Identification of potential prognostic markers associated with lung metastasis in breast cancer by coexpression network analysis

2021 ◽  
pp. 1-12
Author(s):  
Xixun Zhang
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Lung Metastasis ◽  
Prognostic Markers ◽  
Clinical Information ◽  
Preventive Treatment ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Breast cancer (BC) is an aggressive cancer with a high percentage recurrence and metastasis. As one of the most common distant metastasis organ in BC, lung metastasis has a worse prognosis than that of liver and bone. Therefore, it’s important to explore some potential prognostic markers associated with the lung metastasis in BC for preventive treatment. In this study, transcriptomic data and clinical information of BC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Co-expression modules constructed by weighted gene co-expression network analysis (WGCNA) found the royal blue module was significantly associated with lung metastasis in BC. Then, co-expression genes of this module were analyzed for functional enrichment. Furthermore, the prognostic value of these genes was assessed by GEPIA Database and Kaplan-Meier Plotter. Results showed that the hub genes, LMNB and CDC20, were up-regulated in BC and had a worse survival of the patients. Therefore, we speculate that these two genes play crucial roles in the process of lung metastasis in BC, which can be used as potential prognostic markers in lung metastasis of BC. Collectively, our study identified two potential key genes in the lung metastasis of BC, which might be applied as the prognostic markers of the precise treatment in breast cancer with lung metastasis.

Co‑expression Network Analysis by WGCNA and Identify Potential Prognostic Markers Associated with Lung Metastasis in Breast Cancer

2021 ◽  
Author(s):  
xixun zhang
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Lung Metastasis ◽  
Prognostic Markers ◽  
Clinical Information ◽  
Preventive Treatment ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Breast Cancer Patients ◽  
Kaplan Meier

Abstract Backgroud: Breast cancer (BC) is an aggressive cancer with a high percentage recurrence and metastasis. As one of the most common distant metastasis organ in breast cancer, lung metastasis has a worse prognosis than that of liver and bone. Therefore, it’s important to explore some potential prognostic markers associated with the lung metastasis in breast cancer for preventive treatment. Methods: In our study, transcriptomic data and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Co-expression modules was built by Weighted gene co-expression network analysis (WGCNA) to find out the royalbule modules which is significantly associated with lung metastasis in breast cancer. Then, co-expression genes were analyzed for functional enrichment. Furthermore, the prognostic value of these genes was assessed by GEPIA Database and Kaplan-Meier Plotter. Results: Results showed that the hub genes, LMNB and CDC20, were up-regulated in breast cancer and indicated worse survival. Therefore, we speculate that these two genes play crucial roles in the process of lung metastasis in breast cancer, and can be used as potential prognostic markers in lung metastasis of breast cancer. Conclusion: Collectively, our study identified two potential key genes in the lung metastasis of breast cancer, which might be applied as the prognostic markers of the precise treatment in breast cancer with lung metastasis.

scholarly journals Characterization of ceRNA network to reveal potential prognostic biomarkers in triple-negative breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7522 ◽  
Author(s):  
Xiang Song ◽  
Chao Zhang ◽  
Zhaoyun Liu ◽  
Qi Liu ◽  
Kewen He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Quantitative Polymerase Chain Reaction ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Non Coding Rnas

Triple-negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Previous studies have demonstrated that some abnormal expression of non-coding RNAs including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) were closely related to tumor cell proliferation, apoptosis, invasion, migration and drug sensitivity. However, the role of non-coding RNAs in the pathogenesis of TNBC is still unclear. In order to characterize the molecular mechanism of non-coding RNAs in TNBC, we downloaded RNA data and miRNA data from the cancer genome atlas database. We successfully identified 686 message RNAs (mRNAs), 26 miRNAs and 50 lncRNAs as key molecules for high risk of TNBC. Then, we hypothesized that the lncRNA–miRNA–mRNA regulatory axis positively correlates with TNBC and constructed a competitive endogenous RNA (ceRNA) network of TNBC. Our series of analyses has shown that five molecules (TERT, TRIML2, PHBP4, mir-1-3p, mir-133a-3p) were significantly associated with the prognosis of TNBC, and there is a prognostic ceRNA sub-network between those molecules. We mapped the Kaplan–Meier curve of RNA on the sub-network and also suggested that the expression level of the selected RNA is related to the survival rate of breast cancer. Reverse transcription-quantitative polymerase chain reaction showed that the expression level of TRIML2 in TNBC cells was higher than normal. In general, our findings have implications for predicting metastasis, predicting prognosis and discovering new therapeutic targets for TNBC.

scholarly journals Identification of Metastasis-Associated Genes in Triple-Negative Breast Cancer Using Weighted Gene Co-expression Network Analysis

2020 ◽  
Vol 16 ◽  
pp. 117693432095486
Author(s):  
Wenting Xie ◽  
Zhongshi Du ◽  
Yijie Chen ◽  
Naxiang Liu ◽  
Zhaoming Zhong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Candidate Genes ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Characteristic Curve ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Diagnosis And Prognosis

Triple-negative breast cancer (TNBC) is the most aggressive and fatal sub-type of breast cancer. This study aimed to identify metastasis-associated genes that could serve as biomarkers for TNBC diagnosis and prognosis. RNA-seq data and clinical information on TNBC from the Cancer Genome Atlas were used to conduct analyses. Expression data were used to establish co-expression modules using average linkage hierarchical clustering. We used weighted gene co-expression network analysis to explore the associations between gene sets and clinical features and to identify metastasis-associated candidate biomarkers. The K-M plotter website was used to explore the association between the expression of candidate biomarkers and patient survival. In addition, receiver operating characteristic curve analysis was used to illustrate the diagnostic performance of candidate genes. The pale turquoise module was significantly associated with the occurrence of metastasis. In this module, 64 genes were identified, and its functional enrichment analysis revealed that they were mainly associated with transcriptional misregulation in cancer, microRNAs in cancer, and negative regulation of angiogenesis. Further, 4 genes, IGSF10, RUNX1T1, XIST, and TSHZ2, which were negatively associated with relapse-free survival and have seldom been reported before in TNBC, were selected. In addition, the mRNA expression levels of the 4 candidate genes were significantly lower in TNBC tumor tissues compared with healthy tissues. Based on the K-M plotter, these 4 genes were correlated with poor prognosis of TNBC. The area under the curve of IGSF10, RUNX1T1, TSHZ2, and XIST was 0.918, 0.957, 0.977, and 0.749. These findings provide new insight into TNBC metastasis. IGSF10, RUNX1T1, TSHZ2, and XIST could be used as candidate biomarkers for the diagnosis and prognosis of TNBC metastasis.

scholarly journals Analysis of LAGEs Family Gene Signature and Prognostic Relevance in Breast Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 726
Author(s):  
Hoang Dang Khoa Ta ◽  
Wan-Chun Tang ◽  
Nam Nhut Phan ◽  
Gangga Anuraga ◽  
Sz-Ying Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Family Members ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Breast Cancer Patients ◽  
Kaplan Meier

Breast cancer (BRCA) is one of the most complex diseases and involves several biological processes. Members of the L-antigen (LAGE) family participate in the development of various cancers, but their expressions and prognostic values in breast cancer remain to be clarified. High-throughput methods for exploring disease progression mechanisms might play a pivotal role in the improvement of novel therapeutics. Therefore, gene expression profiles and clinical data of LAGE family members were acquired from the cBioportal database, followed by verification using the Oncomine and The Cancer Genome Atlas (TCGA) databases. In addition, the Kaplan-Meier method was applied to explore correlations between expressions of LAGE family members and prognoses of breast cancer patients. MetaCore, GlueGo, and GluePedia were used to comprehensively study the transcript expression signatures of LAGEs and their co-expressed genes together with LAGE-related signal transduction pathways in BRCA. The result indicated that higher LAGE3 messenger (m)RNA expressions were observed in BRCA tissues than in normal tissues, and they were also associated with the stage of BRCA patients. Kaplan-Meier plots showed that overexpression of LAGE1, LAGE2A, LAGE2B, and LAGE3 were highly correlated to poor survival in most types of breast cancer. Significant associations of LAGE family genes were correlated with the cell cycle, focal adhesion, and extracellular matrix (ECM) receptor interactions as indicated by functional enrichment analyses. Collectively, LAGE family members’ gene expression levels were related to adverse clinicopathological factors and prognoses of BRCA patients; therefore, LAGEs have the potential to serve as prognosticators of BRCA patients.

scholarly journals Potential Therapeutic and Prognostic Values of LSM Family Genes in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4902
Author(s):  
Hoang Dang Khoa Ta ◽  
Wei-Jan Wang ◽  
Nam Nhut Phan ◽  
Nu Thuy An Ton ◽  
Gangga Anuraga ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Cycle ◽  
Signaling Pathways ◽  
Family Members ◽  
The Cancer Genome Atlas ◽  
Protein Coding ◽  
Expression Levels ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cycle Regulation

In recent decades, breast cancer (BRCA) has become one of the most common diseases worldwide. Understanding crucial genes and their signaling pathways remain an enormous challenge in evaluating the prognosis and possible therapeutics. The “Like-Smith” (LSM) family is known as protein-coding genes, and its member play pivotal roles in the progression of several malignancies, although their roles in BRCA are less clear. To discover biological processes associated with LSM family genes in BRCA development, high-throughput techniques were applied to clarify expression levels of LSMs in The Cancer Genome Atlas (TCGA)-BRCA dataset, which was integrated with the cBioPortal database. Furthermore, we investigated prognostic values of LSM family genes in BCRA patients using the Kaplan–Meier database. Among genes of this family, LSM4 expression levels were highly associated with poor prognostic outcomes with a hazard ratio of 1.35 (95% confidence interval 1.21–1.51, p for trend = 3.4 × 10−7). MetaCore and GlueGo analyses were also conducted to examine transcript expression signatures of LSM family members and their coexpressed genes, together with their associated signaling pathways, such as “Cell cycle role of APC in cell cycle regulation” and “Immune response IL-15 signaling via MAPK and PI3K cascade” in BRCA. Results showed that LSM family members, specifically LSM4, were significantly correlated with oncogenesis in BRCA patients. In summary, our results suggested that LSM4 could be a prospective prognosticator of BRCA.

scholarly journals Comprehensive Transcriptomic Analysis Identifies ST8SIA1 as a Survival-Related Sialyltransferase Gene in Breast Cancer

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1436
Author(s):  
Jung-Yu Kan ◽  
Sin-Hua Moi ◽  
Wen-Chun Hung ◽  
Ming-Feng Hou ◽  
Fang-Ming Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Area Under The Curve ◽  
Disease Free Survival ◽  
Comprehensive Analysis ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Breast Cancer Patients ◽  
Clinical Database ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Hypersialylation caused by the overexpression of sialyltransferases (STs) is a common feature in cancer that is associated with several characteristics of tumorigenesis. Thus, identifying cancer-associated STs is critical for cancer therapy. However, ST screening has been frequently conducted in cell line models. In this study, we conducted a comprehensive analysis of STs in the clinical database and identified the STs related with the survival of breast cancer patients. RNA sequencing (RNA-Seq) data of 496 patients were obtained from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Of the eight mapped STs, ST3GAL5, and ST8SIA1 met the acceptable area under the curve (AUC) criteria for overall survival (OS). Using Kaplan–Meier methods, we determined that high expression of ST8SIA1 was associated with poor 10-year OS in all patients, triple-negative breast cancer (TNBC), and non-TNBC patients, and poor disease-free survival (DFS) rates particularly in TNBC. ST8SIA1 also had superior AUC values in terms of OS/DFS. High ST8SIA1 levels showed a higher risk for poor OS in different groups of patients and a higher risk for poor DFS particularly in TNBC. In summary, we conducted a comprehensive analysis of STs from the clinical database and identified ST8SIA1 as a crucial survival-related ST, which might be a potential therapeutic target for breast cancer and TNBC patients.

scholarly journals Gene and lncRNA co-expression network analysis reveals novel ceRNA network for triple-negative breast cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kehao Le ◽  
Hui Guo ◽  
Qiulei Zhang ◽  
Xiaojuan Huang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Protein Coding ◽  
Cerna Network

Abstract Breast cancer is the most frequently diagnosed malignancy among women, and triple-negative breast cancer (TNBC) is a highly aggressive subtype. Increasing evidence has shown that lncRNAs are involved in tumor growth, cell-cycle, and apoptosis through interactions with miRNAs or mRNAs. However, there is still limited data on ceRNAs involved in the molecular mechanisms underlying TNBC. In this study, we applied the weighted gene co-expression network analysis to the existing microarray mRNA and lncRNA expression data obtained from the breast tissues of TNBC patients to find the hub genes and lncRNAs involved in TNBC. Functional enrichment was performed on the module that correlated with Ki-67 status the most (Turquoise module). The hub genes in the Turquoise module were found to be associated with DNA repair, cell proliferation, and the p53 signaling pathway. We performed co-expression analysis of the protein-coding and lncRNA hub genes in the Turquoise module. Analysis of the RNA-seq data obtained from The Cancer Genome Atlas database revealed that the protein-coding genes and lncRNAs that were co-expressed were also differentially expressed in the TNBC tissues compared with the normal mammary tissues. On the basis of establishing the ceRNA network, two mRNAs (RAD51AP1 and TYMS) were found to be correlated with overall survival in TNBC. These results suggest that TNBC-specific mRNA and lncRNAs may participate in a complex ceRNA network, which represents a potential therapeutic target for the treatment of TNBC.

Prognostic and diagnostic roles of prolyl 4-hydroxylase subunit α members in breast cancer

2021 ◽  
Author(s):  
Mingjie Li ◽  
Qianyun Wang ◽  
Qinqin Zheng ◽  
Lin Wu ◽  
Bin Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Stage ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Poor Survival ◽  
Free Survival ◽  
Kaplan Meier ◽  
Human Protein Atlas ◽  
Cancer Genome Atlas ◽  
Kaplan Meier Plotter

Aim: We aimed to evaluate the diagnostic and prognostic values of P4HAs in breast cancer (BC) patients. Materials & methods: Kaplan–Meier plotter was used to evaluate the prognostic values of P4HAs and correlations between their expression and clinical characteristics were assessed based on The Cancer Genome Atlas and the Human Protein Atlas. Results: The current study showed that P4HAs were highly expressed in BC patients with clinical stage I compared with nontumor control and elevated P4HAs were correlated with poor survival outcomes. Subtypes analysis revealed that P4HA1 and P4HA2 were most expressed in HER2+ subtypes patients. Univariate analysis displayed that elevated P4HA1 and P4HA3 correlated with unfavorable recurrence-free survival in mutated TP53 patients. Conclusion: This study indicated the diagnostic and prognostic roles of P4HAs members and broadened the biomarker fields of early diagnosis and prognostic monitoring of BC patients.

scholarly journals CENPL, ISG20L2, LSM4 and MRPL3 Are Four Novel Hub Genes Involved in Breast Cancer Development and Progression

2020 ◽  
Author(s):  
Jinbao Yin ◽  
Chen Lin ◽  
Meng jiang ◽  
Xinbing Tang ◽  
Danlin Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Candidate Genes ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Rank Aggregation ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Ppi Networks ◽  
Kaplan Meier

Abstract BackgroundAs a highly prevalent tumor disease worldwide, Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remain an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil its mechanism and provide novel diagnostic and prognostic markers for breast cancer.MethodsIn this study, we systematically integrated multiple bioinformatic methods, including robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), ROC and Kaplan-Meier analyses, DNA methylation analyses and genomic mutation analyses, GSEA and GSVA, based on ten mRNA datasets to identify and investigate novel hub genes involved in breast cancer. In parallel, RNA in situ detection technology was applied to validate those novel hub gene.ResultsEZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4 and MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among those five hub genes, many studies on EZH2 gene in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These novel four hub genes were up-regulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. The preliminary analysis revealed those novel hub genes are four potentially candidate genes for further exploring the molecular mechanism of breast cancer.ConclusionWe identify four novel hub genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. Those hub genes are four potentially candidate genes served as promising candidate diagnostic biomarkers and prognosis predictors for breast cancer, and their exact functional mechanisms in breast cancer deserve further in-depth study.

NAT1 is a critical prognostic biomarker and inhibits proliferation of colorectal cancer through modulation of PI3K/Akt/mTOR

2021 ◽  
Author(s):  
JiaQin Cai ◽  
Hong Sun ◽  
Li Chen ◽  
MuMu Xie ◽  
Jie Zhuang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Wilcoxon Test ◽  
Sequencing Data ◽  
Potential Therapeutic Target ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

The aim of this study was to analyze the correlations between NAT1 and clinicopathological features of and prognosis in colorectal cancer (CRC). RNA sequencing data and clinical information were retrieved from The Cancer Genome Atlas database. Wilcoxon test, logistic regression and Kaplan–Meier method were used to estimate the association between NAT1 and prognosis in CRC. In vitro experiments were conducted to confirm the role of NAT1. NAT1 is significantly less expressed in CRC and independently associated with poor prognosis in CRC patients. The authors further confirmed that expression of NAT1 was significantly lower in SW116 colon cancer cells than in NCM460 cells. Overexpressed NAT1 obviously inhibited the growth of CRC cells by downregulating phosphorylation of the PI3K/Akt/mTOR signaling pathway. NAT1 may be a potential therapeutic target for CRC.

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