New serum biomarkers for detection of esophageal carcinoma using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Evaluation of Salivary Diagnostics: Applications, Benefits, Challenges, and Future Prospects in Dental and Systemic Disease Detection

Saliva is a multifaceted biological fluid that plays a pivotal role in oral health and overall well-being. It is primarily produced by major salivary glands, with additional contributions from minor glands. Saliva is essential for various physiological functions, including oral lubrication, digestion, and defense against pathogens. Its intricate composition comprises proteins, electrolytes, enzymes, hormones, and microbial DNA, enabling it to act as a dynamic indicator of both local and systemic health. A literature search was conducted using PubMed, Web of Science, and Google Scholar to identify relevant studies published up to June 2024. The included studies involved human participants and provided original data or comprehensive reviews on salivary biomarkers. The findings indicate that salivary diagnostics show promise in diagnosing and monitoring systemic conditions such as diabetes and cardiovascular diseases, with salivary glucose levels correlating well with blood glucose levels. Biomarkers such as C-reactive protein (CRP) have been linked to cardiovascular risk, while saliva has been explored for cancer detection, including pancreatic and prostate cancers. Advances in techniques such as enzyme-linked immunosorbent assay (ELISA), saliva omics, and single-cell sequencing have furthered salivary diagnostics, providing insights into disease mechanisms. Additionally, quantitative mass spectrometry (qMS) and Raman spectroscopy (RS) contribute to non-invasive diagnostics for various conditions, including cancer. Collecting saliva samples from healthy individuals is crucial for early disease detection and evaluating treatment efficacy. This review underscores the growing importance of salivary tests in dental practice and their potential for diagnosing various health conditions. Further research is essential to address challenges related to variability and standardization. Dentists and healthcare professionals should consider incorporating salivary tests into clinical decision-making to enhance patient outcomes.

MALDI imaging on large-scale tissue microarrays identifies molecular features associated with tumour phenotype in oesophageal cancer

AIMS

Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) and tissue microarray (TMA) technologies were jointly utilized to search for molecular features associated with clinicopathological parameters in oesophageal cancer.

METHODS AND RESULTS

Two TMAs from formalin-fixed tissue samples, including 300 adenocarcinomas and 177 squamous cell carcinomas with clinical follow-up data, were analysed. MALDI-MSI analysis revealed 72 distinct mass per charge (m/z) signals associated with tumour cells, 48 of which were found in squamous cell carcinomas only, and 12 of which were specific for adenocarcinomas. In adenocarcinomas, six signals were linked to early-stage (pT1-T2) tumours (two signals) and the presence (one signal) or absence (three signals) of lymph node metastasis. In squamous cell carcinomas, 24 signals were strongly linked to different phenotypic features, including tumour stage (four signals), histological grade (four signals), and lymph node metastasis (three signals).

CONCLUSIONS

The high number of m/z signals that were found to be significantly linked to one or more phenotypic features of oesophageal cancer highlights the power of MALDI-MSI in the analysis of high-density TMAs. The data also emphasise substantial biological differences between adenocarcinomas and squamous cell carcinomas.

Identification of novel low molecular weight serum peptidome biomarkers for non-small cell lung cancer (NSCLC)

AIM

To identify discriminating protein patterns in serum samples among non-small cell lung cancer (NSCLC), chronic obstructive pulmonary disease (COPD), pneumonia, and healthy controls. To discover specific low molecular weight (LMW) serum peptidome biomarkers and establish a diagnostic pattern for NSCLCby using proteomic technology.

METHODS

We used magnetic bead-based separation followed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) to identify patients with NSCLC, COPD, and pneumonia. A total of 154 serum samples were analyzed in this study, among which there were 60 serum samples from NSCLC patients, 30 from patients with other lung-related diseases (16 pneumonia patients and 14 patients with COPD) as disease controls, and 64 from healthy volunteers as healthy control. The mass spectra, analyzed using ClinProTools software, distinguished between cancer patients and healthy individuals based on GA algorithm model.

RESULTS

In this study, we generated numerous discriminating m/z peaks as well as disease-specific discrimination peaks. A set of five potential biomarkers (m/z: 7,763.24, 1,012.61, 4,153.16, 1,450.55, and 2,878.89) could be used as the diagnostic biomarkers to distinguish NSCLCpatients from healthy controls. In the training set, patients with NSCLC could be identified with sensitivity of 97.5% and specificity of 98.8%. Similar results were obtained in the testing set, showing 80.7% sensitivity and 91.2% specificity.

CONCLUSION

Our study demonstrated that a combined application of magnetic beads with MALDI-TOF MS technique was suitable for identification of serum biomarkers for NSCLC.

Protein analytical assays for diagnosing, monitoring, and choosing treatment for cancer patients

Cancer treatment is often hindered by inadequate methods for diagnosing the disease or insufficient predictive capacity regarding therapeutic efficacy. Targeted cancer treatments, including Bcr-Abl and EGFR kinase inhibitors, have increased survival for some cancer patients but are ineffective in other patients. In addition, many patients who initially respond to targeted inhibitor therapy develop resistance during the course of treatment. Molecular analysis of cancer cells has emerged as a means to tailor treatment to particular patients. While DNA analysis can provide important diagnostic information, protein analysis is particularly valuable because proteins are more direct mediators of normal and diseased cellular processes. In this review article, we discuss current and emerging protein assays for improving cancer treatment, including trends toward assay miniaturization and measurement of protein activity.

Serum peptidomic biomarkers for pulmonary metastatic melanoma identified by means of a nanopore-based assay

The significant mortality rate associated with metastatic melanoma, which exceeds the number of deaths attributed to the primary tumor, is primarily due to poor diagnosis and increased resistance to systemic therapy. Early detection and treatment of invasive melanoma are therefore crucial to increase survival rates. Low molecular weight proteins and peptides have garnered significant interest as biomarker candidates as they potentially represent a snap shot of pathological condition within the body and, by extension, the organism as a whole. We have developed a nanoporous silica-based platform to segregate the low molecular weight from the high molecular weight protein fraction to aid in the detection of peptides from serum samples using mass spectrometry. The combination of sample treatment with our platform, MALDI-TOF MS and following biostatistical analysis led to the discovery and identification of 27 peptides that are potential biomarkers associated with the development of pulmonary metastatic melanoma. We strongly believe our findings can assist to discover stage-specific peptide signatures and lead to more specific and personalized treatments for patients suffering from pulmonary metastatic melanoma.

Discovery of serum protein biomarkers in rheumatoid arthritis using MALDI-TOF-MS combined with magnetic beads

The aim of this study was to discover potential biomarkers for rheumatoid arthritis (RA) using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with magnetic beads. Proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS was used to profile and compare the proteomes in serum samples from 60 patients with RA, 35 patients with osteoarthritis and 36 healthy controls. The proteomic pattern associated with RA was identified by Biomarker Patterns Software. Model of biomarkers was constructed and evaluated through the Biomarker Patterns Software. A total of 33 discriminative peaks were identified to be related with RA, in which the 5 peaks with the mass-charge ratio (m/z) peaks at 15,715.5, 7,771.4, 8,959.4, 8,469.8 and 8,710.8 Da were used to construct a model for the diagnosis of RA by pattern recognition software. The blind testing data indicated a sensitivity of 86.7% and a specificity of 90.0% in RA diagnosis. These results demonstrated that potential protein biomarkers for RA could be discovered in serum by MALDI-TOF-MS combined with WCX magnetic beads. The diagnosis mode tree based on the five candidate biomarkers could provide a powerful and reliable diagnostic method for RA with high sensitivity and specificity.

Identification of a new protein biomarker for colorectal cancer diagnosis

As one of the most common cancers, colorectal cancer (CRC) is a major public health issue worldwide. Thus, the identification of novel biomarkers to aid in the early diagnosis of CRC is crucial. The aim of the present study was to identify a novel protein biomarker for CRC, and to identify its structure. In this study, a total of 99 serum samples from 73 CRC patients and 26 healthy controls were collected and analyzed by SELDI-TOF-MS. The biomarkers were separated using HPLC and detected with MALDI-TOF-MS. The qualified peaks were ranked by p-value of non-parametric tests and the top 10 peaks displaying significant differences were selected. Among the 10 protein biomarkers, the concentration of a 3.9‑kDa protein in the serum of the CRC patients was much lower than that in the healthy controls. Therefore, the 3.9‑kDa protein was selected as a biomarker for CRC and its separation and purification were performed. The structure of the 3.9-kDa protein biomarker was determined by LC-MS/MS, and was confirmed to be a fragment of serine/theonine kinase 4 (MST1/STK4). The 3.9‑kDa protein biomarker had high sensitivity and specificity for CRC, and its potential clinical application warrants further investigation.

Selective enrichment and sensitive detection of peptide and protein biomarkers in human serum using polymeric reverse micelles and MALDI-MS

Reverse-micelle forming amphiphilic homopolymers with carboxylic acid and quaternary amine substituents are used to selectively enrich biomarker peptides and protein fragments from human serum prior to matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS) analysis. After depletion of human serum albumin (HSA) and immunoglobulin G (IgG), low abundance peptide biomarkers can be selectively enriched and detected by MALDI-MS at clinically relevant concentrations by using the appropriate homopolymer(s) and extraction pH value(s). Three breast cancer peptide biomarkers, bradykinin, C4a, and ITIH(4), were chosen to test this new approach, and detection limits of 0.5 ng mL(-1), 0.08 ng mL(-1), and 0.2 ng mL(-1), respectively, were obtained. In addition, the amphiphilic homopolymers were used to detect prostate specific antigen (PSA) at concentrations as low as 0.5 ng mL(-1) by targeting a surrogate peptide fragment of this protein biomarker. Selective enrichment and sensitive MS detection of low abundance peptide/protein biomarkers by these polymeric reverse micelles should be a sensitive and straightforward approach for biomarker screening in human serum.

SELDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of a boosting decision tree model for diagnosis of pancreatic cancer

AIM

New technologies for the early detection of pancreatic cancer (PC) are urgently needed. The aim of the present study was to screen for the potential protein biomarkers in serum using proteomic fingerprint technology.

METHODS

Magnetic beads combined with surface-enhanced laser desorption/ionization (SELDI) TOF MS were used to profile and compare the protein spectra of serum samples from 85 patients with pancreatic cancer, 50 patients with acute-on-chronic pancreatitis and 98 healthy blood donors. Proteomic patterns associated with pancreatic cancer were identified with Biomarker Patterns Software.

RESULTS

A total of 37 differential m/z peaks were identified that were related to PC (P<0.01). A tree model of biomarkers was constructed with the software based on the three biomarkers (7762 Da, 8560 Da, 11654 Da), this showing excellent separation between pancreatic cancer and non-cancer., with a sensitivity of 93.3% and a specificity of 95.6%. Blind test data showed a sensitivity of 88% and a specificity of 91.4%.

CONCLUSIONS

The results suggested that serum biomarkers for pancreatic cancer can be detected using SELDI-TOF-MS combined with magnetic beads. Application of combined biomarkers may provide a powerful and reliable diagnostic method for pancreatic cancer with a high sensitivity and specificity.

Pituitary adenoma biomarkers identified using proteomic fingerprint technology

OBJECTIVE

To determine whether pituitary adenomas can be diagnosed by identifying protein biomarkers in the serum.

METHODS

We compared serum proteins from 65 pituitary adenoma patients and 90 healthy donors using proteomic fingerprint technology combining magnetic beads with matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS).

RESULTS

A total of 42 M/Z peaks were identified as related to pituitary adenoma (P<0.01). A diagnostic model established based on three biomarkers (3382.0, 4601.9, 9191.2) showed that the sensitivity of diagnosing pituitary adenoma was 90.0% and the specificity was 88.3%. The model was further tested by blind analysis showing that the sensitivity was 88.0% and the specificity was 83.3%.

CONCLUSIONS

These results suggest that proteomic fingerprint technology can be used to identify pituitary adenoma biomarkers and the model based on three biomarkers (3382.0, 4601.9, 9191.2) provides a powerful and reliable method for diagnosing pituitary adenoma.

Usefulness of serum mass spectrometry to identify women diagnosed with higher grades of cervical intraepithelial neoplasia may differ by race

BACKGROUND

An early detection of precursor lesions of cervical cancer will help to eliminate the worldwide burden of cervical cancer.

METHODS

This exploratory study aimed to identify, by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS), serum protein profiles that distinguish cervical intraepithelial neoplasia grades CIN 1 or lower (≤CIN 1) from CIN 2+ among 127 women infected with human papillomavirus (HPV) 16. Of these 127 women, 25 and 23 were diagnosed with CIN 2 or CIN 3, respectively (cases), and 79 were diagnosed with ≤CIN 1 (non-cases). Serum protein profiles were generated by MALDI-TOF-MS. A total of 95 m/z peaks were tested for association with case status by two racial groups, African American (AAs) and Caucasian American (CAs).

RESULTS

Overall, 2 protein peaks identified by our study demonstrated higher specificity for identifying CIN 2+ than previously published studies. An increasing intensity of [m/z 4459] was associated with a higher risk of being a case, regardless of race with a specificity of 58% for CIN 2 and a specificity of 75% for CIN 3. An increasing intensity of [m/z 4154] was not only associated with a higher risk of being a case only among CAs, but also had an opposite effect among AAs.

CONCLUSION

Identification of specific proteins associated with the peaks detected in serum and development of antibody-based tests such as ELISA should lead to the development of race-specific, non-invasive and cost effective screening tests with higher specificity for identifying HPV 16 associated CIN 2+.

Identification of serum biomarkers for ovarian cancer using MALDI-TOF-MS combined with magnetic beads

BACKGROUND

The objective of this study was to search for potential protein biomarkers in serum for diagnosis of ovarian cancer, by use of proteomic fingerprint techniques.

METHOD

MALDI-TOF-MS was combined with magnetic beads to profile and compare serum protein spectra from 40 ovarian cancer patients and from 60 healthy controls.

RESULTS

The tree analysis model of potential cancer biomarkers was constructed with Biomarker Patterns software on the basis of three identified biomarkers (5486, 6440, and 13720 Da), resulting in excellent discrimination between the ovarian cancer and non-cancer in our tests. The sensitivity was 90% and the specificity was 86.7%. In a blind test the sensitivity was 88% and the specificity was 83.3%.

CONCLUSION

The results suggested that biomarkers for ovarian cancer diagnosis in serum could be identified by MALDI-TOF-MS combined with the use of magnetic beads. The use of combined biomarkers would further enable powerful and reliable diagnosis of ovarian cancer with high sensitivity and specificity.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.