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

Abstract

Background Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally, with late diagnosis, low survival rate, and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods. Methods Demographic data, clinical characteristics, and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins was evaluated. To construct diagnostic algorithms, we used the machine learning method. Results The mean salivary expression of CSTB in GC patients was significantly lower (115.55 ± 7.06, p = 0.001), and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88 ± 39.67, p = 0.002) than the control. Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R2 = 0.20, p < 0.001). Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC were 83.87 and 70.97%, respectively. The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity = 80.65% and specificity = 64.52%). The area under the ROC curve was up to 0.741. As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics, and food intake habits was 0.95. The machine learning model’s sensitivity, specificity, and accuracy were 100, 70.8, and 80.5%, respectively. Conclusion Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical, and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

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

“…Multiple drugs that are targeting IL‐6 and its receptor received US Food and Drug Administration approval for rheumatic diseases, such as rheumatoid arthritis and Crohn's disease, and display a reasonable safety profile, thereby indicating that exploratory trials are promising approaches for widening therapeutic options in PD 68 . CSTB is effective as a biomarker in different cancer entities 69 and has been recently reported to be altered in Alzheimer's disease 69 . To date, no drugs directly targeting CSTB are available, but because it binds to cathepsins, it would be feasible to test cathepsin addressing therapies in their potential to affect CSTB levels 70 …”

“…68 CSTB is effective as a biomarker in different cancer entities 69 and has been recently reported to be altered in Alzheimer's disease. 69 To date, no drugs directly targeting CSTB are available, but because it binds to cathepsins, it would be feasible to test cathepsin addressing therapies in their potential to affect CSTB levels. 70…”

Gastric cancer detection by non‐blood‐based liquid biopsies: A systematic review looking into the last decade of research

“…In saliva, microbiota was assessed in three studies 18,23,29 . Serine peptidase inhibitor kazal type 7 ( SPINK7 ), periplakin ( PPL ), semaphoring 4B ( SEMA4B ), and SMAD family member 4 ( SMAD4 ) messenger RNAs (mRNAs) were assessed in two studies, 21,25 as well as salivary glycans, 19,20 and proteins, namely CSTB and DMTB1 26,27 . Amino acids were characterized only once 16 .…”

“…Regarding techniques, most heterogeneity was noticed across different classes of biomarkers: microbiota has been analyzed by sequencing, a high‐throughput technique; RNA molecules by RT‐qPCR, a more targeted approach; glycans and proteins mostly by mass spectrometry, microarrays, ELISA, and western blot; and metabolites by mass spectrometry, from gas chromatography‐mass spectrometry to high performance liquid chromatography‐tandem mass spectrometry, as well as proton nuclear magnetic resonance, which allow the possibility of large‐scale analysis 109 . Overall, a difference in the techniques used was not clearly noticed over time 27,93 . However, radioimmunoassay for protein or metabolite detection has only been performed in studies from 2011 80,97 …”

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

“…Since the rapid progress of computational facilities, artificial intelligence using machine learning (ML) has developed rapidly and has been used in some of the research areas in the medical field, including cancer, cardiovascular disease, neurological disease, emergency medicine and even in the pharmacological field, etc. [ 17 , 18 , 19 , 20 , 21 ]. The definition of ML is the study of computer algorithms that can improve automatically through experience and by the use of data [ 22 ].…”