Plasma proteomics-based identification of novel biomarkers in early gastric cancer

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

Therapeutic Targets for Gastric Cancer: Mendelian Randomization and Colocalization Analysis

BACKGROUND

Gastric cancer (GC) is one of the most prevalent malignancies in the world. Most patients are diagnosed at advanced stages of the disease, primarily attributable to the insidious nature of early symptoms and the infrequent occurrence of routine screening. Further biomarkers are still needed for more comprehensive analysis, targeted prognostication, and effective treatment strategies. Plasma proteins are promising biomarkers and potential drug targets in GC. This study aims to identify potential therapeutic targets for GC by conducting a comprehensive proteome-wide Mendelian randomization (MR) and colocalization analyses.

METHODS

Plasma proteins were obtained from the UK Biobank Pharma Proteomics Project (UKB-PPP), including Genome-Wide Association Study(GWAS)data of 1463 plasma proteins. Genetic associations with cancer were derived from the European Bioinformatics Institute (EBI) database, including 1029 patients and 475,087 controls (dataset: ebi-a-gcst90018849). MR analysis was conducted to assess the association between plasma proteins and the risk of developing cancer. Additionally, colocalization analysis was employed to investigate whether the identified proteins and gastric cancer exhibited shared incidental variants. Finally, using the extensive Finnish database in the R9 version, the potential harmful effects of target proteins on the treatment of gastric cancer were explored through the whole phenomenon association study (PheWAS).

RESULT

The results showed that 15 proteins may be associated with the risk of gastric cancer, and one protein is expected to become a therapeutic target for gastric cancer. There was a positive genetic association between plasma levels of 11 proteins and increased GC risk, while 4 proteins exhibited an inverse association with GC risk (P < 0.05). Colocalization analysis revealed that PPCDC and GC exhibited shared genetic loci among the 15 proteins examined, indicating that PPCDC may serve as potential direct target for intervention in GC. Further phenotype wide association studies showed that PPCDC (P < 0.05) could be associated with certain potential side effects.

CONCLUSION

Our research examined the causal relationship between plasma proteins and gastric cancer, shedding light on potential therapeutic targets. These findings have significant implications for the development of early diagnostic markers and targeted therapies for GC, potentially improving patient outcomes and survival rates. Future studies should validate these findings in diverse populations and explore the clinical applications of these targets.

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study

BACKGROUND

Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis.

METHODS

To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis.

RESULTS

A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9.

CONCLUSION

These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

Gut microbiome, T cell subsets, and cytokine analysis identify differential biomarkers in tuberculosis

Introduction

The gut microbiota, T cell subsets, and cytokines participate in tuberculosis (TB) pathogenesis. To date, the mechanisms by which these factors interactively promote TB development at different time points remain largely unclear. In the context of this study, We looked into the microorganisms in the digestive tract, T cell types, and cytokines related to tuberculosis.

Methods

According to QIIME2, we analyzed 16SrDNA sequencing of the gut microbiome on the Illumina MiSeq. Enzyme-linked immunosorbent assay was used to measure the concentrations of cytokines.

Results

We showed the presence of 26 identifiable differential microbiomes in the gut and 44 metabolic pathways between healthy controls and the different time points in the development of TB in patients. Five bacterial genera (Bacteroides, Bifidobacterium, Faecalibacterium, Collinsella, and Clostridium) were most closely associated with CD4/CD8, whereas three bacterial taxa (Faecalibacterium, Collinsella, and Clostridium) were most closely associated with CD4. Three bacterial taxa (Faecalibacterium, Ruminococcus, and Dorea) were most closely associated with IL-4. Ruminococcus was most closely associated with IL-2 and IL-10.

Conclusion

Diverse microorganisms, subsets of T cells, and cytokines, exhibiting varying relative abundances and structural compositions, were observed in both healthy controls and patients throughout distinct phases of tuberculosis. Gaining insight into the function of the gut microbiome, T cell subsets, and cytokines may help modulate therapeutic strategies for TB.

Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery

Nanotechnology has emerged as a promising frontier in revolutionizing the early diagnosis and surgical management of gastric cancers. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high surgical and pharmacological therapy recurrence rates. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for detecting and treating cancer. Considering the limitations of GIC MRI and endoscopy and the complexity of gastric surgery, the early diagnosis and prompt treatment of gastric illnesses by nanotechnology has been a promising development. Nanoparticles directly target tumor cells, allowing their detection and removal. It also can be engineered to carry specific payloads, such as drugs or contrast agents, and enhance the efficacy and precision of cancer treatment. In this research, the boosting technique of machine learning was utilized to capture nonlinear interactions between a large number of input variables and outputs by using XGBoost and RNN-CNN as a classification method. The research sample included 350 patients, comprising 200 males and 150 females. The patients' mean ± SD was 50.34 ± 13.04 with a mean age of 50.34 ± 13.04. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.034), distant metastasis (P = 0.004), and tumor stage (P = 0.014) were shown to have a statistically significant link with GC patient survival. AUC was 93.54%, Accuracy 93.54%, F1-score 93.57%, Precision 93.65%, and Recall 93.87% when analyzing stomach pictures. Integrating nanotechnology with advanced machine learning techniques holds promise for improving the diagnosis and treatment of gastric cancer, providing new avenues for precision medicine and better patient outcomes.

Proteomic insights into mental health status: plasma markers in young adults

Global emphasis on enhancing prevention and treatment strategies necessitates an increased understanding of the biological mechanisms of psychopathology. Plasma proteomics is a powerful tool that has been applied in the context of specific mental disorders for biomarker identification. The p-factor, also known as the "general psychopathology factor", is a concept in psychopathology suggesting that there is a common underlying factor that contributes to the development of various forms of mental disorders. It has been proposed that the p-factor can be used to understand the overall mental health status of an individual. Here, we aimed to discover plasma proteins associated with the p-factor in 775 young adults in the FinnTwin12 cohort. Using liquid chromatography-tandem mass spectrometry, 13 proteins with a significant connection with the p-factor were identified, 8 of which were linked to epidermal growth factor receptor (EGFR) signaling. This exploratory study provides new insight into biological alterations associated with mental health status in young adults.

The Causal Relationship between PCSK9 Inhibitors and Malignant Tumors: A Mendelian Randomization Study Based on Drug Targeting

Objective: This study explores the potential causal association between proprotein convertase subtilisin/kexin 9 (PCSK9) inhibitors and tumor development using Mendelian randomization (MR) based on drug targets. Methods: Instrumental variables within ±100 kb of the PCSK9 gene locus, impacting low-density lipoprotein cholesterol (LDL-C), were utilized for MR analysis. Coronary heart disease (CHD) served as a positive control to validate the causal relationship between PCSK9 inhibitors and various cancers. We employed reverse MR to address the reverse causation concerns. Data from positive controls and tumors were sourced from OpenGWAS. Results: MR analysis suggested a negative causal relationship between PCSK9 inhibitors and both breast and lung cancers (95%CIBreast cancer 0.81~0.99, p = 2.25 × 10-2; 95%CILung cancer 0.65~0.94, p = 2.55 × 10-3). In contrast, a positive causal link was observed with gastric, hepatic, and oral pharyngeal cancers and cervical intraepithelial neoplasia (95%CIGastric cancer 1.14~1.75, p = 1.88 × 10-2; 95%CIHepatic cancer 1.46~2.53, p = 1.16 × 10-2; 95%CIOral cavity and pharyngeal cancer 4.49~6.33, p = 3.36 × 10-4; 95%CICarcinoma in situ of cervix uteri 4.56~7.12, p = 6.91 × 10-3), without heterogeneity or pleiotropy (p > 0.05). Sensitivity analyses confirmed these findings. The results of MR of drug targets suggested no causal relationship between PCSK9 inhibitors and bladder cancer, thyroid cancer, pancreatic cancer, colorectal cancer, malignant neoplasms of the kidney (except for renal pelvis tumors), malignant neoplasms of the brain, and malignant neoplasms of the esophagus (p > 0.05). Reverse MR helped mitigate reverse causation effects. Conclusions: The study indicates a divergent causal relationship of PCSK9 inhibitors with certain cancers. While negatively associated with breast and lung cancers, a positive causal association was observed with gastric, hepatic, oral cavity, and pharyngeal cancers and cervical carcinoma in situ. No causal links were found with bladder, thyroid, pancreatic, colorectal, certain kidney, brain, and esophageal cancers.

Targeting proprotein convertase subtilisin/kexin type 9 (PCSK9): from bench to bedside

Proprotein convertase subtilisin/kexin type 9 (PCSK9) has evolved as a pivotal enzyme in lipid metabolism and a revolutionary therapeutic target for hypercholesterolemia and its related cardiovascular diseases (CVD). This comprehensive review delineates the intricate roles and wide-ranging implications of PCSK9, extending beyond CVD to emphasize its significance in diverse physiological and pathological states, including liver diseases, infectious diseases, autoimmune disorders, and notably, cancer. Our exploration offers insights into the interaction between PCSK9 and low-density lipoprotein receptors (LDLRs), elucidating its substantial impact on cholesterol homeostasis and cardiovascular health. It also details the evolution of PCSK9-targeted therapies, translating foundational bench discoveries into bedside applications for optimized patient care. The advent and clinical approval of innovative PCSK9 inhibitory therapies (PCSK9-iTs), including three monoclonal antibodies (Evolocumab, Alirocumab, and Tafolecimab) and one small interfering RNA (siRNA, Inclisiran), have marked a significant breakthrough in cardiovascular medicine. These therapies have demonstrated unparalleled efficacy in mitigating hypercholesterolemia, reducing cardiovascular risks, and have showcased profound value in clinical applications, offering novel therapeutic avenues and a promising future in personalized medicine for cardiovascular disorders. Furthermore, emerging research, inclusive of our findings, unveils PCSK9's potential role as a pivotal indicator for cancer prognosis and its prospective application as a transformative target for cancer treatment. This review also highlights PCSK9's aberrant expression in various cancer forms, its association with cancer prognosis, and its crucial roles in carcinogenesis and cancer immunity. In conclusion, this synthesized review integrates existing knowledge and novel insights on PCSK9, providing a holistic perspective on its transformative impact in reshaping therapeutic paradigms across various disorders. It emphasizes the clinical value and effect of PCSK9-iT, underscoring its potential in advancing the landscape of biomedical research and its capabilities in heralding new eras in personalized medicine.

A Systems Biology Approach Unveils a Critical Role of DPP4 in Upper Gastrointestinal Cancer Patient Outcomes

Gastrointestinal (GI) cancers comprise of cancers that affect the digestive system and its accessory organs. The late detection and poor prognosis of GI cancer emphasizes the importance of identifying reliable and precise biomarkers for early diagnosis and prediction of prognosis. The membrane-bound glycoprotein dipeptidyl-peptidase 4 (DPP4), also known as CD26, is ubiquitously expressed and has a wide spectrum of biological roles. The role of DPP4/CD26 in tumor progression in different types of cancers remains elusive. However, the link between DPP4 and tumor-infiltrating cells, as well as its prognostic significance in malignancies, still require further investigation. This study was intended to elucidate the correlation of DPP4 expression and survival along with prognosis, followed by its associated enriched molecular pathways and immune cell marker levels in upper GI cancers. Results demonstrated a strong correlation between increased DPP4 expression and a worse prognosis in esophageal and gastric cancer and the co-expressed common genes with DPP4 were associated with crucial molecular pathways involved in tumorigenesis. Additionally, DPP4 was shown to be significantly linked to several immune infiltrating cell marker genes, including Macrophages (M1, M2 and Tumor Associated Macrophages), neutrophils, Treg, T-cell exhaustion, Th1 and Th2. Overall, our findings suggest that DPP4 may serve as a substantial prognostic biomarker, a possible therapeutic target, as well as it can play a critical role in the regulation of immune cell invasion in patients with gastroesophageal (esophageal, gastroesophageal junction and gastric) cancer. KEY WORDS: DPP4, integrated analysis, GI cancer, gastroesophageal cancer, gastroesophageal junction, prognosis.

Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. Several studies have been conducted to investigate early biomarkers of gastric cancer using genomics, transcriptomics, proteomics, and metabolomics, respectively. However, endogenous substances associated with various omics are concurrently altered during gastric cancer development. Furthermore, environmental exposures and family history can also induce modifications in endogenous substances. Therefore, in this study, we primarily investigated alterations in DNA mutation, DNA methylation, mRNA, lncRNA, miRNA, circRNA, and protein, as well as glucose, amino acid, nucleotide, and lipid metabolism levels in the context of GC development, employing genomics, transcriptomics, proteomics, and metabolomics. Additionally, we elucidate the impact of exposure factors, including HP, EBV, nitrosamines, smoking, alcohol consumption, and family history, on diagnostic biomarkers of gastric cancer. Lastly, we provide a summary of the application of machine learning in integrating multi-omics data. Thus, this review aims to elucidate: i) the biomarkers of gastric cancer related to genomics, transcriptomics, proteomics, and metabolomics; ii) the influence of environmental exposure and family history on multiomics data; iii) the integrated analysis of multi-omics data using machine learning techniques.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

Urinary Proteomics for Discovery of Gastric Cancer Biomarkers to Enable Precision Clinical Oncology

For precision in clinical oncology practice, detection of tumor-derived peptides and proteins in urine offers an attractive and noninvasive alternative for diagnostic or screening purposes. In this study, we report comparative quantitative proteomic profiling of urine samples from patients with gastric cancer and healthy controls using tandem mass tags-based multiplexed mass spectrometry approach. We identified 1504 proteins, of which 246 were differentially expressed in gastric cancer cases. Notably, ephrin A1 (EFNA1), pepsinogen A3 (PGA3), sortilin 1 (SORT1), and vitronectin (VTN) were among the upregulated proteins, which are known to play crucial roles in the progression of gastric cancer. We also found other overexpressed proteins, including shisa family member 5 (SHISA5), mucin like 1 (MUCL1), and leukocyte cell derived chemotaxin 2 (LECT2), which had not previously been linked to gastric cancer. Using a novel approach for targeted proteomics, SureQuant, we validated changes in abundance of a subset of proteins discovered in this study. We confirmed the overexpression of vitronectin and sortilin 1 in an independent set of urine samples. Altogether, this study provides molecular candidates for biomarker development in gastric cancer, and the findings also support the promise of urinary proteomics for noninvasive diagnostics and personalized/precision medicine in the oncology clinic.

Bovine Omasum-Inspired Interfacial Carbon-Based Nanocomposite for Saliva Metabolic Screening of Gastric Cancer

Gastric cancer is one of the most common malignant digestive cancers, and its diagnostic has still faced challenges based on metabolic analysis due to complex sample pretreatment and low metabolite abundance. In this study, inspired by the structure of bovine omasum, we in situ synthesized a novel interfacial carbon-based nanocomposite of graphene supported nickel nanoparticles-encapsulated in the nitrogen-doped carbon nanotube (Ni/N-CNT/rGO), which was served as a novel matrix with enhanced ionization efficiency for the matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) saliva metabolic analysis of gastric cancer. Benefiting from its high sp² graphitic degree, large surface area, strong UV absorption, and rich active sites, Ni/N-CNT/rGO matrix exhibited excellent performances of reproducibility, coverage, salt-tolerance, sensitivity, and adsorption ability in MALDI-TOF MS. The differential scanning calorimetry (DSC) and thermal conversion behaviors explained the highly efficient LDI mechanism. Based on saliva metabolic fingerprints, Ni/N-CNT/rGO assisted LDI MS with cross-validation analysis could successfully distinguish gastric cancer patients from healthy controls through the screening of four potential biomarkers with an accuracy of 92.50%, specificity of 88.03%, and sensitivity of 97.12%. This work provided a fast and sensitive MS sensing platform for the metabolomics characterization of gastric cancer and might have potential value for precision medicine in the future.

The evolving landscape of PCSK9 inhibition in cancer

Cancer is a disease with a significant global burden in terms of premature mortality, loss of productivity, healthcare expenditures, and impact on mental health. Recent decades have seen numerous advances in cancer research and treatment options. Recently, a new role of cholesterol-lowering PCSK9 inhibitor therapy has come to light in the context of cancer. PCSK9 is an enzyme that induces the degradation of low-density lipoprotein receptors (LDLRs), which are responsible for clearing cholesterol from the serum. Thus, PCSK9 inhibition is currently used to treat hypercholesterolemia, as it can upregulate LDLRs and enable cholesterol reduction through these receptors. The cholesterol-lowering effects of PCSK9 inhibitors have been suggested as a potential mechanism to combat cancer, as cancer cells have been found to increasingly rely on cholesterol for their growth needs. Additionally, PCSK9 inhibition has demonstrated the potential to induce cancer cell apoptosis through several pathways, increase the efficacy of a class of existing anticancer therapies, and boost the host immune response to cancer. A role in managing cancer- or cancer treatment-related development of dyslipidemia and life-threatening sepsis has also been suggested. This review examines the current evidence regarding the effects of PCSK9 inhibition in the context of different cancers and cancer-associated complications.

Novel urinary protein panels for the non-invasive diagnosis of non-alcoholic fatty liver disease and fibrosis stages

BACKGROUND & AIMS

There is an unmet clinical need for non-invasive tests to diagnose non-alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2).

METHODS

We collected urine samples from 100 patients with biopsy-confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy-confirmed NAFLD and 45 healthy controls, urinary enzyme-linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models.

RESULTS

The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725-1.000) and 0.858 (95% CI: .712-1.000) in the training set; and .837 (95% CI: .711-.963) and .916 (95% CI: .825-1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80.

CONCLUSIONS

Our newly developed models constructed from urine protein biomarkers have good accuracy for non-invasively diagnosing liver fibrosis in NAFLD.

Novel biomarkers for early detection of gastric cancer

Gastric cancer (GC) remains a leading cause of cancer-related death worldwide. Less than half of GC cases are diagnosed at an advanced stage due to its lack of early symptoms. GC is a heterogeneous disease associated with a number of genetic and somatic mutations. Early detection and effective monitoring of tumor progression are essential for reducing GC disease burden and mortality. The current widespread use of semi-invasive endoscopic methods and radiologic approaches has increased the number of treatable cancers: However, these approaches are invasive, costly, and time-consuming. Thus, novel molecular noninvasive tests that detect GC alterations seem to be more sensitive and specific compared to the current methods. Recent technological advances have enabled the detection of blood-based biomarkers that could be used as diagnostic indicators and for monitoring postsurgical minimal residual disease. These biomarkers include circulating DNA, RNA, extracellular vesicles, and proteins, and their clinical applications are currently being investigated. The identification of ideal diagnostic markers for GC that have high sensitivity and specificity would improve survival rates and contribute to the advancement of precision medicine. This review provides an overview of current topics regarding the novel, recently developed diagnostic markers for GC.

Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for high-throughput proteomic research. Limited by the peak capacity, the separation performance of conventional single-dimensional LC hampers the development of proteomics. Combining different separation modes orthogonally, multidimensional liquid chromatography (MDLC) with high peak capacity was developed to address this challenge. MDLC has evolved rapidly since its establishment, and the progress of proteomics has been greatly facilitated by the advent of novel MDLC-MS-based methods. In this paper, we will review the advances of MDLC-MS-based methodologies and technologies in proteomics studies, from different perspectives including novel application scenarios and proteomic targets, automation, miniaturization, and the improvement of the classic methods in recent years. In addition, attempts regarding new MDLC-MS models are also mentioned together with the outlook of MDLC-MS-based proteomics methods.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors

BACKGROUND

Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors.

METHODS

This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six  ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models' performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation.

RESULTS

This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer.

CONCLUSIONS

The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Tandem mass tagging combined with liquid chromatography-tandem mass spectrometry technique to detect protein markers in gastroesophageal junction adenocarcinoma

BACKGROUND

Gastroesophageal junction adenocarcinoma (GEJA) is a malignant tumor located at the junction of the esophagus and stomach, the incidence of which is increasing year by year, while screening for early biomarkers is limited. Tandem mass tagging (TMT) coupled with liquid chromatography-tandem mass spectrometry (LC/MS/MS) has been used to screen for differential proteins in various cancers.

METHODS

Differential proteins in GEJA and precancerous lesions were screened using TMT-LC/MS/MS, and then proteins that met expectations were selected for trend clustering analysis, combined with GO and KEGG analysis for functional annotation of differential proteins in GEJA. Then, parallel reaction monitoring and immunohistochemistry techniques were used to validate the accuracy of the proteomics data.

RESULTS

Our group screened the differential proteins during GEJA progression using proteomics technology, analyzed the expression trends and functional regions involved in the differential proteins during carcinogenesis, and validated the accuracy of the experimental results.

CONCLUSIONS

The screening of differential proteins in GEJA carcinogenesis based on TMT-LC/MS/MS technology provides detailed information for the elucidation of GEJA progression process, pathogenesis, early screening and screening of candidate markers.

alpha2,3 sialic acid processing enzymes expression in gastric cancer tissues reveals that ST3Gal3 but not Neu3 are associated with Lauren's classification, angiolymphatic invasion and histological grade

Gastric cancer (GC) is one of the leading causes of cancer-related deaths worldwide. Despite progress in the last decades, there are still no reliable biomarkers for the diagnosis of and prognosis for GC. Aberrant sialylation is a widespread critical event in the development of GC. Neuraminidases (Neu) and sialyltransferases (STs) regulate the ablation and addition of sialic acid during glycoconjugates biosynthesis, and they are a considerable source of biomarkers in various cancers. This study retrospectively characterized Neu3 and ST3Gal3 expression by immunohistochemistry in 71 paraffin-embedded GC tissue specimens and analyzed the relationship between their expression and the clinicopathological parameters. Neu3 expression was markedly increased in GC tissues compared with non-tumoral tissues (p<0.0001). Intratumoral ST3Gal3 staining was significantly associated with intestinal subtype (p=0.0042) and was negatively associated with angiolymphatic invasion (p=0.0002) and higher histological grade G3 (p=0.0066). Multivariate analysis revealed that ST3Gal3 positivity is able to predict Lauren's classification. No associations were found between Neu3 staining and clinical parameters. The in silico analysis of mRNA expression in GC validation cohorts corroborates the significant ST3Gal3 association with higher histological grade observed in our study. These findings suggest that ST3Gal3 expression may be an indicator for aggressiveness of primary GC.

Improved profiling of low molecular weight serum proteome for gastric carcinoma by data-independent acquisition

Low molecular weight proteins (LMWPs) in the bloodstream participate in various biological processes and are closely associated with disease status, whereas identification of serous LMWPs remains a great technical challenge due to the wide dynamic range of protein components. In this study, we constructed an integrated LMWP library by combining the LMWPs obtained by three enrichment methods (50% ACN, 20% ACN + 20 mM ABC, and 30 kDa) and their fractions identified by the data-dependent acquisition method. With this newly constructed library, we comprehensively profiled LMWPs in serum using data-independent acquisition and reliably achieved quantitative results for 75% serous LMWPs. When applying this strategy to quantify LMWPs in human serum samples, we could identify 405 proteins on average per sample, of which 136 proteins were with a MW less than 30 kDa and 293 proteins were with a MW less than 65 kDa. Of note, pre- and post-operative gastric carcinoma (GC) patients showed differentially expressed serous LWMPs, which was also different from the pattern of LWMP expression in healthy controls. In conclusion, our results showed that LMWPs could efficiently distinguish GC patients from healthy controls as well as between pre- and post-operative statuses, and more importantly, our newly developed LMWP profiling platform could be used to discover candidate LMWP biomarkers for disease diagnosis and status monitoring.

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.

Insights on Proteomics-Driven Body Fluid-Based Biomarkers of Cervical Cancer

Cervical cancer is one of the top malignancies in women around the globe, which still holds its place despite being preventable at early stages. Gynecological conditions, even maladies like cervical cancer, still experience scrutiny from society owing to prevalent taboo and invasive screening methods, especially in developing economies. Additionally, current diagnoses lack specificity and sensitivity, which prolong diagnosis until it is too late. Advances in omics-based technologies aid in discovering differential multi-omics profiles between healthy individuals and cancer patients, which could be utilized for the discovery of body fluid-based biomarkers. Body fluids are a promising potential alternative for early disease detection and counteracting the problems of invasiveness while also serving as a pool of potential biomarkers. In this review, we will provide details of the body fluids-based biomarkers that have been reported in cervical cancer. Here, we have presented our perspective on proteomics for global biomarker discovery by addressing several pertinent problems, including the challenges that are confronted in cervical cancer. Further, we also used bioinformatic methods to undertake a meta-analysis of significantly up-regulated biomolecular profiles in CVF from cervical cancer patients. Our analysis deciphered alterations in the biological pathways in CVF such as immune response, glycolytic processes, regulation of cell death, regulation of structural size, protein polymerization disease, and other pathways that can cumulatively contribute to cervical cancer malignancy. We believe, more extensive research on such biomarkers, will speed up the road to early identification and prevention of cervical cancer in the near future.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

Proteomic characterization of serum proteins from Atlantic salmon (Salmo salar L.) from an outbreak with cardiomyopathy syndrome

Cardiomyopathy syndrome (CMS), caused by piscine myocarditis virus (PMCV), is a serious challenge to Atlantic salmon (Salmo salar L.) aquaculture. Regrettably, husbandry techniques are the only tool to manage CMS outbreaks, and no prophylactic measures are available at present. Early diagnosis of CMS is therefore desirable, preferably with non-lethal diagnostic methods, such as serum biomarkers. To identify candidate biomarkers for CMS, the protein content of pools of sera (4 fish/pool) from salmon with a CMS outbreak (3 pools) and from clinically healthy salmon (3 pools) was compared using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Overall, seven proteins were uniquely identified in the sera of clinically healthy fish, while 27 proteins were unique to the sera of CMS fish. Of the latter, 24 have been associated with cardiac disease in humans. These were grouped as leakage enzymes (creatine kinase, lactate dehydrogenase, glycogen phosphorylase and carbonic anhydrase); host reaction proteins (acute-phase response proteins-haptoglobin, fibrinogen, α2-macroglobulin and ceruloplasmin; and complement-related proteins); and regeneration/remodelling proteins (fibronectin, lumican and retinol). Clinical evaluation of the suitability of these proteins as biomarkers of CMS, either individually or as part of a panel, is a logical next step for the development of early diagnostic tools for CMS.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Biomarkers for Gastric Cancer Screening and Early Diagnosis

Gastric cancer is one of the most common cancers worldwide, with a bad prognosis associated with late-stage diagnosis, significantly decreasing the overall survival. This highlights the importance of early detection to improve the clinical course of these patients. Although screening programs, based on endoscopic or radiologic approaches, have been useful in countries with high incidence, they are not cost-effective in low-incidence populations as a massive screening strategy. Additionally, current biomarkers used in daily routine are not specific and sensitive enough, and most of them are obtained invasively. Thus, it is imperative to discover new noninvasive biomarkers able to diagnose early-stage gastric cancer. In this context, liquid biopsy is a promising strategy. In this review, we briefly discuss some of the potential biomarkers for gastric cancer screening and diagnosis identified in blood, saliva, urine, stool, and gastric juice.

Proprotein convertase subtilisin/kexin type 9 (PCSK9): A potential multifaceted player in cancer

Proprotein convertase subtilisin/kexin type 9 (PCSK9) has emerged as a novel pharmacological target for hypercholesterolemia and associated cardiovascular diseases owing to its function to mediate the degradation of low-density lipoprotein receptor (LDLR). Findings over the past two decades have identified novel binding partners and cellular functions of PCSK9. Notably, PCSK9 is aberrantly expressed in a broad spectrum of cancers and apparently contributes to disease prognosis, indicating that PCSK9 could be a valuable cancer biomarker. Experimental studies demonstrate the contribution of PCSK9 in various aspects of cancer, including cell proliferation, apoptosis, invasion, metastasis, anti-tumor immunity and radioresistance, strengthening the idea that PCSK9 could be a promising therapeutic target. Here, we comprehensively review the involvement of PCSK9 in cancer, summarizing its aberrant expression, association with disease prognosis, biological functions and underlying mechanisms in various malignancies. Besides, we highlight the potential of PCSK9 as a future therapeutic target in personalized cancer medicine.

Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma

BACKGROUND

African American/Black adults have a disproportionate incidence of Alzheimer's disease (AD) and are underrepresented in biomarker discovery efforts.

OBJECTIVE

This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults.

METHODS

We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates.

RESULTS

In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD.

CONCLUSION

These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.

Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics

Background

The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.

Aims

To construct a predictive model for the diagnosis of gastric cancer with high accuracy based on noninvasive characteristics.

Methods

A retrospective study of 709 patients at Zhejiang Provincial People's Hospital was conducted. Variables of age, gender, blood cell count, liver function, kidney function, blood lipids, tumor markers and pathological results were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the accuracy of the model.

Results

Of the 709 patients, 398 were diagnosed with gastric cancer; 311 were health people or diagnosed with benign gastric disease. Multivariate analysis showed that gender, age, neutrophil lymphocyte ratio, hemoglobin, albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) were independent characteristics associated with gastric cancer. We constructed a predictive model using GBDT, and the area under the receiver operating characteristic curve (AUC) of the model was 91%. For the test dataset, sensitivity was 87.0% and specificity 84.1% at the optimal threshold value of 0.56. The overall accuracy was 83.0%. Positive and negative predictive values were 83.0% and 87.8%, respectively.

Conclusion

We construct a predictive model to diagnose gastric cancer with high sensitivity and specificity. The model is noninvasive and may reduce the medical cost.

Identification of salivary volatile organic compounds as potential markers of stomach and colorectal cancer: A pilot study

OBJECTIVES

The purpose of the pilot study was to determine the potential diagnostic capabilities for the analysis of oxygen-containing salivary volatile organic compounds (VOCs) in stomach and colorectal cancer.

METHODS

Saliva samples of 11 patients with stomach cancer, 18 patients with colorectal cancer, and 16 healthy volunteers were analyzed through capillary gas chromatography. The levels of lipid peroxidation products and catalase activity were determined in all samples. To assess saliva diagnostic potential, we constructed a Classification and Regression Tree (CART).

RESULTS

It was shown that the use of a combination of saliva VOCs (acetaldehyde, acetone, propanol-2, and ethanol) allowed classification into Cancer/Control groups with a sensitivity and specificity of 95.7 and 90.9%, respectively. To clarify the location of the tumor, it was necessary to add a methanol level; in this case, the sensitivity for detecting stomach and colorectal cancer was 80.0% and 92.3%, respectively, while the specificity in both cases was 100%. When the lipid peroxidation product content was added to the VOC indicators, they were selected as the main factors for constructing the decision tree. For classification into Cancer/Control groups, only the triene conjugate and Schiff base content in saliva was sufficient. The combination of VOCs in saliva and lipid peroxidation indices improved the sensitivity and specificity for classification to 100%.

CONCLUSION

Preliminary data were obtained on the sensitivity and specificity of the diagnosis of stomach and colorectal cancer, which confirmed the promise of further studies on saliva VOCs for the purpose of clinical laboratory diagnostics.