scholarly journals A Pipeline to Call Multilevel Expression Changes between Cancer and Normal Tissues and Its Applications in Repurposing Drugs Effective for Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wei Gao ◽  
Jianwei Yang ◽  
Changhua Zhuo ◽  
Sha Huang ◽  
Jinyuan Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Interleukin 4 ◽  
Data Quality Control ◽  
Expression Change ◽  
Normal Tissues ◽  
Differential Coexpression ◽  
Cancer Pathogenesis ◽  
Differential Gene

Differential gene analyses on gastric cancer usually focus on expression change of single genes between tumor and adjacent normal tissues. However, besides changes on single genes, there are also coexpression and expression network module changes during the development of gastric cancer. In this study, we proposed a pipeline to investigate various levels of changes between gastric cancer and adjacent normal tissues, which were used to repurpose potential drugs for treating gastric cancer. Specifically, we performed a series of analyses on 242 gastric cancer samples (33-normal, 209-cancer) downloaded from the cancer genome atlas (TCGA), including data quality control, differential gene analysis, gene coexpression network analysis, module function enrichment analysis, differential coexpression analysis, differential pathway analysis, and screening of potential therapeutic drugs. In the end, we discovered some genes and pathways that are significantly different between cancer and adjacent normal tissues (such as the interleukin-4 and interleukin-13 signaling pathway) and screened perturbed genes by 2703 drugs that have a high overlap with the identified differentially expressed genes. Our pipeline might be useful for understanding cancer pathogenesis as well as gastric cancer treatment.

scholarly journals Identification of potential biomarkers and their clinical significance in gastric cancer using bioinformatics analysis methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9174
Author(s):  
Jie Liu ◽  
Miao Zhou ◽  
Yangyang Ouyang ◽  
Laifeng Du ◽  
Lingbo Xu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Alternative Splicing ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Independent Prognostic Marker ◽  
Normal Tissues ◽  
Gastric Cancer Patients

Background Alternative splicing (AS) is an important mechanism for regulating gene expression and proteome diversity. Tumor-alternative splicing can reveal a large class of new splicing-associated potential new antigens that may affect the immune response and can be used for immunotherapy. Methods The RNA-seq transcriptome data and clinical information of stomach adenocarcinoma (STAD) cohort were downloaded from The Cancer Genome Atlas (TCGA) database data portal, and data of splicing events were obtained from the SpliceSeq database. Predicting genes were validated by Asian cancer research group (ACRG) cohort and Oncomine database. RT-qPCR was used to analysis the expression of ECT2 in STAD. Results A total of 32,166 AS events were identified, among which 2,042 AS events were significantly associated with patients survival. Biological pathway analysis indicated that these genes play an important role in regulating gastric cancer-related processes such as GTPase activity and PI3K-Akt signaling pathway. Next, we derived a risk signature, using alternate acceptor, that is an independent prognostic marker. Moreover, high ECT2 expression was associated with poorer prognosis in STAD. Multivariate survival analysis demonstrated that high ECT2 expression was an independent risk factor for overall survival. Gene set enrichment analysis revealed that high ECT2 expression was enriched for hallmarks of malignant tumors. The ACRG cohort and Oncomine also showed that high ECT2 expression was associated with poorer prognosis in gastric cancer patients. Finally, RT-qPCR showed ECT2 expression was higher in STAD compared to the normal tissues. Conclusion This study excavated the alternative splicing events in gastric cancer, and found ECT2 might be a biomarkers for diagnosis and prognosis.

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

2020 ◽  
Vol 15 ◽  
Author(s):  
Jujuan Zhuang ◽  
Shuang Dai ◽  
Lijun Zhang ◽  
Pan Gao ◽  
Yingmin Han ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Cancer Tissue ◽  
Connectivity Analysis ◽  
Cancer Drugs ◽  
Normal Tissues ◽  
Differential Gene ◽  
Differential Modules ◽  
Differential Connectivity

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

scholarly journals ADAMTS12 acts as a tumor microenvironment related cancer promoter in gastric cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yangming Hou ◽  
Yingjuan Xu ◽  
Dequan Wu
Keyword(s):  
Gastric Cancer ◽  
Tumor Microenvironment ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Tumor Promoter ◽  
Protein Protein Interaction ◽  
Oxidative Phosphorylation Pathway ◽  
Cox Proportional Hazard Regression

AbstractThe infiltration degree of immune and stromal cells has been shown clinically significant in tumor microenvironment (TME). However, the utility of stromal and immune components in Gastric cancer (GC) has not been investigated in detail. In the present study, ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cell (TIC) in GC cohort, including 415 cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were screened by Cox proportional hazard regression analysis and protein–protein interaction (PPI) network construction. Then ADAMTS12 was regarded as one of the most predictive factors. Further analysis showed that ADAMTS12 expression was significantly higher in tumor samples and correlated with poor prognosis. Gene Set Enrichment Analysis (GSEA) indicated that in high ADAMTS12 expression group gene sets were mainly enriched in cancer and immune-related activities. In the low ADAMTS12 expression group, the genes were enriched in the oxidative phosphorylation pathway. CIBERSORT analysis for the proportion of TICs revealed that ADAMTS12 expression was positively correlated with Macrophages M0/M1/M2 and negatively correlated with T cells follicular helper. Therefore, ADAMTS12 might be a tumor promoter and responsible for TME status and tumor energy metabolic conversion.

scholarly journals Comprehensive Analysis of ESRRA in Endometrial Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382199208
Author(s):  
Shufang Wang ◽  
Xinlong Huo
Keyword(s):  
Endometrial Cancer ◽  
Protein Expression ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Operating Characteristics ◽  
Protein Expression Level ◽  
Normal Tissues

Background: Estrogen-related receptor alpha (ESRRA) was reported to play an important role in multiple biological processes of neoplastic diseases. The roles of ESRRA in endometrial cancer have not been fully investigated yet. Methods: Expression data and clinicopathological data of patients with uteri corpus endometrial carcinoma (UCEC) were obtained from The Cancer Genome Atlas (TCGA). Comprehensive bioinformatics analysis was performed, including receiver operating characteristics (ROC) curve analysis, Kaplan-Meier survival analysis, gene ontology (GO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). Immunohistochemistry was used to detect the protein expression level of ESRRA and CCK-8 assay was performed to evaluate the effect of ESRRA on the proliferation ability. Results: A total of 552 UCEC tissues and 35 normal tissues were obtained from the TCGA database. The mRNA and protein expression level of ESRRA was highly elevated in UCEC compared with normal tissues, and was closely associated with poor prognosis. ROC analysis indicated a very high diagnostic value of ESRRA for patients with UCEC. GO and GSEA functional analysis showed that ESRRA might be mainly involved in cellular metabolism processes, in turn, tumorigenesis and progression of UCEC. Knockdown of ESRRA inhibited the proliferation of UCEC cells in vitro. Further immune cell infiltration demonstrated that ESRRA enhanced the infiltration level of neutrophil cell and reduced that of T cell (CD4+ naïve), NK cell, and cancer associated fibroblast (CAF). The alteration of immune microenvironment will greatly help in developing immune checkpoint therapy for UCEC. Conclusions: Our study comprehensively analyzed the expression level, clinical value, and possible mechanisms of action of ESRRA in UCEC. These findings showed that ESRRA might be a potential diagnostic and therapeutic target.

scholarly journals Evaluation of the prognostic value of CBXs in gastric cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mengya He ◽  
Limin Yue ◽  
Haiyan Wang ◽  
Feiyan Yu ◽  
Mingyang Yu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Target Genes ◽  
Prognostic Significance ◽  
Disease Free Survival ◽  
Genetic Alterations ◽  
Genetic Mutation ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
Gastric Cancer Patients

AbstractChromobox (CBX) proteins were suggested to exert epigenetic regulatory and transcriptionally repressing effects on target genes and might play key roles in the carcinogenesis of a variety of carcinomas. Nevertheless, the functions and prognostic significance of CBXs in gastric cancer (GC) remain unclear. The current study investigated the roles of CBXs in the prognosis of GC using the Oncomine, The Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, The Cancer Genome Atlas (TCGA), and cBioPortal databases. CBX1/2/3/4/5 were significantly upregulated in GC tissues compared with normal tissues, and CBX7 was downregulated. Multivariate analysis showed that high mRNA expression levels of CBX3/8 were independent prognostic factors for prolonged OS in GC patients. In addition, the genetic mutation rate of CBXs was 37% in GC patients, and genetic alterations in CBXs showed no association with OS or disease-free survival (DFS) in GC patients. These results indicated that CBX3/8 can be prognostic biomarkers for the survival of GC patients.

Knockdown of PGM1 Synergistically Enhances Anticancer Effects of Orlistat in Gastric Cancer Under Glucose Deprivation

2021 ◽  
Author(s):  
Bo Cao ◽  
Huan Deng ◽  
Hao Cui ◽  
Ruiyang Zhao ◽  
Hanghang Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cell Proliferation ◽  
Lipid Metabolism ◽  
Fatty Acid Synthase ◽  
Enrichment Analysis ◽  
Glucose Deprivation ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas

Abstract Background Phosphoglucomutase 1 (PGM1) acts as an important regulator in glucose metabolism. However, the role of PGM1 in gastric cancer (GC) remains unclear. This study aims to investigate the role of PGM1 and develop novel regimens based on metabolic reprogramming in GC. MethodsCorrelation and enrichment analysis of PGM1 was conducted based on The Cancer Genome Atlas database. Data derived from the Kaplan-Meier Plotter database were analyzed for correlations between PGM1 expression and survival time of GC patients. CCK-8, EdU, flow cytometry assays, generation of subcutaneous tumor and lung metastasis mouse models were used to determine growth and metastasis in vitro and in vivo. Cell glycolysis was detected by a battery of glycolytic indicators, including lactate, pyruvic acid, ATP production and glucose uptake. Fatty Acid Synthase (FASN) activity and detection of lipid regulators levels by western blot were used to reflect on the cell lipid metabolism. ResultsCorrelation and enrichment analysis suggested that PGM1 was closely associated with cell proliferation and metabolism. PGM1 was overexpressed in GC tissues and cell lines. High PGM1 expression served as an indicator of shorter survival for specific subpopulation of GC patients, which was also correlated with some clinicopathological features, including T stage and TNM stage. Under low glucose conditions, knockdown of PGM1 significantly suppressed cell proliferation and glycolysis levels, whereas lipid metabolism was enhanced. Orlistat, as a drug that was designed to inhibit FASN activity for obesity treatment, effectively induced apoptosis, suppressed FASN activity. However, orlistat conversely increased glycolytic levels in GC cells. Orlistat exhibited more significant inhibitive effects on GC progression after knockdown of PGM1 under glucose deprivation due to combination of glycolysis and lipid metabolism. ConclusionsDownregulation of PGM1 expression under glucose deprivation synergistically enhanced anti-cancer effects of orlistat. This combination application may serve as a novel strategy for GC treatment.

scholarly journals Enolase 1 Correlated With Cancer Progression and Immune-Infiltrating in Multiple Cancer Types: A Pan-Cancer Analysis

2021 ◽  
Vol 10 ◽  
Author(s):  
Wenhua Xu ◽  
Wenna Yang ◽  
Chunfeng Wu ◽  
Xiaocong Ma ◽  
Haoyu Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Stress Protein ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Clinical Prognosis ◽  
Multiple Tumor ◽  
Normal Tissues

Enolase 1 (ENO1) is an oxidative stress protein expressed in endothelial cells. This study aimed to investigate the correlation of ENO1 with prognosis, tumor stage, and levels of tumor-infiltrating immune cells in multiple cancers. ENO1 expression and its influence on tumor stage and clinical prognosis were analyzed by UCSC Xena browser, Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and GTEx Portal. The ENO1 mutation analysis was performed by cBio Portal, and demonstrated ENO1 mutation (1.8%) did not impact on tumor prognosis. The relationship between ENO1 expression and tumor immunity was analyzed by Tumor Immune Estimation Resource (TIMER) and GEPIA. The potential functions of ENO1 in pathways were investigated by Gene Set Enrichment Analysis. ENO1 expression was significantly different in tumor and corresponding normal tissues. ENO1 expression in multiple tumor tissues correlated with prognosis and stage. ENO1 showed correlation with immune infiltrates including B cells, CD8+ and CD4+ T cells, macrophages, neutrophils, and dendritic cells, and tumor purity. ENO1 was proved to be involved in DNA replication, cell cycle, apoptosis, glycolysis process, and other processes. These findings indicate that ENO1 is a potential prognostic biomarker that correlates with cancer progression immune infiltration.

scholarly journals The expression characteristics and prognostic roles of autophagy-related genes in gastric cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10814
Author(s):  
Mengya Wang ◽  
Jingjing Jing ◽  
Hao Li ◽  
Jingwei Liu ◽  
Yuan Yuan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Factors ◽  
Differential Expression Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Genes Expression ◽  
Expression Characteristics ◽  
Cellular Components

Background Autophagy is an evolutionally highly conserved process, accompanied by the dynamic changes of various molecules, which is necessary for the orderly degradation and recycling of cellular components. The aim of the study was to identify the role of autophagy-related (ATG) genes in the occurrence and development of gastric cancer (GC). Methods Data from Oncomine dataset was used for the differential expression analysis between cancer and normal tissues. The association of ATG genes expression with clinicopathologic indicators was evaluated by The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Moreover, using the TCGA datasets, the prognostic role of ATG genes was assessed. A nomogram was further built to assess the independent prognostic factors. Results The expression of autophagy-related genes AMBRA1, ATG4B, ATG7, ATG10, ATG12, ATG16L2, GABARAPL2, GABARAPL1, ULK4 and WIPI2 showed differences between cancer and normal tissues. After verification, ATG14 and ATG4D were significantly associated with TNM stage. ATG9A, ATG2A, and ATG4D were associated with T stage. VMP1 and ATG4A were low-expressed in patients without lymph node metastasis. No gene in autophagy pathway was associated with M stage. Further multivariate analysis suggested that ATG4D and MAP1LC3C were independent prognostic factors for GC. The C-index of nomogram was 0.676 and the 95% CI was 0.628 to 0.724. Conclusion Our study provided a comprehensive illustration of ATG genes expression characteristics in GC. Abnormal expressions of the ubiquitin-like conjugated system in ATG genes plays a key role in the occurrence of GC. ATG8/LC3 sub-system may play an important role in development and clinical outcome of GC. In the future, it is necessary to further elucidate the alterations of specific ATG8/LC3 forms in order to provide insights for the discovery, diagnosis, or targeting for GC.

scholarly journals Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Xiangchou Yang ◽  
Liping Chen ◽  
Yuting Mao ◽  
Zijing Hu ◽  
Muqing He
Keyword(s):  
Gastric Cancer ◽  
Extracellular Matrix ◽  
Risk Score ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Clinicopathologic Features ◽  
Normal Tissues ◽  
Prognostic Application ◽  
Score Model

The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t -test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081 ∗ log 2   CD 36 + 0.043 ∗ log 2   COL 5 A 2 + 0.001 ∗ log 2   ITGB 5 + 0.039 ∗ log 2   SDC 2 + 0.135 ∗ log 2   SV 2 B + 0.012 ∗ log 2   THBS 1 + 0.068 ∗ log 2   VTN + 0.023 ∗ log 2   VWF . The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351 , HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046 ) and validation ( n = 300 , HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014 ) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001 ). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001 ) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001 ) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

scholarly journals Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Honglan Guo ◽  
Qinqiao Fan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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