scholarly journals Integrated Weighted Gene Co-expression Network Analysis Reveals ALB Associated With Prognosis of High-grade Serous Ovarian Cancer

2021 ◽  
Author(s):  
Bo Wang ◽  
Shan Chao ◽  
Bo Guo
Keyword(s):  
Ovarian Cancer ◽  
Survival Analysis ◽  
Network Analysis ◽  
Single Gene ◽  
Enrichment Analysis ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Hub Genes ◽  
Hub Gene ◽  
Proteomic Data

Abstract Background: Ovarian cancer is the gynecologic tumor with the highest fatality rate, and high-grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer. One important reason for the poor prognosis of HGSOC is the lack of effective diagnostic and prognostic biomarkers. New biomarkers are necessary for improvement of treatment strategies and to ensure appropriate healthcare decisions.Methods: To construct the co-expression network of HGSOC samples, we applied weighted gene co-expression network analysis (WGCNA) to assess the proteomic data downloaded from Clinical Proteomic Tumor Analysis Consortium (CPTAC), and module-trait relationship was then analyzed and plotted in a heat map to choose key module associated with HGSOC. Enrichment analysis was performed on the genes in the key modules to explore the functional information in which these genes participate. Hub genes with high connectivity in key module were identified by Cytoscape software. Furthermore, the true hub gene was selected through survival analysis, followed by expression verification with transcriptome dataset from TCGA and GTEx. Finally, the potential biological functions of hub gene were analyzed via single-gene Gene Set Enrichment Analysis (GSEA).Results: After merging similar modules, a total of 9 modules were identified. Module-trait analysis revealed that the brown module (cor = 0.7) was significantly associated with HGSOC. The results of enrichment analysis of the genes in the brown module show that these genes were related to the functions of the extracellular matrix, the complement system and the blood system. Ten hub genes with the highest connectivity were selected by protein-protein interaction analysis. After survival analysis and expression verification of hub genes, only ALB was confirmed to be the true hub gene and positively correlated with HGSOC prognosis. Single gene GSEA revealed that ALB was associated with cell material degradation.Conclusion: We conducted the first gene co-expression analysis based on proteomic data from HGSOC samples, and found that ALB had prognostic value, which could be applied in the treatment of HGSOC in the future.

Four genes relevant to pathological grade and prognosis in ovarian cancer

2020 ◽  
Vol 29 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Xue Pan ◽  
Ying Chen ◽  
Song Gao
Keyword(s):  
Ovarian Cancer ◽  
Gene Ontology ◽  
Survival Analysis ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Progression Free Survival ◽  
Receptor Interaction ◽  
Pathological Grade ◽  
Hub Genes ◽  
Tissue Samples

BACKGROUND: Ovarian cancer is the common tumor in female, the prognostic of which is influenced by a series of factors. In this study, 4 genes relevant to pathological grade in ovarian cancer were screened out by the construction of weighted gene co-expression network analysis. METHODS: GSE9891 with 298 ovarian cancer cases had been used to construct co-expression networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses was used to analyze the possible mechanism of genes involved in the malignant process of ovarian cancer. Hub genes were validated in other independent datasets, such as GSE63885, GSE26193 and GSE30161. Survival analysis based on the hub genes was performed by website of Kaplan Meier-plotter. RESULTS: The result based on weighted gene co-expression network analysis indicated that turquoise module has the highest association with pathological grade. Gene Ontology enrichment analysis revealed that the genes in turquoise module main enrichment in inflammatory response and immune response. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that the genes in turquoise module main enrichment in cytokine-cytokine receptor interaction and chemokine signaling pathway. In turquoise module, a total of 4 hub genes (MS4A4A, CD163, CPR65, MS4A6A) were identified. Then, 4 hub genes were effectively verified in the test datasets (GSE63885, GSE26193 and GSE30161) and tissue samples from Shengjing Hospital of China Medical University. Survival analysis indicated that the 4 hub genes were associated with poor progression-free survival of ovarian cancer. CONCLUSIONS: In conclusion, 4 hub genes (MS4A4A, CD163, CPR65, MS4A6A) were verified associated with pathological grade of ovarian cancer. Moreover, MS4A4A, CD163, MS4A6A may serve as a surface marker for M2 macrophages. Targeting the 4 hub genes may can improve the prognosis of ovarian cancer.

scholarly journals Identifying SLC27A5 as a potential prognostic marker of hepatocellular carcinoma by weighted gene co-expression network analysis and in vitro assays

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fan Zhang ◽  
Mengjuan Xue ◽  
Xin Jiang ◽  
Huiyuan Yu ◽  
Yixuan Qiu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Network Analysis ◽  
Prognostic Marker ◽  
In Vitro Assays ◽  
Hub Genes ◽  
Hub Gene ◽  
Tumor Tissues ◽  
Potential Biomarker

Abstract Background The incidence and mortality rates of hepatocellular carcinoma are among the highest of all cancers all over the world. However the survival rates are relatively low due to lack of effective treatments. Efforts to elucidate the mechanisms of HCC and to find novel prognostic markers and therapeutic targets are ongoing. Here we tried to identify prognostic genes of HCC through co-expression network analysis. Methods We conducted weighted gene co-expression network analysis with a microarray dataset GSE14520 of HCC from Gene Expression Omnibus database and identified a hub module associated with HCC prognosis. Function enrichment analysis of the hub module was performed. Clinical information was analyzed to select candidate hub genes. The expression profiles and survival analysis of the selected genes were performed using additional datasets (GSE45267 and TCGA-LIHC) and the hub gene was identified. GSEA and in vitro experiments were conducted to further verify the function of the hub gene. Results Genes in the hub module were mostly involved in the metabolism pathway. Four genes (SLC27A5, SLC10A1, PCK2 and FMO4) from the module were identified as candidate hub genes according to correlation analysis with prognostic indicators. All these genes were significantly down-regulated in tumor tissues compared with non-tumor tissues in additional datasets. After survival analysis and network construction, SLC27A5 was selected as a prognostic marker. GSEA analysis and in vitro assays suggested that SLC27A5 downregulation promoted tumor cell migration via enhancing epithelial-mesenchymal transition. Conclusion SLC27A5 is a potential biomarker of HCC and SLC27A5 downregulation promoted HCC progression by enhancing EMT.

scholarly journals Differences in Gene Expression Between High- and Low-Grade Serous Ovarian Cancers: Implications for Diagnosis and Prognosis

2021 ◽  
Author(s):  
Jianhao Xu ◽  
Qian Wang ◽  
Fang Cao ◽  
Zhiyong Deng ◽  
Xiaojiao Gao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Low Grade ◽  
Serous Ovarian Cancer ◽  
Hub Genes ◽  
Diagnosis And Prognosis ◽  
Potential Marker

Abstract Background The clinical presentations of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) differ. In this study, we aimed to identify the essential molecules for the diagnosis and prognosis of these OC subtypes. Methods Differentially expressed genes (DEGs) between HGSOC and LGSOC were identified using three GEO series. The functional enrichment analysis was performed to investigate different biological processes and pathways. The protein–protein interaction network was constructed, and hub genes were screened to narrow the focus of the study. The discovered hub genes were frequently validated using prognostic correlation, co-expression, and immunohistochemistry (IHC) in GTEx, Oncomine, GEPIA, cBioportal, HPA, and other databases. Results In comparison with LGSOC, 79 upregulated genes and 85 downregulated genes were identified in HGSOC, and the biological roles of these genes were mostly centered on the cell cycle process and chromosomal segregation. Among the 10 hub genes chosen, BIRC5 was favorably linked with overall survival of patients with ovarian cancer (p = 0.014), whereas RRM2 was negatively correlated with the ovarian cancer stage (p = 0.0251). In IHC studies, the intensity of BIRC5 expression in ovarian cancer was greater than that in normal ovarian tissues; however, RRM2 was not substantially expressed in either ovarian cancer tissues or normal ovarian tissues. Conclusions BIRC5 is a potential marker that can distinguish HGSOC from LGSOC, guide prognosis, and be utilized in clinical IHC.

Neoadjuvant Chemotherapy Modulates the Tumor Microenvironment in High-Grade Serous Ovarian Cancer

2021 ◽  
Author(s):  
Zhongling Zhuo ◽  
Min Tang ◽  
Hexin Li ◽  
Lili Zhang ◽  
Bingqing Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
T Cells ◽  
Survival Analysis ◽  
Tumor Microenvironment ◽  
Neoadjuvant Chemotherapy ◽  
Mapk Pathway ◽  
Treatment Options ◽  
High Grade ◽  
Serous Ovarian Cancer

Abstract Background While surgical reduction with adjuvant chemotherapy is the traditional treatment for high-grade serous ovarian cancer (HGSOC), neoadjuvant chemotherapy (NACT) has increasingly been applied. This work aims to investigate the expression profiles before and after NACT, explore changes in the tumor microenvironment, expand current treatments, and design a combination of treatment options for patients. Methods We downloaded 326 pre-NACT RNA sequencing data and 37 matched pre- and post-NACT samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were determined with EdgeR, and Gene Ontology analysis was performed to identify the clusters responsible for the biological processes and pathways of HGSOC. Immune infiltration was analyzed using Single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT. Kaplan-Meier (KM) survival analysis was performed to assess prognosis, and the potential correlations between modules and phenotypes were explored using weighted gene co-expression network analysis (WGCNA). Results After NACT, a total of 352 genes showed significant changes in RNA expression, among which 180 genes were up-regulated and 172 down-regulated. The most influential pathway was the positive regulation of mitogen-activated protein kinase (MAPK) cascade. Correlation analysis and KM survival analysis showed that overexpression of MAPK cascade genes correlated with shorter survival time in HGSOC patients. ssGSEA results showed that the expressions of anti-tumor cells (central memory CD4+ T cell and central memory CD8+ T cell) and pro-tumor cells (neutrophil and dendritic cells) were significantly increased after NACT. CIBERSORT showed that the abundances of memory B cells, NK cells, and monocytes were increased and the abundance of plasma cells was decreased after NACT. WGCNA and KM survival analysis showed that a lower abundance of Regulatory T cells (Tregs) was correlated with a better prognosis. Conclusions Gene expression of the MAPK pathway is up-regulated and the abundance of CD4+ T regulation cell decreases after NACT. Thus, the MAPK pathway may promote the differentiation of CD4+ T cells into Th17 cells while inhibiting Tregs development. The inhibited Tregs' development can lead to a better prognosis. Therefore, it is speculated that Tregs inhibitors combined with platinum-based NACT are potential treatment options for HGSOC.

scholarly journals Weighted Gene Co-Expression Network Analysis and Treatment Strategies of Tumor Recurrence-Associated Hub Genes in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhengze Shen ◽  
Shengwei Liu ◽  
Jie Liu ◽  
Jingdong Liu ◽  
Caoyuan Yao
Keyword(s):  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Tumor Recurrence ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Tissue Samples ◽  
Hub Gene

Despite the recent progress of lung adenocarcinoma (LUAD) therapy, tumor recurrence remained to be a challenging factor that impedes the effectiveness of treatment. The objective of the present study was to predict the hub genes affecting LUAD recurrence via weighted gene co-expression network analysis (WGCNA). Microarray samples from LUAD dataset of GSE32863 were analyzed, and the modules with the highest correlation to tumor recurrence were selected. Functional enrichment analysis was conducted, followed by establishment of a protein–protein interaction (PPI) network. Subsequently, hub genes were identified by overall survival analyses and further validated by evaluation of expression in both myeloid populations and tissue samples of LUAD. Gene set enrichment analysis (GSEA) was then carried out, and construction of transcription factors (TF)–hub gene and drug–hub gene interaction network was also achieved. A total of eight hub genes (ACTR3, ARPC5, RAB13, HNRNPK, PA2G4, WDR12, SRSF1, and NOP58) were finally identified to be closely correlated with LUAD recurrence. In addition, TFs that regulate hub genes have been predicted, including MYC, PML, and YY1. Finally, drugs including arsenic trioxide, cisplatin, Jinfukang, and sunitinib were mined for the treatment of the eight hub genes. In conclusion, our study may facilitate the invention of targeted therapeutic drugs and shed light on the understanding of the mechanism for LUAD recurrence.

scholarly journals Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Dechao Wei ◽  
Qiankun Li ◽  
Xiaobing Yang ◽  
Yili Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Single Gene ◽  
Characteristic Curve ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Hub Genes ◽  
Gene Set

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e–26) and tumor stage (r = 0.38, p = 2e–17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

scholarly journals Characterization of Candidate Factors Associated With the Metastasis and Progression of High Grade Serous Ovarian Cancer

2021 ◽  
Author(s):  
Huiping Liu ◽  
Ling Zhou ◽  
Hongyan Cheng ◽  
Shang Wang ◽  
Wenqing Luan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Primary Tumor ◽  
Gynecological Cancer ◽  
Wnt Signaling Pathway ◽  
The Cancer Genome Atlas ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Factors Associated

Abstract Background. High grade serous ovarian cancer (HGSOC) is the highest cause of gynecological cancer-related mortality due to the extremely metastatic nature of this disease. The goal of this study is to explore and evaluate the profiles and characteristics of candidate factors associated with metastasis and progression of HGSOC.Methods. Transcriptomic data of HGSOC patients’ samples collected from the primary tumor and matched omental metastatic tumor were obtained from three independent studies in the NCBI GEO database. Genes significantly up-regulated and down-regulated were selected to evaluate the effects to prognosis and progression of ovarian cancer using data of ovarian cancer patients from The Cancer Genome Atlas (TCGA) database. Enrichment analysis for biological processes and pathways was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis. Furthermore, the hub genes immune landscapes were estimated by Tumor Immune Estimation Resource (TIMER) database.Results. 14 candidate genes included ADIPOQ, ALPK2, BARX1, CD37, CNR2, COL5A3, FABP4, FAP, GPR68, ITGBL1, MOXD1, PODNL1, SFRP2 and TRAF3IP3 were selected as up-regulated genes in metastatic tumors in every database while CADPS, GATA4, STAR and TSPAN8 were down-regulated. These 14 genes were significantly enriched for negative regulation of Wnt signaling pathway, fat cell differentiation, extracellular matrix organization. Finally, ALPK2, FAP, SFRP2 and GATA4, STAR, TSPAN8 were selected as hub genes that were found to be significantly associated with the survival and recurrence. All hub genes were correlated with several types of tumor microenvironmental cells infiltration significantly, especially for cancer associated fibroblasts and NK cells.Conclusions. This study indicates that screening for differentially expressed genes and pathways in HGSOC primary tumor and matched metastasis tumor using integrated bioinformatics analyses. In sum, we identify six hub genes correlated with the progression of HGSOC in our study, which might provide effective targets to predict prognosis and provide novel insights into immune-based therapy strategies of HGSOC well.

scholarly journals RBBP4 Enhances Platinum Chemo Resistance in Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Nianwu Wang ◽  
Wei Wang ◽  
Wenli Mao ◽  
Nazuke Kuerbantayi ◽  
Nuan Jia ◽  
...  
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Primary Treatment ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Hub Gene ◽  
Prognostic Analysis ◽  
Platinum Based Chemotherapy ◽  
Gene Modules

Background. The majority of lung cancers are adenocarcinomas, with the proportion being 40%. The patients are mostly diagnosed in the middle and late stages with metastasis and easy recurrence, which poses great challenge to the treatment and prognosis. Platinum-based chemotherapy is a primary treatment for adenocarcinoma, which frequently causes drug resistance. As a result, it is important to uncover the mechanisms of the chemoresponse of adenocarcinoma to platinum-based chemotherapy. Methods. The genes from the dataset GSE7880 were gathered into gene modules with the assistance of weighted gene coexpression network analysis (WGCNA), the gene trait significance absolute value (|GS|), and gene module memberships (MM). The genes from hub gene modules were calculated with a protein-protein interaction (PPI) network analysis in order to obtain a screening map of hub genes. The hub genes with both a high |GS| and MM and a high degree were selected. Furthermore, genes in the hub gene modules also went through a Gene Ontology (GO) functional enrichment analysis. Results. 11 hub genes in four hub gene modules (LY86, ACTR2, CDK2, CKAP4, KPNB1, RBBP4, SMAD4, MYL6, RPS27, TSPAN2, and VAMP2) were chosen as the significant hub genes. Through the GO function enrichment analysis, it was indicated that four modules were abundant in immune system functions (floralwhite), amino acid biosynthetic process (lightpink4), cell chemotaxis (navajowhite2), and targeting protein (paleturquoise). Four hub genes with the highest |GS| were verified by prognostic analysis.

Identification of lncRNA-associated ceRNA network in high-grade serous ovarian cancer metastasis

Epigenomics ◽  
2020 ◽  
Vol 12 (14) ◽  
pp. 1175-1191
Author(s):  
Xi Li ◽  
Sihui Yu ◽  
Rui Yang ◽  
Qi Wang ◽  
Xiangnan Liu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Metastasis ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Sequencing Data ◽  
Expression Levels

Aim: To uncover a novel lncRNA–miRNA–mRNA network associated with high-grade serous ovarian cancer metastasis. Material & methods: The candidate differentially expressed lncRNAs were obtained from RNA-sequencing data and determined by functional experiments. The downstream miRNAs and mRNAs were identified by bioinformatic prediction and subjected to functional enrichment analysis. Results: The expression levels of lncRNA ENTPD1-AS1/PRANCR/NR2F2-AS1 were reduced in omental metastatic tissues. Similar differential expression patterns of these lncRNAs were also found in lnCAR database and we verified their tumor suppressive roles by performing functional experiments. Furthermore, we predicted miRNAs and mRNAs via bioinformatic tools and validated their alteration in expression levels in presence of lncRNA interference. Conclusion: We proposed a potential ceRNA regulatory mechanism in high-grade serous ovarian cancer omental metastasis

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