Sulfatase 1 expression in pancreatic cancer and its correlation with clinicopathological features and postoperative prognosis

Editor's Choice Development of a method to measure the activity of heparan sulfate 6-endosulfatase for biological research

Heparan sulfate 6-endosulfatases (SULFs) remove 6-O-sulfo groups from heparan sulfate polysaccharide chains. SULFs modify the functions of heparan sulfate and contribute to the development of cancers, organ development and endothelial inflammatory responses. However, direct measurement of the activity of SULFs from human and mouse plasma is not currently possible. Here, we report a liquid chromatography coupled with tandem mass spectrometry (LS-MS/MS) assay to measure the activity of SULFs. The method uses a structurally homogeneous heparan sulfate dodecasaccharide (12-mer) in which the glucuronic and iduronic acid residues are labeled with both 13C- and 2H-atoms. The 12-mers desulfated by the SULFs is subjected to degradation with heparin lyases to yield disaccharides, which is followed by LC-MS/MS. The amount of two specific disaccharides, ΔIIIS and ΔIVS, quantified by LC-MS/MS reports the activity of the SULFs with high sensitivity and specificity. This method allows for the determination of the activity from conditioned cell media and mouse plasma. Our findings offer an essential novel tool to delineate many roles of SULFs in biological processes.

Comprehensive analysis of inhibin-β A as a potential biomarker for gastrointestinal tract cancers through bioinformatics approaches

Inhibin, β, which is also known as INHBA, encodes a protein that belongs to the Transforming Growth factor-β (TGF-β) superfamily, which plays a pivotal role in cancer. Gastrointestinal tract (GI tract) cancer refers to the cancers that develop in the colon, liver, esophagus, stomach, rectum, pancreas, and bile ducts of the digestive system. The role of INHBA in all GI tract cancers remains understudied. By utilizing GEPIA2, which uses transcriptomic data from TCGA, we examined the expression of INHBA across different GI tract cancers. The results revealed consistent upregulation of INHBA in all TCGA GI tract cancers, except for liver hepatocellular carcinoma, where it showed downregulation compared to normal tissues, along with GTEx normal samples. Significant differences in INHBA expression were noted in adenocarcinomas of the colon, pancreas, rectum, and stomach, while no such differences were observed in cholangiocarcinoma and liver cancer. Moreover, a comprehensive bioinformatics analysis has been done to demonstrate that the differences in expression levels are significantly related to pathological tumor stages and prognosis in different GI tract cancers. Mucinous adenocarcinoma, esophageal squamous cell carcinoma, and stomach adenocarcinoma show a higher frequency of INHBA alteration and are primarily linked to mutations and amplifications. DNA methylation, immune infiltration, functional enrichment analysis, the genes associated with INHBA, and survival analysis in all TCGA GI tract cancers have been extensively analyzed. In colon and stomach cancers, increased INHBA expression significantly correlates with poorer overall survival (OS). However, in colon and pancreatic adenocarcinoma, higher expression is significantly associated with worse disease-free survival (DFS). Additionally, INHBA expression exhibited a positive correlation with cancer-associated fibroblasts across all gastrointestinal (GI) tract cancers. The KEGG pathway analysis revealed that INHBA and its interacting proteins are involved in several pathways, including TGF-beta signaling, Signalling pathways regulating pluripotency of stem cells, colorectal cancer, pancreatic cancer, AGE-RAGE signaling, and so on as major pathways. These findings demonstrate that INHBA could serve as a potential biomarker therapeutic target for GI tract cancer.

SUMF1 overexpression promotes tumorous cell growth and migration and is correlated with the immune status of patients with glioma

BACKGROUND

Glioma is a prevalent type of malignant tumor. To date, there is a lack of literature reports that have examined the association between sulfatase modifying factor 1 (SUMF1) and glioma.

METHODS

The levels of SUMF1 were examined, and their relationships with the diagnosis, prognosis, and immune microenvironment of patients with glioma were investigated. Cox and Lasso regression analysis were employed to construct nomograms and risk models associated with SUMF1. The functions and mechanisms of SUMF1 were explored and verified using gene ontology, cell counting kit-8, wound healing, western blotting, and transwell experiments.

RESULTS

SUMF1 expression tended to increase in glioma tissues. SUMF1 overexpression was linked to the diagnosis of cancer, survival events, isocitrate dehydrogenase status, age, and histological subtype and was positively correlated with poor prognosis in patients with glioma. SUMF1 overexpression was an independent risk factor for poor prognosis. SUMF1-related nomograms and high-risk scores could predict the outcome of patients with glioma. SUMF1 co-expressed genes were involved in cytokine, T-cell activation, and lymphocyte proliferation. Inhibiting the expression of SUMF1 could deter the proliferation, migration, and invasion of glioma cells through epithelial mesenchymal transition. SUMF1 overexpression was significantly associated with the stromal score, immune cells (such as macrophages, neutrophils, activated dendritic cells), estimate score, immune score, and the expression of the programmed cell death 1, cytotoxic T-lymphocyte associated protein 4, CD79A and other immune cell marker.

CONCLUSION

SUMF1 overexpression was found to be correlated with adverse prognosis, cancer detection, and immune status in patients with glioma. Inhibiting the expression of SUMF1 was observed to deter the proliferation, migration, and invasion of cancer cells. The nomograms and risk models associated with SUMF1 could predict the prognosis of patients with glioma.

SULF1 Activates the VEGFR2/PI3K/AKT Pathway to Promote the Development of Cervical Cancer

BACKGROUND AND PURPOSE

Sulfatase 1 (SULF1) can regulate the binding of numerous signaling molecules by removing 6-O-sulfate from heparan sulfate proteoglycans (HSPGs) to affect numerous physiological and pathological processes. Our research aimed to investigate the effect of the SULF1-mediated VEGFR2/PI3K/AKT signaling pathway on tumorigenesis and development of cervical cancer (CC).

METHODS

The expression and prognostic values of SULF1 in patients with CC were analyzed through bioinformatics analysis, qRT-PCR, immunohistochemistry, and western blot. The function and regulatory mechanism of SULF1 in proliferation, migration, and invasion of cervical cancer cells were examined through lentivirus transduction, CCK8, flow cytometry analysis, plate colony formation assay, scratch assay, transwell assay, western blot, VEGFR2 inhibitor (Ki8751), and mouse models.

RESULTS

SULF1 expression was significantly upregulated in CC tissues, which was significantly associated with poor prognosis of patients with CC. In vitro, the upregulation of SULF1 expression in HeLa cells promoted cell proliferation, colony formation, migration, and invasion while inhibiting apoptosis. Conversely, the downregulation of SULF1 expression had the opposite effect. In vivo, the upregulation of SULF1 expression resulted in a significant increase in both tumor growth and angiogenesis, while its downregulation had the opposite effect. Furthermore, western blot detection and cell function rescue assay confirmed that the upregulation of SULF1 in HeLa cells promoted the tumorigenic behaviors of cancer cells by activating the VEGFR2/PI3K/AKT signaling pathway.

CONCLUSION

SULF1 plays an oncogenic role in the tumorigenesis and development of CC, indicating its potential as a novel molecular target for gene-targeted therapy in patients with CC.

Heparan sulfate proteoglycans in cancer: Pathogenesis and therapeutic potential

The heparan sulfate proteoglycans (HSPGs) are glycoproteins that consist of a proteoglycan "core" protein and covalently attached heparan sulfate (HS) chain. HSPGs are ubiquitously expressed in mammalian cells on the cell surface and in the extracellular matrix (ECM) and secretory vesicles. Within HSPGs, the protein cores determine when and where HSPG expression takes place, and the HS chains mediate most of HSPG's biological roles through binding various protein ligands, including cytokines, chemokines, growth factors and receptors, morphogens, proteases, protease inhibitors, and ECM proteins. Through these interactions, HSPGs modulate cell proliferation, adhesion, migration, invasion, and angiogenesis to display essential functions in physiology and pathology. Under physiological conditions, the expression and localization of HSPGs are finely regulated to orchestrate their physiological functions, and this is disrupted in cancer. The HSPG dysregulation elicits multiple oncogenic signaling, including growth factor signaling, ECM and Integrin signaling, chemokine and immune signaling, cancer stem cell, cell differentiation, apoptosis, and senescence, to prompt cell transformation, proliferation, tumor invasion and metastasis, tumor angiogenesis and inflammation, and immunotolerance. These oncogenic roles make HSPGs an attractive pharmacological target for anti-cancer therapy. Several therapeutic strategies have been under development, including anti-HSPG antibodies, peptides and HS mimetics, synthetic xylosides, and heparinase inhibitors, and shown promising anti-cancer efficacy. Therefore, much progress has been made in this line of study. However, it needs to bear in mind that the roles of HSPGs in cancer can be either oncogenic or tumor-suppressive, depending on the HSPG and the cancer cell type with the underlying mechanisms that remain obscure. Further studies need to address these to fill the knowledge gap and rationalize more efficient therapeutic targeting.

Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis

Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The “limma” package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell−substrate adhesion cytokine−cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC.

Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients

BACKGROUND

Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only "curative" treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC.

METHODS

In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database.

RESULTS

LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19.

CONCLUSIONS

In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs.

Expression and Role of Heparan Sulfated Proteoglycans in Pancreatic Cancer

Pancreatic cancer is a lethal condition with poor outcomes and an increasing incidence. The unfavourable prognosis is due to the lack of early symptoms and consequent late diagnosis. An effective method for the early diagnosis of pancreatic cancer is therefore sought by many researchers in the field. Heparan sulfated proteoglycan-related genes are often expressed differently in tumors than in normal tissues. Alteration of the tumor microenvironment is correlated with the ability of heparan sulfated proteoglycans to bind cytokines and growth factors and eventually to influence tumor progression. Here we discuss the importance of glypicans, syndecans, perlecan and extracellular matrix modifying enzymes, such as heparanases and sulfatases, as potential diagnostics in pancreatic cancer. We also ran an analysis on a multidimensional cancer genomics database for heparan sulfated proteoglycan-related genes, and report altered expression of some of them.

Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference

Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments.

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

BACKGROUND

The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.

METHODS

Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.

RESULTS

Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).

CONCLUSION

Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.