Prognostic Value of Upregulation of Myristoylated Alanine-Rich C-Kinase Substrate in Gastric Cancer

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC

BACKGROUND

Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations.

PATIENTS AND METHODS

The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC.

RESULTS

TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05).

CONCLUSIONS

Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

LINC01268 promotes epithelial-mesenchymal transition, invasion and metastasis of gastric cancer via the PI3K/Akt signaling pathway and targeting MARCKS

BACKGROUND

Long non-coding RNAs (lncRNAs) with differential expression characteristics have been found to be closely related to the tumorigenesis and development of gastric cancer (GC), but their specific mechanisms and roles still need to be further elucidated.

AIM

To investigate the expression of LINC01268 in GC and its mechanism of affecting GC progression.

METHODS

Real-time quantitative polymerase chain reaction was used to detect the expression of LINC01268 in GC tissues, cell lines and plasma. The Kaplan-Meier method was used to evaluate the value of LINC01268 in the prognostication of GC patients. An receiver operating characteristic curve was constructed to evaluate the value of LINC01268 in the diagnosis of GC. Transwell migration and invasion assays and wound healing assays were used to confirm the effect of LINC01268 on the invasion and migration of GC cells. The regulatory relationship between LINC01268 and myristoylated alanine rich protein kinase C substrate (MARCKS), the PI3K/Akt signaling pathway, and the epithelial-mesenchymal transition (EMT) process in GC was demonstrated by western blot analysis.

RESULTS

The expression of LINC01268 was increased in GC tissues and cell lines. The expression level of LINC01268 was significantly correlated with lymph node metastasis, TNM stage, and tumor differentiation in patients with GC. Over-expression of LINC01268 indicated a poor prognosis for patients with GC, and it had a certain auxiliary diagnostic value for GC. In vitro functional experiments proved that the abnormal expression of LINC01268 further activated the PI3K/Akt signaling pathway and promoted EMT by targeting and regulating MARCKS and ultimately promoted the invasion and metastasis of GC.

CONCLUSION

This study elucidates that LINC01268 in GC may be an oncogene that further activates the PI3K/Akt signaling pathway and EMT by targeting and regulating MARCKS, and ultimately promotes the invasion and metastasis of GC. LINC01268 may be a potential effective target for the treatment of GC.

Patent landscape highlighting therapeutic implications of peptides targeting myristoylated alanine-rich protein kinase-C substrate (MARCKS)

INTRODUCTION

MARCKS protein, a protein kinase C (PKC) substrate, is known to be at the intersection of several intracellular signaling pathways and plays a pivotal role in cellular physiology. Unlike PKC inhibitors, MARCKS-targeting drug (BIO-11006) has shown early success in clinical trials involving lung diseases. Recent research investigations have identified two MARCKS-targeting peptides which possess multifaceted implications against asthma, cancer, inflammation, and lung diseases.

AREAS COVERED

This review article provides the patent landscape and recent developments on peptides targeting MARCKS for therapeutic purposes. Online free open-access databases were used to fetch out the patent information, and research articles were fetched using PubMed.

EXPERT OPINION

Research studies highlighting the intriguing role of MARCKS in human disease and physiology have dramatically increased in recent years. A similar increasing trend in the number of patents has also been observed related to the MARCKS-targeting peptides. Thus, there is a need to amalgamate and translate such a trend into therapeutic intervention. Our review article provides an overview of such recent advances, and we believe that our compilation will fetch the interest of researchers around the globe to develop MARCKS-targeting peptides in future for human diseases.

The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.

MiR-652 serves as a prognostic biomarker in gastric cancer and promotes tumor proliferation, migration, and invasion via targeting RORA

PURPOSE

MicroRNAs (miRNAs) have been reported to be involved in tumorigenesis. The aim of this study was to investigate the functional role and prognostic value of miR-652 in gastric cancer (GC).

METHODS

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to determine the expression levels of miR-652 in human GC tissue samples and GC cell lines. The Kaplan-Meier survival curves and Cox regression analysis were performed to measure the prognostic value of miR-652 in GC. The tumor cell proliferation capacity was estimated by MTT assay, and cell migration and invasion were assessed by Transwell assays. The luciferase reporter assay was performed to confirm the target gene of miR-652.

RESULTS

MiR-652 was significantly elevated in GC tissues and cell lines (all P< 0.001). And the expression level of miR-652 was significantly associated with TNM stage and lymph node metastasis (all P< 0.05). GC patients with high expression of miR-652 had a shorter overall survival rate than those with low miR-652 expression (log-rank P< 0.001). The miR-652 and TNM stage were proven to be independent prognostic predictors for the GC patients. Overexpressing miR-652 could enhance cell proliferation, migration and invasion (all P< 0.01). RORA was proved to be the target gene of miR-652.

CONCLUSION

MiR-652 functions as an oncogene in GC and promotes tumor progression via targeting RORA. MiR-652 might be a novel predictive marker for the poor prognosis of GC patients.

Identification of Hub Genes and Pathways in Gastric Adenocarcinoma Based on Bioinformatics Analysis

BACKGROUND Gastric adenocarcinoma accounts for 95% of all gastric malignant tumors. The purpose of this research was to identify differentially expressed genes (DEGs) of gastric adenocarcinoma by use of bioinformatics methods. MATERIAL AND METHODS The gene microarray datasets of GSE103236, GSE79973, and GSE29998 were imported from the GEO database, containing 70 gastric adenocarcinoma samples and 68 matched normal samples. Gene ontology (GO) and KEGG analysis were applied to screened DEGs; Cytoscape software was used for constructing protein-protein interaction (PPI) networks and to perform module analysis of the DEGs. UALCAN was used for prognostic analysis. RESULTS We identified 2909 upregulated DEGs (uDEGs) and 7106 downregulated DEGs (dDEGs) of gastric adenocarcinoma. The GO analysis showed uDEGs were enriched in skeletal system development, cell adhesion, and biological adhesion. KEGG pathway analysis showed uDEGs were enriched in ECM-receptor interaction, focal adhesion, and Cytokine-cytokine receptor interaction. The top 10 hub genes - COL1A1, COL3A1, COL1A2, BGN, COL5A2, THBS2, TIMP1, SPP1, PDGFRB, and COL4A1 - were distinguished from the PPI network. These 10 hub genes were shown to be significantly upregulated in gastric adenocarcinoma tissues in GEPIA. Prognostic analysis of the 10 hub genes via UALCAN showed that the upregulated expression of COL3A1, COL1A2, BGN, and THBS2 significantly reduced the survival time of gastric adenocarcinoma patients. Module analysis revealed that gastric adenocarcinoma was related to 2 pathways: including focal adhesion signaling and ECM-receptor interaction. CONCLUSIONS This research distinguished hub genes and relevant signal pathways, which contributes to our understanding of the molecular mechanisms, and could be used as diagnostic indicators and therapeutic biomarkers for gastric adenocarcinoma.