Quantitative Proteomic Approach Targeted to Fibrinogen β Chain in Tissue Gastric Carcinoma

Label-based comparative proteomics of oral mucosal tissue to understand progression of precancerous lesions to oral squamous cell carcinoma

Introduction

Oral squamous cell carcinomas typically arise from precancerous lesions such as leukoplakia and erythroplakia. These lesions exhibit a range of histological changes from hyperplasia to dysplasia and carcinoma in situ, during their transformation to malignancy. The molecular mechanisms driving this multistage transition remain incompletely understood. To bridge this knowledge gap, our current study utilizes label based comparative proteomics to compare protein expression profiles across different histopathological grades of leukoplakia, erythroplakia, and oral squamous cell carcinoma samples, aiming to elucidate the molecular changes underlying lesion evolution.

Methodology

An 8-plex iTRAQ proteomics of 4 biological replicates from 8 clinical phenotypes of leukoplakia and erythroplakia, with hyperplasia, mild dysplasia, moderate dysplasia; along with phenotypes of well differentiated squamous cell carcinoma and moderately differentiated squamous cell carcinoma was carried out using the Orbitrap Fusion Lumos mass spectrometer. Raw files were processed with Maxquant, and statistical analysis across groups was conducted using MetaboAnalyst. Statistical tools such as ANOVA, PLS-DA VIP scoring, and correlation analysis were employed to identify differentially expressed proteins that had a linear expression variation across phenotypes of hyperplasia to cancer. Validation was done using Bioinformatic tools such as ClueGO + Cluepedia plugin in Cytoscape to extract functional annotations from gene ontology and pathway databases.

Results and discussion

A total of 2685 protein groups and 12,397 unique peptides were identified, and 61 proteins consistently exhibited valid reporter ion corrected intensities across all samples. Of these, 6 proteins showed linear varying expression across the analysed sample phenotypes. Collagen type VI alpha 2 chain (COL6A2), Fibrinogen β chain (FGB), and Vimentin (VIM) were found to have increased linear expression across pre-cancer phenotypes of leukoplakia to cancer, while Annexin A7 (ANXA7) was seen to be having a linear decreasing expression. Collagen type VI alpha 2 chain (COL6A2) and Annexin A2 (ANXA2) had increased linear expression across precancer phenotypes of erythroplakia to cancer. The mass spectrometry proteomics data have been deposited to the ProteomeXchanger Consortium via the PRIDE partner repository with the data set identifier PXD054190. These differentially expressed proteins mediate cancer progression mainly through extracellular exosome; collagen-containing extracellular matrix, hemostasis, platelet aggregation, and cell adhesion molecule binding.

Conclusion

Label-based proteomics is an ideal platform to study oral cancer progression. The differentially expressed proteins provide insights into the molecular mechanisms underlying the progression of oral premalignant lesions to malignant phenotypes. The study has translational value for early detection, risk stratification, and potential therapeutic targeting of oral premalignant lesions and in its prevention to malignant forms.

FGA influences invasion and metastasis of hepatocellular carcinoma through the PI3K/AKT pathway

Fibrinogen is an important plasma protein composed of three polypeptide chains, fibrinogen alpha (FGA), beta, and gamma. Apart from being an inflammation regulator, fibrinogen also plays a role in tumor progression. Liver cancer usually has a poor prognosis, with chronic hepatitis being the main cause of liver cirrhosis and hepatocellular carcinoma (HCC). FGA serves as a serological marker for chronic hepatitis, but its relationship with liver cancer remains unclear. Through bioinformatics analysis and agarose gel electrophoresis, we found that FGA was downregulated in HCC and correlated with tumor stage and grade. By constructing both FGA gene knockout and overexpression cell models, we demonstrated that overexpressing FGA inhibited migration and invasion of liver cancer cells through Transwell migration/invasion and wound healing assays. Western blotting experiments showed that FGA overexpression increased the expression of the epithelial-mesenchymal transition marker protein E-cadherin while decreasing N-cadherin and slug protein expression. In addition, FGA knockout activated the PI3K/AKT pathway. In a mouse model of metastatic tumors, overexpression of FGA restricted the spread of tumor cells. In conclusion, FGA exhibits an inhibitory effect on tumor metastasis, providing new insights for the treatment of advanced HCC metastatic tumors.

Proteomic profiling of extracellular vesicles derived from human serum for the discovery of biomarkers in Avascular necrosis

BACKGROUND

Avascular necrosis (AVN) is a medical condition characterized by the destruction of bone tissue due to a diminished blood supply. When the rate of tissue destruction surpasses the rate of regeneration, effective treatment becomes challenging, leading to escalating pain, arthritis, and bone fragility as the disease advances. A timely diagnosis is imperative to prevent and initiate proactive treatment for osteonecrosis. We explored the potential of differentially expressed proteins in serum-derived extracellular vesicles (EVs) as biomarkers for AVN of the femoral head in humans. We analyzed the genetic material contained in serum-derived exosomes from patients for early diagnosis, treatment, and prognosis of avascular necrosis.

METHODS

EVs were isolated from the serum of both patients with AVN and a control group of healthy individuals. Proteomic analyses were conducted to compare the expression patterns of these proteins by proteomic analysis using LC-MS/MS.

RESULTS

Our results show that the levels of IGHV3-23, FN1, VWF, FGB, PRG4, FCGBP, and ZSWIM9 were upregulated in the EVs of patients with AVN compared with those of healthy controls. ELISA results showed that VWF and PRG4 were significantly upregulated in the patients with AVN.

CONCLUSIONS

These findings suggest that these EV proteins could serve as promising biomarkers for the early detection and diagnosis of AVN. Early diagnosis is paramount for effective treatment, and the identification of new osteonecrosis biomarkers is essential to facilitate swift diagnosis and proactive intervention. Our study provides novel insights into the identification of AVN-related biomarkers that can enhance clinical management and treatment outcomes.

Intracellular peptides in SARS-CoV-2-infected patients

Intracellular peptides (InPeps) generated by the orchestrated action of the proteasome and intracellular peptidases have biological and pharmacological significance. Here, human plasma relative concentration of specific InPeps was compared between 175 patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and 45 SARS-CoV-2 non-infected patients; 2,466 unique peptides were identified, of which 67% were InPeps. The results revealed differences of a specific group of peptides in human plasma comparing non-infected individuals to patients infected by SARS-CoV-2, following the results of the semi-quantitative analyses by isotope-labeled electrospray mass spectrometry. The protein-protein interactions networks enriched pathways, drawn by genes encoding the proteins from which the peptides originated, revealed the presence of the coronavirus disease/COVID-19 network solely in the group of patients fatally infected by SARS-CoV-2. Thus, modulation of the relative plasma levels of specific InPeps could be employed as a predictive tool for disease outcome.

Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Bioinformatics and Next-Generation Sequencing Data Analysis

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the molecular pathogenesis, but the molecular mechanisms of PDAC are still not well understood. The purpose of this study is to further explore the molecular mechanism of PDAC through integrated bioinformatics analysis.

Methods

To identify the candidate genes in the carcinogenesis and progression of PDAC, next-generation sequencing (NGS) data set GSE133684 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and Gene Ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using Integrated Interactions Database (IID) interactome database and Cytoscape. Subsequently, miRNA-DEG regulatory network and TF-DEG regulatory network were constructed using miRNet database, NetworkAnalyst database, and Cytoscape software. The expression levels of hub genes were validated based on Kaplan-Meier analysis, expression analysis, stage analysis, mutation analysis, protein expression analysis, immune infiltration analysis, and receiver operating characteristic (ROC) curve analysis.

Results

A total of 463 DEGs were identified, consisting of 232 upregulated genes and 233 downregulated genes. The enriched GO terms and pathways of the DEGs include vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis, and adaptive immune system. Four hub genes (namely, cathepsin B [CCNB1], four-and-a-half LIM domains 2 (FHL2), major histocompatibility complex, class II, DP alpha 1 (HLA-DPA1) and tubulin beta 1 class VI (TUBB1)) were obtained via taking interaction of different analysis results.

Conclusions

On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of PDAC and provide potential targets for further investigation.

Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer

Objectives

It is significant to develop effective prognostic strategies and techniques for improving the survival rate of gallbladder carcinoma (GBC). We aim to develop the prediction model from multi-clinical indicators combined artificial intelligence (AI) algorithm for the prognosis of GBC.

Methods

A total of 122 patients with GBC from January 2015 to December 2019 were collected in this study. Based on the analysis of correlation, relative risk, receiver operator characteristic curve, and importance by AI algorithm analysis between clinical factors and recurrence and survival, the two multi-index classifiers (MIC1 and MIC2) were obtained. The two classifiers combined eight AI algorithms to model the recurrence and survival. The two models with the highest area under the curve (AUC) were selected to test the performance of prognosis prediction in the testing dataset.

Results

The MIC1 has ten indicators, and the MIC2 has nine indicators. The combination of the MIC1 classifier and the "avNNet" model can predict recurrence with an AUC of 0.944. The MIC2 classifier and "glmet" model combination can predict survival with an AUC of 0.882. The Kaplan-Meier analysis shows that MIC1 and MIC2 indicators can effectively predict the median survival of DFS and OS, and there is no statistically significant difference in the prediction results of the indicators (MIC1: χ² = 6.849, P = 0.653; MIC2: χ² = 9.14, P = 0.519).

Conclusions

The MIC1 and MIC2 combined with avNNet and mda models have high sensitivity and specificity in predicting the prognosis of GBC.

A machine learning-based approach to predicting the malignant and metastasis of thyroid cancer

Background

Thyroid Cancer (TC) is the most common malignant disease of endocrine system, and its incidence rate is increasing year by year. Early diagnosis, management of malignant nodules and scientific treatment are crucial for TC prognosis. The first aim is the construction of a classification model for TC based on risk factors. The second aim is the construction of a prediction model for metastasis based on risk factors.

Methods

We retrospectively collected approximately 70 preoperative demographic and laboratory test indices from 1735 TC patients. Machine learning pipelines including linear regression model ridge, Logistic Regression (LR) and eXtreme Gradient Boosting (XGBoost) were used to select the best model for predicting deterioration and metastasis of TC. A comprehensive comparative analysis with the prediction model using only thyroid imaging reporting and data system (TI-RADS).

Results

The XGBoost model achieved the best performance in the final thyroid nodule diagnosis (AUC: 0.84) and metastasis (AUC: 0.72-0.77) predictions. Its AUCs for predicting Grade 4 TC deterioration and metastasis reached 0.84 and 0.97, respectively, while none of the AUCs for Only TI-RADS reached 0.70. Based on multivariate analysis and feature selection, age, obesity, prothrombin time, fibrinogen, and HBeAb were common significant risk factors for tumor progression and metastasis. Monocyte, D-dimer, T3, FT3, and albumin were common protective factors. Tumor size (11.14 ± 7.14 mm) is the most important indicator of metastasis formation. In addition, GGT, glucose, platelet volume distribution width, and neutrophil percentage also contributed to the development of metastases. The abnormal levels of blood lipid and uric acid were closely related to the deterioration of tumor. The dual role of mean erythrocytic hemoglobin concentration in TC needs to be verified in a larger patient cohort. We have established a free online tool (http://www.cancer-thyroid.com/) that is available to all clinicians for the prognosis of patients at high risk of TC.

Conclusion

It is feasible to use XGBoost algorithm, combined with preoperative laboratory test indexes and demographic characteristics to predict tumor progression and metastasis in patients with TC, and its performance is better than that of Only using TI-RADS. The web tools we developed can help physicians with less clinical experience to choose the appropriate clinical decision or secondary confirmation of diagnosis results.

Proteins in human body fluids contain in vivo antigen analog of the melibiose-derived glycation product: MAGE

Melibiose-derived AGE (MAGE) is an advanced glycation end-product formed in vitro in anhydrous conditions on proteins and protein-free amino acids during glycation with melibiose. Our previous studies revealed the presence of MAGE antigen in the human body and tissues of several other species, including muscles, fat, extracellular matrix, and blood. MAGE is also antigenic and induces generation of anti-MAGE antibody. The aim of this paper was to identify the proteins modified by MAGE present in human body fluids, such as serum, plasma, and peritoneal fluids. The protein-bound MAGE formed in vivo has been isolated from human blood using affinity chromatography on the resin with an immobilized anti-MAGE monoclonal antibody. Using mass spectrometry and immunochemistry it has been established that MAGE epitope is present on several human blood proteins including serum albumin, IgG, and IgA. In serum of diabetic patients, mainly the albumin and IgG were modified by MAGE, while in healthy subjects IgG and IgA carried this modification, suggesting the novel AGE can impact protein structure, contribute to auto-immunogenicity, and affect function of immunoglobulins. Some proteins in peritoneal fluid from cancer patients modified with MAGE were also observed and it indicates a potential role of MAGE in cancer.

Gastric Cancer Pre-Stage Detection and Early Diagnosis of Gastritis Using Serum Protein Signatures

Background: A gastric cancer (GC) diagnosis relies on histopathology. Endoscopy rates are increasing. Helicobacter pylori infection is a major GC risk factor. In an effort to elucidate abundant blood biomarkers, and potentially reduce the number of diagnostic surgical interventions, we investigated sera and biopsies from a cohort of 219 H. pylori positive and negative patients diagnosed with GC, gastritis, and ulcers. This allowed the comparative investigation of the different gastroduodenal diseases, and the exclusion of protein changes resulting from bacterial infection or inflammation of the gastric mucosa when searching for GC-dependent proteins. Methods: High-definition mass spectrometry-based expression analysis of tryptically digested proteins was performed, followed by multivariate statistical and network analyses for the different disease groups, with respect to H. pylori infection status. Significantly regulated proteins differing more than two-fold between groups were shortlisted, and their role in gastritis and GC discussed. Results: We present data of comparative protein analyses of biopsies and sera from patients suffering from mild to advanced gastritis, ulcers, and early to advanced GC, in conjunction with a wealth of metadata, clinical information, histopathological evaluation, and H. pylori infection status. We used samples from pre-malignant stages to extract prospective serum markers for early-stage GC, and present a 29-protein marker panel containing, amongst others, integrin β-6 and glutathione peroxidase. Furthermore, ten serum markers specific for advanced GC, independent of H. pylori infection, are provided. They include CRP, protein S100A9, and kallistatin. The majority of these proteins were previously discussed in the context of cancer or GC. In addition, we detected hypoalbuminemia and increased fibrinogen serum levels in gastritis. Conclusion: Two protein panels were suggested for the development of multiplex tests for GC serum diagnostics. For most of the elements contained in these panels, individual commercial tests are available. Thus, we envision the design of multi-protein assays, incorporating several to all of the panel members, in order to gain a level of specificity that cannot be achieved by testing a single protein alone. As their development and validation will take time, gastritis diagnosis based on the fibrinogen to albumin serum ratio may be a quick way forward. Its determination at the primary/secondary care level for early diagnosis could significantly reduce the number of referrals to endoscopy. Preventive measures are in high demand. The protein marker panels presented in this work will contribute to improved GC diagnostics, once they have been transferred from a research result to a practical tool.

C8B in Complement and Coagulation Cascades Signaling Pathway is a predictor for Survival in HBV-Related Hepatocellular Carcinoma Patients

OBJECTIVE

The role of the complement and coagulation cascades signaling pathway in the pathogenesis of cancers remains uncertain. This study aimed to investigate the associations between enriched differentially expressed genes (DEGs) in this pathway and hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients.

MATERIALS AND METHODS

Clinical and gene expression data of the Gene Expression Omnibus (GEO) series profile GSE14520 were downloaded. The "Limma" package was used to screen the DEGs and the "clusterProfiler" package was used to identify the complement and coagulation cascades pathway and enriched significant genes. Cox regression analysis, the Kaplan-Meier method, and the nomogram model were used to address the correlations between significantly enriched DEGs in the complement and coagulation cascades pathway and HCC survival.

RESULTS

A total of 220 HBV-related HCC patients were enrolled in this study. The complement and coagulation cascades pathway was significantly enriched by 37 DEGs (p-value < 0.05 and adjusted p-value < 0.05). Complement 8 beta chain (C8B) expression levels had protective effects on overall survival (OS) and recurrence-free survival (RFS) in HBV-related HCC patients. High levels of C8B contributed to favorable OS and RFS in this population (both p < 0.01), even after adjustment of clinicopathological characteristics including tumor node metastasis (TNM) staging, Barcelona Clinic liver cancer (BCLC) staging, gender, and fibrinogen beta chain (FGB) expression (all p < 0.05).

CONCLUSION

C8B in the complement and coagulation cascades signaling pathway serves as a predictive candidate for survival in HBV-related HCC patients.

Classical Hodgkin's Lymphoma in the Era of Immune Checkpoint Inhibition

The ligation of programmed cell death 1 (PD-1) with programmed cell death ligand PD-L activates the immune checkpoint leading to T-cell dysfunction, exhaustion, and tolerance, especially in Hodgkin lymphoma (HL) where the PD-L/ Janus kinase (Jak) signaling was frequently found altered. Anti-PD-1 or anti-PD-L1 monoclonal antibodies can reverse this immune checkpoint, releasing the brake on T-cell responses. The characterization of the mechanisms regulating both the expression of PD-1 and PD-L and their function(s) in HL is ongoing. We provide in this review the recent findings focused on this aim with special attention on the major research topics, such as adverse events and resistance to PD-1–PD-L1 inhibitor treatment, together with a part about angiogenesis, extracellular vesicles, and microbiome in HL pathogenesis.

Early diagnosis of colorectal cancer via plasma proteomic analysis of CRC and advanced adenomatous polyp

AIM

This paper aimed to identify new candidate biomarkers in blood for early diagnosis of CRC.

BACKGROUND

Colorectal cancer (CRC) is the third most widespread malignancies increasing globally. The high mortality rate associated with colorectal cancer is due to the delayed diagnosis in an advanced stage while the metastasis has occurred. For better clinical management and subsequently to reduce mortality of CRC, early detection biomarkers are in high demand.

METHODS

A 2D-PAGE separation of proteins was performed followed by tandem mass Spectrometry (MALDI-TOF-TOF) to discover potential plasma protein markers for CRC and AA (advanced adenomas). Furthermore, western blot method was used to confirm a part of the results in colorectal tissue samples.

RESULTS

The significantly altered proteins including HPR, HP, ALB, KRT1, APOA1, FGB, IGJ and C4A were down-regulated in polyp relative to normal, and CRC compare to polyp surprisingly, and inversely, ORM2 was up-regulated with the fold change ≥ 2 and p-value ≤ 0.05. We also surveyed APOA1, FGB, and C4A for further confirmation of their expression changes by western blotting. All three of them showed a decreasing trend from normal toward CRC tissue samples as it mentioned before, but just changes of FGB and C4A were significant.

CONCLUSION

The results demonstrated that plasma proteins can be less invasive markers for the detection of CRC. FGB and C4A can be considered as plasma potential biomarkers to early diagnosis of CRC patients and understanding the underlying procedures in tumorigenesis. Undoubtedly, the additional study must be conducted on large scale cohorts to verify the results.

Proteomic Identification of Plasma Biomarkers in Children and Adolescents with Recurrent Hodgkin Lymphoma

The treatment of paediatric Hodgkin lymphoma (HL) has steadily improved over the years, so that 10- years survival exceed 80%. The purpose of this study was to identify prognostic markers for relapsed HL that might contribute to optimize therapeutic approaches. To this aim we retrospectively analysed differential protein expression profiles obtained from plasma of children/adolescents with HL (age ranging from 10 to 18 years) collected at diagnosis. We examined the protein profiles of 15 HL relapsed (R) patients compared with 14 HL not relapsed (NR) patients treated with the same LH-2004 protocol. Two dimensional difference in gel electrophoresis (2D-DIGE) revealed significant differences (fold change > 1.5; Student's T-test p<0.01) between R and NR patients in 10 proteins: α-1-antitrypsin chain a, apolipoprotein A-IV precursor; inter-α-trypsin inhibitor heavy chain; antithrombin-III; vitronectin; fibrinogen α, β and γ chains, complement C3, and ceruloplasmin. An up-regulation of fibrinogen α (spots 78, 196, 230, 234, 239) and β (spots 98, 291, 296, 300) chains together with a lower level of α-1-antitrypsin (spots 255, 264, 266, 272, 273) were found in R patients, and this difference was validated by immunoblotting. The functional role(s) of these proteins in the coagulation and inflammation associated with paediatric/adolescent HL progression and relapse deserves further investigations.