scholarly journals Identification of Potential Biomarkers and Therapy Targets involved in Gastric Cancer Using Bioinformatics Analysis

2021 ◽  
Author(s):  
YangYang Teng ◽  
Na Shan ◽  
GuangRong Lu ◽  
LeYi Ni ◽  
ZeJun Gao ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Bioinformatics Analysis ◽  
Risk Model ◽  
Malignant Tumors ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Normal Tissues ◽  
Therapy Targets ◽  
Selection Operator ◽  
Cancer Tissues

Abstract Gastric cancer remains one of the five major malignant tumors in the world, posing a great threat to public life and health. As gene sequencing technology develops, it is urgent to find out specific molecular markers for cancer therapy. In this study, datasets of GSE13911, GSE30727, GSE63089 and GSE118916 were investigated by bioinformatics analysis, and differentially expressed genes (DEGs) between GC tissues and normal tissues were screened for potential cancer therapeutic targets. Furthermore, the GSE63089 dataset was analyzed by Weighted Gene Co-expression Network Analysis (WGCNA), and the highly related genes were clustered. Then, the hub genes were searched using co-expression network and Molecular Complex Detection (MCODE) plug-in from Cytoscape software. Finally, ASPM, COL11A1 and CDC20 were obtained by intersection of hub genes and DEGs. The expressions of ASPM, COL11A1 and CDC20 gene in gastric cancer tissues and normal tissues from TCGA database were detected. For these genes, the least absolute shrink and selection operator (LASSO) Cox expression analysis was used to establish the prognostic risk model. COL11A1 and CDC20 genes were identified as candidate prognostic risk markers for GC. Analysis using qRT-PCR has shown that COL11A1 and CDC20 were significant differentially expressed between gastric cancer tissues and normal gastric tissues (P < 0.01). In conclusion, our study identifies specific DEGs involved in ECM process and metabolism by cytochrome P450 process, and these DEGs may be potential targets for GC therapy. The model constructed by COL11A1 and CDC20 genes can predict the prognosis risk of GC patients. Taken together, these findings provide reference for further analyses of key alterations during GC progression.

scholarly journals Bioinformatics Analysis and Identification of Potential Biomarkers in Gastric Cancer

2020 ◽  
Author(s):  
Jingdi Yang ◽  
Bo Peng ◽  
Xianzheng Qin ◽  
Tian Zhou
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.

scholarly journals Identification of Aberrantly Expressed miRNAs in Gastric Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dan Liu ◽  
Xiaowei Hu ◽  
Hongfeng Zhou ◽  
Guangyue Shi ◽  
Jin Wu
Keyword(s):  
Gastric Cancer ◽  
Gene Ontology ◽  
Real Time ◽  
Real Time Pcr ◽  
Malignant Disease ◽  
Gene Ontology Analysis ◽  
Normal Tissues ◽  
Therapy Targets ◽  
New Perspective ◽  
Cancer Tissues

The noncoding components of the genome, including miRNA, can contribute to pathogenesis of gastric cancer. Their expression has been profiled in many human cancers, but there are a few published studies in gastric cancer. It is necessary to identify novel aberrantly expressed miRNAs in gastric cancer. In this study, the expression profile of 1891 miRNAs was analyzed using a miRCURY array LNA miRNA chip from three gastric cancer tissues and three normal tissues. The expression levels of 4 miRNAs were compared by real-time PCR between cancerous and normal tissues. We found that 31 miRNAs are upregulated in gastric cancer(P<0.05)and 10 miRNAs have never been reported by other studies; 30 miRNA are downregulated(P<0.05)in gastric cancer tissues. Gene ontology analysis revealed that those dysregulated miRNAs mainly take part in regulating cell proliferation. The levels of has-miR-105, -213*, -514b, and -548n were tested by real-time PCR and have high levels in cancerous tissues. Here, we report a miRNA profile of gastric cancer and provide new perspective to understand this malignant disease. This novel information suggests the potential roles of these miRNAs in the diagnosis, prognosis biomarkers, or therapy targets of gastric cancer.

scholarly journals Application of Bioinformatics Analysis to Identify Important Pathways and Hub Genes in Ovarian Cancer Affected by WT1

2021 ◽  
Vol 9 ◽  
Author(s):  
Kai Meng ◽  
Jinghe Cao ◽  
Yehao Dong ◽  
Mengchen Zhang ◽  
Chunfeng Ji ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Mrna Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Wilms Tumor ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Normal Tissues ◽  
Cancer Tissues

Wilms tumor gene (WT1) is used as a marker for the diagnosis and prognosis of ovarian cancer. However, the molecular mechanisms involving WT1 in ovarian cancer require further study. Herein, we used bioinformatics and other methods to identify important pathways and hub genes in ovarian cancer affected by WT1. The results showed that WT1 is highly expressed in ovarian cancer and is closely related to the overall survival and progression-free survival (PFS) of ovarian cancer. In ovarian cancer cell line SKOV3, WT1 downregulation increased the mRNA expression of 638 genes and decreased the mRNA expression of 512 genes, which were enriched in the FoxO, AMPK, and the Hippo signaling pathways. The STRING online tool and Cytoscape software were used to construct a Protein-protein interaction (PPI) network and for Module analysis, and 18 differentially expressed genes (DEGs) were selected. Kaplan-Meier plotter analysis revealed that 16 of 18 genes were related to prognosis. Analysis of GEPIA datasets indicated that 7 of 16 genes were differentially expressed in ovarian cancer tissues and in normal tissues. The expression of IGFBP1 and FBN1 genes increased significantly after WT1 interference, while the expression of the SERPINA1 gene decreased significantly. The correlation between WT1 expression and that of these three genes was consistent with that of ovarian cancer tissues and normal tissues. According to the GeneMANIA online website analysis, there were complex interactions between WT1, IGFBP1, FBN1, SERPINA1, and 20 other genes. In conclusion, we have identified important signaling pathways involving WT1 that affect ovarian cancer, and distinguished three differentially expressed genes regulated by WT1 associated with the prognosis of ovarian cancer. Our findings provide evidence outlining mechanisms involving WT1 gene expression in ovarian cancer and provides a rational for novel treatment of ovarian cancer.

scholarly journals Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Gu ◽  
Ying Sun ◽  
Xiong Zheng ◽  
Jin Ma ◽  
Xiao-Ying Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Microarray Analysis ◽  
Cancer Progression ◽  
Pathway Analysis ◽  
Bioinformatics Analysis ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Circular Rnas ◽  
Target Mrnas

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar’s home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.

scholarly journals Identification of key genes related to macrophage infiltration in gastric cancer based on WGCNA

2020 ◽  
Author(s):  
Haiyan Chen ◽  
Cangang Zhang ◽  
Shuai Cao ◽  
Meng Cao ◽  
Nana Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Immune Cell ◽  
Accurate Diagnosis ◽  
Marker Genes ◽  
Hub Genes ◽  
Normal Tissues ◽  
Tumor Tissues ◽  
New Strategy ◽  
Tumor Environment ◽  
Advanced Stages

Abstract Background: Gastric cancer (GC) is rampant around the world. Most of the GC cases are detected in advanced stages with poor prognosis. The identification of marker genes for early diagnosis is of great significance. Studying the tumor environment is helpful to acknowledge the process of tumorigenesis, development, and metastasis.Methods: In GEO, 22 kinds of immune cell infiltration were calculated by CIBERSORT. Macrophages were discovered remarkably infiltrated higher in GC compared with normal tissues. WGCNA was utilized to construct the network and then identify key modules and genes related to macrophages in TCGA.Results: Finally, 18 hub genes were verified. In the PPI bar chart, the top 3 genes were chosen as hub genes involved in most pathways. On the TIMER and THPA websites, it is verified that the expression levels of CYBB, CD86 and C3AR1 genes in tumor tissues were higher than those in normal tissues.Conclusion: These genes may work as biomarkers or targets for accurate diagnosis and treatment of GC in the future. Our findings may be a new strategy for the treatment of GC.

scholarly journals Genomic Expression Profiling of Colorectal Cancer in Silico

2021 ◽  
Author(s):  
Feifei Liu ◽  
Yu Wang ◽  
Wenxue Li ◽  
Diancheng Li ◽  
Yuwei Xin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Mrna Expression ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Analysis Tool ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies of the digestive system; the progression and prognosis of which are affected by a complicated network of genes and pathways. The aim of this study was to identify potential hub genes associated with the progression and prognosis of colorectal cancer (CRC).Methods: We obtained gene expression profiles from GEO database to search differentially expressed genes (DEGs) between CRC tissues and normal tissue. Subsequently, we conducted a functional enrichment analysis, generated a protein–protein interaction (PPI) network to identify the hub genes, and analyzed the expression validation of the hub genes. Kaplan–Meier plotter survival analysis tool was performed to evaluate the prognostic value of hub genes expression in CRC patients.Results: A total of 370 samples, involving CRC and normal tissues were enrolled in this article. 283 differentially expressed genes (DEGs), including 62 upregulated genes and 221 downregulated genes between CRC and normal tissues were selected. We finally filtered out 6 hub genes, including INSL5, MTIM, GCG, SPP1, HSD11B2, and MAOB. In the database of TCGA-COAD, the mRNA expression of INSL5, MT1M, HSD11B2, MAOB in tumor is lower than that in normal; the mRNA expression of SPP1 in tumor is higher than that in normal. In the HPA database, the expression of INSL5, GCG, HSD11B2, MAOB in tumor is lower than that in normal tissues; the expression of SPP1 in the tumor is higher than that in normal tissues. Survival analysis revealed that INSL5, GCG, SPP1 and MT1M may serve as prognostic biomarkers in CRC. Conclusions: We screened out six hub genes to predict the occurrence and prognosis of patients with CRC using bioinformatics methods, which may provide new targets and ideas for diagnosis, prognosis and individualized treatment for CRC.

scholarly journals CircRNA-miRNA-mRNA Regulatory Network in Colorectal Cancer

2021 ◽  
Author(s):  
Liyuan Liu ◽  
Shan Wu ◽  
Dan Jiang ◽  
Yuliang Qu ◽  
Hongxia Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regulatory Network ◽  
Research Direction ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Qrt Pcr ◽  
Cancer Tissues ◽  
Chip Analysis

Abstract Background: Abnormal expression of Circular RNAs (circRNAs) occurs in the occurrence and progression of colorectal cancer (CRC) and plays an important role in the pathogenesis of tumors. We combined bioinformatics and laboratory-validated methods to search for key circRNAs and possible potential mechanisms. Methods: Colorectal cancer tissues and normal paracancerous tissues were detected by microarray analysis and qRT-PCR validation, and differentially expressed circRNAs were screened and identified. The circRNA-miRNA-mRNA regulatory network (cirReNET) was constructed, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to ascertain the functions of circRNAs in CRCs. In addition, a protein-protein interaction (PPI) network of hub genes which acquired by string and plugin app CytoHubba in cytoscape was established. Validation of expression of hub genes was identified by GEPIA database. Results: 564 differentially expressed circRNAs which include 207 up-regulated and 357 down-regulated circRNAs were detected. The top 3 up-regulated circRNAs (hsa_circRNA_100833, hsa_circRNA_103828, hsa_circRNA_103831) and the top 3 down-regulated circRNAs (hsa_circRNA_103752, hsa_circRNA_071106, hsa_circRNA_102293) in chip analysis were chosen to be verified in 33 pairs of CRCs by qRT-PCR. The cirReNET include of 6 circRNAs, 19 miRNAs and 210 mRNA. And the targeted mRNAs were associated with cellular metabolic process, cell cycle and glandular epithelial cell differentiation and so on. 12 and 10 target hub genes were shown separately in upregulated circRNA-downregulated miRNA-upregulated mRNA (UcDiUm-RNA) group and downregulated circRNA-upregulated miRNA-downregulated mRNA (DcUiDm-RNA) group. Finally, we may have predicted and discovered several critical circRNA-miRNA-mRNA regulatory axes (cirReAXEs) which may play important roles in colorectal cancer. Conclusion: We constructed a cirReNET including 6 candidate circRNAs, which were crucial in CRCs, may become potential diagnostic markers and predictive indicators of CRCs, and we may provide a research direction for the pathogenesis of colorectal cancer.

scholarly journals A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

scholarly journals The expression and clinical significance of PDCD4 and Her-2 in human gastric cancer

2019 ◽  
Vol 17 ◽  
pp. 205873921982823
Author(s):  
Yuelou Yang ◽  
Xiangjun Jiang ◽  
Dong Li ◽  
Feiyan Wang ◽  
Qun Yang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Protein Expression ◽  
Western Blot ◽  
Clinical Significance ◽  
Normal Mucosa ◽  
Normal Tissues ◽  
Positive Rate ◽  
Her 2 ◽  
Cancer Tissues ◽  
Pdcd4 Protein

To investigate the correlation and clinical significance between programmed cell death factor 4 (PDCD4) and epidermal growth factor receptor 2 (Her-2) expressions and clinicopathological parameters in patients with gastric cancer, a total of 65 cases of gastric cancer and the corresponding normal mucosa with PDCD4 and Her-2 protein expressions were detected by SP immunohistochemical staining, and 50 cases of gastric cancer and the corresponding normal mucosa with PDCD4 and Her-2 protein expression quantities were detected by Western blot, in order to analyze the relationship between the positive expressions of PDCD4 and Her-2 protein and the clinicopathological features of patients with gastric cancer. The results showed that the positive rate of PDCD4 protein expression in gastric cancer tissues was 7.7%, which was significantly lower than that in the corresponding normal tissues, that is, 77.5% ( P < 0.05); the positive rate of Her-2 expression was 41.5%, which was significantly higher than that of the corresponding normal tissues, which is 2.5% ( P < 0.05). The Western blot test showed that the expression of PDCD4 protein in gastric cancer was 0.3105 ± 0.0073, which was significantly lower than that in the corresponding normal tissues, that is, 0.9428 ± 0.0127 ( P < 0.05); the expression level of Her-2 protein in gastric cancer tissues was 0.9428 ± 0.0127, which was significantly higher than that of the corresponding normal mucosa, which is 0.2054 ± 0.0264 ( P < 0.05). The positive expressions of PDCD4 (5/65) and Her-2 (27/65) were significantly correlated with the differentiation degrees and TNM stages of gastric cancer ( P < 0.05). However, no significant correlation can be observed from Table 2 ( P > 0.05), regarding sex, age, tumor size, and lymph node metastasis. Our research claimed that PDCD4 and Her-2 may play an important role in the invasion and metastasis of gastric cancer, which has a negative correlation with biological behaviors of gastric cancer. The low expression of PDCD4 and the high expression of Her-2 in gastric cancer may promote the occurrence and progression of cancer. The PDCD4 and Her-2 test can be used as an index to evaluate the malignant biological behaviors of gastric cancer and prognosis, and provide a theoretical basis for targeted therapy.

scholarly journals Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482097667
Author(s):  
Ju-Yueh Li ◽  
Chia-Jung Li ◽  
Li-Te Lin ◽  
Kuan-Hao Tsui
Keyword(s):  
Ovarian Cancer ◽  
Protein Interactions ◽  
Malignant Tumors ◽  
Single Gene ◽  
Protein Protein Interactions ◽  
Normal Tissues ◽  
Human Protein Atlas ◽  
Copy Number Changes ◽  
And Function ◽  
Cancer Tissues

Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein–protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the “Human Protein Atlas” database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.

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