Endocan Promotes Pro-Tumorigenic Signaling in Lung Cancer Cells: Modulation of Cell Proliferation, Migration and lncRNAs H19 and HULC Expression

The predictive value of endocan as a novel biomarker: an umbrella study on meta-analyses

BACKGROUND AND AIM

In recent years, endocan has emerged as a potential biomarker in various medical conditions. This multifaceted molecule, involved in key processes such as inflammation and endothelial dysfunction, has shown promise in predicting disease progression and therapeutic response across a spectrum of pathologies. However, the heterogeneity of studies and the complexity of endocan's role in different diseases necessitate a comprehensive review. This umbrella review aimed to systematically synthesize and evaluate the evidence from multiple meta-analyses, offering a view of endocan's effectiveness as a predictive biomarker in medical diseases.

METHODS

An extensive search was carried out on March 12, 2024, using the following four databases: PubMed, Scopus, Web of Science, and Cochrane Library. The goal was to identify meta-analyses that assess endocan's predictive efficacy. The pooled effect size and its 95% confidence interval were taken out of each discovered meta-analysis. Furthermore, power analyses were performed to assess the robustness and dependability of the results. An additional GRADE assessment was carried out to gauge the epidemiological reliability of the findings.

RESULTS

In the final analysis, 12 meta-analyses were included in the current umbrella review. The results showed that there is a significant correlation between a higher endocan level and COVID-19 (SMD: 1.40, 95% CI 0.21-2.58, P = 0.02), followed by chronic kidney disease (SMD: 1.34, 95% CI 0.20 to 2.48, P < 0.01), obstructive sleep apnea (SMD: 1.30, 95% CI 1.06-1.54, P < 0.01), diabetes mellitus (SMD: 1.00, 95% CI 0.81 to 1.19, P < 0.01), coronary artery disease (SMD: 0.99, 95% CI 0.58-1.39, P < 0.01), hypertension (SMD: 0.91, 95% CI 0.44-1.38, P < 0.01), and preeclampsia (SMD: 0.37, 95% CI 0.13-0.62, P < 0.01).

CONCLUSION

Endocan has emerged as a highly promising biomarker with considerable potential across various medical conditions. Its relevance spans critical areas such as COVID-19, chronic kidney disease, obstructive sleep apnea, diabetes mellitus, coronary artery disease, and preeclampsia. The broad applicability of endocan highlights its value in improving diagnostic accuracy and enhancing our understanding of these diseases. Clinically, incorporating endocan testing could aid in early detection, monitoring disease progression, and refining patient management, particularly for high-risk populations. However, additional research is needed to fully assess its specificity, sensitivity, and overall clinical utility, paving the way for its integration into routine healthcare practices and enabling more precise, individualized treatment strategies.

Endocan, a novel glycoprotein with multiple biological activities, may play important roles in neurological diseases

Endothelial cell specific-1 (ESM-1), also known as endocan, is a soluble dermatan sulfate proteoglycan that is mainly secreted by endothelial cells. Endocan is associated with tumorigenesis and cancer progression and is also related to cardiovascular disorders, autoimmune diseases, and sepsis. The phenylalanine-rich region and linear polysaccharide of endocan are necessary for the protein to exert its biological functions. Elevated plasma endocan levels reflect endothelial activation and dysfunction. In addition, endocan participates in complex inflammatory responses and proliferative processes. Here, we reviewed current research on endocan, elaborated the protein's structure and biological functions, and speculated on its possible clinical value in nervous system diseases. We conclude that endocan may be a glycoprotein that plays an important role in neurological disorders.

Glycosylation in cancer as a source of biomarkers

INTRODUCTION

Glycosylation, the process of glycan synthesis and attachment to target molecules, is a crucial and common post-translational modification (PTM) in mammalian cells. It affects the protein's hydrophilicity, charge, solubility, structure, localization, function, and protection from proteolysis. Aberrant glycosylation in proteins can reveal new detection and therapeutic Glyco-biomarkers, which help to improve accurate early diagnosis and personalized treatment. This review underscores the pivotal role of glycans and glycoproteins as a source of biomarkers in human diseases, particularly cancer.

AREAS COVERED

This review delves into the implications of glycosylation, shedding light on its intricate roles in cancer-related cellular processes influencing biomarkers. It is underpinned by a thorough examination of literature up to June 2024 in PubMed, Scopus, and Google Scholar; concentrating on the terms: (Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).

EXPERT OPINION

Glyco-biomarkers enhance early cancer detection, allow early intervention, and improve patient prognoses. However, the abundance and complex dynamic glycan structure may make their scientific and clinical application difficult. This exploration of glycosylation signatures in cancer biomarkers can provide a detailed view of cancer etiology and instill hope in the potential of glycosylation to revolutionize cancer research.

Emerging roles of long noncoding RNA H19 in human lung cancer

Lung cancer holds the position of being the primary cause of cancer-related fatalities on a global scale. Furthermore, it exhibits the highest mortality rate among all types of cancer. The survival rate within a span of 5 years is less than 20%, primarily due to the fact that the disease is often diagnosed at an advanced stage, resulting in less effective treatment options compared to earlier stages. There are two main types of primary lung cancer: nonsmall-cell lung cancer, which accounts for approximately 80%-85% of all cases, and small-cell lung cancer, which is categorized based on the specific type of cells in which the cancer originates. The understanding of the biology of this disease and the identification of oncogenic driver alterations have significantly transformed the landscape of therapeutic approaches. Long noncoding RNAs (lncRNAs) play a crucial role in regulating various physiological and pathological processes through diverse molecular mechanisms. Among these lncRNAs, lncRNA H19, initially identified as an oncofetal transcript, has garnered significant attention due to its elevated expression in numerous tumors. Extensive research has confirmed its involvement in tumorigenesis and malignant progression by promoting cell growth, invasion, migration, epithelial-mesenchymal transition, metastasis, and therapy resistance. This comprehensive review aims to provide an overview of the aberrant overexpression of lncRNA H19 and the molecular pathways through which it contributes to the advancement of lung cancer. The findings of this review highlight the potential for further investigation into the diagnosis and treatment of this disease, offering promising avenues for future research.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

ESM1 Interacts with c-Met to Promote Gastric Cancer Peritoneal Metastasis by Inducing Angiogenesis

The peritoneum is the most common metastatic site of advanced gastric cancer and is associated with extremely poor prognosis. Endothelial-specific molecule 1 (ESM1) was found to be significantly associated with gastric cancer peritoneal metastasis (GCPM); however, the biological functions and molecular mechanisms of ESM1 in regulating GCPM remain unclear. Herein, we demonstrated that ESM1 expression was significantly upregulated in gastric cancer tissues and positively correlated with platelet endothelial cell adhesion molecule-1 (CD31) levels. Moreover, clinical validation, in in vitro and in vivo experiments, confirmed that ESM1 promoted gastric cancer angiogenesis, eventually promoting gastric cancer peritoneal metastasis. Mechanistically, ESM1 promoted tumor angiogenesis by binding to c-Met on the vascular endothelial cell membrane. In addition, our results confirmed that ESM1 upregulated VEGFA, HIF1α, and MMP9 expression and induced angiogenesis by activating the MAPK/ERK pathway. In conclusion, our findings identified the role of ESM1 in gastric cancer angiogenesis and GCPM, thus providing insights into the diagnosis and treatment of advanced gastric cancer.