCC. Thus, analysis of the salivary protease spectrum may be an innovative and cost-efficient approach to evaluating the health status of the oral cavity. Specifically, increases in cathepsin V, kallikrein 5 and ADAM9 may be useful biomarkers in the screening and diagnosis of OSCC.
| S-EPMC6315043 | biostudies-literature
Project description:The prognosis of a gastric cancer (GC) diagnosis is poor due to the current lack of effective early diagnostic methods. Extracellular vesicle (EV) biomarkers have previously demonstrated strong diagnostic efficiency for certain types of cancer, including pancreatic and lung cancer. The present review aimed to summarize the diagnostic value of circulating EV biomarkers for early stage GC. The PubMed, Medline and Web of Science databases were searched from May 1983 to September 18, 2022. All studies that reported the diagnostic performance of EV biomarkers for GC were included for analysis. Overall, 27 studies were selected containing 2,831 patients with GC and 2,117 controls. A total of 58 EV RNAs were reported in 26 studies, including 39 microRNAs (miRNAs), 10 long non-coding RNAs (lncRNAs), five circular RNAs, three PIWI-interacting RNAs and one mRNA, in addition to one protein in the remaining study. Meta-analysis of the aforementioned studies demonstrated that the pooled sensitivity, specificity and AUC value of the total RNAs were 84, 67% and 0.822, respectively. The diagnostic values of miRNAs were consistent with the total RNA, as the pooled sensitivity, specificity and AUC value were 84, 67% and 0.808, respectively. The pooled sensitivity, specificity and AUC values of lncRNAs were 89, 69% and 0.872, respectively, markedly higher compared with that of miRNAs. A total of five studies reported the diagnostic performance of EV RNA panels for early stage GC and reported powerful diagnostic values with a pooled sensitivity, specificity and AUC value of 80, 77% and 0.879, respectively. Circulating EV RNAs could have the potential to be used in the future as effective, noninvasive biomarkers for early GC diagnosis. Further research in this field is necessary to translate these findings into clinical practice.
| S-EPMC10472029 | biostudies-literature
Project description:BackgroundColorectal cancer (CRC) is common and associated with significant mortality. Current screening methods for CRC lack patient compliance. microRNAs (miRNAs), identified in body fluids, are negative regulators of gene expression and are dysregulated in many cancers, including CRC. This paper summarises studies identifying blood-based miRNAs dysregulated in CRC compared with healthy controls in an attempt to evaluate their use as a screening tool for the diagnosis of CRC.MethodsA search of electronic databases (PubMed and EMBASE) and grey literature was performed between January 2002 and April 2016. Studies reporting plasma or serum miRNAs in the diagnosis of CRC compared with healthy controls were selected. Patient demographics, type of patient sample (serum or plasma), method of miRNA detection, type of normalisation, and the number of significantly dysregulated miRNAs identified were recorded. Statistical evaluation of dysregulated miRNAs using sensitivity, specificity, and area under the curve (AUC) was performed.ResultsThirty-four studies investigating plasma or serum miRNAs in the diagnosis of CRC were included. A total of 31 miRNAs were found to be either upregulated (n=17) or downregulated (n=14) in CRC cases as compared with controls. Fourteen studies identified panels of ⩾2 dysregulated miRNAs. The highest AUC, 0.943, was identified using a panel of 4 miRNAs with 83.3% sensitivity and 93.1% specificity. Meta-analysis of studies identifying a single dysregulated miRNA in CRC cases compared with controls was performed. Overall sensitivity and specificity of 28 individual miRNAs in the diagnosis of CRC were 76% (95% CI 72%-80%) and 76% (95% CI 72%-80%), respectively, indicating good discriminative ability of miRNAs as biomarkers for CRC. These data did not change with sensitivity analyses.ConclusionsBlood-based miRNAs distinguish patients with CRC from healthy controls with high sensitivity and specificity comparable to other common and invasive currently used screening methods for CRC. In future, miRNAs may be used as a relatively non-invasive blood-based marker for detection of CRC.
| S-EPMC5355921 | biostudies-literature
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 subjects’ 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.
| S-ECPF-GEOD-14245 | biostudies-other