Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

Expression of Salivary miRNAs, Clinical, and Demographic Features in the Early Detection of Gastric Cancer: A Statistical and Machine Learning Analysis

OBJECTIVE

Gastric cancer ranks as one of the top five deadliest cancers worldwide and is often diagnosed at late stages. Analysis of saliva may provide a non-invasive approach for detection of malignancies in organs associated with the oral cavity. This research aims to analyze salivary microRNA expression together with clinical and demographic features with the aim of diagnosing gastric cancer.

MATERIALS

The study included 19 patients with early-stage gastric cancer and 19 healthy controls. Saliva samples were collected and processed for RNA isolation. Salivary expression of miR-223-3p and miR-21-5p were measured using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curves were generated to evaluate the accuracy of diagnostic models. Machine learning algorithms, multiple logistic regression, and principal component analysis (PCA) were used to assess the predictive power of miRNAs in conjunction with clinical-demographic features.

RESULTS

Significant upregulation of miR-223-3p and downregulation of miR-21-5p in saliva were observed in patients with gastric cancer. The area under ROC curve (AUC) values for salivary miR-21-5p, salivary miR-223-3p, and their multiple logistic regression were determined to be 0.723, 0.791, and 0.850, respectively. The AUC for multiple logistic regression model was 0.919. The PCA model led to the highest diagnostic odds ratio (DOR) of 134.33 (sensitivity = 0.785, specificity = 1.00, AUC = 903). Application of machine learning methods, and in particular a random forest algorithm, showed high accuracy in diagnosing patients with gastric cancer (sensitivity = 1.00, specificity = 0.857, AUC = 0.93).

CONCLUSION

The application of validated salivary diagnostics in clinical practice could help facilitate earlier diagnosis of gastric cancer and improve medical outcome. Expression of miR-21 and miR-223-3p in saliva together with clinical and demographic features, appears promising in screening for GC.

B-cell specific Moloney murine leukemia virus insertion site 1 contributes to invasion, metastasis, and poor prognosis in salivary adenoid cystic carcinoma

Background/purpose

Upregulation of B-cell specific Moloney murine leukemia virus insertion site 1 (BMI-1) has been involved in the invasion, metastasis, and poor prognosis of many cancers. The aim of this study was to evaluate the levels and clinical significance of BMI-1 in saliva of patients with salivary adenoid cystic carcinoma (SACC), and to analyze biological function and mechanism of BMI-1 in the invasion and metastasis of SACC.

Materials and methods

The levels of BMI-1 in saliva and tumor tissues of SACC patients were determined. The correlation of salivary BMI-1 levels with clinicopathological parameters and clinical outcomes in patients with SACC was analyzed. Additionally, the effects of BMI-1 on wound-healing, transwell invasion, and epithelial-mesenchymal transition (EMT)-related protein expression in vitro as well as on tumorigenicity and experimental lung metastasis in vivo were investigated through exogenous overexpression and silencing of BMI-1 in SACC cells.

Results

BMI-1 levels increased in saliva and tumor tissues in SACC patients with invasion or metastasis. High salivary BMI-1 levels were correlated with poor TNM stage, poor overall survival, and disease-free survival. Exogenous expression of BMI-1 in SACC-83 promoted its migration and invasion, while silencing BMI-1 in SACC-LM inhibited its migration and invasion in vitro and suppressed tumorigenesis and lung metastasis in vivo. Furthermore, BMI-1 regulated the expression of EMT-related proteins in SACC.

Conclusion

Our study shows that BMI-1 can serve as a valuable biomarker to identify tumor invasion and metastasis in SACC, predict its prognosis, and act as a promising therapeutic target for SACC.

Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes

BACKGROUND

The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships.

AIM

To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS.

METHODS

556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods.

RESULTS

Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS.

CONCLUSION

Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

Comprehensive analysis of disulfidptosis related genes and prognosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is a common malignant tumor of the digestive system. Disulfidptosis is a new programmed cell death mechanism, although its specific mechanism in GC is incompletely understood.

AIM

In this study, we used bioinformatics analysis to explore a disulfidptosis-based predictive model related to GC prognosis and to identify potential therapeutic targets and sensitive drugs for GC.

METHODS

We extracted GC-related data from The Cancer Genome Atlas and Gene Expression Omnibus databases. R software (version 4.2.1) was used for correlation analysis.

RESULTS

Through the above analysis, we found that the disulfidptosis related gene may be related to the prognosis of GC. Six genes, namely, PLS3, GRP, APOD, SGCE, COL8A1, and VAMP7, were found to constitute a predictive model for GC prognosis. APOD is a potential therapeutic target for treating GC. Bosutinib and other drugs are sensitive for the treatment of GC.

CONCLUSION

The results of this study indicate that disulfidptosis is related to the prognosis and treatment of GC, while APOD represents a potential therapeutic target for GC.

Multi-faceted attributes of salivary cell-free DNA as liquid biopsy biomarkers for gastric cancer detection

BACKGROUND

Recent advances in circulating cell-free DNA (cfDNA) analysis from biofluids have opened new avenues for liquid biopsy (LB). However, current cfDNA LB assays are limited by the availability of existing information on established genotypes associated with tumor tissues. Certain cancers present with a limited list of established mutated cfDNA biomarkers, and thus, nonmutated cfDNA characteristics along with alternative biofluids are needed to broaden the available cfDNA targets for cancer detection. Saliva is an intriguing and accessible biofluid that has yet to be fully explored for its clinical utility for cancer detection.

METHODS

In this report, we employed a low-coverage single stranded (ss) library NGS pipeline "Broad-Range cell-free DNA-Seq" (BRcfDNA-Seq) using saliva to comprehensively investigate the characteristics of salivary cfDNA (ScfDNA). The identification of cfDNA features has been made possible by applying novel cfDNA processing techniques that permit the incorporation of ultrashort, ss, and jagged DNA fragments. As a proof of concept using 10 gastric cancer (GC) and 10 noncancer samples, we examined whether ScfDNA characteristics, including fragmentomics, end motif profiles, microbial contribution, and human chromosomal mapping, could differentiate between these two groups.

RESULTS

Individual and integrative analysis of these ScfDNA features demonstrated significant differences between the two cohorts, suggesting that disease state may affect the ScfDNA population by altering nuclear cleavage or the profile of contributory organism cfDNA to total ScfDNA. We report that principal component analysis integration of several aspects of salivary cell-free DNA fragmentomic profiles, genomic element profiles, end-motif sequence patterns, and distinct oral microbiome populations can differentiate the two populations with a p value of < 0.0001 (PC1).

CONCLUSION

These novel features of ScfDNA characteristics could be clinically useful for improving saliva-based LB detection and the eventual monitoring of local or systemic diseases.

Multi-Faceted Attributes of Salivary Cell-free DNA as Liquid Biopsy Biomarkers for Gastric Cancer Detection

Background

Recent advances in circulating cell-free DNA (cfDNA) analysis from biofluids have opened new avenues for liquid biopsy (LB). However, current cfDNA LB assays are limited by the availability of existing information on established genotypes associated with tumor tissues. Certain cancers present with a limited list of established mutated cfDNA biomarkers, and thus, nonmutated cfDNA characteristics along with alternative biofluids are needed to broaden the available cfDNA targets for cancer detection. Saliva is an intriguing and accessible biofluid that has yet to be fully explored for its clinical utility for cancer detection.

Methods

In this report, we employed a low-coverage single stranded (ss) library NGS pipeline "Broad-Range cell-free DNA-Seq" (BRcfDNA-Seq) using saliva to comprehensively investigate the characteristics of salivary cfDNA (ScfDNA). The identification of cfDNA features has been made possible by applying novel cfDNA processing techniques that permit the incorporation of ultrashort, ss, and jagged DNA fragments. As a proof of concept using 10 gastric cancer (GC) and 10 noncancer samples, we examined whether ScfDNA characteristics, including fragmentomics, end motif profiles, microbial contribution, and human chromosomal mapping, could differentiate between these two groups.

Results

Individual and integrative analysis of these ScfDNA features demonstrated significant differences between the two cohorts, suggesting that disease state may affect the ScfDNA population by altering nuclear cleavage or the profile of contributory organism cfDNA to total ScfDNA. We report that principal component analysis integration of several aspects of salivary cell-free DNA fragmentomic profiles, genomic element profiles, end-motif sequence patterns, and distinct oral microbiome populations can differentiate the two populations with a p value of < 0.0001 (PC1).

Conclusion

These novel features of ScfDNA characteristics could be clinically useful for improving saliva-based LB detection and the eventual monitoring of local or systemic diseases.

Site-specific protein biomarkers in gastric cancer: a comprehensive review of novel biomarkers and clinical applications

INTRODUCTION

Gastric cancer (GC) is the fifth most common cancer and the fourth leading cause of cancer-related death worldwide, thus representing a significant global health burden. Early detection and monitoring of GC are essential to improve patient outcomes. While traditional cancer biomarkers such as carcinoembryonic antigen, carbohydrate antigen (CA) 19-9, and CA 72-4 are widely used, their limited sensitivity and specificity necessitate the exploration of alternative biomarkers.

AREAS COVERED

This review comprehensively analyzes the landscape of GC protein biomarkers identified from 2019 to 2022, with a focus on tissue, blood, urine, saliva, gastric juice, ascites, and exhaled breath as sample sources. We address the potential clinical applications of these biomarkers in early diagnosis, monitoring recurrence, and predicting survival and therapeutic response of GC patients.

EXPERT OPINION

The discovery of novel protein biomarkers holds great promise for improving the clinical management of GC. However, further validation in large, diverse cohorts is needed to establish the clinical utility of these biomarkers. Integrating these biomarkers with existing diagnostic and monitoring approaches will likely lead to improved personalized treatment plans and patient outcomes.

Blood Markers of Inflammation, Neurodegeneration, and Cardiovascular Risk in Early Parkinson's Disease

BACKGROUND

Recent studies point toward a significant impact of cardiovascular processes and inflammation on Parkinson's disease (PD) progression.

OBJECTIVE

The aim of this study was to assess established markers of neuronal function, inflammation, and cardiovascular risk by high-throughput sandwich immune multiplex panels in deeply phenotyped PD.

METHODS

Proximity Extension Assay technology on 273 markers was applied in plasma of 109 drug-naive at baseline (BL) patients with PD (BL, 2-, 4-, and 6-year follow-up [FU]) and 96 healthy control patients (HCs; 2- and 4-year FU) from the de novo Parkinson's cohort. BL plasma from 74 individuals (37 patients with PD, 37 healthy control patients) on the same platform from the Parkinson Progression Marker Initiative was used for independent validation. Correlation analysis of the identified markers and 6 years of clinical FU, including motor and cognitive progression, was evaluated.

RESULTS

At BL, 35 plasma markers were differentially expressed in PD, showing downregulation of atherosclerotic risk markers, eg, E-selectin and ß₂ -integrin. In contrast, we found a reduction of markers of the plasminogen activation system, eg, urokinase plasminogen activator. Neurospecific markers indicated increased levels of peripheral proteins of neurodegeneration and inflammation, such as fibroblast growth factor 21 and peptidase inhibitor 3. Several markers, including interleukin-6 and cystatin B, correlated with cognitive decline and progression of motor symptoms during FU. These findings were independently validated in the Parkinson Progression Marker Initiative.

CONCLUSIONS

We identified and validated possible PD plasma biomarker candidates for state, fate, and disease progression, elucidating new molecular processes with reduced endothelial/atherosclerotic processes, increased thromboembolic risk, and neuroinflammation. Further investigations and validation in independent and larger longitudinal cohorts are needed. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

[Developments in Research on Salivary Biomarkers in the Diagnosis of Systemic Diseases]

Saliva, an important biological fluid secreted by oral glands, serves multiple functions. It performs cleaning and protective functions for oral tissues, safeguarding against biological, mechanical and chemical stimuli, while allowing for the sensory perception of taste and temperature. It is also responsible for the preliminary digestion of food. These functions and properties of saliva are attributed to the presence of electrolytes, buffers, proteins, glycoproteins, and lipids in saliva. Recent studies have found that saliva contains biomarkers that are closely connected with the pathophysiological status of the human body, suggesting that saliva makes an ideal biological fluid for drug monitoring and biomarker screening. Therefore, salivary biomarkers can be used as an instrument for physical monitoring and localization of the occurrence of diseases, thereby accomplishing early diagnosis of diseases and assessment of the overall health status of patients. However, the actual application of salivary biomarkers in the diagnosis and treatment of systemic diseases is still not widely available, and the establishment of evaluation criteria and the exploration of its mechanism are not sufficiently investigated. Herein, we reviewed the latest research findings on applying the salivary biomarkers in the diagnosis of systemic diseases.

Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.