Serum biomarker panels for the diagnosis of gastric adenocarcinoma

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Explainable machine learning models for early gastric cancer diagnosis

Gastric cancer remains a significant global health concern, with a notably high incidence in East Asia. This paper explores the potential of explainable machine learning models in enhancing the early diagnosis of gastric cancer. Through comprehensive evaluations, various machine learning models, including WeightedEnsemble, CatBoost, and RandomForest, demonstrated high potential in accurately diagnosing early gastric cancer. The study emphasizes the importance of model explainability in medical diagnostics, showing how transparent, explainable models can increase trust and clinical acceptance, thereby improving diagnostic accuracy and patient outcomes. This research not only highlights key biomarkers and clinical features critical for early detection but also presents a versatile approach that could be applied to other medical diagnostics, promoting broader adoption of machine learning in clinical settings.

PG I and PG II show unique value in diagnosing postoperative biochemical recurrence in patients with gastric cancer after total gastrectomy

OBJECTIVE

To investigate the potential of group I pepsinogen (PG I) and group II pepsinogen (PG II) as diagnostic markers for recurrence in gastric cancer (GC) patients post-total gastrectomy.

METHODS

Ninety-six patients who underwent total gastrectomy for GC between June 2022 and June 2023 were included in this study. Clinical data, serum samples, and ascites samples were collected. Patients were categorized based on recurrence status at the time of sample collection and the primary tumor site. PG I and PG II levels were determined using a chemiluminescent immunoassay, and their clinical utility following total gastrectomy for GC was evaluated via receiver operating characteristic (ROC) curve analysis.

RESULTS

This study included 96 GC patients who underwent total gastrectomy, 55 of whom experienced postoperative recurrence (57.29%). The levels of serum PG I (27.86 (27.04, 30.97) vs. 26.05 (24.16, 27.09) ng/mL; P < 0.0001) and PG II (1.95 (1.23, 3.05) vs. 0.63 (0.47, 0.90) ng/mL; P < 0.0001) were significantly greater in the recurrent group compared to the non-recurrent group. The secretion of PG I and/or PG II by metastatic cancer cells correlated with the primary lesion site. When the cut-off value for serum PG I was 26.93 ng/mL, the area under the curve (AUC) for PG I was 0.77. When the cut-off value for serum PG II was 0.96 ng/mL, the AUC reached 0.90. The combined AUC was 0.97.

CONCLUSION

These findings suggest that serum PG I and PG II are valuable biomarkers for identifying GC patients with biochemical recurrence post-total gastrectomy.

Good or bad: Paradox of plasminogen activator inhibitor 1 (PAI-1) in digestive system tumors

The fibrinolytic system is involved in many physiological functions, among which the important members can interact with each other, either synergistically or antagonistically to participate in the pathogenesis of many diseases. Plasminogen activator inhibitor 1 (PAI-1) acts as a crucial element of the fibrinolytic system and functions in an anti-fibrinolytic manner in the normal coagulation process. It inhibits plasminogen activator, and affects the relationship between cells and extracellular matrix. PAI-1 not only involved in blood diseases, inflammation, obesity and metabolic syndrome but also in tumor pathology. Especially PAI-1 plays a different role in different digestive tumors as an oncogene or cancer suppressor, even a dual role for the same cancer. We term this phenomenon "PAI-1 paradox". PAI-1 is acknowledged to have both uPA-dependent and -independent effects, and its different actions can result in both beneficial and adverse consequences. Therefore, this review will elaborate on PAI-1 structure, the dual value of PAI-1 in different digestive system tumors, gene polymorphisms, the uPA-dependent and -independent mechanisms of regulatory networks, and the drugs targeted by PAI-1 to deepen the comprehensive understanding of PAI-1 in digestive system tumors.

Real-World Practice of Gastric Cancer Prevention and Screening Calls for Practical Prediction Models

INTRODUCTION

Some gastric cancer prediction models have been published. Still, the value of these models for application in real-world practice remains unclear. We aim to summarize and appraise modeling studies for gastric cancer risk prediction and identify potential barriers to real-world use.

METHODS

This systematic review included studies that developed or validated gastric cancer prediction models in the general population.

RESULTS

A total of 4,223 studies were screened. We included 18 development studies for diagnostic models, 10 for prognostic models, and 1 external validation study. Diagnostic models commonly included biomarkers, such as Helicobacter pylori infection indicator, pepsinogen, hormone, and microRNA. Age, sex, smoking, body mass index, and family history of gastric cancer were frequently used in prognostic models. Most of the models were not validated. Only 25% of models evaluated the calibration. All studies had a high risk of bias, but over half had acceptable applicability. Besides, most studies failed to clearly report the application scenarios of prediction models.

DISCUSSION

Most gastric cancer prediction models showed common shortcomings in methods, validation, and reports. Model developers should further minimize the risk of bias, improve models' applicability, and report targeting application scenarios to promote real-world use.

Painless and sensitive pepsinogen I detection: an electrochemical immunosensor based on rhombic dodecahedral Cu3Pt and MoS2 NFs

Gastric cancer (GC) is a common malignant tumour of the digestive tract with a high mortality rate worldwide. However, many patients delay treatment due to the avoidance of the costly and painful procedure of gastroscopy. Therefore, an early convenient screening method is essential to improve the survival rate of GC patients. To address this issue, we constructed an electrochemical immunosensor supported by rhombohedral Cu3Pt and MoS2 nanoflowers (MoS2 NFs) for rapid, painless and quantitative detection of the GC biomarker in vitro. Here, pepsinogen I was employed as a model protein biomarker to analyse the performance of the immunosensor. The rhombohedral dodecahedral Cu3Pt nanoparticles decorated with MoS2-NFs were further functionalized; this allowed the constructed sensor to possess more nano- or micro-structures, thereby improving the detection sensitivity. In specific applications, the corresponding bioactive molecules can be flexibly captured. Under optimal conditions, the immunoassay showed a wide linear range from 500 pg mL-1 to 400 ng mL-1 and a low detection limit of 167 pg mL-1 (S/N = 3). This covers the critical value of 70 ng mL-1, and the results obtained from the analysis of human serum samples were on par with those from the enzyme immunoassay, suggesting significant potential for this new method in daily diagnosis.

Multiple biomarkers are more accurate than a combination of carbohydrate antigen 125 and human epididymis protein 4 for ovarian cancer screening

Objective

The objective of this study was to compare and evaluate the diagnostic value of serum carbohydrate antigen 125 (CA125) and/or human epididymis protein 4 (HE4) and a panel of novel multiple biomarkers in patients with ovarian tumors to identify more accurate and effective markers for screening ovarian cancer.

Methods

Candidate ovarian cancer biomarkers were selected based on a literature search. Dozens of candidate biomarkers were examined using 143 serum samples from patients with ovarian cancer and 157 healthy serum samples as non-cancer controls. To select the optimal marker panel for an ovarian cancer classification model, a set of biomarker panels was created with the number of possible combinations of 8 biomarkers. Using the set of biomarkers as an input variable, the optimal biomarker panel was selected by examining the performance of the biomarker panel set using the Random Forest algorithm as a non-linear classification method and a 10-fold cross-validation technique.

Results

The final selected optimal combination of five biomarkers (CA125, HE4, CA15.3, ApoA1, and ApoA2) exhibited a sensitivity of 93.71% and specificity of 93.63% for ovarian cancer detection during validation.

Conclusion

Combining multiple biomarkers is a valid strategy for ovarian cancer diagnosis and can be used as a minimally invasive screening method for early ovarian cancer. A panel of five optimal biomarkers, including CA125 and HE4, was verified in this study. These can potentially be used as clinical biomarkers for early detection of ovarian cancer.

The diagnostic value of interleukin 6 as a biomarker for gastric cancer: A meta-analysis and systematic review

BACKGROUND

Gastric cancer is one of the most common cancers and a main cause of global cancer death. The expression of interleukin 6 is associated with the risk of gastric cancer. But the diagnostic accuracy of interleukin 6 remains unclear. This study was designed to assess the diagnostic performance of interleukin 6 in gastric cancer diagnosis.

METHODS

The related data was obtained from Oncomine and studied using bioinformatics analysis. The PubMed, Embase, Cochrane Library, Web of science databases were searched for related studies published from inception to July 14, 2020. Measuring tools of diagnostic performance including sensitivity, specificity, and diagnostic odds ratio were pooled using bivariate mixed-effects meta-analysis model. The summery receiver operator characteristic curves were plotted.

RESULTS

The result from Oncomine showed that the expression of interleukin 6 in gastric cancer (GC) patients was higher than the normal groups (P < .05). Furthermore, a total of 4 eligible articles were enrolled, containing 390 cases and 404 controls. The diagnostic results were as follows: a sensitivity of 0.80 (95% confidence interval [CI] 0.57-0.92), a specificity of 0.86 (95% CI 0.74-0.93), a positive likelihood ratio of 5.76 (95% CI 3.49-9.49), a negative likelihood ratio of 0.23 (95% CI 0.11-0.51) and a diagnostic odds ratio of 24.58 (95% CI 14.14-42.73). The summary area under the receiver operating characteristic curves was 0.90 (95% CI 0.87-0.93).

CONCLUSION

Higher interleukin 6 expression was detected in GC patients, and interleukin 6 could be a helpful indicator of diagnosis of gastric cancer. Further large-scale prospective studies are required for identifying the diagnostic value of interleukin 6 in gastric cancer.

Diagnostic value of combining tumor and inflammatory biomarkers in detecting common cancers in Korea

BACKGROUND

The ultimate goal of cancer screening is to diagnose invasive cancers early, while they are still curable. We aimed to validate the diagnostic value of blood-derived protein biomarkers that we developed for six common cancer in Korea.

METHODS

We have discovered 12 protein biomarkers that are useful in differentiating cancer patients from healthy controls using two-dimensional gel electrophoresis (2-DE), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), and literature review. Cancer patients (stomach, colon, liver, lung, breast, and prostate) and control subjects were collected and tested data sets were used to generate predictive models that identify risk scores for each cancer. The validation study was done in serum samples of an independent patient cohort Receiver operating characteristic (ROC) analyses were conducted to evaluate the diagnostic performance of the biomarkercombinations.

RESULTS

The AUCs of the model in the test set were 0.971, 0.960, 0.969, 0.942, 0.834, and 0.985 for stomach, colon, liver, lung, breast, and prostate cancer, respectively.

CONCLUSIONS

Combining multiple tumor and systemic inflammatory biomarkers proved to be a valid strategy in the diagnosis of six common cancers in Korea. Further validation of appropriate screening populations through large-scale clinical trials are warranted.

Multiplexed measurement of candidate blood protein biomarkers of heart failure

AIMS

There is a critical need for better biomarkers so that heart failure can be diagnosed at an earlier stage and with greater accuracy. The purpose of this study was to design a robust mass spectrometry (MS)-based assay for the simultaneous measurement of a panel of 35 candidate protein biomarkers of heart failure, in blood. The overall aim was to evaluate the potential clinical utility of this biomarker panel for prediction of heart failure in a cohort of 500 patients.

METHODS AND RESULTS

Multiple reaction monitoring (MRM) MS assays were designed with Skyline and Spectrum Mill PeptideSelector software and developed using nanoflow reverse phase C18 chromatographic Chip Cube-based separation, coupled to a 6460 triple quadrupole mass spectrometer. Optimized MRM assays were applied, in a sample-blinded manner, to serum samples from a cohort of 500 patients with heart failure and non-heart failure (non-HF) controls who had cardiovascular risk factors. Both heart failure with reduced ejection fraction (HFrEF) patients and heart failure with preserved ejection fraction (HFpEF) patients were included in the study. Peptides for the Apolipoprotein AI (APOA1) protein were the most significantly differentially expressed between non-HF and heart failure patients (P = 0.013 and P = 0.046). Four proteins were significantly differentially expressed between non-HF and the specific subtypes of HF (HFrEF and HFpEF); Leucine-rich-alpha-2-glycoprotein (LRG1, P < 0.001), zinc-alpha-2-glycoprotein (P = 0.005), serum paraoxanse/arylesterase (P = 0.013), and APOA1 (P = 0.038). A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90. This was a notable improvement on predictive capacity of BNP measurements alone, which achieved 77.1% accuracy and an AUC of 0.86 (P = 0.005). The protein peptides for LRG1, which contributed most significantly to model performance, were significantly associated with future new onset HF in the non-HF cohort [Peptide 1: odds ratio (OR) 2.345 95% confidence interval (CI) (1.456-3.775) P = 0.000; peptide 2: OR 2.264 95% CI (1.422-3.605), P = 0.001].

CONCLUSIONS

This study has highlighted a number of promising candidate biomarkers for (i) diagnosis of heart failure and subtypes of heart failure and (ii) prediction of future new onset heart failure in patients with cardiovascular risk factors. Furthermore, this study demonstrates that multiplexed measurement of a combined biomarker signature that includes BNP is a more accurate predictor of heart failure than BNP alone.

A Deep Learning-Based Tumor Classifier Directly Using MS Raw Data

Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), large-scale mass spectrometry (MS) based proteomic profiling of different kinds of human tumor samples have provided huge amount of valuable data for both basic and clinical researchers. Accurate prediction for tumor and non-tumor samples, as well as the tumor types has become a key step for biological and medical research, such as biomarker discovery, diagnosis, and monitoring of diseases. The traditional MS-based classification strategy mainly depends on the identification and quantification results of MS data, which has some inherent limitations, such as the low identification rate of MS data. Here, a deep learning-based tumor classifier directly using MS raw data is proposed, which is independent of the identification and quantification results of MS data. The potential precursors with intensities and retention times from MS data as input is first detected and extracted. Then, a deep learning-based classifier is trained, which can accurately distinguish between the tumor and non-tumor samples. Finally, it is demonstrated the deep learning-based classifier has a good performance compared with other machine learning methods and may help researchers find the potential biomarkers which are likely to be missed by the traditional strategy.

Biomarker Panel for the Diagnosis of Pancreatic Ductal Adenocarcinoma

A single tumor marker has a low diagnostic value in pancreatic cancer. Combinations of multiple biomarkers and unique analysis algorithms can be applied to overcome these limitations. This study sought to develop diagnostic algorithms using multiple biomarker panels and to validate their performance in the diagnosis of pancreatic ductal adenocarcinoma (PDAC). We used blood samples from 180 PDAC patients and 573 healthy controls. Candidate markers consisted of 11 markers that are commonly expressed in various cancers and which have previously demonstrated increased expression in pancreatic cancer. Samples were divided into training and validation sets. Five linear or non-linear classification methods were used to determine the optimal model. Differences were identified in 10 out of the 11 markers tested. We identified 2047 combinations, all of which were applied to 5 separate algorithms. The new biomarker combination consisted of 6 markers (ApoA1, CA125, CA19-9, CEA, ApoA2, and TTR). The area under the curve, specificity, and sensitivity were 0.992, 95%, and 96%, respectively, in the training set. Meanwhile, the measures were 0.993, 96%, and 93% in the validation set. This study demonstrated the utility of multiple biomarker combinations in the early detection of PDAC. A diagnostic panel of 6 biomarkers was developed and validated. These algorithms will assist in the early diagnosis of PDAC.

Serum levels of ANOS1 serve as a diagnostic biomarker of gastric cancer: a prospective multicenter observational study

BACKGROUND

Development of high-performance serum biomarkers will likely improve treatment outcomes of patients with gastric cancer (GC). We previously identified the candidate serum markers, anosmin 1 (ANOS1), dihydropyrimidinase-like 3 (DPYSL3), and melanoma-associated antigen D2 (MAGE-D2) and evaluated their clinical significance through a single-center retrospective analysis. Here we conducted a prospective multicenter observational study aimed at validating the diagnostic performance of these potential markers.

METHODS

We analyzed serum levels before and after surgery of the three potential biomarkers in patients with GC and healthy volunteers. Quantification of serum and GC tissue levels was performed using an ELISA.

RESULTS

Area under the curve (AUC) values that discriminated patients with GC from healthy controls were - 0.7058, 0.6188, and 0.5031 for ANOS1, DPYSL3, and MAGED2, respectively. The sensitivity and specificity of the ANOS1 assay were 0.36 and 0.85, respectively. The AUC value of ANOS1 that discriminated patients with stage I GC from healthy controls was 0.7131. Serum ANOS1 levels were significantly elevated in patients with stage I GC compared with those of healthy controls (median 1179 ng/ml and 461 ng/ml, respectively, P < 0.0001) and decreased after resection of primary GC lesions (P < 0.0001). The combination of serum ANOS1 and DPYSL3 levels increased the AUC value that discriminated patients with GC from healthy controls. Serum levels of ANOS1 did not significantly correlate with those of carcinoembryonic antigen, carbohydrate antigen 19-9, or other markers of inflammation.

CONCLUSIONS

Serum levels of ANOS1 may serve as a useful diagnostic tool for managing GC.

ANKRD33 is overexpressed in gastric adenocarcinoma and predictive for poor prognosis

The aim of the current study was to investigate and discuss the function of ANKRD33 gene in the pathogenesis of gastric adenocarcinoma. The marked up-regulated expression of ANKRD33 gene in gastric adenocarcinoma tissues compared to normal tissues was found by bioinformatics analysis. Kaplan-Meier analysis revealed that high expression of ANKRD33 is correlated with lower overall survival of gastric adenocarcinoma patients. The results of qPCR revealed that mRNA expression level of ANKRD33 was dramatically higher in AGS, SGC7901, and BGC823 cell lines than that in the GES1 cells. Knockdown of ANKRD33 remarkably inhibited the viability, invasion, and migration of AGS and BGC823 cells. Furthermore, the ratio of p-AKT/AKT and p-mTOR/mTOR was significantly decreased in AGS cells which transfected with si- ANKRD33. All the above results illustrated that ANKRD33 would act as a tumor forwarder in gastric adenocarcinoma development and have a high potential to be a marker molecule in the diagnosis and treatment of gastric tumors.

Serum Biomarkers for Racial Disparities in Breast Cancer Progression

African American (AA) women are often diagnosed with more aggressive breast cancers and have worse survival outcomes than their Caucasian American (CA) counterparts. However, a comprehensive understanding of this disparity remains unclear. In this study, we attempted to identify the race-specific non-invasive protein biomarkers that may particularly benefit interventions aimed at reducing the risk of recurrence and metastasis in breast cancers (BrCa). Our technical strategy has been to discover candidate protein biomarkers in patient sera using a high throughput antibody microarray platform. A total of 240 subjects were selected, composed of controls and all immunohistochemistry-based subtypes of breast cancer cases, subdivided by pre- and post-menopausal status and by race. A global Wilcoxon analysis comparing no-cancer controls and cancer patients identified Pyk2, SAPK/JNK, and phosphatase and tensin homolog as present in higher concentrations in cancer patient serum. A paired t-test revealed that c-kit and Rb are significantly over-represented in AA cancer serum when compared to CA cancer serum. Interestingly, VEGFR2, a protein linked to BrCa metastasis and poor prognosis, was significantly over-represented in AA cancer serum compared to AA controls; however, this was not found in CA cancer serum compared to CA controls, suggesting a possible explanation for the higher incidence of aggressive BrCa in AA versus CA patients. Through examining race-specific differences in the protein landscape of BrCa patient serum, the identified proteins could lay the groundwork for the development of an all-inclusive "liquid mammogram test."

Serum prealbumin is a prognostic indicator in idiopathic pulmonary fibrosis

INTRODUCTION

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease characterized by variable progression. The prealbumin (PA) is a parameter in a routine blood biochemistry examination. We sought to investigate the prognostic value in IPF patients.

OBJECTIVES

To evaluate the prognosis value in patients with IPF.

METHODS

Blood biochemistry examination, demographics, pulmonary function data from patients with IPF consulted in Beijing Chao-Yang Hospital and China-Japan Friendship Hospital between July 2012 and December 2016 were collected. Infection, liver and kidney dysfunction and lung transplantation are excluded from the cohort.

RESULTS

The result of multivariate Cox analysis showed that PA was significant prognostic indicator of survival along with BMI, FVC, serum albumin protein and serum global protein. The patients with PA concentration <0.2 mg/L had shorter survival compared with those whose PA were normal. Although the survival had no significant difference between the patients with PA concentration < 0.2 mg/L and albumin < 35 g/L and those with PA concentration < 0.2 mg/L, the average survival time of patients with PA concentration < 0.2 mg/L and albumin < 35 g/L were shorter.

CONCLUSIONS

Our study indicated that IPF patients with PA concentration < 0.2 mg/L have poorer outcome. Further studies are warranted to indentify PA as a predictor for IPF patients outcomes and explore the role of PA in the pathogenesis of IPF.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Diagnostic value of apolipoprotein C-I, transthyretin and apolipoprotein C-III in gastric cancer

Diagnostic value of apolipoprotein C-I (ApoC-I), transthyretin (TTR) and ApoC-III in gastric cancer were evaluated. Retrospective analysis methods were used to collect 60 patients with gastric cancer first diagnosed in The First Affiliated Hospital of Jiaxing University. There were 60 patients with chronic atrophic gastritis in the benign lesion group and 60 healthy individuals in the control group. The expression levels of serum ApoC-I, TTR and ApoC-III was detected by enzyme-linked immunosorbent assay. Differences existed in the expression levels of ApoC-I, TTR and ApoC-III in the gastric cancer group, benign lesion group and control group (P<0.001), with the expression levels of ApoC-I, TTR and ApoC-III in the gastric cancer group being lower than that of the benign lesion group (P<0.05), and the expression levels of ApoC-I, TTR and ApoC-III in the benign lesion group being lower than that of the control group (P<0.05). The expression levels of ApoC-I, TTR and ApoC-III in the gastric cancer group were to a certain degree correlated with the clinical stage, lymph node metastasis and differentiation of patients in the gastric cancer group (P<0.05). The specificity and negative predictive value of combined detection were proven to be higher than the separate detection of the three factors (P<0.05). The detection of serum ApoC-I, TTR and ApoC-III was of great significance in the diagnosis of gastric cancer and the estimation of its severity. The method of combined detection is worth a further in-depth study as it could improve the specificity of diagnosis and have a higher negative predictive value.

The expansion of targetable biomarkers for CAR T cell therapy

Biomarkers are an integral part of cancer management due to their use in risk assessment, screening, differential diagnosis, prognosis, prediction of response to treatment, and monitoring progress of disease. Recently, with the advent of Chimeric Antigen Receptor (CAR) T cell therapy, a new category of targetable biomarkers has emerged. These biomarkers are associated with the surface of malignant cells and serve as targets for directing cytotoxic T cells. The first biomarker target used for CAR T cell therapy was CD19, a B cell marker expressed highly on malignant B cells. With the success of CD19, the last decade has shown an explosion of new targetable biomarkers on a range of human malignancies. These surface targets have made it possible to provide directed, specific therapy that reduces healthy tissue destruction and preserves the patient's immune system during treatment. As of May 2018, there are over 100 clinical trials underway that target over 25 different surface biomarkers in almost every human tissue. This expansion has led to not only promising results in terms of patient outcome, but has also led to an exponential growth in the investigation of new biomarkers that could potentially be utilized in CAR T cell therapy for treating patients. In this review, we discuss the biomarkers currently under investigation and point out several promising biomarkers in the preclinical stage of development that may be useful as targets.

Discovery of gastric cancer specific biomarkers by the application of serum proteomics

Current diagnostic markers for gastric cancer are not sufficiently specific or sensitive for use in clinical practice. The aims of this study are to compare the proteomes of serum samples from patients with gastric cancers and normal controls, and to develop useful tumor markers of gastric cancer by quantitative proteomic analysis. We identified a total of 388 proteins with a ≤1% FDR and with at least two unique peptides from the sera of each group. Among them, 215, 251, and 260 proteins were identified in serum samples of patients in an advanced cancer group, early cancer group, and normal control group, respectively. We selected differentially expressed proteins in cancer patients compared with those of normal controls via semiquantitative analyses comparing the spectral counts of identified proteins. These differentially expressed proteins were successfully verified using an MS-based quantitative assay, multiple reactions monitoring analysis. Four proteins (vitronectin, clusterin isoform 1, thrombospondin 1, and tyrosine-protein kinase SRMS) were shown to have significant changes between the cancer groups and the normal control group. These four serum proteins were able to discriminate gastric cancer patients from normal controls with sufficient specificity and selectivity.

Elevated level of some chemokines in plasma of gastric cancer patients

INTRODUCTION

Gastric cancer is one of the most common cancer-related causes of death. This is mainly due to the lack of good noninvasive method/biomarkers suitable for early-tumour diagnosis and planning of further therapy modalities. Chemokines play an important role in cancer progression and metastasis formation. In gastric cancer patients, clinical relevance of CXCL12 and CCL5 level has been postulated.

AIM OF THE STUDY

Efforts were undertaken to examine whether expanded chemokine range may be relevant for evaluation of preoperative staging of gastric cancer patients.

MATERIAL AND METHODS

Plasma from 66 gastric cancer patients and 11 healthy controls was obtained, and CCL2, CCL3, CCL4, CCL5, CXCL8, CXCL9, and CXCL10 levels were determined by flow cytometry FlexSet system.

RESULTS

In gastric cancer patients' plasma an increased level of CCL2, CCL4, CCL5, CXCL8, CXCL9, and CXCL10 was observed. In the case of CCL2, CXCL9, and CXCL10, the chemokine levels correlated with advanced (III and IV in TNM classification) disease stage. In the case of CCL4, CCL5, and CXCL8, elevated levels were observed in all cancer patients in comparison to healthy donors.

CONCLUSIONS

The accuracy of preoperative diagnosis in gastric cancer may include the monitoring of a wide range of chemokines in patients' plasma. Increased levels of chemokines may warn that the disease is more advanced than conventional diagnostic procedures suggest.

Interleukins 17 and 23 in patients with gastric neoplasms

Recently there has been heightened interest in the potential significance of interleukin (IL)-17 and IL-23 in the development/progression of human malignancies. Here, we analyzed the systemic levels of these cytokines in 75 patients with different types of gastric neoplasms (carcinoma, gastrointestinal stromal tumors, neuroendocrine neoplasms, and lymphomas) and 42 healthy volunteers. We found that patients with all types of gastric neoplasms have significantly lower IL-23 levels. However, in comparison to the levels in healthy individuals, IL-17 concentrations were lower only in patients with types of gastric neoplasms other than carcinoma. Interestingly, IL-17 levels significantly differed between patients with early and advanced gastric carcinoma. No significant associations were detected between the systemic levels of examined interleukins and TNM staging. However, peripheral levels of IL-23 were correlated with the absolute numbers of circulating populations of bone marrow-derived mesenchymal and very small embryonic/epiblast-like stem cells in patients with gastric carcinoma. ROC curve analyses demonstrated that systemic levels of IL-17 seem to meet basic criteria for consideration as a helpful diagnostic marker in the detection of gastric carcinoma. In conclusion, our study provides translational evidence confirming the clinical significance of IL-17 and IL-23 in the pathogenesis of different types of gastric neoplasms in humans.

Progress and prospects for the discovery of biomarkers for gastric cancer: a focus on proteomics

INTRODUCTION

Patient outcomes from gastric cancer vary due to the complexity of stomach carcinogenesis. Recent research using proteomic technologies has targeted components of all of these systems in order to develop biomarkers to aid the early diagnosis of gastric cancer and to assist in prognostic stratification. Areas covered: This review is comprised of evidence obtained from literature searches from PubMed. It covers the evidence of diagnostic, prognostic, and predictive biomarkers for gastric cancer using proteomic technologies, and provides up-to-date references. Expert commentary: The proteomic technologies have not only enabled the screening of a large number of samples, but also enabled the identification of diagnostic, prognostic and predictive biomarkers for gastric cancer. While major challenges still remain, to date, proteomic studies in gastric cancer have provided a wealth of information in revealing proteome alterations associated with the disease.

Proteomics approach to identify biomarkers for upper gastrointestinal cancer

INTRODUCTION

The prognosis for patients with upper gastrointestinal cancers remains dismal despite the development of multimodality therapies that incorporate surgery, chemotherapy, and radiotherapy. Early diagnosis and personalized treatment should lead to better prognosis. Given the advances in proteomic technologies over the past decades, proteomics promises to be the most effective technique to identify novel diagnostics and therapeutic targets. Areas covered: For this review, keywords were searched in combination with 'proteomics' and 'gastric cancer' or 'esophageal cancer' in PubMed. Studies that evaluated proteomics associated with upper gastrointestinal cancer were identified through reading, with several studies quoted at second hand. We summarize the proteomics involved in upper gastrointestinal cancer and discuss potential biomarkers and therapeutic targets. Expert commentary: In particular, the development of mass spectrometry has enabled detection of multiple proteins and peptides in more biological samples over a shorter time period and at lower cost than was previously possible. In addition, more sophisticated protein databases have allowed a wider variety of proteins in samples to be quantified. Novel biomarkers that have been identified by new proteomic technologies should be applied in a clinical setting.

Potential Diagnostic, Prognostic and Therapeutic Targets of MicroRNAs in Human Gastric Cancer

Human gastric cancer (GC) is characterized by a high incidence and mortality rate, largely because it is normally not identified until a relatively advanced stage owing to a lack of early diagnostic biomarkers. Gastroscopy with biopsy is the routine method for screening, and gastrectomy is the major therapeutic strategy for GC. However, in more than 30% of GC surgical patients, cancer has progressed too far for effective medical resection. Thus, useful biomarkers for early screening or detection of GC are essential for improving patients’ survival rate. MicroRNAs (miRNAs) play an important role in tumorigenesis. They contribute to gastric carcinogenesis by altering the expression of oncogenes and tumor suppressors. Because of their stability in tissues, serum/plasma and other body fluids, miRNAs have been suggested as novel tumor biomarkers with suitable clinical potential. Recently, aberrantly expressed miRNAs have been identified and tested for clinical application in the management of GC. Aberrant miRNA expression profiles determined with miRNA microarrays, quantitative reverse transcription-polymerase chain reaction and next-generation sequencing approaches could be used to establish sample specificity and to identify tumor type. Here, we provide an up-to-date summary of tissue-based GC-associated miRNAs, describing their involvement and that of their downstream targets in tumorigenic and biological processes. We examine correlations among significant clinical parameters and prognostic indicators, and discuss recurrence monitoring and therapeutic options in GC. We also review plasma/serum-based, GC-associated, circulating miRNAs and their clinical applications, focusing especially on early diagnosis. By providing insights into the mechanisms of miRNA-related tumor progression, this review will hopefully aid in the identification of novel potential therapeutic targets.

Identification of cysteinylated transthyretin, a predictive biomarker of treatment response to partially hydrolyzed guar gum in type 2 diabetes rats, by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Recent evidence has indicated that total fiber intake is inversely related to type 2 diabetes risk. The present study aimed to investigate the effects of chronic administration of partially hydrolyzed guar gum (PHGG), a water-soluble dietary fiber, on the occurrence of diabetes and its complications, fatty liver and nephropathy. We also identified predictive serum biomarkers of treatment response to PHGG by mass spectroscopy-based proteomic analysis using Otsuka Long-Evans Tokushima Fatty (OLETF) rats, a good model of human non-insulin-dependent diabetes mellitus. In this study, at 5 weeks of age, OLETF rats and control strain Long-Evans Tokushima Otsuka (LETO) rats were fed a control diet or a high-fiber diet (5% PHGG) for 57 weeks. Body weight, food intake, oral glucose tolerance test, plasma insulin levels, and urine glucose and protein levels were regularly measured. Oral glucose tolerance tests (OGTT) and storage of serum in a deep freezer were conducted at the beginning of the experiment and every 4 weeks after overnight fasting during the experiments. PHGG treatment affected neither meal patterns nor the body weight of OLETF and LETO rats. Repeated measure analysis of variance revealed significant differences in fasting plasma glucose and plasma glucose at 2 h after OGTT between control OLETF (OLETF-C) rats and OLETF rats treated with PHGG (OLETF-F). The glucose response determined by the area under the curve of OGTT was significantly greater in OLETF-C rats than that in OLETF-F rats at 25 weeks of age. HOMA-IR, an index of insulin resistance, increased at 25 weeks of age in OLETF-C rats, while this increase was significantly inhibited in OLETF-F rats. At 62 weeks of age, PHGG treatment significantly improved hepatic steatosis as well as renal mesangial matrix accumulation in OLETF rats. To identify the risk marker for diabetes mellitus by SELDI-TOF MS, we collected sera from 21-week-old individuals. Among the 12 specific peaks that were risk marker candidates for diabetes mellitus, the m/z 13,720 peak was identified as that of cysteinylated transthyretin by sequencing of four tryptic peptides using tandem mass spectrometry and peak distribution around the m/z 13,720 peak in the SELDI-TOF spectra. In conclusion, we found that chronic treatment with PHGG improved insulin resistance, delayed the onset of diabetes, and inhibited the development of diabetic complications, as well as identified cysteinylated transthyretin as a predictive biomarker of treatment response to PHGG in OLETF rats.

Emerging blood-based biomarkers for detection of gastric cancer

Early detection and efficient monitoring of tumor dynamics are prerequisites for reducing disease burden and mortality, and for improving the management of patients with gastric cancer (GC). Blood-based biomarker assays for the detection of early-stage GC could be of great relevance both for population-wide or risk group-based screening programs, while circulating biomarkers that reflect the genetic make-up and dynamics of the tumor would allow monitoring of treatment efficacy, predict recurrences and assess the genetic heterogeneity of the tumor. Recent research to identify blood-based biomarkers of GC has resulted in the identification of a wide variety of cancer-associated molecules, including various proteins, autoantibodies against tumor associated antigens, cell-free DNA fragments, mRNAs and various non-coding RNAs, circulating tumor cells and cancer-derived extracellular vesicles. Each type of these biomarkers provides different information on the disease status, has different advantages and disadvantages, and distinct clinical usefulness. In the current review, we summarize the recent developments in blood-based GC biomarker discovery, discuss the origin of various types of biomarkers and their clinical usefulness and the technological challenges in the development of biomarker assays for clinical use.

Challenges of deciphering gastric cancer heterogeneity

Gastric cancer is in decline in most developed countries; however, it still accounts for a notable fraction of global mortality and morbidity related to cancer. High-throughput methods are rapidly changing our view and understanding of the molecular basis of gastric carcinogenesis. Today, it is widely accepted that the molecular complexity and heterogeneity, both inter- and intra-tumour, of gastric adenocarcinomas present significant obstacles in elucidating specific biomarkers for early detection of the disease. Although genome-wide sequencing and gene expression studies have revealed the intricate nature of the molecular changes that occur in tumour landscapes, the collected data and results are complex and sometimes contradictory. Several aberrant molecules have already been tested in clinical trials, although their diagnostic and prognostic utilities have not been confirmed thus far. The gold standard for the detection of sporadic gastric cancer is still the gastric endoscopy, which is considered invasive. In addition, genome-wide association studies have confirmed that genetic variations are important contributors to increased cancer risk and could participate in the initiation of malignant transformation. This hypothesis could in part explain the late onset of sporadic gastric cancers. The elaborate interplay of polymorphic low penetrance genes and lifestyle and environmental risk factors requires additional research to decipher their relative impacts on tumorigenesis. The purpose of this article is to present details of the molecular heterogeneity of sporadic gastric cancers at the DNA, RNA, and proteome levels and to discuss issues relevant to the translation of basic research data to clinically valuable tools. The focus of this work is the identification of relevant molecular changes that could be detected non-invasively.

Expression analysis of apolipoprotein E and its associated genes in gastric cancer

Gastric cancer is a common type of cancer worldwide, and has a poor prognosis, in part due to the low rates of early diagnosis and the limited treatment methods available. Apolipoprotein E (ApoE) is involved in exogenous cholesterol transport and may be important in enabling tumor cells to fulfill their high cholesterol requirements. A number of reports have indicated that ApoE affects the development and prognosis of gastric cancer. Therefore, the aim of the present study was to investigate the genes and transcription factors that interact with ApoE during the development of gastric cancer. Using gene expression profiling, the BioGRID database and the transcriptional regulatory element database, gene expression and regulatory networks in gastric cancer tissues and adjacent normal tissues were analyzed. The data demonstrated that eight genes associated with ApoE were differentially expressed, with six of these upregulated and two downregulated. Functionally, these genes were involved in the JAK-STAT cascade, acute-phase response, acute inflammatory response, and the steroid hormone response. Among these ApoE-associated genes, expression of the signal transducer and activator of transcription 2 (STAT2) and STAT3 transcription factors was upregulated. To the best of our knowledge, this is the first study to demonstrate the network of ApoE-related genes and transcription factors in gastric cancer. Additional studies are required in order to confirm these data and to translate the results into the identification of clinical biomarkers and novel treatment strategies for gastric cancer.

Implementation of gastric cancer screening - the global experience

Gastric cancer (GC) is still an important global healthcare problem, and in absolute figures it is going to remain at the present level in foreseeable future. In general, survival of patients with GC is poor mainly due to advanced-stage diagnosis. Early-stage GC can be cured by endoscopic resection or less invasive surgical treatment. Unfortunately, there is no appropriate screening strategy available for global application. This article provides a description of established national and regional GC screening programs and the screening modalities used. This review also summarizes current approaches to develop cancer-screening biomarkers. Although candidates with initial promising results have been suggested, moving discovery into clinical practice is still a major challenge. Well-designed biomarker studies, with systematic validation steps, are needed to decrease the burden of this fatal disease.

Potential prognostic, diagnostic and therapeutic markers for human gastric cancer

The high incidence of gastric cancer (GC) and its consequent mortality rate severely threaten human health. GC is frequently not diagnosed until a relatively advanced stage. Surgery is the only potentially curative treatment. Thus, early screening and diagnosis are critical for improving prognoses in patients with GC. Gastroscopy with biopsy is an appropriate method capable of aiding the diagnosis of specific early GC tumor types; however, the stress caused by this method together with it being excessively expensive makes it difficult to use it as a routine method for screening for GC on a population basis. The currently used tumor marker assays for detecting GC are simple and rapid, but their use is limited by their low sensitivity and specificity. In recent years, several markers have been identified and tested for their clinical relevance in the management of GC. Here, we review the serum-based tumor markers for GC and their clinical significance, focusing on discoveries from microarray/proteomics research. We also review tissue-based GC tumor markers and their clinical application, focusing on discoveries from immunohistochemical research. This review provides a brief description of various tumor markers for the purposes of diagnosis, prognosis and therapeutics, and we include markers already in clinical practice and various forthcoming biomarkers.

D-dimer: not just an indicator of venous thrombosis but a predictor of asymptomatic hematogenous metastasis in gastric cancer patients

Background

Plasma D-dimer levels have been shown to be high in advanced tumor stage patients and can be used to predict clinical outcome in cancer patients. As most advanced tumor stage patients exhibit asymptomatic metastasis, which contributes to early tumor recurrence after surgery, we hypothesized that plasma D-dimer levels can be used to predict patients with potential metastasis.

Methods

We enrolled 1042 primary gastric cancer patients in three multiple cancer centers in Northwest China and examined plasma D-dimer levels using the latex-enhanced immunoturbidimetric assay (LEIA) method. Plasma D-dimer levels were compared with the clinicopathological characteristics in this large-scale case-control study with follow up. We also performed regular follow-up studies for 395 patients to analyze the 2-year survival rate and early tumor recurrence.

Results

In this large-scale clinical study, we found that plasma D-dimer levels were increased in patients with distant metastasis and especially hematogenous metastasis patients. The cut-off value of the D-dimer levels was determined to be 1.5 mg/ml based on the ROC curve, and the sensitivity and specificity for metastasis prediction were 61.9% and 86.6%, respectively. Additionally, patients with high D-dimer levels displayed early tumor recurrence and poor outcome during the follow-up study.

Conclusion

Plasma D-dimer may represent an easy to measure and lower cost marker for the testing of gastric cancer patients to predict asymptomatic hematogenous metastasis.

Elevated plasma D-dimer levels correlate with long term survival of gastric cancer patients

Background

Increasing evidence indicated plasma D-dimer could be regarded as a marker in cancers, however, its role in gastric cancer is still largely unknown.

Methods

Plasma D-dimer levels were measured by enzyme linked fluorescent immunoassays and evaluated by receiver operating characteristic (ROC) curves for peritoneal dissemination in gastric cancer and healthy subjects. The overall survival (OS) characteristics were determined using Kaplan–Meier and Cox regression analyses.

Results

The average of the plasma D-dimer levels for gastric cancer patients was significantly higher than the healthy subjects. A Spearman correlation analysis showed that plasma D-dimer levels correlated with the depth of invasion, lymph node metastasis, peritoneal dissemination, distant metastasis, tumor size and TNM stage. The mean plasma D-dimer level was 2.20±1.51 µg/mL in peritoneal dissemination patients and 1.01±0.79 µg/mL in non-peritoneal dissemination patients (P<0.001). Additionally, the mean plasma D-dimer concentration in patients alive at the final follow-up evaluation was 0.79±0.72 µg/mL,which was significantly lower than the amounts determined for the deceased patients (1.36±1.13 µg/mL) (P<0.001). The AUC of D-dimer was 0.833 (95%CI: 0.780–0.885). At a cut-off value of 1.465 µg/mL, the D-dimer measurement had a sensitivity of 78.00%, a specificity of 83.76% and an accuracy of 82.59%. The median OS was 48.10 months (95% CI: 43.88–52.31) in patients with plasma D-dimer levels less than 1.465 µg/mL and 22.39 months (95% CI: 16.95–27.82) in patients with plasma D-dimer levels exceeding 1.465 µg/mL (log-rank test, P<0.001). Importantly, plasma D-dimer levels exceeding 1.465 µg/mL were significantly associated with poor OS, as determined using a multivariate Cox regression analysis (hazard ratio [HR], 2.28; 95%CI: 1.36–3.81; P = 0.002).

Conclusions

Plasma D-dimer levels are increased in gastric cancer patients and may be a valuable biomarker for peritoneal dissemination, with high D-dimer levels predicting poor outcomes for gastric cancer patients.

Serum protein and genetic tumor markers of gastric carcinoma

The high incidence of gastric cancer and consequent mortality pose severe threats to human health. Early screening, diagnosis and treatment are the key to improve the prognosis of the patients with gastric cancer. Gastroscopy with biopsy is an efficient method for the diagnosis of early gastric cancer, but the associated discomfort and high cost make it difficult to be a routine method for screening gastric cancer. Serum tumor marker assay is a simple and practical method for detection of gastric cancer, but it is limited by poor sensitivity and specificity. Therefore, people have been looking for novel serum markers of gastric cancer in recent years. Here we review the novel serum tumor markers of gastric cancer and their diagnostic significance, focusing on the discoveries from serum proteomics analyses and epigenetics researches.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

Differential association of RANTES-403 and IL-1B-1464 polymorphisms on histological subtypes in male Korean patients with gastric cancer

The aims of this study were to elucidate the association between RANTES-403 and an increased risk of gastric cancer in Korean males and to investigate the gene-gene interaction between IL-1B and RANTES. In total, 218 male patients with gastric cancer (114 diffuse types, 97 intestinal types, and 7 mixed types) and 377 male controls were included. RANTES-403 was genotyped, and age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were estimated by logistic regression. A multifactor dimensionality reduction (MDR) test with three-way split interval validation confirmed by likelihood ratio and permutation analysis was carried out. A significant increase in the risk of gastric cancer for the intestinal-type group was observed for IL-1B-1464G carriers (OR = 2.535; 95% CI = 1.121-5.732; P = 0.02) as well as for those with IL-1B-1464 CG (OR = 2.342; 95% CI = 0.998-5.500; P = 0.05) or IL-1B-1464 GG (OR = 2.819; 95% CI = 1.170-6.793; P = 0.02). For the RANTES-403 genotype, there was no significant difference in the risk of gastric cancer between the overall gastric cancer and the control groups. When further stratified according to histological types, RANTES-403A carriers (OR = 1.743; 95% CI = 1.086-2.798; P = 0.021) or heterozygotes (OR = 1.791; 95% CI = 1.092-2.935; P = 0.021) showed increased risk for developing diffuse-type gastric cancer. MDR revealed a three-way locus-locus interaction between RANTES-403AA, IL-1B-1464GG, and IL-1B-511CT for diffuse-type gastric cancer in Korean males. We demonstrated that RANTES-403 was significantly associated with the risk of developing diffuse-type gastric cancer in men and found a possible gene-gene interaction between RANTES and IL-1B polymorphisms in gastric cancer carcinogenesis.

N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications

Protein biomarkers have the potential to transform medicine as they are clinically used to diagnose diseases, stratify patients, and follow disease states. Even though a large number of potential biomarkers have been proposed over the past few years, almost none of them have been implemented so far in the clinic. One of the reasons for this limited success is the lack of technologies to validate proposed biomarker candidates in larger patient cohorts. This limitation could be alleviated by the use of antibody-independent validation methods such as selected reaction monitoring (SRM). Similar to measurements based on affinity reagents, SRM-based targeted mass spectrometry also requires the generation of definitive assays for each targeted analyte. Here, we present a library of SRM assays for 5568 N-glycosites enabling the multiplexed evaluation of clinically relevant N-glycoproteins as biomarker candidates. We demonstrate that this resource can be utilized to select SRM assay sets for cancer-associated N-glycoproteins for their subsequent multiplexed and consistent quantification in 120 human plasma samples. We show that N-glycoproteins spanning 5 orders of magnitude in abundance can be quantified and that previously reported abundance differences in various cancer types can be recapitulated. Together, the established N-glycoprotein SRMAtlas resource facilitates parallel, efficient, consistent, and sensitive evaluation of proposed biomarker candidates in large clinical sample cohorts.