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Multiscale Embedded Gene Co-expression Network Analysis.

Song WM, Zhang B - PLoS Comput. Biol. (2015)

Bottom Line: Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases.However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness.MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(/V/3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

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Kaplan-Meier plots of the subgroups defined by median expression of ROPN1 in A) all the patients, B) the ER+ patients and C) the PR+ patients.Blue and red curves correspond to the lower and higher expression levels of ROPN1, respectively.
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pcbi.1004574.g011: Kaplan-Meier plots of the subgroups defined by median expression of ROPN1 in A) all the patients, B) the ER+ patients and C) the PR+ patients.Blue and red curves correspond to the lower and higher expression levels of ROPN1, respectively.

Mentions: This multiscale hub set also includes a number of novel genes as promising targets of breast cancer for further studies. One example is ROPN1, which is an immediate neighborhood of FOXC1 and FOXA1. ROPN1, also known as ropporin, is a cancer-testis antigen[56] and a potential immune-therapeutic target for multiple myeloma as Chiriva-Internati et al. generated human leukocyte antigen class I-restricted cytotoxic lymphocytes to kill autologous multiple myeloma cells[57]. ROPN1 is significantly associated with the overall survival for all the BRCA patients in TCGA (Cox p-value < 2.6e-2, logrank p-value < 5.8 e-2), PR+ (Cox p-value < 9.7e-5, logrank p-value < 3.0e-4), and ER+ (Cox p-value < 3.3e-4, logrank p-value < 2.7e-4). Higher expression of ROPN1 was associated to better prognosis. Furthermore, ROPN1 is mostly down-regulated in tumor samples in comparison to normal samples (FDR corrected test p-value < 8.4e-14, the ratio of the average expression in the tumor samples to that in the adjacent normal samples = 0.14). Fig 11 shows a significant survival difference between the ROPN1-low and -high groups for all, ER+ and PR+ patients. Moreover, ROPN1 is predictive of survival of the Her2 subtype identified by PAM50 biomarkers (Cox p-value < 2.2e-3) [58]. These results suggest ROPN1 as a desirable immunotherapeutic target for further validation.


Multiscale Embedded Gene Co-expression Network Analysis.

Song WM, Zhang B - PLoS Comput. Biol. (2015)

Kaplan-Meier plots of the subgroups defined by median expression of ROPN1 in A) all the patients, B) the ER+ patients and C) the PR+ patients.Blue and red curves correspond to the lower and higher expression levels of ROPN1, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664553&req=5

pcbi.1004574.g011: Kaplan-Meier plots of the subgroups defined by median expression of ROPN1 in A) all the patients, B) the ER+ patients and C) the PR+ patients.Blue and red curves correspond to the lower and higher expression levels of ROPN1, respectively.
Mentions: This multiscale hub set also includes a number of novel genes as promising targets of breast cancer for further studies. One example is ROPN1, which is an immediate neighborhood of FOXC1 and FOXA1. ROPN1, also known as ropporin, is a cancer-testis antigen[56] and a potential immune-therapeutic target for multiple myeloma as Chiriva-Internati et al. generated human leukocyte antigen class I-restricted cytotoxic lymphocytes to kill autologous multiple myeloma cells[57]. ROPN1 is significantly associated with the overall survival for all the BRCA patients in TCGA (Cox p-value < 2.6e-2, logrank p-value < 5.8 e-2), PR+ (Cox p-value < 9.7e-5, logrank p-value < 3.0e-4), and ER+ (Cox p-value < 3.3e-4, logrank p-value < 2.7e-4). Higher expression of ROPN1 was associated to better prognosis. Furthermore, ROPN1 is mostly down-regulated in tumor samples in comparison to normal samples (FDR corrected test p-value < 8.4e-14, the ratio of the average expression in the tumor samples to that in the adjacent normal samples = 0.14). Fig 11 shows a significant survival difference between the ROPN1-low and -high groups for all, ER+ and PR+ patients. Moreover, ROPN1 is predictive of survival of the Her2 subtype identified by PAM50 biomarkers (Cox p-value < 2.2e-3) [58]. These results suggest ROPN1 as a desirable immunotherapeutic target for further validation.

Bottom Line: Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases.However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness.MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(/V/3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Show MeSH
Related in: MedlinePlus