Identification of Fucosylated SERPINA1 as a Novel Plasma Marker for Pancreatic Cancer Using Lectin Affinity Capture Coupled with iTRAQ-Based Quantitative Glycoproteomics

Glycosylation: mechanisms, biological functions and clinical implications

Protein post-translational modification (PTM) is a covalent process that occurs in proteins during or after translation through the addition or removal of one or more functional groups, and has a profound effect on protein function. Glycosylation is one of the most common PTMs, in which polysaccharides are transferred to specific amino acid residues in proteins by glycosyltransferases. A growing body of evidence suggests that glycosylation is essential for the unfolding of various functional activities in organisms, such as playing a key role in the regulation of protein function, cell adhesion and immune escape. Aberrant glycosylation is also closely associated with the development of various diseases. Abnormal glycosylation patterns are closely linked to the emergence of various health conditions, including cancer, inflammation, autoimmune disorders, and several other diseases. However, the underlying composition and structure of the glycosylated residues have not been determined. It is imperative to fully understand the internal structure and differential expression of glycosylation, and to incorporate advanced detection technologies to keep the knowledge advancing. Investigations on the clinical applications of glycosylation focused on sensitive and promising biomarkers, development of more effective small molecule targeted drugs and emerging vaccines. These studies provide a new area for novel therapeutic strategies based on glycosylation.

Predictive potentials of glycosylation-related genes in glioma prognosis and their correlation with immune infiltration

Glycosylation is currently considered to be an important hallmark of cancer. However, the characterization of glycosylation-related gene sets has not been comprehensively analyzed in glioma, and the relationship between glycosylation-related genes and glioma prognosis has not been elucidated. Here, we firstly found that the glycosylation-related differentially expressed genes in glioma patients were engaged in biological functions related to glioma progression revealed by enrichment analysis. Then seven glycosylation genes (BGN, C1GALT1C1L, GALNT13, SDC1, SERPINA1, SPTBN5 and TUBA1C) associated with glioma prognosis were screened out by consensus clustering, principal component analysis, Lasso regression, and univariate and multivariate Cox regression analysis using the TCGA-GTEx database. A glycosylation-related prognostic signature was developed and validated using CGGA database data with significantly accurate prediction on glioma prognosis, which showed better capacity to predict the prognosis of glioma patients than clinicopathological factors do. GSEA enrichment analysis based on the risk score further revealed that patients in the high-risk group were involved in immune-related pathways such as cytokine signaling, inflammatory responses, and immune regulation, as well as glycan synthesis and metabolic function. Immuno-correlation analysis revealed that a variety of immune cell infiltrations, such as Macrophage, activated dendritic cell, Regulatory T cell (Treg), and Natural killer cell, were increased in the high-risk group. Moreover, functional experiments were performed to evaluate the roles of risk genes in the cell viability and cell number of glioma U87 and U251 cells, which demonstrated that silencing BGN, SDC1, SERPINA1, TUBA1C, C1GALT1C1L and SPTBN5 could inhibit the growth and viability of glioma cells. These findings strengthened the prognostic potentials of our predictive signature in glioma. In conclusion, this prognostic model composed of 7 glycosylation-related genes distinguishes well the high-risk glioma patients, which might potentially serve as caner biomarkers for disease diagnosis and treatment.

Plasma/Serum Proteomics based on Mass Spectrometry

Human blood is a window of physiology and disease. Examination of biomarkers in blood is a common clinical procedure, which can be informative in diagnosis and prognosis of diseases, and in evaluating treatment effectiveness. There is still a huge demand on new blood biomarkers and assays for precision medicine nowadays, therefore plasma/serum proteomics has attracted increasing attention in recent years. How to effectively proceed with the biomarker discovery and clinical diagnostic assay development is a question raised to researchers who are interested in this area. In this review, we comprehensively introduce the background and advancement of technologies for blood proteomics, with a focus on mass spectrometry (MS). Analyzing existing blood biomarkers and newly-built diagnostic assays based on MS can shed light on developing new biomarkers and analytical methods. We summarize various protein analytes in plasma/serum which include total proteome, protein post-translational modifications, and extracellular vesicles, focusing on their corresponding sample preparation methods for MS analysis. We propose screening multiple protein analytes in the same set of blood samples in order to increase success rate for biomarker discovery. We also review the trends of MS techniques for blood tests including sample preparation automation, and further provide our perspectives on their future directions.

Screening of Exosome-Derived Proteins and Their Potential as Biomarkers in Diagnostic and Prognostic for Pancreatic Cancer

In the oncological area, pancreatic cancer is one of the most lethal diseases, with 5-year survival rising just 10% in high-development countries. This disease is genetically characterized by KRAS as a driven mutation followed by SMAD4, CDKN2, and TP53-associated mutations. In clinical aspects, pancreatic cancer presents unspecific clinical symptoms with the absence of screening and early plasmatic biomarker, being that CA19-9 is the unique plasmatic biomarker having specificity and sensitivity limitations. We analyzed the plasmatic exosome proteomic profile of 23 patients with pancreatic cancer and 10 healthy controls by using Nanoscale liquid chromatography coupled to tandem mass spectrometry (NanoLC-MS/MS). The pancreatic cancer patients were subdivided into IPMN and PDAC. Our findings show 33, 34, and 7 differentially expressed proteins when comparing the IPMN vs. control, PDAC-No treatment vs. control, and PDAC-No treatment vs. IPMN groups, highlighting proteins of the complement system and coagulation, such as C3, APOB, and SERPINA. Additionally, PDAC with no treatment showed 11 differentially expressed proteins when compared to Folfirinox neoadjuvant therapy or Gemcitabine adjuvant therapy. So here, we found plasmatic exosome-derived differentially expressed proteins among cancer patients (IPMN, PDAC) when comparing with healthy controls, which could represent alternative biomarkers for diagnostic and prognostic evaluation, supporting further scientific and clinical studies on pancreatic cancer.

Proteomics-Driven Biomarkers in Pancreatic Cancer

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

Serpin Family A Member 1 Is Prognostic and Involved in Immunological Regulation in Human Cancers

Serpin family A member 1 (SERPINA1) encodes a protease inhibitor participating in many human diseases, but its value in immunoregulation and prognosis of human cancers remains unclear. In this study, through comprehensive analysis of data from The Cancer Genome Atlas (TCGA) datasets, we found that SERPINA1 was dysregulated in many cancers compared with normal tissues. SERPINA1 expression was significantly associated with prognosis, immune subtype, molecular subtype, immune checkpoint (ICP) genes, tumor mutational burden (TMB), microsatellite instability (MSI), and the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) score. There was a strong connection between SERPINA1 expression and tumor-infiltrating lymphocytes, and SERPINA1 showed significant relation to gene markers of immune cells in digestive tumors. Fluorescence-based multiplex immunohistochemistry confirmed that SERPINA1 protein expression was related to clinicopathologic features and immune infiltrates in hepatic cancer. This study suggests that SERPINA can potentially serve as a novel biomarker for cancer prognosis and immunotherapy.

Proteome-Wide Analysis Reveals TFEB Targets for Establishment of a Prognostic Signature to Predict Clinical Outcomes of Colorectal Cancer

Dephosphorylation of transcription factor EB (TFEB) at Ser142 and Ser138 determines its nuclear localization and transcriptional activity. The link between TFEB-associated genes and colorectal cancer (CRC) progression and prognosis remains unclear. To systematically identify the targets of TFEB, we performed data-independent acquisition (DIA)-based quantitative proteomics to compare global protein changes in wild-type (WT) DLD1 cells and TFEB^WT- or TFEB^S142A/S138A (activated status)-expressing DLD1 cells. A total of 6048 proteins were identified and quantified in three independent experiments. The differentially expressed proteins in TFEB^S142A/S138A versus TFEB^WT and TFEB^WT versus control groups were compared, and 60 proteins were identified as products of TFEB transcriptional regulation. These proteins were significantly associated with vesicular endocytic trafficking, the HIF-1 signaling pathway, and metabolic processes. Furthermore, we generated a TFEB-associated gene signature using a univariate and LASSO Cox regression model to screen robust prognostic markers. An eight-gene signature (PLSCR3, SERPINA1, ATP6V1C2, TIMP1, SORT1, MAP2, KDM4B, and DDAH2) was identified. According to the signature, patients were assigned to high-risk and low-risk groups. Higher risk scores meant worse overall survival and higher epithelial-mesenchymal transition (EMT) scores. Additionally, as per the clinicopathological parameters and gene signature, a nomogram was constructed that was utilized to enhance the quantification capacity in risk assessment for individual patients. This research shows that TFEB directly mediates network effects in CRC, and the identified TFEB gene signature-based model may provide important information for the clinical judgment of prognosis.

Differential Plasma Proteins Identified via iTRAQ-Based Analysis Serve as Diagnostic Markers of Pancreatic Ductal Adenocarcinoma

Objective

We aimed to identify differentially expressed proteins in the plasma of patients with pancreatic cancer and control subjects, which could serve as potential tumor biomarkers.

Methods

Differentially expressed proteins were determined via isostatic labeling and absolute quantification (iTRAQ). Potential protein biomarkers were identified via enzyme-linked immunosorbent assay (ELISA) in 40 patients and 40 control subjects, and those eventually selected were further validated in 40 pancreatic cancer and normal pancreatic tissues.

Results

In total, 30 proteins displayed significant differences in expression among which 21 were downregulated and 9 were upregulated compared with the control group. ELISA revealed downregulation of peroxiredoxin-2 (PRDX2) and upregulation of alpha-1-antitrypsin (AAT), Ras-related protein Rab-2B (RAB2B), insulin-like growth factor-binding protein 2 (IGFBP2), Rho-related GTP-binding protein RhoC (RHOC), and prelamin-A/C (LMNA) proteins in 40 other samples of pancreatic cancer. Notably, only AAT, RAB2B, and IGFBP2 levels were consistent with expression patterns obtained with iTRAQ. Moreover, all three proteins displayed a marked increase in pancreatic cancer tissues. Data from ROC curve analysis indicated that the diagnostic ability of AAT, RAB2B, and IGFBP2 combined with carbohydrate antigen 19-9 (CA19-9) for pancreatic cancer was significantly greater than that of the single indexes (area under the curve (AUC): 90% vs. 75% (CA19-9), 76% (AAT), 71% (RAB2B), and 71% (IGFBP2), all P < 0.01).

Conclusion

AAT, RAB2B, and IGFBP2 could serve as effective biomarkers to facilitate the early diagnosis of pancreatic cancer.

Heat Shock Transcription Factor 2 Is Significantly Involved in Neurodegenerative Diseases, Inflammatory Bowel Disease, Cancer, Male Infertility, and Fetal Alcohol Spectrum Disorder: The Novel Mechanisms of Several Severe Diseases

HSF (heat shock transcription factor or heat shock factor) was discovered as a transcription factor indispensable for heat shock response. Although four classical HSFs were discovered in mammals and two major HSFs, HSF1 and HSF2, were cloned in the same year of 1991, only HSF1 was intensively studied because HSF1 can give rise to heat shock response through the induction of various HSPs' expression. On the other hand, HSF2 was not well studied for some time, which was probably due to an underestimate of HSF2 itself. Since the beginning of the 21st century, HSF2 research has progressed and many biologically significant functions of HSF2 have been revealed. For example, the roles of HSF2 in nervous system protection, inflammation, maintenance of mitosis and meiosis, and cancer cell survival and death have been gradually unveiled. However, we feel that the fact HSF2 has a relationship with various factors is not yet widely recognized; therefore, the biological significance of HSF2 has been underestimated. We strongly hope to widely communicate the significance of HSF2 to researchers and readers in broad research fields through this review. In addition, we also hope that many readers will have great interest in the molecular mechanism in which HSF2 acts as an active transcription factor and gene bookmarking mechanism of HSF2 during cell cycle progression, as is summarized in this review.

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.

Strategies for Proteome-Wide Quantification of Glycosylation Macro- and Micro-Heterogeneity

Protein glycosylation governs key physiological and pathological processes in human cells. Aberrant glycosylation is thus closely associated with disease progression. Mass spectrometry (MS)-based glycoproteomics has emerged as an indispensable tool for investigating glycosylation changes in biological samples with high sensitivity. Following rapid improvements in methodologies for reliable intact glycopeptide identification, site-specific quantification of glycopeptide macro- and micro-heterogeneity at the proteome scale has become an urgent need for exploring glycosylation regulations. Here, we summarize recent advances in N- and O-linked glycoproteomic quantification strategies and discuss their limitations. We further describe a strategy to propagate MS data for multilayered glycopeptide quantification, enabling a more comprehensive examination of global and site-specific glycosylation changes. Altogether, we show how quantitative glycoproteomics methods explore glycosylation regulation in human diseases and promote the discovery of biomarkers and therapeutic targets.

Application of Mass Spectrometry in Pancreatic Cancer Translational Research

Pancreatic cancer (PC) is one of the most common malignant tumors in the digestive tract worldwide, with increased morbidity and mortality. In recent years, with the development of surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and the change of the medical thinking model, remarkable progress has been made in researching comprehensive diagnosis and treatment of PC. However, the present situation of diagnostic and treatment of PC is still unsatisfactory. There is an urgent need for academia to fully integrate the basic research and clinical data from PC to form a research model conducive to clinical translation and promote the proper treatment of PC. This paper summarized the translation progress of mass spectrometry (MS) in the pathogenesis, diagnosis, prognosis, and PC treatment to promote the basic research results of PC into clinical diagnosis and treatment.