Predicting novel salivary biomarkers for the detection of pancreatic cancer using biological feature-based classification

Pancreatic Cancer Biomarkers: Oncogenic Mutations, Tissue and Liquid Biopsies, and Radiomics-A Review

Pancreatic cancer is one of the most fatal malignancies, as approximately 80% of patients are at advanced stages by the time of diagnosis. The main reason for the poor overall survival is late diagnosis that is partially due to the lack of tools for early-stage detection. In addition, there are several challenges in evaluating response to treatment and predicting prognosis. In this article, we do a review of the most common pancreatic cancer biomarkers with emphasis in new and promising approaches. Liquid biopsies seem to have important clinical applications in early detection, screening, prognosis, and longitudinal monitoring of on-treatment patients. Together with biomarkers in imaging, can represent valuable alternative non-invasive tools in order to achieve a more effective management of pancreatic cancer patients.

Pancreatic Cancer in Chronic Pancreatitis: Pathogenesis and Diagnostic Approach

Chronic pancreatitis is one of the main risk factors for pancreatic cancer, but it is a rare event. Inflammation and oncogenes work hand in hand as key promoters of this disease. Tobacco is another co-factor. During alcoholic chronic pancreatitis, the cumulative risk of cancer is estimated at 4% after 15 to 20 years. This cumulative risk is higher in hereditary pancreatitis: 19 and 12% in the case of PRSS1 and SPINK1 mutations, respectively, at an age of 60 years. The diagnosis is difficult due to: (i) clinical symptoms of cancer shared with those of chronic pancreatitis; (ii) the parenchymal and ductal remodeling of chronic pancreatitis rendering imaging analysis difficult; and (iii) differential diagnoses, such as pseudo-tumorous chronic pancreatitis and paraduodenal pancreatitis. Nevertheless, the occurrence of cancer during chronic pancreatitis must be suspected in the case of back pain, weight loss, unbalanced diabetes, and jaundice, despite alcohol withdrawal. Imaging must be systematically reviewed. Endoscopic ultrasound-guided fine-needle biopsy can contribute by targeting suspicious tissue areas with the help of molecular biology (search for KRAS, TP53, CDKN2A, DPC4 mutations). Short-term follow-up of patients is necessary at the clinical and paraclinical levels to try to diagnose cancer at a surgically curable stage. Pancreatic surgery is sometimes necessary if there is any doubt.

Salivary noncoding RNA in the diagnosis of pancreatic cancer: Systematic review and meta-analysis

BACKGROUND

Pancreatic cancer is considered one of the most deadly malignancies, primarily because of its diagnostic challenges. We performed a systematic review and diagnostic meta-analysis to evaluate the diagnostic value of noncoding salivary RNAs in pancreatic cancer diagnosis.

METHODS

Our investigation involved pertinent studies published in PubMed, Scopus, Web of Science, LIVIVO, Ovid and also the Google Scholar search engine. Specificity and sensitivity were calculated, as were positive and negative likelihood ratios (PLR and NLR), and the diagnostic odds ratio (DOR). The summary receiver-operating characteristics and area under the curve were plotted and assessed.

RESULTS

This meta-analysis and systematic review involved and examined five studies that contained 145 study units with a total of 2731 subjects (1465 pancreatic cancer patients versus 1266 noncancer controls). The pooled specificity, sensitivity, NLR, PLR and DOR were 0.783 (95% CI: 0.759-0.805), 0.829 (95% CI: 0.809-0.848), 0.309 (95% CI: 0.279-0.343), 3.386 (95% CI: 2.956-3.879) and 18.403 (95% CI: 14.753-22.954), respectively, with the area under the curve (AUC) equal to 0.882. Subgroup analyses were conducted based on the saliva type (unstimulated and stimulated), mean age of patients, sample size, type of control, serum carbohydrate antigen 19-9 (CA19-9) level and type of salivary noncoding RNA (microRNA (miRNA) and long noncoding RNA (lncRNA)).

CONCLUSIONS

The results of our systematic review and meta-analysis suggest that noncoding RNA biomarkers in the stimulated saliva could be a promising approach for accurate pancreatic cancer diagnosis in the early stages.

Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review

Pancreatic cancer is a highly malignant digestive system tumor with a poor prognosis. Most pancreatic cancer patients are diagnosed at an advanced stage or even metastasis due to its highly aggressive characteristics and lack of typical early symptoms. Thus, an early diagnosis of pancreatic cancer is crucial for improving its prognosis. Currently, screening is often applied in high‐risk individuals to achieve the early diagnosis of pancreatic cancer. Fully understanding the risk factors of pancreatic cancer and pathogenesis could help us identify the high‐risk population and achieve early diagnosis and timely treatment of pancreatic cancer. Notably, accumulating studies have been undertaken to improve the detection rate of different imaging methods and the diagnostic accuracy of endoscopic ultrasound‐guided fine‐needle aspiration (EUS‐FNA) which is the golden standard for pancreatic cancer diagnosis. In addition, there are currently no biomarkers with sufficient sensitivity and specificity for the diagnosis of pancreatic cancer to be applied in the clinic. As the only serum biomarker approved by the United States Food and Drug Administration, carbohydrate antigen 19‐9 (CA19‐9) is not recommended to be used in the early screening of pancreatic cancer because of its limited specificity. Recently, increasing numbers of studies focused on the discovering of novel serum biomarkers and exploring their combination with CA19‐9 in the detection of pancreatic cancer. Besides, the application of liquid biopsy involving circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs (miRNAs), and exosomes in blood and biomarkers in urine, and saliva in pancreatic cancer diagnosis are drawing more and more attention. Furthermore, many innovative technologies such as artificial intelligence, computer‐aided diagnosis system, metabolomics technology, ion mobility spectrometry (IMS) associated technologies, and novel nanomaterials have been tested for the early diagnosis of pancreatic cancer and have shown promising prospects. Hence, this review aims to summarize the recent progress in the development of early screening and diagnostic methods, including imaging, pathological examination, serological examination, liquid biopsy, as well as other potential diagnostic strategies for pancreatic cancer.

Potential biomarkers for early detection of pancreatic ductal adenocarcinoma

Pancreatic cancer has the highest mortality amongst all major organ cancers. Early detection is key to reduce deaths related to pancreatic cancer. However, early detection has been challenged by the lack of non-invasive biomarkers with enough sensitivity and specificity to allow for screening. The gold standard is still carbohydrate antigen (CA 19-9), against which all new biomarkers must be evaluated. In this paper, we describe recent progress in the development of new pancreatic cancer biomarkers, focusing on proteins, metabolites, and genetic and epigenetic biomarkers. Although several promising biomarkers have been identified, they are all derived from retrospective studies and additional prospective studies are needed to confirm their clinical validity.

Biomarkers for Early Cancer Diagnosis: Prospects for Success through the Lens of Tumor Genetics

Thousands of candidate cancer biomarkers have been proposed, but so far, few are used in cancer screening. Failure to implement these biomarkers is attributed to technical and design flaws in the discovery and validation phases, but a major obstacle stems from cancer biology itself. Oncogenomics has revealed broad genetic heterogeneity among tumors of the same histology and same tissue (or organ) from different patients, while tumors of different tissue origins also share common genetic mutations. Moreover, there is wide intratumor genetic heterogeneity among cells within any single neoplasm. These findings seriously limit the prospects of finding a single biomarker with high specificity for early cancer detection. Current research focuses on developing biomarker panels, with data assessment by machine-learning algorithms. Whether such approaches will overcome the inherent limitations posed by tumor biology and lead to tests with true clinical value remains to be seen.

Salivary biomarkers for diagnosis of systemic diseases and malignant tumors. A systematic review

Background

Saliva evaluation could be a possible alternative to blood and/or tissue analyses, for researching specific molecules associated to the presence of systemic diseases and malignancies. The present systematic review has been designed in order to answer to the question “are there significant associations between specific salivary biomarkers and diagnosis of systemic diseases or malignancies?”.

Material and Methods

The Preferred Reporting Item for Systematic Reviews and Meta-analysis (PRISMA) statement was used to guide the review. The combinations of “saliva” and “systemic diseases” or “diagnosis” or “biomarkers” or “cancers” or “carcinoma” or “tumors”, were used to search Medline, Scopus and Web of Science databases. Endpoint of research has been set at May 2019. Studies were classified into 3 groups according to the type of disease investigated for diagnosis: 1) malignant tumors; 2) neurologic diseases and 3) inflammatory/metabolic/cardiovascular diseases. Assessment of quality has been assigned according to a series of questions proposed by the National Institute of Health. Level of evidence was assessed using the categories proposed in the Oxford Center for Evidence-Based medicine (CEMB) levels for diagnosis (2011).

Results

Seventy-nine studies met the inclusion and exclusion criteria. Fifty-one (64%) investigated malignant tumors, 14 (17.5%) neurologic and 14 (18.5%) inflammatory/cardiovascular/metabolic diseases. Among studies investigating malignant tumors, 12 (23.5%) were scored as “good” and 11 of these reported statistically significant associations between salivary molecules and pathology. Two and 5 studies were found to have a good quality, among those evaluating the association between salivary biomarkers and neurologic and inflammatory/metabolic/cardiovascular diseases, respectively.

Conclusions

The present systematic review confirms the existence of some “good” quality evidence to support the role of peculiar salivary biomarkers for diagnosis of systemic diseases (e.g. lung cancer and EGFR).

Key words:Salivary diagnostics, biomarkers, systemic diseases, malignant tumors, early diagnosis.

Carious Lesion Severity Induces Higher Antioxidant System Activity and Consequently Reduces Oxidative Damage in Children's Saliva

Oxidative stress biomarkers can be found at detectable concentrations in saliva. These salivary biomarkers reflect specific oxidation pathways associated with caries and periodontitis. Our study evaluated the influence of dental caries severity (assessed using the ICCMS™ criteria) on the levels of oxidative stress biomarkers in saliva from children. Unstimulated saliva samples were collected from patients (from one to three years old) in a day care center in Birigui, SP, Brazil, two hours after fasting. Children were divided into four groups (n = 30/group), according to caries severity: caries free (group A), early carious lesions (group B), moderate carious lesions (group C), and advanced carious lesions (group D). The following salivary biomarkers were determined: total proteins (TP), measured by the Lowry method; oxidative damage, measured by the TBARS method; total antioxidant capacity (TAC); superoxide dismutase (SOD) enzymatic antioxidant activity; and uric acid (UA) non-enzymatic antioxidant activity. Data were analyzed by ANOVA, followed by the Student-Newman-Keuls test, Pearson and Spearman correlation coefficients, and multivariable linear regression (p < 0.05). TP, TAC, SOD enzymatic antioxidant activity, and UA non-enzymatic antioxidant activity increased with caries severity, consequently reducing salivary oxidative damage. It was concluded that higher caries severity increases salivary antioxidant system activity, with consequent reduction in salivary oxidative damage.

An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection

Aim: Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers. Materials & methods: Modern transcriptomic and methylomic profiling technologies detect the status of tens of thousands or even millions of probing residues in the human genome, and introduce a major computational challenge for the existing feature selection algorithms. This study proposes a three-step feature selection algorithm, TriVote, to detect a subset of transcriptomic or methylomic residues with highly accurate binary classification performance. Results & conclusion: TriVote outperforms both filter and wrapper feature selection algorithms with both higher classification accuracy and smaller feature number on 17 transcriptomes and two methylomes. Biological functions of the methylome biomarkers detected by TriVote were discussed for their disease associations. An easy-to-use Python package is also released to facilitate the further applications.

A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis

With the rapid accumulation of gene expression data from various technologies, e.g., microarray, RNA-sequencing (RNA-seq), and single-cell RNA-seq, it is necessary to carry out dimensional reduction and feature (signature genes) selection in support of making sense out of such high dimensional data. These computational methods significantly facilitate further data analysis and interpretation, such as gene function enrichment analysis, cancer biomarker detection, and drug targeting identification in precision medicine. Although numerous methods have been developed for feature selection in bioinformatics, it is still a challenge to choose the appropriate methods for a specific problem and seek for the most reasonable ranking features. Meanwhile, the paired gene expression data under matched case-control design (MCCD) is becoming increasingly popular, which has often been used in multi-omics integration studies and may increase feature selection efficiency by offsetting similar distributions of confounding features. The appropriate feature selection methods specifically designed for the paired data, which is named as matched-pairs feature selection (MPFS), however, have not been maturely developed in parallel. In this review, we compare the performance of 10 feature-selection methods (eight MPFS methods and two traditional unpaired methods) on two real datasets by applied three classification methods, and analyze the algorithm complexity of these methods through the running of their programs. This review aims to induce and comprehensively present the MPFS in such a way that readers can easily understand its characteristics and get a clue in selecting the appropriate methods for their analyses.

Clinical multi-omics strategies for the effective cancer management

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis.

BIOLOGICAL SIGNIFICANCE

In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.