Screening and Survival Analysis of Hub Genes in Gastric Cancer Based on Bioinformatics

Identification of gastric cancer biomarkers through in-silico analysis of microarray based datasets

Gastric cancer is among the most prevalent cancers worldwide including in Pakistan. Late diagnosis of gastric cancer leads to reduced survival. The present study aimed to investigate biomarkers for early diagnosis and prognosis of gastric cancer. For this purpose, the ten microarray-based gene expression datasets (GSE54129, GSE79973, GSE161533, GSE103236, GSE33651, GSE19826, GSE118916, GSE112369, GSE13911, and GSE81948) were retrieved from GEO database and analyzed by GEO2R to identify differentially expressed genes. Datasets were arranged in subsets of different dataset combinations to identify common DEGs. The gene ontology and functional pathway enrichment analysis of common DEGs was performed by DAVID tool. Pan-cancer analysis was conducted by UALCAN database. Survival analysis of common DEGs was done by Kaplan-Meier plotter. A total of 71 common DEGs were identified in different combinations of datasets. Among them, only 5 DEGs namely ATP4B, ATP4A, CCKBR, KCNJ15, and KCNJ16 were detected to be common in all the datasets. The GO and pathway analysis represented that the identified DEGs are involved in gastric acid secretion and collecting duct acid secretion pathways. Further expression validation of these five genes using three additional datasets (GSE31811, GSE26899, and GSE26272) confirmed their differential expression in gastric cancer samples. The pan-cancer analysis also revealed aberrant expression of DEGs in various cancers. The survival analysis showed the association of these 5 DEGs with poor survival of gastric cancer patients. To conclude, this study revealed a panel of 5 genes, which can be employed as diagnostic and prognostic biomarkers of gastric cancer patients.

G-protein-coupled estrogen receptor 1 promotes peritoneal metastasis of gastric cancer through nicotinamide adenine dinucleotide kinase 1-mediated redox modulation

Adipose tissue is the second most important site of estrogen production, where androgens are converted into estrogen by aromatase. While gastric cancer patients often develop adipocyte-rich peritoneal metastasis, the underlying mechanism remains unclear. In this study, we identified the G-protein-coupled estrogen receptor (GPER1) as a promoter of gastric cancer peritoneal metastasis. Functional in vitro studies revealed that β-Estradiol (E2) or the GPER1 agonist G1 inhibited anoikis in gastric cancer cells. Additionally, genetic overexpression or knockout of GPER1 significantly inhibited or enhanced gastric cancer cell anoikis in vitro and peritoneal metastasis in vivo, respectively. Mechanically, GPER1 knockout disrupted the NADPH pool and increased reactive oxygen species (ROS) generation. Conversely, overexpression of GPER1 had the opposite effects. GPER1 suppressed nicotinamide adenine dinucleotide kinase 1(NADK1) ubiquitination and promoted its phosphorylation, which were responsible for the elevated expression of NADK1 at protein levels and activity, respectively. Moreover, genetic inhibition of NADK1 disrupted NADPH and redox homeostasis, leading to high levels of ROS and significant anoikis, which inhibited lung and peritoneal metastasis in cell-based xenograft models. In summary, our study suggests that inhibiting GPER1-mediated NADK1 activity and its ubiquitination may be a promising therapeutic strategy for peritoneal metastasis of gastric cancer.

The G protein-coupled oestrogen receptor GPER in health and disease: an update

Oestrogens and their receptors contribute broadly to physiology and diseases. In premenopausal women, endogenous oestrogens protect against cardiovascular, metabolic and neurological diseases and are involved in hormone-sensitive cancers such as breast cancer. Oestrogens and oestrogen mimetics mediate their effects via the cytosolic and nuclear receptors oestrogen receptor-α (ERα) and oestrogen receptor-β (ERβ) and membrane subpopulations as well as the 7-transmembrane G protein-coupled oestrogen receptor (GPER). GPER, which dates back more than 450 million years in evolution, mediates both rapid signalling and transcriptional regulation. Oestrogen mimetics (such as phytooestrogens and xenooestrogens including endocrine disruptors) and licensed drugs such as selective oestrogen receptor modulators (SERMs) and downregulators (SERDs) also modulate oestrogen receptor activity in both health and disease. Following up on our previous Review of 2011, we herein summarize the progress made in the field of GPER research over the past decade. We will review molecular, cellular and pharmacological aspects of GPER signalling and function, its contribution to physiology, health and disease, and the potential of GPER to serve as a therapeutic target and prognostic indicator of numerous diseases. We also discuss the first clinical trial evaluating a GPER-selective drug and the opportunity of repurposing licensed drugs for the targeting of GPER in clinical medicine.

Gene Regulatory Network-Classifier: Gene Regulatory Network-Based Classifier and Its Applications to Gastric Cancer Drug (5-Fluorouracil) Marker Identification

The complex mechanisms of diseases involve the disturbance of the molecular network, rather than disorder in a single gene, implying that single gene-based analysis is insufficient to understand these mechanisms. Gene regulatory networks (GRNs) have attracted a lot of interest and various approaches have been developed for their statistical inference and gene network-based analysis. Although various computational methods have been developed, relatively little attention has been paid to incorporation of biological knowledge into the computational approaches. Furthermore, existing studies on network-based analysis perform prediction/classification of status of cell lines based on preconstructed GRNs, implying that we cannot extract prediction/classification-specific gene networks, leading to difficulty in interpretation of biological mechanisms and marker identification related to the status of cancer cell lines. We developed a novel strategy to build a GRN-based classifier, called a GRN-classifier. The proposed GRN-classifier estimates GRNs and classifies cell lines simultaneously, where the gene network is estimated to minimize error in gene network estimation and the negative log-likelihood for classifying cell lines. Thus, we can identify biological status-specific gene regulatory systems, enabling us to achieve biologically reliable interpretation of the classification. We also propose an algorithm to implement the GRN-classifier based on coordinate descent update. Monte Carlo simulations were conducted to examine performance of the GRN-classifier. Results: Our strategy provides effective results in feature selection in the classification model and edge selection in gene network estimation. The GRN-classifier also shows outstanding classification accuracy. We apply the GRN-classifier to classify cancer cell lines into anticancer drug-related status, that is, 5-fluorouracil (5-FU)-sensitive/resistant and 5-FU target/nontarget cancer cell lines. We then identified 5-FU markers based on 5-FU-related status classification-specific gene networks. The mechanisms of the identified markers were verified through literature survey. Our results suggest that the molecular interplay between MYOF and AHNAK2 may play a crucial role in drug resistance and can provide information on the chemotherapy efficiency of 5-FU. It is also suggested that suppression of the identified 5-FU markers, including MYOF/AHNAK2 and AKR1C1/AKR1C3 may improve 5-FU resistance of cancer cell lines.

PredictiveNetwork: predictive gene network estimation with application to gastric cancer drug response-predictive network analysis

Background

Gene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology.

Results

To effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks.

Conclusion

We identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04871-z.

Prognostic and Functional Analysis of NPY6R in Uveal Melanoma Using Bioinformatics

Neuropeptides can mediate tumor cell proliferation and differentiation through autocrine, paracrine, neurosecretory, and endocrine mechanisms. This study investigated the expression and prognostic significance of neuropeptide Y receptor Y6 (NPY6R) in uveal melanoma (UVM) and preliminarily investigated the biological function of NPY6R in UVM. NPY6R was poorly expressed in most tumors and was associated with better prognosis in UVM. Among the clinicopathological features of UVM, NPY6R expression was lower in male patients. The area under the curve (AUC) value of NPY6R for the diagnosis of UVM was 0.676 (95% CI: 0.556–0.795). A nomogram including four clinical predictors was constructed. NPY6R expression was significantly associated with features of the UVM immune microenvironment. ESTIMATE and CIBERSORT algorithms were used to calculate the fraction of immune cells and the percentage of infiltration in each patient, respectively. NPY6R expression-related gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analyses were performed. GO and KEGG enrichment analyses revealed that NPY6R-related genes are mainly enriched in pathways and functions related to visual light perception. Gene set enrichment analysis suggested that NPY6R is associated with tumor progression in UVM. NPY6R is involved in the tumor progression of UVM and has a good predictive value as a prognostic marker of UVM.

Role of estrogen receptors in health and disease

Estrogen receptors (ERs) regulate multiple complex physiological processes in humans. Abnormal ER signaling may result in various disorders, including reproductive system-related disorders (endometriosis, and breast, ovarian, and prostate cancer), bone-related abnormalities, lung cancer, cardiovascular disease, gastrointestinal disease, urogenital tract disease, neurodegenerative disorders, and cutaneous melanoma. ER alpha (ERα), ER beta (ERβ), and novel G-protein-coupled estrogen receptor 1 (GPER1) have been identified as the most prominent ERs. This review provides an overview of ERα, ERβ, and GPER1, as well as their functions in health and disease. Furthermore, the potential clinical applications and challenges are discussed.

A novel strategy to identify candidate diagnostic and prognostic biomarkers for gastric cancer

Background

Gastric cancer (GC) is one of the most common cancer worldwide. It is essential to identify non-invasive diagnostic and prognostic biomarkers of GC. The aim of the present study was to screen candidate biomarkers associated with the pathogenesis and prognosis of GC by a novel strategy.

Methods

The expression level of gene higher in cancer than in adjacent non-cancer tissue was defined as “positive”, and the top 5% genes with “positive rate” were filtered out as candidate diagnostic biomarkers in three Gene Expression Omnibus (GEO) datasets. Further, a prognostic risk model was constructed by multivariate Cox regression analysis in GEO dataset and validated in The Cancer Genome Atlas (TCGA). The expression level of candidate biomarkers was determined in serum and serum-derived exosomes of GC patients. Moreover, the effect of biomarkers in exosomes on migration of GC cells was analyzed by transwell assay.

Results

Ten candidate biomarkers (AGT, SERPINH1, WNT2, LIPG, PLAU, COL1A1, MMP7, MXRA5, CXCL1 and COL11A1) were identified with efficient diagnostic value in GC. A prognostic gene signature consisted of AGT, SERPINH1 and MMP7 was constructed and showed a good performance in predicting overall survivals in TCGA. Consistently, serum levels of the three biomarkers also showed high sensitivity and specificity in distinguishing GC patients from controls. In addition, the expression level of the three biomarkers were associated with malignant degree and decreased after surgery in GC patients. Moreover, the expression level of AGT and MMP7 in exosomes correlated positively with serum level. The exosomes derived from serum of GC patients can promote migration of SGC‐7901 cells. After neutralized the expression level of three proteins in exosomes with antibodies, the migration of GC cells was obviously suppressed.

Conclusions

Our findings provided a novel strategy to identify diagnostic biomarkers based on public datasets, and suggested that the three-gene signature was a candidate diagnostic and prognostic biomarker for patients with GC.

Role of G Protein-Coupled Estrogen Receptor in Digestive System Carcinomas: A Minireview

Digestive system carcinomas are one of the leading causes of cancer-related deaths worldwide. G protein-coupled estrogen receptor (GPER), a novel estrogen receptor, has been recognized as an important mediator in numerous cancer types. Recently, the function and clinical significance of GPER in digestive system carcinomas has been a subject of interest. Increasing evidence has revealed that GPER plays an important role as a potential biomarker in digestive system carcinomas. This work summarizes the recent literature and focuses on the emerging functional role of GPER in digestive system carcinomas, including gastric cancer, hepatocellular carcinoma, pancreatic cancer, and colorectal cancer. The potential application of GPER in novel strategies for the diagnosis and treatment of digestive system carcinomas is discussed and highlighted.

Identification and Validation of an Individualized EMT-Related Prognostic Risk Score Formula in Gastric Adenocarcinoma Patients

Background

The epithelial-mesenchymal transition (EMT) is a pivotal process for fibrotic disease, embryonic development, and wound healing. Moreover, some evidence has proven that the disorder of EMT also plays an important role in carcinogenesis, especially invasion and metastasis of various tumors (Ritchie et al., 2015). Additionally, gastric adenocarcinoma (GAC) is a common gastrointestinal malignancy which is the fourth most commonly diagnosed tumor. Our study is aimed at identifying the prognostic value of EMT-related genes in gastric adenocarcinoma.

Methods

Firstly, high-throughput and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. 99 differentially expressed EMT-related genes (ERGs) were obtained in these gastric adenocarcinoma data. Secondly, GO and KEGG enrichment analyses show that EMT may promote gastric carcinogenesis. Next, 10 ERGs associated with prognosis of gastric adenocarcinoma patients are screened out by univariate Cox regression, and 6 pivotal prognostic ERGs (MMP8, MMP11, TFDP3, MYB, F2, and CNTN1) are identified through multivariate Cox regression. These 6 genes are confirmed with significant prognostic value in gastric adenocarcinoma through overall survival (OS) analysis. Finally, a risk score formula is constructed and tested in another gastric adenocarcinoma cohort from GEO.

Results

99 differentially expressed EMT-related genes (ERGs) and their enriched pathways are identified. 10 ERGs are strongly related to the prognosis of GAC patients. A risk score formula of 6 prognosis-related ERGs used to predict the prognosis of gastric adenocarcinoma patients is identified and tested (risk score = 0.448115∗expression value of MMP8 + 0.378892∗expression value of MMP11 − 0.3226∗expression value of MYB + 1.322812∗expression value of TFDP3 + 0.325063∗expression value of F2 + 0.334197∗expression value of CNTN1).

Conclusion

This study provides a potential prognostic signature for predicting prognosis of gastric adenocarcinoma patients and molecular insights of EMT in gastric adenocarcinoma, and the formula focusing on the prognosis of gastric adenocarcinoma can be effective.

SERPINH1 regulates EMT and gastric cancer metastasis via the Wnt/β-catenin signaling pathway

In this study, we investigated the role of SERPINH1 in gastric cancer (GC) progression. GC patient tissues show significantly higher SERPINH1 mRNA and protein levels than normal gastric mucosal tissues. GC patients with high SERPINH1 expression are associated with lymph node metastasis and poor prognosis. SERPINH1 mRNA levels negatively correlate with E-cadherin mRNA levels and positively correlate with levels of N-cadherin, MMP2, and MMP9 mRNA levels. This suggests SERPINH1 regulates epithelial to mesenchymal transition (EMT). SERPINH1 expression was significantly higher in the HGC-27, AGS, MGC-803, and SGC-7901 GC cell lines than in the GES-1 normal gastric mucosal cell line. In SERPINH1-silenced SGC-7901 cells, survival, colony formation, migration and invasion were all reduced, whereas they were all enhanced in SERPINH1-overexpressing MGC-803 cells. Levels of WNT/β-catenin signaling pathway proteins, including β-catenin, Wnt2, GSK-3β, p-GSK-3β, NF-κB P65, Snail1, Slug and TWIST, were all reduced in SERPINH1-silenced SGC-7901 cells, and increased in the SERPINH1-overexpressing MGC-803 cells. Inhibition of SERPINH1 protein using Co1003 significantly decreased survival, invasion, and migration of GC cells. SERPINH1 thus appears to regulate EMT and GC progression via the Wnt/β-catenin pathway, making SERPINH1 a potential prognostic biomarker and therapeutic target in GC patients.