Prognostic marker identification based on weighted gene co-expression network analysis and associated in vitro confirmation in gastric cancer

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Research on the biological mechanism and potential application of CEMIP

Cell migration-inducing protein (CEMIP), also known as KIAA1199 and hyaluronan-binding protein involved in hyaluronan depolymerization, is a new member of the hyaluronidase family that degrades hyaluronic acid (HA) and remodels the extracellular matrix. In recent years, some studies have reported that CEMIP can promote the proliferation, invasion, and adhesion of various tumor cells and can play an important role in bacterial infection and arthritis. This review focuses on the pathological mechanism of CEMIP in a variety of diseases and expounds the function of CEMIP from the aspects of inhibiting cell apoptosis, promoting HA degradation, inducing inflammatory responses and related phosphorylation, adjusting cellular microenvironment, and regulating tissue fibrosis. The diagnosis and treatment strategies targeting CEMIP are also summarized. The various functions of CEMIP show its great potential application value.

CEMIP, a Promising Biomarker That Promotes the Progression and Metastasis of Colorectal and Other Types of Cancer

Simple Summary

CEMIP (cell migration-inducing and hyaluronan-binding protein) has been implicated in the pathogenesis of numerous diseases, including colorectal and other forms of cancer. The molecular functions of CEMIP are currently under investigation and include the degradation of the extracellular matrix component hyaluronic acid (HA), as well as the regulation of a number of signaling pathways. In this review, we survey our current understanding of how CEMIP contributes to tumor growth and metastasis, focusing particularly on colorectal cancer, for which it serves as a promising biomarker.

Abstract

Originally discovered as a hypothetical protein with unknown function, CEMIP (cell migration-inducing and hyaluronan-binding protein) has been implicated in the pathogenesis of numerous diseases, including deafness, arthritis, atherosclerosis, idiopathic pulmonary fibrosis, and cancer. Although a comprehensive definition of its molecular functions is still in progress, major functions ascribed to CEMIP include the depolymerization of the extracellular matrix component hyaluronic acid (HA) and the regulation of a number of signaling pathways. CEMIP is a promising biomarker for colorectal cancer. Its expression is associated with poor prognosis for patients suffering from colorectal and other types of cancer and functionally contributes to tumor progression and metastasis. Here, we review our current understanding of how CEMIP is able to foster the process of tumor growth and metastasis, focusing particularly on colorectal cancer. Studies in cancer cells suggest that CEMIP exerts its pro-tumorigenic and pro-metastatic activities through stimulating migration and invasion, suppressing cell death and promoting survival, degrading HA, regulating pro-metastatic signaling pathways, inducing the epithelial–mesenchymal transition (EMT) program, and contributing to the metabolic reprogramming and pre-metastatic conditioning of future metastatic microenvironments. There is also increasing evidence indicating that CEMIP may be expressed in cells within the tumor microenvironment that promote tumorigenesis and metastasis formation, although this remains in an early stage of investigation. CEMIP expression and activity can be therapeutically targeted at a number of levels, and preliminary findings in animal models show encouraging results in terms of reduced tumor growth and metastasis, as well as combating therapy resistance. Taken together, CEMIP represents an exciting new player in the progression of colorectal and other types of cancer that holds promise as a therapeutic target and biomarker.

Polymorphisms of an oncogenic gene, mesothelin, predict the risk and prognosis of gastric cancer in a Chinese Han population

Mesothelin (MSLN) is a cell surface protein associated with tumor invasion and metastasis. This study aims to explore the biological function of MSLN in gastric cancer and to evaluate the association of MSLN polymorphism (rs3764247, rs3764246, rs12597489, rs1057147, rs3765319) with the risk and prognosis of gastric cancer. Small interfering RNA (siRNA) transfection and MSLN overexpression were performed in human gastric cancer cell lines, respectively. The proliferation of tumor cells was evaluated by Cell counting kit 8(CCK-8) and colony formation assay. Wound healing assay and transwell assay were used to elucidate gastric cancer cell migration and invasion rates. We conducted a case-control study involving 860 patients with gastric cancer and 870 controls. All mutation sites were genotyped by PCR-LDR sequencing. First, our study revealed the cancer-promoting role of MSLN in gastric cancer. Second, we also demonstrated that rs3764247 and rs3764246 were associated with a reduced risk of gastric cancer (OR = 0.83, p = 0.010; OR = 0.84, p = 0.011; respectively). The clinicopathological analysis further showed that rs3764247 was closely related to T stage, vascular infiltration, and HER2 expression. In addition, in the survival analysis of 392 patients with gastric cancer, patients with rs3764247 CC genotype had poorer survival than patients with AA + AC genotype after adjusting for age, sex, TNM stage, and Lauren classification (HR = 2.07, p = 0.029). Our findings indicated that MSLN could be an oncogene whose polymorphisms were closely related to the risk and prognosis of gastric cancer.

Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma

PURPOSE

Uveal melanoma (UM) is an aggressive intraocular malignancy, leading to systemic metastasis in half of the patients. However, the mechanism of the high metastatic rate remains unclear. This study aimed to identify key genes related to metastasis and construct a gene-based signature for better prognosis prediction of UM patients.

METHODS

Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate, Lasso-penalized and multivariate Cox regression analyses were performed to establish a prognostic signature for UM patients.

RESULTS

The tan and greenyellow modules were significantly associated with the metastasis of UM patients. Significant genes related to the overall survival (OS) in these two modules were then identified. Additionally, an OS-predicting signature was established. The UM patients were divided into a low- or high-risk group. The Kaplan-Meier curve indicated that high-risk patients had poorer OS than low-risk patients. The receiver operating curve (ROC) was used to validate the stability and accuracy of the final five-gene signature. Based on the signature and clinical traits of UM patients, a nomogram was established to serve in clinical practice.

CONCLUSIONS

We identified key genes involved in the metastasis of UM. A robust five-gene-based prognostic signature was constructed and validated. In addition, the gene signature-based nomogram was created that can optimize the prognosis prediction and identify possible factors causing the poor prognosis of high-risk UM patients.