Identification of differentially-expressed genes in intestinal gastric cancer by microarray analysis

Artificial intelligence-guided discovery of gastric cancer continuum

BACKGROUND

Detailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.

METHODS

We built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.

RESULTS

Our model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.

CONCLUSIONS

A Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.

Molecular Characteristics and Promoter Analysis of Porcine COL1A1

COL1A1 encodes the type I collagen α1 chain, which shows the highest abundance among members of the collagen family and is widely expressed in different mammalian cells and tissues. However, its molecular characteristics are not completely elucidated. In this study, the molecular profiles of COL1A1 and characteristics of the COL1A1 protein were investigated using a promoter activity assay and multiple bioinformatics tools. The results showed that the 5' flanking region of porcine COL1A1 contained two CpG islands, five core promoter sequences, and twenty-six transcription factor-binding sites. In the luciferase assay, the upstream 294 bp region of the initiation codon of COL1A1 showed the highest activity, confirming that this section is the core region of the porcine COL1A1 promoter. Bioinformatic analysis revealed that COL1A1 is a negatively charged, hydrophilic secreted protein. It does not contain a transmembrane domain and is highly conserved in humans, mice, sheep, and pigs. Protein interaction analysis demonstrated that the interaction coefficient of COL1A1 with COL1A2, COL3A1, ITGB1, and ITGA2 was greater than 0.9, suggesting that this protein plays a crucial role in collagen structure formation and cell adhesion. These results provide a theoretical basis for further investigation of the functions of porcine COL1A1.

Systems biology and OMIC data integration to understand gastrointestinal cancers

Gastrointestinal (GI) cancers are a set of diverse diseases affecting many parts/ organs. The five most frequent GI cancer types are esophageal, gastric cancer (GC), liver cancer, pancreatic cancer, and colorectal cancer (CRC); together, they give rise to 5 million new cases and cause the death of 3.5 million people annually. We provide information about molecular changes crucial to tumorigenesis and the behavior and prognosis. During the formation of cancer cells, the genomic changes are microsatellite instability with multiple chromosomal arrangements in GC and CRC. The genomically stable subtype is observed in GC and pancreatic cancer. Besides these genomic subtypes, CRC has epigenetic modification (hypermethylation) associated with a poor prognosis. The pathway information highlights the functions shared by GI cancers such as apoptosis; focal adhesion; and the p21-activated kinase, phosphoinositide 3-kinase/Akt, transforming growth factor beta, and Toll-like receptor signaling pathways. These pathways show survival, cell proliferation, and cell motility. In addition, the immune response and inflammation are also essential elements in the shared functions. We also retrieved information on protein-protein interaction from the STRING database, and found that proteins Akt1, catenin beta 1 (CTNNB1), E1A binding protein P300, tumor protein p53 (TP53), and TP53 binding protein 1 (TP53BP1) are central nodes in the network. The protein expression of these genes is associated with overall survival in some GI cancers. The low TP53BP1 expression in CRC, high EP300 expression in esophageal cancer, and increased expression of Akt1/TP53 or low CTNNB1 expression in GC are associated with a poor prognosis. The Kaplan Meier plotter database also confirmed the association between expression of the five central genes and GC survival rates. In conclusion, GI cancers are very diverse at the molecular level. However, the shared mutations and protein pathways might be used to understand better and reveal diagnostic/prognostic or drug targets.

Comprehensive analysis of the differential expression and prognostic value of COL1A2 in colon adenocarcinoma

Background: Colon adenocarcinoma (COAD) is a highly heterogeneous disease, which is the second most common cancer in females and third in males. Collagen type I alpha 2 (COL1A2) has been documented to be involved in the carcinogenesis of multiple tumors; however, the expression and prognostic significance of COL1A2 and its underlying mechanism in COAD remains unclarified.

Materials and Methods: The general profile of COL1A2, its expression pattern, and prognostic value were systematically assessed through various bioinformatics tools. The protein level of COL1A2 was verified in COAD patients using immunohistochemistry analysis. In addition, enrichment analyses were performed to explore the possible regulatory pathways of COL1A2 in COAD.

Results: The mRNA and protein levels of COL1A2 were significantly increased in COAD than that in normal tissues (P < 0.05). The COL1A2 expression tended to increase along with cancer stages and nodal metastasis status in COAD, while the promoter methylation levels of COL1A2 might negatively related to its mRNA expression. Survival analysis showed that COL1A2 was a reliable predictor for distinguishing the status of disease-specific survival (DSS), overall survival (OS), and progression-free survival (PFS), and might serve as a robust independent prognostic biomarker for DSS and OS in COAD patients (P < 0.05). The enrichment analysis showed focal adhesion as the most possible regulatory pathway by COL1A2.

Conclusion: Collectively, COL1A2 functioned as an independent prognostic biomarker and might be a potential therapeutic target in COAD.

Downregulated genes by silencing MYC pathway identified with RNA-SEQ analysis as potential prognostic biomarkers in gastric adenocarcinoma

MYC overexpression is a common phenomenon in gastric carcinogenesis. In this study, we identified genes differentially expressed with a downregulated profile in gastric cancer (GC) cell lines with silenced MYC. The TTLL12, CDKN3, CDC16, PTPRA, MZT2B, UBE2T genes were validated using qRT-PCR, western blot and immunohistochemistry in tissues of 213 patients with diffuse and intestinal GC. We identified high levels of TTLL12, MZT2B, CDC16, UBE2T, associated with early and advanced stages, lymph nodes, distant metastases and risk factors such as H. pylori. Our results show that in the diffuse GC the overexpression of CDC16 and UBE2T indicate markers of poor prognosis higher than TTLL12. That is, patients with overexpression of these two genes live less than patients with overexpression of TTLL12. In the intestinal GC, patients who overexpressed CDC16 had a significantly lower survival rate than patients who overexpressed MZT2B and UBE2T, indicating in our data a worse prognostic value of CDC16 compared to the other two genes. PTPRA and CDKN3 proved to be important for assessing tumor progression in the early and advanced stages. In summary, in this study, we identified diagnostic and prognostic biomarkers of GC under the control of MYC, related to the cell cycle and the neoplastic process.

lncRNA TUG1 Promotes Cell Proliferation, Migration, and Invasion in Hepatocellular Carcinoma via Regulating miR-29c-3p/COL1A1 Axis

Background

Taurine upregulated gene 1 (TUG1) has been recognized as a novel oncogenic gene. The current study was established to explore the function and regulatory mechanism of TUG1 in hepatocellular carcinoma (HCC).

Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression of TUG1, miR-29c-3p, and COL1A1 in tissues and cell lines. MTT assay, wound-healing and transwell assay were utilized for the detection of cell viability, migration and invasion, respectively. The interactions between miR-29c-3p and TUG1/COL1A1 were predicted by starBase v2.0 (http://starbase.sysu.edu.cn/) and verified by the dual-luciferase reporter or RNA immunoprecipitation assay. Western blot assay was performed to determine the protein levels of COL1A1, cyclin D1, E-cadherin, N-cadherin, Bcl-2, and Bax.

Results

Dramatically increased expression of TUG1 was noticed in HCC tissues and cell lines. TUG1 knockdown restrained the proliferation, migration, and invasion, and promoted the apoptosis of HCC cells. TUG1 targeted miR-29c-3p and inhibited miR-29c-3p expression. Overexpression of miR-29c-3p inhibited the proliferation, migration and invasion of HCC cells. MiR-29c-3p directly targeted COL1A1 and down-regulated COL1A1 expression. In addition, downregulation of miR-29c-3p and upregulation of COL1A1 both reversed the effects of TUG1 knockdown on the proliferation, apoptosis, migration, and invasion of HCC cells.

Conclusion

TUG1 could promote the proliferation, migration and invasion of HCC cells through regulating miR-29c-3p/COL1A1 axis. This novel finding might provide a latent target for the treatment of HCC.

Translation of gastric disease progression at gene level expression

Helicobacter pylori is associated with the development of several lesions in the human stomach. This chronic infection produces gastritis, which can progress to intestinal metaplasia and gastric cancer. To date, there is very little information regarding gene-expression in the different phases of progression caused by chronic H. pylori infection. In this study, we performed a genome-wide gene-expression analysis in gastric biopsies of patients chronically infected with H. pylori, using the potential of high-throughput technologies that have not been fully exploited in this area. Here we illustrate the potential correlation of H. pylori infection with the gene expression changes in follicular gastritis, chronic gastritis and intestinal metaplasia. We also suggest its potential as biomarkers of each condition. An exploratory set of 21 biopsies from patients with follicular gastritis, chronic gastritis, and intestinal metaplasia were analyzed by gene-expression microarrays in order to identify the biological processes altered in each lesion. The microarray data was corroborated by real-time PCR, while 79 Formalin-Fixed Paraffin-Embeded samples were analyzed by immunohistochemistry. Follicular gastritis exhibited significant enrichment in genes associated with glutamate signaling, while chronic gastritis showed a down-regulation in metallothionein 1 and 2 and in oxidative phosphorylation-related genes, which could be associated with the chronic infecton of H. pylori. Intestinal metaplasia exhibited an over-expression of gastrointestinal stem cell markers, such as LGR5 and PROM1, as well as messenger RNA and nucleic acid metabolism-related genes. The gene-expression patterns found in this study provide new comparative information about chronic gastritis, follicular gastritis and intestinal metaplasia that may play an important role in the development of gastric cancer.

Collagen 1A1 (COL1A1) Is a Reliable Biomarker and Putative Therapeutic Target for Hepatocellular Carcinogenesis and Metastasis

Increasing evidence shows that hepatocellular carcinoma (HCC) is a principal cause of cancer-related mortality globally, especially among Asian and African populations. Collagen type I α1 (COL1A1) is the major component of type I collagen. While aberrant expression of COL1A1 and COL1A2 is implicated in numerous cancers, the differential role of COL1A1 in malignant, premalignant and normal tissues remains unclear, and its clinical significance in HCC has not been elucidated. In this study, using bioinformatics analysis of publicly-available HCC microarray data from Gene Expression Omnibus (GEO) and RNAseq data from The Cancer Genome Atlas (TCGA) database, we determined that COL1A1 is significantly upregulated in HCC tumor tissues in comparison to normal tissues. Our analysis also revealed that COL1A1 confers survival advantage and enhanced oncogenicity on HCC cells. Interestingly, the siRNA-mediated silencing of COL1A1 expression (siCOLIA1) suppressed HCC cells clonogenicity, motility, invasiveness and tumorsphere formation. Concomitantly, siCOL1A1 abrogated Slug-dependent epithelial-to-mesenchymal transition (EMT) and HCC stemness gene-signature, by attenuating expression of stemness markers SOX2, OCT4 and CD133. The present study provides some mechanistic insight into COL1A1 activity in HCC and highlights its putative role as an important diagnostic biomarker and potential therapeutic target in early development and metastasis of HCC.

Differentially expressed circulating miRNAs in postmenopausal osteoporosis: a meta-analysis

MicroRNAs (miRNAs) have been proven to play a crucial role in postmenopausal osteoporosis (PMO), and studies on their diagnostic value have been increasing. In our study, we aim to identify the key miRNAs in the PMO that might be potential biomarkers. A comprehensive systematic literature search was conducted by searching PubMed, Web of Science, Embase and Cochrane Library databases. In the total of 16 independent miRNA expression studies which contained 327 PMO patients and 328 postmenopausal (PM) healthy control samples, miRNAs were evaluated by using robust rank aggregation (RRA) method. A statistically significant meta-signature of up-regulated hsa-miR-133a-3p (P = 1.38e-03) was determined. Then bioinformatics analysis to recruit putative target genes prediction of hsa-miR-133a-3p and pathway enrichment analysis to reveal what biological processes this miRNA may affect were conducted. It was indicated that pathways were commonly associated with adrenergic signaling in cardiomyocytes, adherens junction, PI3K-Akt signaling pathway and AMPK signaling pathway. Furthermore, STRING and Cytoscape tools were used to visualize the interactions between target genes of hsa-miR-133a-3p. Six genes were detected as hub genes among 576 targets which were CDC42, RHOA, EGFR, VAMP2, PIK3R2 and FN1. After Kyoto Encyclopedia of Genes and Genomes pathway analysis, it was detected that these hub genes were mostly enriched in signaling pathways and cancer. In this meta-analysis, it is stated that circulating hsa-miR-133a-3p may serve as a potential non-invasive biomarker and therapeutic target in PMO.

[Bioinformatics analysis of COL1A1 regulated by miR-129-5p as a potential therapeutic target for gastric cancer]

OBJECTIVE

To explore the pathogenesis of gastric cancer through a bioinformatic approach to provide evidence for the prevention and treatment of gastric cancer.

METHODS

The differentially expressed genes (DEGs) in gastric cancer and normal gastric mucosa in GSE79973 dataset were analyzed using GEO2R online tool. GO analysis and KEGG pathway enrichment analysis of the DEGs in DAVID database were performed. The protein interaction network was constructed using STRING database, and the key genes (Hub genes) were screened and their functional modules were analyzed using Cytoscape software. The GEPIA database was used to validate the Hub genes, and the Target Scan database was used to predict the microRNAs that regulate the target genes; OncomiR was used to analyze the expressions of the microRNAs in gastric cancer tissues and their relationship with the survival outcomes of the patients.

RESULTS

A total of 181 DEGs were identified in gastric cancer, and 10 hub genes were screened by the protein- protein interaction network. Functional analysis showed that these DEGs were involved mainly in protein digestion and absorption, PI3K-Akt signaling pathway, ECM-receptor interaction and platelet activation signal pathway. GEPIA database validation showed that COL1A1 was highly expressed in gastric cancer tissues and was associated with a poor prognosis of patients with gastric cancer. MiR-129-5p was found to bind to the 3'UTR of COL1A1 mRNA, and compared with that in normal tissues, miR-129-5p expression was obviously down-regulated in gastric cancer tissues, and was correlated with the prognosis of the patients.

CONCLUSIONS

COL1A1 under regulation by MiR-129-5p is a potential therapeutic target for gastric cancer.

Gastric Cancer Cell Lines Have Different MYC-Regulated Expression Patterns but Share a Common Core of Altered Genes

MYC is an oncogene responsible for excessive cell growth in cancer, enabling transcriptional activation of genes involved in cell cycle regulation, metabolism, and apoptosis, and is usually overexpressed in gastric cancer (GC). By using siRNA and Next-Generation Sequencing (NGS), we identified MYC-regulated differentially expressed Genes (DEGs) in three Brazilian gastric cancer cell lines representing the histological subtypes of GC (diffuse, intestinal, and metastasis). The DEGs were picked using Sailfish software, followed by Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using KEGG. We found 11 significantly enriched gene sets by using enrichment score (ES), False Discovery Rate (FDR), and nominal P-values. We identified a total of 5.471 DEGs with correlation over (80%). In diffuse-type and in metastatic GC cell lines, MYC-silencing caused DEGs downregulation, while the intestinal-type GC cells presented overall DEGs upregulation after MYC siRNA depletion. We were able to detect 11 significant gene sets when comparing our samples to the hallmark collection of gene expression, enriched mostly for the following hallmarks: proliferation, pathway, signaling, metabolic, and DNA damage response. When we analyzed our DEGs considering KEGG metabolic pathways, we found 12 common branches covering a wide range of biological functions, and three of them were common to all three cell lines: ubiquitin-mediated proteolysis, ribosomes, and system and epithelial cell signaling in Helicobacter pylori infection. The GC cell lines used in this study share 14 MYC-regulated genes, but their gene expression profile is different for each histological subtype of GC. Our results present a computational analysis of MYC-related signatures in GC, and we present evidence that GC cell lines representing distinct histological subtypes of this disease have different MYC-regulated expression profiles but share a common core of altered genes. This is an important step towards the understanding of MYC's role in gastric carcinogenesis and an indication of probable new drug targets in stomach cancer.

Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods

Selecting differentially expressed genes (DEGs) based on integrated bioinformatics analyses has been used in previous studies to explore potential biomarkers in gastric cancer (GC) with microarray and RNA sequencing data. However, the genes obtained may be inaccurate because of noisy data and errors, as well as insufficient clinical sample sizes. Thus, we aimed to find robust and strong DEGs with prognostic value for GC, where the robust rank aggregation method was employed to select significant DEGs from eight Gene Expression Omnibus data sets with a total of 140 up‐regulated and 206 down‐regulated genes. Network data mining was then used to screen hub genes, and 11 genes were filtered using Fisher's exact test. Based on these results, we built a prognostic signature with seven genes (FBN1,MMP1,PLAU,SPARC,COL1A2,COL2A1 and ATP4A) using stepwise multivariate Cox proportional hazard regression. According to the risk score for each patient, we found that high‐risk group patients had significantly worse survival results compared with those in the low‐risk group (log‐rank test P‐value < 0.001). This seven‐gene signature was then validated with an external data set. Thus, we established a signature based on seven DEGs with prognostic value for GC patients using multi‐steps bioinformatics methods, which may provide novel insights and potential biomarkers for prognosis, as well as possibly serving as new therapeutic targets in clinical applications.

Gene expression study and pathway analysis of histological subtypes of intestinal metaplasia that progress to gastric cancer

Background

Intestinal metaplasia (IM) is a precursor lesion that precedes gastric cancer (GC). There are two IM histological subtypes, complete (CIM) and incomplete (IIM), the latter having higher progression rates to GC. This study was aimed at analysing gene expression and molecular processes involved in the progression from normal mucosa to IM, and also from IM subtypes to GC.

Methodology

We used expression data to compare the transcriptome of healthy gastric mucosa to that of IM not progressing to GC, and the transcriptome of IM subtypes that had progressed to GC to those that did not progress. Some deregulated genes were validated and pathway analyses were performed.

Results

Comparison of IM subtypes that had progressed to GC with those that did not progress showed smaller differences in the expression profiles than the comparison of IM that did not progress with healthy mucosa. New transcripts identified in IM not progressing to GC included TRIM, TMEM, homeobox and transporter genes and SNORD116. Comparison to normal mucosa identified non tumoral Warburg effect and melatonin degradation as previously unreported processes involved in IM. Overexpressed antigen processing is common to both IM-subtypes progressing to GC, but IIM showed more over-expressed oncogenic genes and molecular processes than CIM.

Conclusions

There are greater differences in gene expression and molecular processes involved in the progression from normal healthy mucosa to IM than from IM to gastric cancer. While antigen processing is common in both IM-subtypes progressing to GC, more oncogenic processes are observed in the progression of IIM.

Identification of COL1A1 and COL1A2 as candidate prognostic factors in gastric cancer

Background

The role of type I collagen, composed of collagen type I alpha 1 (COL1A1) and collagen type I alpha 2 (COL1A2), has been studied in several cancers. However, the expression of COL1A1 and COL1A2 in malignant, premalignant, and normal gastric tissues and their clinical significances in gastric cancer have not been elucidated.

Methods

Real-time quantitative PCR was performed in 55 malignant, 27 premalignant, and 19 normal tissues to measure COL1A1 and COL1A2 messenger RNA (mRNA) expression, and the correlations between COL1A1 and COL1A2 expression and clinicopathological parameters and patients’ survival rate were analyzed.

Results

We found that COL1A1 mRNA expression was significantly upregulated in premalignant and malignant tissues than in normal tissues, whereas COL1A2 mRNA expression was significantly higher in malignant tissues than in premalignant and normal tissues. Moreover, COL1A1 expression was unrelated to clinicopathological parameters, while COL1A2 expression was positively related to tumor size and depth of invasion. Besides, higher COL1A1 and COL1A2 expression levels were related to lower overall survival.

Conclusions

We find that COL1A1 might have its potential as a monitoring factor to screen early gastric cancer, and COL1A1 and COL1A2 might predict poor clinical outcomes in gastric cancer patients.

Glyco-genes change expression in cancer through aberrant methylation

BACKGROUND

Most eukaryotic proteins are modified by covalent addition of glycan molecules that considerably influence their function. Aberrant glycosylation is profoundly involved in malignant transformation, tumor progression and metastasis. Some glycan structures are tumor-specific and reflect disturbed glycan biosynthesis pathways.

METHODS

We analyzed DNA methylation and expression of 86 glyco-genes in melanoma, hepatocellular, breast and cervical cancers using data from publicly available databases. We also analyzed methylation datasets without the available matching expression data for glyco-genes in lung cancer, and progression of melanoma into lymph node and brain metastases.

RESULTS

Ten glyco-genes (GALNT3, GALNT6, GALNT7, GALNT14, MGAT3, MAN1A1, MAN1C1, ST3GAL2, ST6GAL1, ST8SIA3) showing changes in both methylation and expression in the same type of cancer belong to GalNAc transferases, GlcNAc transferases, mannosidases and sialyltransferases, which is in line with changes in glycan structures already reported in the same type of tumors. Some of those genes were additionally identified as potentially valuable for disease prognosis. The MGAT5B gene, so far identified as specifically expressed in brain, emerged as a novel candidate gene that is epigenetically dysregulated in different cancers other than brain cancer. We also report for the first time aberrant expression of the GALNT and MAN genes in cancer by aberrant promoter methylation.

CONCLUSIONS

Aberrant expression of glyco-genes due to aberrant promoter methylation could be a way leading to characteristic glycosylation profiles commonly described in cancer.

GENERAL SIGNIFICANCE

Methylation status in promoters of candidate glyco-genes might serve as prognostic markers for specific tumors and point to potential novel targets for epigenetic drugs. This article is part of a Special Issue entitled "Glycans in personalised medicine" Guest Editor: Professor Gordan Lauc.