ITIH4 is a novel serum biomarker for early gastric cancer diagnosis

Protein Biomarkers of Gastric Preneoplasia and Cancer Lesions in Blood: A Comprehensive Review

Gastric cancer (GC) is a major cause of cancer-related mortality worldwide. It is often associated with a bad prognosis because of its asymptomatic phenotype until advanced stages, highlighting the need for its prevention and early detection. GC development is preceded by the emergence of gastric preneoplasia lesions (GPNLs), namely atrophic gastritis (AG), intestinal metaplasia (IM), and dysplasia (DYS). GC is currently diagnosed by endoscopy, which is invasive and costly and has limited effectiveness for the detection of GPNLs. Therefore, the discovery of non-invasive biomarkers in liquid biopsies, such as blood samples, in order to identify the presence of gastric preneoplasia and/or cancer lesions at asymptomatic stages is of paramount interest. This comprehensive review provides an overview of recently identified plasma/serum proteins and their diagnostic performance for the prediction of GPNLs and early cancer lesions. Autoantibodies appear to be promising biomarkers for AG, IM and early gastric cancer detection, along with inflammation and immunity-related proteins and antibodies against H. pylori virulence factors. There is a lack of specific protein biomarkers with which to detect DYS. Despite the need for further investigation and validation, some emerging candidates could pave the way for the development of reliable, non-invasive diagnostic tests for the detection and prevention of GC.

The function of the inter-alpha-trypsin inhibitors in the development of disease

Through the formation of covalent connections with hyaluronic acid (HA), the inter-α-trypsin inhibitor (IαI) family collaborates to preserve the stability of the extracellular matrix (ECM). The five distinct homologous heavy chains (ITIH) and one type of light chain make up the IαI family. ITIH alone or in combination with bikunin (BK) has been proven to have important impacts in a number of earlier investigations. This implies that BK and ITIH might be crucial to both physiological and pathological processes. The functions of BK and ITIH in various pathophysiological processes are discussed independently in this paper. In the meanwhile, this study offers suggestions for further research on the roles of BK and ITIH in the course of disease and summarizes the plausible mechanisms of the previous studies.

Proteomic Profile of Endometrial Cancer: A Scoping Review

Proteomics can be a robust tool in protein identification and regulation, allowing the discovery of potential biomarkers. In clinical practice, the management of endometrial cancer can be challenging. Thus, identifying promising markers could be beneficial, helping both in diagnosis and prognostic stratification, even predicting the response to therapy. Therefore, this manuscript systematically reviews the existing evidence of the proteomic profile of human endometrial cancer. The literature search was conducted via Medline (through PubMed) and the Web of Science. The inclusion criteria were clinical, in vitro, and in vivo original studies reporting proteomic analysis using all types of samples to map the human endometrial cancer proteome. A total of 55 publications were included in this review. Most of the articles carried out a proteomic analysis on endometrial tissue, serum and plasma samples, which enabled the identification of several potential diagnostic and prognostic biomarkers. In addition, eight articles were analyzed regarding the identified proteins, where three studies showed a strong correlation, sharing forty-five proteins. This analysis also allowed the identification of the 10 most frequently reported proteins in these studies: EGFR, PGRMC1, CSE1L, MYDGF, STMN1, CASP3 ANXA2, YBX1, ANXA1, and MYH11. Proteomics-based approaches pointed out potential diagnostic and prognostic candidates for endometrial cancer. However, there is a lack of studies exploring novel therapeutic targets.

Serum proteomic analysis uncovers novel serum biomarkers for depression

Objective

The identification of depression primarily relies on the clinical symptoms and psychiatric evaluation of the patient, in the absence of objective and quantifiable biomarkers within clinical settings. This study aimed to explore potential serum biomarkers associated with depression.

Methods

Serum samples from a training group comprising 48 depression patients and 48 healthy controls underwent proteomic analysis. Magnetic bead-based weak cation exchange (MB-WCX) and MALDI-TOF-MS were used in combination. To screen the differential peaks, ClinProTools software was employed. The proteins were identified using LC-MS/MS. ELISA was employed to confirm the expression of entire protein in the serum of the verification cohort, which encompassed 48 individuals who had been diagnosed with Depression and 48 healthy controls who were collected prospectively. Subsequently, logistic regression analysis was conducted to determine the diagnostic efficacy of the aforementioned predictors.

Results

Five potential biomarker peaks indicating depression were identified in serum samples (peak 1, m/z: 1868.21; peak 2, m/z: 1062.35; peak 3, m/z: 1452.12; peak 4, m/z: 1208.72; peak 5, m/z: 1619.58). All of these peaks had higher expression in the pre-therapy group and were confirmed to be Tubulin beta chain (TUBB), Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), Complement component 3 (C3), and Complement C4A precursor (C4A) by ELISA validation. Multivariate logistic regression analysis revealed that serum levels of TUBB, ITIH4, C3, and C4A were significant independent risk factors for the development of depression.

Conclusion

Depression is a prevalent psychiatric condition. Timely detection is challenging, resulting in poor prognoses for patients. Our study on plasma proteomics for depression demonstrated that TUBB, ITIH4, C3, and C4A differentiate between depression patients and healthy controls. The proteins that were identified could potentially function as biomarkers for the diagnosis of depression. Pinpointing these biomarkers could enable early identification of depression, which would advance precise treatment.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Mass spectrometry-based proteomics of cerebrospinal fluid in pediatric central nervous system malignancies: a systematic review with meta-analysis of individual patient data

BACKGROUND

The cerebrospinal fluid (CSF) proteome could offer important insights into central nervous system (CNS) malignancies. To advance proteomic research in pediatric CNS cancer, the current study aims to (1) evaluate past mass spectrometry-based workflows and (2) synthesize previous CSF proteomic data, focusing on both qualitative summaries and quantitative re-analysis. MAIN: In our analysis of 11 studies investigating the CSF proteome in pediatric patients with acute lymphoblastic leukemia (ALL) or primary brain tumors, we observed significant methodological variability. This variability negatively affects comparative analysis of the included studies, as per GRADE criteria for quality of evidence. The qualitative summaries covered 161 patients and 134 non-tumor controls, while the application of validation cohort varied among the studies. The quantitative re-analysis comprised 15 B-ALL vs 6 "healthy" controls and 15 medulloblastoma patients vs 22 non-tumor controls. Certain CSF proteins were identified as potential indicators of specific malignancies or stages of neurotoxicity during chemotherapy, yet definitive conclusions were impeded by inconsistent data. There were no proteins with statistically significant differences when comparing cases versus controls that were corroborated across studies where quantitative reanalysis was feasible. From a gene ontology enrichment, we observed that age disparities between unmatched case and controls may mislead to protein correlations more indicative of age-related CNS developmental stages rather than neuro-oncological disease. Despite efforts to batch correct (HarmonizR) and impute missing values, merging of dataset proved unfeasible and thereby limited meaningful data integration across different studies.

CONCLUSION

Infrequent publications on rare pediatric cancer entities, which often involve small sample sizes, are inherently prone to result in heterogeneous studies-particularly when conducted within a rapidly evolving field like proteomics. As a result, obtaining clear evidence, such as CSF proteome biomarkers for CNS dissemination or early-stage neurotoxicity, is currently impractical. Our general recommendations comprise the need for standardized methodologies, collaborative efforts, and improved data sharing in pediatric CNS malignancy research. We specifically emphasize the possible importance of considering natural age-related variations in CSF due to different CNS development stages when matching cases and controls in future studies.

Identification of an inflammation-related risk signature for prognosis and immunotherapeutic response prediction in bladder cancer

Tumor inflammation is one of the hallmarks of tumors and is closely related to tumor occurrence and development, providing individualized prognostic prediction. However, few studies have evaluated the relationship between inflammation and the prognosis of bladder urothelial carcinoma (BLCA) patients. Therefore, we constructed a novel inflammation-related prognostic model that included six inflammation-related genes (IRGs) that can precisely predict the survival outcomes of BLCA patients. RNA-seq expression and corresponding clinical data from BLCA patients were downloaded from The Cancer Genome Atlas database. Enrichment analysis was subsequently performed to determine the enrichment of GO terms and KEGG pathways. K‒M analysis was used to compare overall survival (OS). Cox regression and LASSO regression were used to identify prognostic factors and construct the model. Finally, this prognostic model was used to evaluate cell infiltration in the BLCA tumor microenvironment and analyze the effect of immunotherapy in high- and low-risk patients. We established an IRG signature-based prognostic model with 6 IRGs (TNFRSF12A, NR1H3, ITIH4, IL1R1, ELN and CYP26B1), among which TNFRSF12A, IL1R1, ELN and CYP26B1 were unfavorable prognostic factors and NR1H3 and ITIH4 were protective indicators. High-risk score patients in the prognostic model had significantly poorer OS. Additionally, high-risk score patients were associated with an inhibitory immune tumor microenvironment and poor immunotherapy response. We also found a correlation between IRS-related genes and bladder cancer chemotherapy drugs in the drug sensitivity data. The IRG signature-based prognostic model we constructed can predict the prognosis of BLCA patients, providing additional information for individualized prognostic judgment and treatment selection.

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.

Gaining knowledge about biomarkers of the immune system and inflammation in the saliva of pigs: The case of myeloperoxidase, S100A12, and ITIH4

An assay for the measurement of myeloperoxidase (Mpx) in porcine saliva was developed and validated, and factors influencing Mpx and another two biomarkers of inflammation and immune system, the protein S100A12 and the inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4), were studied. The spectrophotometric method for Mpx measurement validated in this assay showed an adequate analytical performance including precision and accuracy. When a group of twenty healthy pigs was sampled every 4 h from 8 a.m. until 8 p.m., Mpx and S100A12 showed significant increases at 4 p.m., whereas ITIH4 concentration showed a significant decrease at 12 a.m. Increases were also seen in salivary Mpx, S100A12, and ITIH4 levels 24 h after the intramuscular administration of Escherichia coli lipopolysaccharide in five pigs; whereas in a non-septic inflammation after the subcutaneous administration of turpentine oil to five pigs changes were seen in S100A12 at 3 h and in ITIH4 at 48 h. When a stressful situation consisting of the transportation and stay of 4 h to a slaughterhouse of 24 pigs was performed, all analytes were increased after 4 h of lairage in the slaughterhouse compared with the values that were obtained the day before at the same time of the day. Mpx can be measured in the saliva of pigs with the automated assay described in this report. Mpx, S100A12, and ITIH4 salivary levels can change depending on the hour of the day in which the sample is taken, and increases can be produced due to sepsis, non-septic inflammation and stress.

The diagnostic value of serum insulin-like growth factor binding protein 7 in gastric cancer

Backgrounds

Early detection might help in reducing the burden and promoting the survival rate of gastric cancers. Herein, we tried to explore the diagnostic value of insulin-like growth factor binding protein 7 (IGFBP7) in gastric cancers.

Methods

In this study, we first analyzed the expression levels and prognostic value of IGFBP7 mRNA in gastric cancers from The Cancer Genome Atlas (TCGA) database. Then, we recruited 169 gastric cancer patients and 100 normal controls as training cohort, and 55 gastric cancer patients and 55 normal controls as independent validation cohort. Enzyme-linked immunosorbent assay was applied to test the serum levels of IGFBP7. The receiver operating characteristic curve (ROC) and the area under the curve (AUC) were applied to evaluation the diagnostic value.

Results

TCGA showed that IGFBP7 mRNA was dysregulated and associated with prognosis in gastric cancer patients. Then, we examined the expression of serum IGFBP7 and found that serum IGFBP7 expressed lower in gastric cancer patients than normal controls both in training and independent validation cohorts (p < 0.0001). In training cohort, with the cutoff value of 1.515 ng/ml, the AUC for distinguishing gastric cancer patients was 0.774 (95% CI [0.713-0.836]) with sensitivity of 36.7% (95% CI [29.5-44.5]) and specificity of 90.0% (95% CI [82.0-94.8]). As for early-stage EJA, the AUC was 0.773 (95% CI [0.701-0.845]) with the sensitivity of 33.3% (95% CI [14.4-58.8]). In independent validation cohort, with the same cutoff value, the AUC reached to 0.758 (95% CI [0.664-0.852]). Similarly, for early-stage gastric cancer diagnosis in the independent validation cohort, the AUC value was 0.778 (95% CI [0.673-0.882]).

Conclusions

This study indicated that serum IGFBP7 might act as a potential early diagnostic marker for gastric cancers.

A rapid and high-throughput Helicobacter pylori RPA-CRISPR/Cas12a-based nucleic acid detection system

BACKGROUND

Helicobacter pylori lives in the human stomach and causes gastric cancer and other gastric diseases. The development of molecular technology has facilitated low-cost, rapid, and high-throughput detection of H. pylori.

MATERIALS AND METHODS

The combination of isothermal recombinase polymerase amplification (RPA) and CRISPR-Cas12a was used for early diagnosis and monitoring of H. pylori in clinical settings. The UreB genes from 242 H. pylori strains were subjected to cluster analysis, and we designed corresponding RPA primers and screened 2 sets of CRISPR-derived RNAs (crRNAs) for accurate H. pylori recognition. We then performed specificity and sensitivity validation of seven strains using this RPA-CRISPR/Cas12a method. In addition, the cut-off values of this RPA-CRISPR/Cas12a method based on fluorescence values (i.e., RPA-CRISPR/Cas12a-FT) were determined by comparison with quantitative PCR (qPCR), and further experiments comparing different methods were performed using clinical samples.

RESULTS

We developed a rapid detection system based on the combination of RPA and CRISPR-Cas12a, which was applied to the early diagnosis and monitoring of H. pylori in clinical settings. The RPA-CRISPR/Cas12a system was used to detect the UreB gene. We found that the limit of detection (LOD) for the CRISPR/Cas12a method based on the lateral flow dipstick result (i.e., CRISPR/Cas12a-LFD) was 100 copies, the cut-off value was 1.4; and for CRISPR/Cas12a-FT the LOD was 50 copies. This system was used to assess clinical samples and showed high reproducibility with proof-of-concept sensitivity, and the whole detection process was completed within 40 min.

CONCLUSION

As a diagnostic method that can detect the UreB gene of H. pylori in gastric tissue samples rapidly, sensitively, visually, and in a high throughput manner, our method provides a new diagnostic option for clinicians. This system is ideal for hospitals or testing sites with limited medical resources.

Blood plasma derived extracellular vesicles (BEVs): particle purification liquid chromatography (PPLC) and proteomic analysis reveals BEVs as a potential minimally invasive tool for predicting response to breast cancer treatment

PURPOSE

Circulating blood plasma derived extracellular vesicles (BEVs) containing proteins hold promise for their use as minimally invasive biomarkers for predicting response to cancer therapy. The main goal of this study was to establish the efficiency and utility of the particle purification liquid chromatography (PPLC) BEV isolation method and evaluate the role of BEVs in predicting breast cancer (BC) patient response to neoadjuvant chemotherapy (NAC).

METHODS

PPLC isolation was used to separate BEVs from non-EV contaminants and characterize BEVs from 17 BC patients scheduled to receive NAC. Using LC-MS/MS, we compared the proteome of PPLC-isolated BEVs from patients (n = 7) that achieved a pathological complete response (pCR) after NAC (responders [R]) to patients (n = 10) who did not achieve pCR (non-responders [NR]). Luminal MCF7 and basaloid MDA-MB-231 BC cells were treated with isolated BEVs and evaluated for metabolic activity by MTT assay.

RESULTS

NR had elevated BEV concentrations and negative zeta potential (ζ-potential) prior to receipt of NAC. Eight proteins were enriched in BEVs of NR. GP1BA (CD42b), PECAM-1 (CD31), CAPN1, HSPB1 (HSP27), and ANXA5 were validated using western blot. MTT assay revealed BEVs from R and NR patients increased metabolic activity of MCF7 and MDA-MB-231 BC cells and the magnitude was highest in MCF7s treated with NR BEVs.

CONCLUSION

PPLC-based EV isolation provides a preanalytical separation process for BEVs devoid of most contaminants. Our findings suggest that PPLC-isolated BEVs and the five associated proteins may be established as predictors of chemoresistance, and thus serve to identify NR to spare them the toxic effects of NAC.

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Extracellular vesicles and particles impact the systemic landscape of cancer

Intercellular cross talk between cancer cells and stromal and immune cells is essential for tumor progression and metastasis. Extracellular vesicles and particles (EVPs) are a heterogeneous class of secreted messengers that carry bioactive molecules and that have been shown to be crucial for this cell-cell communication. Here, we highlight the multifaceted roles of EVPs in cancer. Functionally, transfer of EVP cargo between cells influences tumor cell growth and invasion, alters immune cell composition and function, and contributes to stromal cell activation. These EVP-mediated changes impact local tumor progression, foster cultivation of pre-metastatic niches at distant organ-specific sites, and mediate systemic effects of cancer. Furthermore, we discuss how exploiting the highly selective enrichment of molecules within EVPs has profound implications for advancing diagnostic and prognostic biomarker development and for improving therapy delivery in cancer patients. Altogether, these investigations into the role of EVPs in cancer have led to discoveries that hold great promise for improving cancer patient care and outcome.

Systemic Evaluation of the Effect of Diabetes Mellitus on Breast Cancer in a Mouse Model

Breast cancer complicated with diabetes mellitus (DM) is a common disease. To evaluate the effect of preexisting DM on breast cancer progression without drug interference, we used a streptozotocin (STZ)-induced type 2 diabetes mellitus BALB/c mouse model. We found that 4T1 breast cancer complicated with DM decreased the mouse survival time compared with 4T1-bearing mice. The diversity of gut microbiome was affected by DM. The infiltration of mucosal-associated invariant T cell (MAIT), CD8+ T cell, and CD4+ T cell in the tumor was significantly decreased in the DM-4T1 group compared with the 4T1 group. The transcriptome data of tumor tissues indicated that the expressions of inflammatory C–C chemokine- and metabolism-related genes were greatly changed. The abnormal expression of these genes may be related with the decreased T-cell infiltration in DM-4T1. In conclusion, the gut microbiome and tumor microenvironment of diabetic breast cancer patients have unique features. The effect of diabetes on breast cancer should be considered in the treatment for diabetic breast cancer patients.

Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis

Objective

The study aimed to investigate the serum antigenomic profiling in rheumatoid arthritis (RA) and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm.

Method

Serum antigens were captured from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), together with sex- and age-matched 30 osteoarthritis (OA) patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then performed. The significantly upregulated and downregulated proteins with fold change > 1.5 (p < 0.05) were selected. Based on these differentially expressed proteins (DEPs), a machine learning model was trained and validated to classify RA, ACPA-positive RA, and ACPA-negative RA.

Results

We identified 62, 71, and 49 DEPs in RA, ACPA-positive RA, and ACPA-negative RA, respectively, as compared to OA and healthy controls. Typical pathway enrichment and protein-protein interaction networks were shown among these DEPs. Three panels were constructed to classify RA, ACPA-positive RA, and ACPA-negative RA using random forest models algorithm based on the molecular signature of DEPs, whose area under curve (AUC) were calculated as 0.9949 (95% CI = 0.9792-1), 0.9913 (95% CI = 0.9653-1), and 1.0 (95% CI = 1-1).

Conclusion

This study illustrated the serum auto-antigen profiling of RA. Among them, three panels of antigens were identified as diagnostic biomarkers to classify RA, ACPA-positive, and ACPA-negative RA patients.