scholarly journals A gene sets approach for identifying prognostic gene signatures for outcome prediction

BMC Genomics ◽  
2008 ◽  
Vol 9 (1) ◽  
pp. 177 ◽  
Author(s):  
Seon-Young Kim ◽  
Yong Sung Kim
Keyword(s):  
Outcome Prediction ◽  
Gene Signatures ◽  
Prognostic Gene ◽  
Gene Sets

scholarly journals Network Properties of Cancer Prognostic Gene Signatures in the Human Protein Interactome

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 247
Author(s):  
Jifeng Zhang ◽  
Shoubao Yan ◽  
Cheng Jiang ◽  
Zhicheng Ji ◽  
Chenrun Wang ◽  
...  
Keyword(s):  
Clustering Coefficient ◽  
Human Protein ◽  
Random Sets ◽  
P Value ◽  
Gene Signatures ◽  
Prognostic Gene ◽  
Gene Sets ◽  
Protein Interactome ◽  
Network Properties ◽  
Set Distance

Prognostic gene signatures are critical in cancer prognosis assessments and their pinpoint treatments. However, their network properties remain unclear. Here, we obtained nine prognostic gene sets including 1439 prognostic genes of different cancers from related publications. Four network centralities were used to examine the network properties of prognostic genes (PG) compared with other gene sets based on the Human Protein Reference Database (HPRD) and String networks. We also proposed three novel network measures for further investigating the network properties of prognostic gene sets (PGS) besides clustering coefficient. The results showed that PG did not occupy key positions in the human protein interaction network and were more similar to essential genes rather than cancer genes. However, PGS had significantly smaller intra-set distance (IAD) and inter-set distance (IED) in comparison with random sets (p-value < 0.001). Moreover, we also found that PGS tended to be distributed within network modules rather than between modules (p-value < 0.01), and the functional intersection of the modules enriched with PGS was closely related to cancer development and progression. Our research reveals the common network properties of cancer prognostic gene signatures in the human protein interactome. We argue that these are biologically meaningful and useful for understanding their molecular mechanism.

scholarly journals Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis

2013 ◽  
Vol 15 (7) ◽  
pp. 829-839 ◽  
Author(s):  
Y.-W. Kim ◽  
D. Koul ◽  
S. H. Kim ◽  
A. K. Lucio-Eterovic ◽  
P. R. Freire ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gene Signatures ◽  
Prognostic Gene

scholarly journals Multi-dimensional omics characterization in glioblastoma identifies the purity-associated pattern and prognostic gene signatures

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yi Xiong ◽  
Zujian Xiong ◽  
Hang Cao ◽  
Chang Li ◽  
Siyi Wanggou ◽  
...  
Keyword(s):  
Gene Signatures ◽  
Prognostic Gene

scholarly journals Biomarker-based outcome prediction in prostate adenocarcinoma depends on theTMPRSS2-ERGstatus

2019 ◽  
Author(s):  
Julia S. Gerke ◽  
Martin F. Orth ◽  
Yuri Tolkach ◽  
Laura Romero-Pérez ◽  
Fabienne Wehweck ◽  
...  
Keyword(s):  
Outcome Prediction ◽  
Validation Cohort ◽  
Molecular Subtype ◽  
Leading Edge ◽  
Prostate Adenocarcinoma ◽  
Prognostic Biomarkers ◽  
Gene Set Enrichment ◽  
Free Survival ◽  
Gene Signatures ◽  
Kaplan Meier

ABSTRACTBackgroundProstate adenocarcinoma (PCa) with/without theTMPRSS2-ERG(T2E)-fusion represent distinct molecular subtypes.ObjectiveTo investigate gene-signatures associated with metastasis in T2E-positive and -negative PCa, and to identify and validate subtype-specific prognostic biomarkers.Design, setting and participantsGene expression and clinicopathological data of two discovery PCa cohorts (totaln=783) were separately analyzed regarding the T2E-status. Selected subtype-specific biomarkers were validated in two additional cohorts (totaln=405).Outcome measurements and statistical analysisFrom both discovery cohorts, we generated two gene lists ranked by their differential intratumoral expression in patients with/without metastases stratified by T2E-status, which were subjected to gene set enrichment and leading-edge analyses. The resulting top 20 gene-signatures of both gene lists associated with metastasis were analyzed for overlaps between T2E-positive and -negative cases. Genes shared by several functional gene-signatures were tested for their association with event-free survival using the Kaplan-Meier method in a validation cohort. Immunohistochemistry was performed in another validation cohort.Results and limitationsMetastatic T2E-positive and -negative PCa are characterized by different gene-signatures. Five genes (ASPN, BGN, COL1A1, RRM2andTYMS) were identified whose high expression was significantly associated with worse outcome exclusively in T2E-negative PCa. This was validated in an independent cohort for all genes and additionally for RRM2 by immunohistochemistry in a separate validation cohort. No prognostic biomarkers were identified exclusively for T2E-positive tumors.ConclusionsOur study demonstrates that the prognostic value of biomarkers critically depends on the molecular subtype, i.e. the T2E-status, which should be considered when screening for and applying novel prognostic biomarkers for outcome prediction in PCa.Patient summaryOutcome prediction for PCa is complex. The results of this study highlight that the validity of prognostic biomarkers depends on the molecular subtype, specifically the presence/absence of T2E. The reported new subtype-specific biomarkers exemplify that biomarker-based outcome prediction in PCa should consider the T2E-status.

scholarly journals Finding prognostic gene pairs for cancer from patient-specific gene networks

2019 ◽  
Vol 12 (S8) ◽  
Author(s):  
Byungkyu Park ◽  
Wook Lee ◽  
Inhee Park ◽  
Kyungsook Han
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Patient Specific ◽  
Specific Gene ◽  
Gene Signatures ◽  
Gene Pairs ◽  
Prognostic Gene ◽  
Gene Correlation

Abstract Background Molecular characterization of individual cancer patients is important because cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. Many studies have been conducted to identify diagnostic or prognostic gene signatures for cancer from gene expression profiles. However, some gene signatures may fail to serve as diagnostic or prognostic biomarkers and gene signatures may not be found in gene expression profiles. Methods In this study, we developed a general method for constructing patient-specific gene correlation networks and for identifying prognostic gene pairs from the networks. A patient-specific gene correlation network was constructed by comparing a reference gene correlation network from normal samples to a network perturbed by a single patient sample. The main difference of our method from previous ones includes (1) it is focused on finding prognostic gene pairs rather than prognostic genes and (2) it can identify prognostic gene pairs from gene expression profiles even when no significant prognostic genes exist. Results Evaluation of our method with extensive data sets of three cancer types (breast invasive carcinoma, colon adenocarcinoma, and lung adenocarcinoma) showed that our approach is general and that gene pairs can serve as more reliable prognostic signatures for cancer than genes. Conclusions Our study revealed that prognosis of individual cancer patients is associated with the existence of prognostic gene pairs in the patient-specific network and the size of a subnetwork of the prognostic gene pairs in the patient-specific network. Although preliminary, our approach will be useful for finding gene pairs to predict survival time of patients and to tailor treatments to individual characteristics. The program for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is available at http://bclab.inha.ac.kr/pancancer.

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