Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis

Song, Zhenyu; Fan, Chongxi; Zhuo, Zhiyuan; Ma, Zhe; Li, Wenzhi; Chen, Gang

doi:10.18632/aging.102087

Cited by 95 publications

(97 citation statements)

References 51 publications

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“…For hub genes from the module that were significantly associated with clinical features of iCCA patients, we performed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to annotate their biological function using the R package "clusterprofiler" [12]. In our study, we defined hub genes as those with membership (MM) > 0.8 and gene significance (GS) > 0.3 [13].…”

Section: Weighted Gene Co-expression Network Analysis (Wgcna)mentioning

confidence: 99%

Identification of a Three-gene Signature Acting as a Novel Prognostic Biomarker in Intrahepatic Cholangiocarcinoma Patients

Tang¹,

Hunag²,

Li³

et al. 2020

Preprint

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Background: Intrahepatic cholangiocarcinoma (iCCA) patients have poor outcomes due to the lack of biomarkers for the selection of treatment options. The present study was conducted to find biomarkers with independent prognostic vaule in iCCA patients. Methods: Gene transcriptome profiles of E-MTAB-6389, TCGA-CHOL and GSE26566 were obtained from ArrayExpress, The Cancer Genome Atlas and the Gene Expression Omnibus databases, respectively. Bioinformatic analyses were performed to screen novel biomarkers for predicting the prognosis of iCCA patients. Using multivariate Cox regression analyses, a 3-gene signature (BTD-FER-COL12A1) with potential prognostic value was identified and validated in both a training cohort and two validation cohorts. Results: A total of 177 iCCA patients were included in this study. From the key gene modules significantly associated with liver cirrhosis and overall survival (OS) of iCCA patients, we identified 89 hub genes for functional analyses. Cox-regression analyses in both the training and validation cohort indicate that FER, COL12A1 and BTD were independent risk factors for iCCA patients. A 3-gene signature (BTD-FER-COL12A1) with independent prognostic value in iCCA patients was validated in the training cohort, as well as in two validation cohorts. In terms of predicting the prognosis of iCCA patients, the receiver operating characteristics (ROC) curves showed that this 3-gene signature had superior prediction power to BTD, FER, and COL12A1 alone, as well as known biomarkers (MUC1, MUC13) of iCCA. Immunohistochemical staining of samples from The Human Protein Atlas showed that FER and COL12A1 were positively expressed in iCCA tissue, although BTD was not, while none of these genes was detected in normal tissue. These findings were consistent with the expression status of BTD, FER and COL12A1 at the transcriptional level. In addition, we found that FER and COL12A1 were significantly associated with the degree of infiltration by tumor-infiltrating immune cells. Conclusion: We discovered a three-gene signature with independent prognostic value as a novel biomarker for prediction prognosis of iCCA patients. Our findings may help to find novel therapeutic targets for precision treatment of iCCA.

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Section: Weighted Gene Co-expression Network Analysis (Wgcna)mentioning

confidence: 99%

Identification of a Three-gene Signature Acting as a Novel Prognostic Biomarker in Intrahepatic Cholangiocarcinoma Patients

Tang¹,

Hunag²,

Li³

et al. 2020

Preprint

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“…The DEGs from all gene microarray datasets and RNA-seq data were obtained using the RRA method. This method uses a probabilistic model for aggregation, which is resilient to noise, and can calculate the probability of signi cance of all elements and perform the nal ranking [33][34][35][36][37]. The cutoff P value for the RRA was set as 0.05.…”

Section: The Potential Mechanism Of Bsg Expression In Thyroid Cancer mentioning

confidence: 99%

Immunohistochemical Basigin Expression Level in Thyroid Cancer Tissues

Guo

Tang

Pang

et al. 2020

Preprint

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Background: Thyroid cancer (TC) is the most common endocrine malignancy, Basigin (also known as BSG) plays a crucial role in tumor cell invasion, metastasis, and angiogenesis. This study was designed to identify the change of BSG expression in TC and its possible potential mechanism. Methods: The BSG expression levels in TC were demonstrated using data collected from in-house immunohistochemical (IHC), RNA-sequencing (RNA-seq), microarrays and literatures. Integrated analysis was performed to determined BSG expression levels in TC comprehensively. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the integration of BSG co-expressed genes and differentially expressed genes (DEGs) in TC tissues to explore the potential mechanisms of BSG in TC. Results: The protein expression level of BSG was significantly higher in TC cases based on the IHC experiments. In addition, the combined SMD for BSG expression was 0.39 (p<0.0001), the diagnostic odds ratio was 3.69, and the AUC of the sROC curve was 0.6986 using 1182 TC cases and 437 non-cancerous cases from 17 independent datasets. Furthermore, BSG co-expressed genes tended to be enriched in gene terms of the extracellular matrix (ECM), cell adhesion, and cell-cell interactions. The expression levels of nine hub BSG co-expressed genes were markedly up-regulated in TC cases. Conclusion: BSG expression levels were closely correlated with the progression of TC and may affect the signals of the ECM, cell adhesion, and cell-cell interactions.

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“…Recently, the robust rank aggregation (RRA) has been speci cally designed to compare several ranked gene lists and identi ed commonly overlapping genes to reduce or get rid of these mistakes [5]. Numerous studies adopted the RRA method to select and lter differentially expressed microRNA or mRNA pro les on multiple datasets [6][7][8]. However, there is a little study on identifying DEGs using the RRA method in HCC, especially on screening prognostic gene signatures.…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of a Reliable Prognostic Eleven-Genes Signature for Hepatocellular Carcinoma

Liu

Wang

2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and mortality. Although advances in early diagnosis, disease management and treatment of HCC, the outcomes remain unsatisfactory. This study aimed to identify the reliable prognostic biomarkers based integrated bioinformatics analysis to predict and improve the survival of HCC patients. Methods: The gene expression or transcriptome profiles and survival of HCC were acquired from the Gene Expression Omnibus database (GEO) and the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened out by the limma or edgeR package in the R software. Univariate, LASSO and multivariate Cox regression analyses were conducted to explore survival-related signature. Subsequently, a prognostic model and nomogram composed of prognostic signature were constructed for assessing overall survival (OS). Kaplan-Meier analysis, receiver operating characteristic (ROC) curve and stratified analysis were performed to confirm the prognostic performance of the prognostic model.Results: Compared with nontumor samples, 451 reliable DEGs were identified using the robust rank aggregation and overlap validation. Eleven survival-related DEGs were selected for the construction of a risk evaluation model, which could efficiently distinguish high-risk patients from low-risk patients and even be feasible in the subgroups of stages and age. Further analyses suggested the positive and independent prognostic performance of the model compared to other clinical characteristics (P< 0.05, ROC > 0.7). Finally, a prognostic nomogram composed of the model was constructed for assessing the overall survival, and Harrell’s concordance index and calibration curves demonstrated its efficient predictive performance. Conclusion: The predictive model and nomogram will contribute directly to further clinical applications in the individualized survival prediction, the improvement of treatment strategies and more accurate management for patients with HCC.

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Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis

Cited by 95 publications

References 51 publications

Identification of a Three-gene Signature Acting as a Novel Prognostic Biomarker in Intrahepatic Cholangiocarcinoma Patients

Identification of a Three-gene Signature Acting as a Novel Prognostic Biomarker in Intrahepatic Cholangiocarcinoma Patients

Immunohistochemical Basigin Expression Level in Thyroid Cancer Tissues

Identification and Validation of a Reliable Prognostic Eleven-Genes Signature for Hepatocellular Carcinoma

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