Screening and Identification of Key Biomarkers in Breast Cancer with Brain Metastasis: Evidence from Bioinformatic Analysis

Abstract Purpose. Breast cancer (BC) has a poor prognosis when brain metastases (BM) occur, and the treatment effect is limited. In this study, we aim to identify representative candidate biomarkers for clinical prognosis of patients with BM and explore the mechanisms underlying the progression of BC.Methods. Herein, we examined the Microarray datasets (GSE125989) obtained from the Gene Expression Omnibus database to find the target genes in BC patients with BM. We employed the GEO2R tool to filter the differentially expressed genes (DEGs) that participate in primary BC and BC with BM. Subsequently, using the DAVID tool, we conducted an enrichment analysis with the screened DEGs based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional annotation. The STRING database was employed to analyze the protein-protein interactions of the DEGs and visualized using Cytoscape software. Lastly, the Kaplan-Meier plotter database was employed to determine the prognostic potential of hub genes in BC.Results. We screened out 311 upregulated DEGs and 104 downregulated DEGs. The enrichment analyses revealed that all the DEGs were` enriched in the biological process of extracellular matrix organization, cell adhesion, proteolysis, collagen catabolic process and immune response. The significant enrichment pathways were focal adhesion, protein absorption and digestion, ECM-receptor interaction, PI3K-Akt signalling pathway, and Pathways in cancer. The top ten hub nodes screened out included FN1, VEGFA, COL1A1, MMP2, COL3A1, COL1A2, POSTN, DCN, BGN and LOX. The Kaplan-Meier plotter results showed that the three hub genes (FN1, VEGFA and DCN) are candidate biomarkers for clinical prognosis of patients with BM.Conclusion. we identified seven genes related to poor prognosis in BCBM. FN1, VEGFA and DCN can be considered as potential prognostic markers for BCBM. Meantime, COL1A1, POSTN, BGN and LOX may be linked to the distant transformation of BC.

Download Full-text

Screening and identification of key biomarkers in breast cancer with brain metastasis: Evidence from bioinformatic analysis

10.21203/rs.3.rs-36118/v1 ◽

2020 ◽

Author(s):

Tao Ming Shao ◽

Zhi Yang Hu ◽

Wen Wei Li ◽

Long Yun Pan

Keyword(s):

Breast Cancer ◽

Protein Interactions ◽

Poor Prognosis ◽

Target Genes ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Hub Genes ◽

Clinical Prognosis ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Download Full-text

Nomogram Models Based on Gene Expression in Prediction of Breast Cancer Bone Metastasis

10.21203/rs.3.rs-1109324/v1 ◽

2021 ◽

Author(s):

Teng-di Fan ◽

Di-kai Bei ◽

Song-wei Li

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Bone Metastasis ◽

Gene Signature ◽

Functional Enrichment ◽

Training Set ◽

Hub Genes ◽

Kaplan Meier ◽

Patient Prognosis ◽

Kaplan Meier Plotter

Abstract Objective: To design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, and GSE14020 was taken as a validation set. In the training cohort, limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC non-bone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the prognostic value of hub genes in BC was explored. Results: A total of 1858 DEGs were obtained. WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival. While GJA1, IGFBP6, MDFI, ITGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival. Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

Download Full-text

Bioinformatics analysis of potential core genes for glioblastoma

Bioscience Reports ◽

10.1042/bsr20201625 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Yu Zhang ◽

Xin Yang ◽

Xiao-Lin Zhu ◽

Jia-Qi Hao ◽

Hao Bai ◽

...

Keyword(s):

Survival Analysis ◽

Protein Interactions ◽

Poor Prognosis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Normal Brain ◽

Hub Genes ◽

Potential Core ◽

Core Genes

Abstract Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes. Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool. Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis. Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.

Download Full-text

Upregulated CCNE1 Correlates with Poor Prognosis, Tumor Immune Infiltration and Escape in Breast Cancer

10.21203/rs.3.rs-966772/v1 ◽

2021 ◽

Author(s):

Jiaxi Feng ◽

Yanan Hu ◽

Dan Liu ◽

Shanshan Wang ◽

Mengci Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Immune Cells ◽

Poor Prognosis ◽

Immune Cell ◽

High Expression ◽

Expression Level ◽

Immune Cell Infiltration ◽

Immune Infiltration ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Abstract Background Breast cancer (BC) is the most common malignant tumor in women and widely known for its poor prognosis. More and more research has discovered that cyclin E1 (CCNE1) plays an important role in progression of various types of cancer. But its specific mechanism in BC progression still needs further research to explore.Methods At first, we determined the expression and prognostic value of CCNE1 through The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) data. Then, we predicted the upstream non-coding RNAs of CCNE1 through StarBase, GEPIA, and Kaplan-Meier plotter database. We further studied the correlation of CCNE1 expression with BC immune cell infiltration, biomarkers of immune cells and immune checkpoints expression through TIMER and GEPIA databases.Results The results suggested that CCNE1 was significantly upregulated in BC and its high expression was correlated with poor prognosis in BC patients. Next, we identified long noncoding RNA (lncRNA) LINC00511 / microRNA-195-5p (miR-195-5p) / CCNE1 axis as the most potential pathway that could regulate CCNE1 expression in BC through StarBase, GEPIA, and Kaplan-Meier plotter database. Furthermore, our in-depth research discovered that CCNE1 expression level was significantly correlated with tumor immune cell inﬁltration, biomarkers of immune cells, and immune checkpoint expression in BC. conclusions In summary, high expression level of CCNE1 was significantly correlated with poor prognosis, tumor immune infiltration and escape in BC.

Download Full-text

Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases

Bioscience Reports ◽

10.1042/bsr20204370 ◽

2021 ◽

Author(s):

Qiangqiang Zheng ◽

Shihui Min ◽

Qinghua Zhou

Keyword(s):

Signaling Pathway ◽

Clinical Features ◽

Enrichment Analysis ◽

Expression Level ◽

Normal Lung ◽

Receptor Interaction ◽

Hub Genes ◽

Kaplan Meier ◽

Viral Carcinogenesis ◽

Relationship Of

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The current study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan-Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5,930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM-receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression level of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression level of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD, and may be potential therapeutic targets for LUAD.

Download Full-text

Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer

Cancer Biomarkers ◽

10.3233/cbm-200594 ◽

2021 ◽

pp. 1-17

Author(s):

Qiaoyun Zhao ◽

Jun Xie ◽

Jinliang Xie ◽

Rulin Zhao ◽

Conghua Song ◽

...

Keyword(s):

Gastric Cancer ◽

Poor Prognosis ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Correlation Network ◽

Receptor Interaction ◽

Hub Genes ◽

Mesenchymal Transition ◽

Weighted Correlation

BACKGROUND: Gastric cancer (GC) is one of the most deadliest tumours worldwide, and its prognosis remains poor. OBJECTIVE: This study aims to identify and validate hub genes associated with the progression and prognosis of GC by constructing a weighted correlation network. METHODS: The gene co-expression network was constructed by the WGCNA package based on GC samples and clinical data from the TCGA database. The module of interest that was highly related to clinical traits, including stage, grade and overall survival (OS), was identified. GO and KEGG pathway enrichment analyses were performed using the clusterprofiler package in R. Cytoscape software was used to identify the 10 hub genes. Differential expression and survival analyses were performed on GEPIA web resources and verified by four GEO datasets and our clinical gastric specimens. The receiver operating characteristic (ROC) curves of hub genes were plotted using the pROC package in R. The potential pathogenic mechanisms of hub genes were analysed using gene set enrichment analysis (GSEA) software. RESULTS: A total of ten modules were detected, and the magenta module was identified as highly related to OS, stage and grade. Enrichment analysis of magenta module indicated that ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, proteoglycans in cancer were significantly enriched. The PPI network identified ten hub genes, namely COL1A1, COL1A2, FN1, POSTN, THBS2, COL11A1, SPP1, MMP13, COMP, and SERPINE1. Three hub genes (FN1, COL1A1 and SERPINE1) were finally identified to be associated with carcinogenicity and poor prognosis of GC, and all were independent risk factors for GC. The area under the curve (AUC) values of FN1, COL1A1 and SERPINE1 for the prediction of GC were 0.702, 0.917 and 0.812, respectively. GSEA showed that three hub genes share 15 common upregulated biological pathways, including hypoxia, epithelial mesenchymal transition, angiogenesis, and apoptosis. CONCLUSION: We identified FN1, COL1A1 and SERPINE1 as being associated with the progression and poor prognosis of GC.

Download Full-text

CXCL2 as a Prognostic Marker in Breast Cancer is Associated with Immune Infiltration and Regulated by miR-215

10.21203/rs.3.rs-41001/v1 ◽

2020 ◽

Author(s):

Jia-Xiang An ◽

Ying-Ying Chen ◽

Zhao-Sheng Ma ◽

Wen-Jie Yu ◽

Jin-Xi Hu ◽

...

Keyword(s):

Breast Cancer ◽

Survival Time ◽

Cancer Patients ◽

Breast Cancer Patients ◽

Clinical Prognosis ◽

Immune Infiltration ◽

Kaplan Meier ◽

The Impact ◽

Kaplan Meier Plotter ◽

Low Expression

Abstract Background: CXCL2 is a part of chemokine superfamily, which encodes secretory proteins involved in immune regulation and inflammation. The correlation between CXCL2 and prognosis of different cancers, tumor infiltrating lymphocytes are not clear. Methods: We analyzed the expression of CXCL2 and its effect on clinical prognosis through Oncomine database, Tumor Immune Estimation Resource (TIMER) website, Kaplan-Meier plotter, PrognoScan database and Gene Expression Profiling Interactive Analysis (GEPIA). TIMER and GEPIA were used to analyze the correlation between CXCL2 and the gene marker of immune infiltration. StarBase was used to predict the miRNA that may regulate CXCL2. The relationship between miR-532-5p and CXCL2 was detected by qRT-PCR. Kaplan-Meier plotter was used to evaluate the impact of miR-532-5p on clinical prognosis. Results: PrognoScan, Kaplan-Meier plotter and GEPIA database analysis showed that low expression of CXCL2 was associated with poor disease-specific survival time (DSS), relapse-free survival time (RFS) and overall disease survival (OS) in breast cancer patients. In addition, low expression of CXCL2 was associated with poor OS and RFS in patients with lymph node positive breast cancer. CXCL2 expression was positively correlated with the infiltration of B cells, CD4+T and CD8+T cells, neutrophils and dendritic cells (DCs) in BRCA, mainly in Luminal breast cancer. MiR-532-5p can directly regulate CXCL2 expression. High miR-532-5p expression is significantly correlated with HER2 negative, grade 2 and 3 and poor OS in patients with HER+ER- breast cancer. Conclusion: CXCL2 is closely related to the prognosis and immune infiltration level of breast cancer patients, it can be regulated by miR-532-5p.

Download Full-text

Upregulated CCNE1 Correlates with Poor Prognosis, Tumor Immune Infiltration and Escape in Breast Cancer

10.21203/rs.3.rs-966772/v2 ◽

2021 ◽

Author(s):

Jiaxi Feng ◽

Yanan Hu ◽

Dan Liu ◽

Shanshan Wang ◽

Mengci Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Immune Cells ◽

Poor Prognosis ◽

Immune Cell ◽

High Expression ◽

Expression Level ◽

Immune Cell Infiltration ◽

Immune Infiltration ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Abstract BackgroundBreast cancer (BC) is the most common malignant tumor in women and widely known for its poor prognosis. More and more research has discovered that cyclin E1 (CCNE1) plays an important role in progression of various types of cancer. But its specific mechanism in BC progression still needs further research to explore.MethodsAt first, we determined the expression and prognostic value of CCNE1 through The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) data. Then, we predicted the upstream non-coding RNAs of CCNE1 through StarBase, GEPIA, and Kaplan-Meier plotter database. We further studied the correlation of CCNE1 expression with BC immune cell infiltration, biomarkers of immune cells and immune checkpoints expression through TIMER and GEPIA databases.ResultsThe results suggested that CCNE1 was significantly upregulated in BC and its high expression was correlated with poor prognosis in BC patients. Next, we identified long noncoding RNA (lncRNA) LINC00511 / microRNA-195-5p (miR-195-5p) / CCNE1 axis as the most potential pathway that could regulate CCNE1 expression in BC through StarBase, GEPIA, and Kaplan-Meier plotter database. Furthermore, our in-depth research discovered that CCNE1 expression level was significantly correlated with tumor immune cell inﬁltration, biomarkers of immune cells, and immune checkpoint expression in BC.ConclusionIn summary, high expression level of CCNE1 was significantly correlated with poor prognosis, tumor immune infiltration and escape in BC.

Download Full-text

Identifying of a Novel miR-98-5p/IGF1 Axis Contributes the Pathogenesis of Breast Cancer Using Comprehensive Bioinformatic Analyses Methods and Experiments Validation

10.21203/rs.3.rs-46919/v1 ◽

2020 ◽

Author(s):

Dapeng Sun ◽

Xigang Luo ◽

Lingling Ma ◽

Yi Wang ◽

Fengxiang Zhang

Keyword(s):

Breast Cancer ◽

Messenger Rna ◽

Target Genes ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Differentially Expressed Mirnas ◽

Limma Package ◽

Microarray Datasets ◽

Geo Database

Abstract Background: Breast cancer (BC) is a huge threat for the health of women worldwide. Although the numerous microRNAs (miRNA) have been identified to be aberrantly expressed in BC, the construction of a comprehensive miRNA-messenger RNA (mRNA) network is still needed. This study was aimed to identify BC-associated miRNAs through analyzing microarray datasets obtained from GEO database and to construct a miRNA-mRNA network for BC. Methods: Limma package was used to identify differentially expressed miRNAs (DEMs) in microarray datasets. Genes targeted by DEMs were analyzed with mirTarBase. Gene Ontology and pathway enrichment analysis for the predicted target genes were performed at DAVID. Correlation of DEMs and target genes was analyzed at ENCORI. Based on these results, a miRNA-mRNA regulatory network was constructed. Results: A total of 17 overlapping DEMs were identified at these two microarray datasets. Expression of DEMs in BC were further validated by ENCORI. By utilizing miRTarBase, a total of 167 target genes for DEMs were obtained. 10 hub genes (AKT1, MYC, VEGFA, CCND1, PTEN, IL6, CASP3, KRAS, IGF1, ESR1) were identified after network analysis at STRING and CytoScape. Through analyzing the effects of hub genes on overall survival of BC patients and correlation of DEMs and hub genes, we found hsa-miR-98-5p/IGF1 axis may play a crucial role in BC progression. The connections of hsa-miR-98-5p and IGF1 were further validated by luciferase activity reporter assay and functional assays. Conclusion: In this work, a miRNA-mRNA network related to BC progression was built, and identified one important miRNA-mRNA axis in BC.

Download Full-text

Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis

PeerJ ◽

10.7717/peerj.5180 ◽

2018 ◽

Vol 6 ◽

pp. e5180 ◽

Cited By ~ 21

Author(s):

Ling Cao ◽

Yan Chen ◽

Miao Zhang ◽

De-quan Xu ◽

Yan Liu ◽

...

Keyword(s):

Focal Adhesion ◽

Gene Network ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Mirna Gene ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Hub Genes ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Objective Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC. Methods The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein–protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan–Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples. Results A total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan–Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted. Conclusion In conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.

Download Full-text