Bioinformatics Analysis of Hub Genes and Potential Therapeutic Agents Associated with Gastric Cancer

CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling

OBJECTIVE

Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis to be used as biomarkers or therapeutic targets.

METHODS

Differentially expressed genes were explored between human gastric cancerous and noncancerous tissues, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were done. Hub genes were identified based on the protein-protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further investigation using GEPIA2 and DrugBank databases.

RESULTS

Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients.

CONCLUSION

A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. The identified genes could be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC, but further clinical validation is required.

Identification and validation of key genes in gastric cancer: insights from in silico analysis, clinical samples, and functional assays

INTRODUCTION

The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.

METHODS

Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and in vitro assays for cellular behaviors.

RESULTS

A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in in vivo and exhibited mutations in GC patients.

CONCLUSION

We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.

Therapeutic effect of microRNA-21 on differentially expressed hub genes in gastric cancer based on systems biology

Gastric cancer (GC) is a leading cause of mortality for many people. Cancer's initiating factors are poorly understood. miR-21 has a crucial function in several malignancies, particularly GC. Furthermore, it has been shown that miR-21 is critical for the emergence and advancement of GC. This work intends to identify new genes which expression is associated with the activity of mir-21 in GC and to investigate the effect of downregulation of mir-21 on these genes and gastric tumorigenesis. We utilized the gene expression profiles of GCs from an Array database (GSE13911) from the Gene Expression Omnibus (GEO) dataset to find differentially expressed genes (DEGs) between control and gastric cancer groups. Using weighted gene correlation network analysis (WGCNA) in R, the Gene co-expression network was reconstructed. The microRNA-mRNA network was then reconstructed using the miRWalk database, and by investigating the microRNA-mRNA network, the genes that have an association with mir-21 were found. To implement the functional investigation, MKN and AGS cell lines were transfected with anti-miR-21 next. Subsequently, MTT proliferation was utilized to assess the cell's vitality. qRT-PCR was then used to evaluate the anticipated levels of gene expression in both GC cell lines. This study discovered and predicted CCL28, NR3C2, and SNYPO2 as the targets of miR-21 (GC), which are downregulated through gastric tumorigenesis, showing great potential as therapeutic and diagnostic targets. The suppression of miR-21 in gastric GC cells led to the inhibition of cell proliferation and decreased expression of CCL28, NR3C2, and SNYPO2 genes. This study established that miR-21, via downregulating these genes, contributes significantly to the development of GC. In addition, systems biology techniques identified CCL28, NR3C2, and SNYPO2 genes as possible GC surveillance and therapy components.

Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis

AIM

Gastric cancer (GC) is one of the most diagnosed cancers worldwide. GC is a heterogeneous disease whose pathogenesis has not been entirely understood. Besides, the GC prognosis for patients remains poor. Hence, finding reliable biomarkers and therapeutic targets for GC patients is urgently needed.

METHODS

GSE54129 and GSE26942 datasets were downloaded from Gene Expression Omnibus (GEO) database to detect differentially expressed genes (DEGs). Then, gene set enrichment analyses and protein-protein interactions were investigated. Afterward, ten hub genes were identified from the constructed network of DEGs. Then, the expression of hub genes in GC was validated. Performing survival analysis, the prognostic value of each hub gene in GC samples was investigated. Finally, the databases were used to predict microRNAs that could regulate the hub genes. Eventually, top miRNAs with more interactions with the list of hub genes were introduced.

RESULTS

In total, 203 overlapping DEGs were identified between both datasets. The main enriched KEGG pathway was "Protein digestion and absorption." The most significant identified GO terms included "primary alcohol metabolic process," "basal part of cell," and "extracellular matrix structural constituent conferring tensile strength." Identified hub modules were COL1A1, COL1A2, TIMP1, SPP1, COL5A2, THBS2, COL4A1, MUC6, CXCL8, and BGN. The overexpression of seven hub genes was associated with overall survival. Moreover, among the list of selected miRNAs, hsa-miR-27a-3, hsa-miR-941, hsa-miR-129-2-3p, and hsa-miR-1-3p, were introduced as top miRNAs targeting more than five hub genes.

CONCLUSIONS

The present study identified ten genes associated with GC, which may help discover novel prognostic and diagnostic biomarkers as well as therapeutic targets for GC. Our results may advance the understanding of GC occurrence and progression.

Bioinformatics and pathway enrichment analysis identified hub genes and potential biomarker for gastric cancer prognosis

Introduction

Gastric cancer is one of the most common cancers in the world. This study aimed to identify genes, biomarkers, and metabolic pathways affecting gastric cancer using bioinformatic analysis and meta-analysis.

Methods

Datasets containing gene expression profiles of tumor lesions and adjacent non-tumor mucosa samples were downloaded. Common differentially expressed genes between data sets were selected to identify hub genes and further analysis. Gene Expression Profiling and Interactive Analyses (GEPIA) and the Kaplan-Meier method were used to further validate the expression level of genes and plot the overall survivalcurve, respectively.

Results and disscussion

KEGG pathway analysis showed that the most important pathway was enriched in ECM-receptor interaction. Hub genes includingCOL1A2, FN1, BGN, THBS2, COL5A2, COL6A3, SPARC and COL12A1 wereidentified. The top interactive miRNAs including miR-29a-3p, miR-101-3p,miR-183-5p, and miR-15a-5p targeted the most hub genes. The survival chart showed an increase in mortality in patients with gastric cancer, which shows the importance of the role of these genes in the development of the disease and can be considered candidate genes in the prevention and early diagnosis of gastric cancer.

A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy

Recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide. While COVID-19 generally affects the lungs, it also damages other organs, including those of the cardiovascular system. Hypertrophic cardiomyopathy (HCM) is a common genetic cardiovascular disorder. Studies have shown that HCM patients with COVID-19 have a higher mortality rate; however, the reason for this phenomenon is not yet elucidated. Herein, we conducted transcriptomic analyses to identify shared biomarkers between HCM and COVID-19 to bridge this knowledge gap. Differentially expressed genes (DEGs) were obtained using the Gene Expression Omnibus ribonucleic acid (RNA) sequencing datasets, GSE147507 and GSE89714, to identify shared pathways and potential drug candidates. We discovered 30 DEGs that were common between these two datasets. Using a combination of statistical and biological tools, protein-protein interactions were constructed in response to these findings to support hub genes and modules. We discovered that HCM is linked to COVID-19 progression based on a functional analysis under ontology terms. Based on the DEGs identified from the datasets, a coregulatory network of transcription factors, genes, proteins, and microRNAs was also discovered. Lastly, our research suggests that the potential drugs we identified might be helpful for COVID-19 therapy.

miR‑29a‑3p inhibits the malignant characteristics of non‑small cell lung cancer cells by reducing the activity of the Wnt/β‑catenin signaling pathway

MicroRNAs (miRNAs) can influence non-small cell lung cancer (NSCLC) in a tumor-suppressive and oncogenic manner. The present study aimed to investigate the effects and underlying mechanisms of miR-29a-3p in NSCLC. NSCLC cell lines (A549, H1299, and H460) and a normal lung epithelial cell line (BEAS-2B) were used. Additionally, a mouse lung tumor xenograft model was established using A549 cells and used to determine the effects of miR-29a-3p on NSCLC in vivo. Tumor volumes were measured every week. The expression of miR-29a-3p in cells and lung tissues were detected by RT-qPCR. Cell proliferation was detected using Cell Counting Kit-8 and EdU assays. Migration and invasion were assessed using wound healing and Transwell invasion assays, respectively. Ki-67 expression was detected using immunohistochemical staining. The expression levels of Wnt3a and β-catenin were determined using western blotting. miR-29a-3p expression was significantly downregulated in NSCLC cells and mice. In contrast to miR-29a-3p knockdown, miR-29a-3p overexpression decreased NSCLC cell proliferation, migration, and invasion as well as tumor growth in in the NSCLC mouse model. Moreover, miR-29a-3p overexpression decreased the protein expression levels of Wnt3a and β-catenin. The inhibitory effects of miR-29a-3p on NSCLC cells were reversed by LiCl (an activator of the Wnt signaling pathway). In conclusion, miR-29a-3p prevented NSCLC tumor growth and cell proliferation, migration, and invasion by inhibiting the Wnt/β-catenin signaling pathway. This finding offers novel insights into the prognosis and treatment of NSCLC.

Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer

Our study aimed to explore the effect of cellular senescence and to find potential therapeutic strategies for gastric cancer. Cellular senescence-related genes were acquired from the CellAge database, while gastric cancer data were obtained from GEO and TCGA databases. SMARCA4 had the highest mutation frequency (6%), and it was linked to higher overall survival (OS) and progression-free survival (PFS). The gastric cancer data in TCGA database served as a training set to construct a prognostic risk score signature, and GEO data were used as a testing set to validate the accuracy of the signature. Patients with the low-risk score group had a longer survival time, while the high-risk score group is the opposite. Patients with low-risk scores had higher immune infiltration and active immune-related pathways. The results of drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Most patients with mutation genes had a lower risk score than the wild type. Therefore, the risk score signature with cellular senescence-related genes can predict gastric cancer prognosis and identify gastric cancer patients who are sensitive to chemotherapy and immunotherapy.