Construct ion of co - expression modules related to survival by WGCNA and identif ication of potential prognostic biomarkers in glioblastoma

Abstract Background: Glioblastoma (GBM) is a malignant brain tumor with poor prognosis. However, the potential pathogenesis and therapeutic target s still needs to be explored.Methods: Herein, The Cancer Genome Atlas (TCGA) expression profile data and clinical information were downloaded, and the Weighted Gene Co-expression Network Analysis (WGCNA) was conducted. Subsequently, hub genes which closely related to the poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM patients, and immune microenvironment were analysed. Patients from TCGA were divided into high- and low-risk groups.Results: Top 10 hub genes (CDC20, NCAPH, CDCA5, BUB1, CDCA8, PBK, KIF2C, TPX2, TTK and TOP2A) were obtained. Then, we performed single-gene analysis on CDCA5 and CDCA8 as genes of interest. We found that their expression levels were closely related to overall survival (OS). They had good correlations with the genes that regulate cell cycle in p53 signaling pathway. Moreover, it revealed that high amplication of CDCA5 was correlated with CD8 + T cells while CDCA8 with CD4 + T cells, and the expression of these two genes showed a significant difference in OS.Conclusions: These results might provide new molecular targets and intervention strategy for GBM.

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Characterization of the prognostic values of the NDRG family in gastric cancer

Therapeutic Advances in Gastroenterology ◽

10.1177/1756284819858507 ◽

2019 ◽

Vol 12 ◽

pp. 175628481985850 ◽

Cited By ~ 4

Author(s):

Chaoran Yu ◽

Xiaohui Hao ◽

Sen Zhang ◽

Wenjun Hu ◽

Jianwen Li ◽

...

Keyword(s):

Gastric Cancer ◽

T Cells ◽

Poor Prognosis ◽

Wide Spectrum ◽

Mitotic Cell ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Follicular Helper ◽

Kaplan Meier ◽

Cancer Genome Atlas

Background: The N-myc downstream-regulated gene ( NDRG) family, NDRG1-4, has been involved in a wide spectrum of biological functions in multiple cancers. However, their prognostic values remain sparse in gastric cancer (GC). Therefore, it is crucial to systematically investigate the prognostic values of the NDRG family in GC. Methods: The prognostic values of the NDRG family were evaluated by Kaplan–Meier Plotter and SurvExpress. The mRNA of the NDRG family was investigated in The Cancer Genome Atlas (TCGA). Transcription factors (TFs) and miRNAs associated with the NDRG family were predicted by NetworkAnalysis. The prognostic values of DNA methylation levels were analyzed by MethSurv. The correlation between immune cells and the NDRG family was evaluated by the Tumor Immune Estimation Resource (TIMER) database. Results: High levels of mRNA expression of NDRG2 and NDRG3 were associated with a favorable prognosis in all GCs. In HER2− GC, NDRG1 was significantly associated with a poor prognosis of GC [hazard ratio (HR) = 1.65, 95% confidence interval (CI) = 1.16–2.33, p = 0.0046]. In HER2+ GC, NDRG4 showed a poor prognosis (HR = 1.4, 95% CI: 1.06–1.85, p = 0.017). NDRG4 was an independent prognostic factor in recurrence-free survival by TCGA cohort. The low-risk NDRG-signature group displayed a significantly favorable survival outcome than the high-risk group (HR = 1.76, 95% CI: 1.2–2.59, p = 0.00385). The phosphorylated protein NDRG1 (NDRG1_pT346) displayed a favorable overall survival and was significantly associated with HER2 and phosphorylated HER2. Epidermis development was the top biological process (BP) for coexpressed genes associated with NDRG1 and NDRG4, while mitotic nuclear division and mitotic cell processes were the top BPs for NDRG2 and NDRG3, respectively. Overall, 6 CpGs of NDRG1, 4 CpGs of NDRG2, 3 CpGs of NDRG3 and 24 CpGs of NDRG4 were associated with significant prognosis. CD4+ T-cells showed the highest correlation with NDRG4 (correlation = 0.341, p = 2.14e−11). Furthermore, BCL6 in follicular helper T-cells (Tfh) cells showed the highest association with NDRG4 (correlation = 0.438, p = 00e+00). Conclusions: This study analyzed the multilevel prognostic values and biological roles of the NDRG family in GC.

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Screening and Interaction Analysis of Key Genes in miR-542-3p Over- Expressed Osteosarcoma Cells by Bioinformatics

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200401103353 ◽

2020 ◽

Vol 23 (5) ◽

pp. 411-418

Author(s):

Zhongqiu Li ◽

Peng Zhang ◽

Feifei Feng ◽

Qiao Zhang

Keyword(s):

Gene Expression ◽

Interaction Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Protein Protein Interaction ◽

P53 Signaling ◽

Cancer Genome Atlas ◽

Key Genes ◽

Gene Expression Profile Data

Background: Osteosarcoma is one of the most serious primary malignant bone tumors that threaten the lives of children and adolescents. However, the mechanism underlying and how to prevent or treat the disease have not been well understood. Aims & Objective: This aim of the present study was to identify the key genes and explore novel insights into the molecular mechanism of miR-542-3p over-expressed Osteosarcoma. Materials & Methods: Gene expression profile data GDS5367 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. And protein-protein interaction (PPI) network was constructed by the STRING database. In addition, the most highly connected module was screened by plugin MCODE and hub genes by plugin CytoHubba. Furthermore, UALCAN and The Cancer Genome Atlas were performed for survival analysis. Result: In total, 1421 DEGs were identified, including 598 genes were up-regulated and 823 genes were down-regulated. GO analysis showed that DEGs were classified into three groups and DEGs mainly enriched in Steroid biosynthesis, Ubiquitin mediated proteolysis and p53 signaling pathway. Six hub genes (UBA52, RNF114, UBE2H, TRIP12, HNRNPC, and PTBP1) may be key genes with the progression of osteosarcoma. Conclusion: The results could better understand the mechanism of osteosarcoma, which may facilitate a novel insight into treatment targets.

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Key pathways in prostate cancer with SPOP mutation identified by bioinformatic analysis

Open Medicine ◽

10.1515/med-2020-0237 ◽

2020 ◽

Vol 15 (1) ◽

pp. 1039-1047

Author(s):

Guanxiong Ding ◽

Jianliang Sun ◽

Lianhua Jiang ◽

Peng Gao ◽

Qidong Zhou ◽

...

Keyword(s):

Prostate Cancer ◽

Poor Prognosis ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

The Cancer Genome Atlas ◽

Oxygen Species ◽

Hub Genes ◽

Atlas Data ◽

Cancer Genome Atlas ◽

Key Pathways

AbstractProstate cancer (PCa) is a leading adult malignant tumor. Recent research has shown that speckle-type BTB/POZ protein (SPOP) mutant is the top frequently mutated gene in PCa, which makes it an important biomarker. In this paper, we aimed at identifying critical genes and pathways related to SPOP mutation in PCa. Recent The Cancer Genome Atlas data showed that 12% of patients with PCa were SPOP mutant. There were 1,570 differentially expressed genes, and online enrichment analysis showed that these genes were mainly enriched in metabolism, pathways in cancer and reactive oxygen species. INS, GNG13, IL6, HTR5A, SAA1, PPY, CXCR5, CXCL13, CD19 and CCL20 were identified as hub genes. The lower SPOP expression level was associated with poor prognosis. In all, our findings showed that various pathways and genes could play critical roles in SPOP mutation in PCa, providing potential options for individualized treatment.

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Association Between Obesity and Incident Colorectal Cancer: An Analysis Based on Colorectal Cancer Database in the Cancer Genome Atlas

10.21203/rs.3.rs-200275/v1 ◽

2021 ◽

Author(s):

Su Yongxian ◽

Chen Tonghua

Keyword(s):

Colorectal Cancer ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Obese Group ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Significant Difference ◽

Cancer Genome Atlas ◽

Genome Atlas

Abstract Background To investigate gene factors of colorectal cancer (CRC) in obesity and potential molecular markers. Methods Clinical data and mRNA expression data from The Cancer Genome Atlas (TCGA) was collected and divided into obese group and non-obese group according to BMI. The differential expressed genes (DEGs) were screened out by “Limma” package of R software based on (|log2(fold change)|>2 and p < 0.05). The functions of DEGs were revealed with Gene Ontology and Kyoto Encyclopedia Genes and Genomes pathway enrichment analysis using the DAVID database. Then STRING database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. Kaplan-Meier analysis was used to assess the potential prognostic genes for CRC patients. Results It has revealed 2055 DEGs in obese group with CRC, 7615 DEGs in non-obese group and 9046 DEGs in total group. MS4A12, TMIGD1, CA2, GBA3 and SLC51B were the top five downregulated genes in obese group. A PPI network consisted of 1042 nodes and 4073 edges, and top ten hub genes SST, PYY, GNG12, CCL13, MCHR2, CCL28, ADCY9, SSTR1, CXCL12 and ADRA2A were identified in obese group. PDCD11 may well predict overall survivals of CRC patients in non-obese group. The survival time of obese group was shorter than that of non-obese group, but there was no significant difference. Conclusions PDCD11 may be a potential molecular marker for non-obese patients with CRC.

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Identification of novel autophagy-related lncRNAs associated with a poor prognosis of colon adenocarcinoma through bioinformatics analysis

Scientific Reports ◽

10.1038/s41598-021-87540-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dejun Wu ◽

Zhenhua Yin ◽

Yisheng Ji ◽

Lin Li ◽

Yunxin Li ◽

...

Keyword(s):

High Risk ◽

Poor Prognosis ◽

Notch Pathway ◽

Colon Adenocarcinoma ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Future Research ◽

Risk Patients ◽

Cancer Genome Atlas ◽

Immune Score

AbstractLncRNAs play a pivotal role in tumorigenesis and development. However, the potential involvement of lncRNAs in colon adenocarcinoma (COAD) needs to be further explored. All the data used in this study were obtained from The Cancer Genome Atlas database, and all analyses were conducted using R software. Basing on the seven prognosis-related lncRNAs finally selected, we developed a prognosis-predicting model with powerful effectiveness (training cohort, 1 year: AUC = 0.70, 95% Cl = 0.57–0.78; 3 years: AUC = 0.71, 95% Cl = 0.6–0.8; 5 years: AUC = 0.76, 95% Cl = 0.66–0.87; validation cohort, 1 year: AUC = 0.70, 95% Cl = 0.58–0.8; 3 years: AUC = 0.73, 95% Cl = 0.63–0.82; 5 years: AUC = 0.68, 95% Cl = 0.5–0.85). The VEGF and Notch pathway were analyzed through GSEA analysis, and low immune and stromal scores were found in high-risk patients (immune score, cor = − 0.15, P < 0.001; stromal score, cor = − 0.18, P < 0.001) , which may partially explain the poor prognosis of patients in the high-risk group. We screened lncRNAs that are significantly associated with the survival of patients with COAD and possibly participate in autophagy regulation. This study may provide direction for future research.

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Transcriptome of Tumor-Infiltrating T Cells in Colorectal Cancer Patients Uncovered a Unique Gene Signature in CD4+ T Cells Associated with Poor Disease-Specific Survival

Vaccines ◽

10.3390/vaccines9040334 ◽

2021 ◽

Vol 9 (4) ◽

pp. 334

Author(s):

Salman M. Toor ◽

Varun Sasidharan Nair ◽

Reem Saleh ◽

Rowaida Z. Taha ◽

Khaled Murshed ◽

...

Keyword(s):

Colorectal Cancer ◽

T Cells ◽

T Cell Activation ◽

Cd4 T Cells ◽

Poor Prognosis ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Disease Specific Survival ◽

Unique Gene ◽

Disease Specific

Colorectal cancer (CRC) is influenced by infiltration of immune cell populations in the tumor microenvironment. While elevated levels of cytotoxic T cells are associated with improved prognosis, limited studies have reported associations between CD4+ T cells and disease outcomes. We recently performed transcriptomic profiling and comparative analyses of sorted CD4+ and CD8+ tumor-infiltrating lymphocytes (TILs) from bulk tumors of CRC patients with varying disease stages. In this study, we compared the transcriptomes of CD4+ with CD8+ TILs. Functional annotation pathway analyses revealed the downregulation of inflammatory response-related genes, while T cell activation and angiogenesis-related genes were upregulated in CD4+ TILs. The top 200 deregulated genes in CD4+ TILs were aligned with the cancer genome atlas (TCGA) CRC dataset to identify a unique gene signature associated with poor prognosis. Moreover, 69 upregulated and 20 downregulated genes showed similar trends of up/downregulation in the TCGA dataset and were used to calculate “poor prognosis score” (ppScore), which was significantly associated with disease-specific survival. High ppScore patients showed lower expression of Treg-, Th1-, and Th17-related genes, and higher expression of Th2-related genes. Our data highlight the significance of T cells within the TME and identify a unique candidate prognostic gene signature for CD4+ TILs in CRC patients.

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Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis

Cancer Biomarkers ◽

10.3233/cbm-203262 ◽

2021 ◽

pp. 1-13

Author(s):

Simei Tu ◽

Hao Zhang ◽

Xiaocheng Yang ◽

Wen Wen ◽

Kangjing Song ◽

...

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Molecular Mechanisms ◽

Disease Free Survival ◽

The Cancer Genome Atlas ◽

Mrna Levels ◽

Hub Genes ◽

Normal Tissues ◽

Significant Difference ◽

Core Genes

BACKGROUND: Since the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer. OBJECTIVE: The study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer. METHODS: The gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database. RESULTS: A total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and high RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples. CONCLUSIONS: RFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

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High RPS27A Expression Predicts Poor Prognosis in Patients With HPV Type 16 Cervical Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.752974 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qiming Wang ◽

Yan Cai ◽

Xuewen Fu ◽

Liang Chen

Keyword(s):

Cervical Cancer ◽

Poor Prognosis ◽

Biological Significance ◽

Enrichment Analysis ◽

Hpv Infection ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Cervical Cancer Cells ◽

Cancer Genome Atlas

In recent years, the incidence and the mortality rate of cervical cancer have been gradually increasing, becoming one of the major causes of cancer-related death in women. In particular, patients with advanced and recurrent cervical cancers present a very poor prognosis. In addition, the vast majority of cervical cancer cases are caused by human papillomavirus (HPV) infection, of which HPV16 infection is the main cause and squamous cell carcinoma is the main presenting type. In this study, we performed screening of differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) database and GSE6791, constructed a protein–protein interaction (PPI) network to screen 34 hub genes, filtered to the remaining 10 genes using the CytoHubba plug-in, and used survival analysis to determine that RPS27A was most associated with the prognosis of cervical cancer patients and has prognostic and predictive value for cervical cancer. The most significant biological functions and pathways of RPS27A enrichment were subsequently investigated with gene set enrichment analysis (GSEA), and integration of TCGA and GTEx database analyses revealed that RPS27A was significantly expressed in most cancer types. In this study, our analysis revealed that RPS27A can be used as a prognostic biomarker for HPV16 cervical cancer and has biological significance for the growth of cervical cancer cells.

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CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

10.21203/rs.3.rs-150866/v1 ◽

2021 ◽

Author(s):

Jun Du ◽

Jinguo Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Poor Prognosis ◽

Early Stage ◽

Human Tumor ◽

The Cancer Genome Atlas ◽

Data Set ◽

Kaplan Meier ◽

Cancer Genome Atlas ◽

Cox Proportional Hazard Regression ◽

The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

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Identification of novel mRNA-miRNA-lncRNA competing endogenous RNA network associated with prognosis of breast cancer

Epigenomics ◽

10.2217/epi-2019-0209 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1501-1518 ◽

Cited By ~ 6

Author(s):

Guansheng Zhong ◽

Weiyang Lou ◽

Minya Yao ◽

Chengyong Du ◽

Haiyan Wei ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Cerna Network ◽

Competing Endogenous Rna Network ◽

Cancer Genome Atlas ◽

Genome Atlas

Aim: To identify novel competing endogenous RNA (ceRNA) network related to patients prognosis in breast cancer. Materials & methods: Dysregulated mRNA based on intersection of three Gene Expression Omnibus and The Cancer Genome Atlas datasets were analyzed by bioinformatics. Results: In total 72 upregulated and 208 downregulated genes were identified. Functional analysis showed that some pathways related to cancer were significantly enriched. By means of stepwise reverse prediction and validation from mRNA to lncRNA, 19 hub genes, nine key miRNA and four key lncRNAs were identified by expression and survival analysis. Ultimately, the coexpression analysis identified RRM2-let-7a-5p- SNHG16/ MAL2 as key ceRNA subnetwork associated with prognosis of breast cancer. Conclusion: We successfully constructed a novel ceRNA network, among which each component was significantly associated with breast cancer prognosis.

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