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Systems biology approach to studying proliferation-dependent prognostic subnetworks in breast cancer.

Song Q, Wang H, Bao J, Pullikuth AK, Li KC, Miller LD, Zhou X - Sci Rep (2015)

Bottom Line: The SPNs were validated on other cohorts of patients, and delivered high accuracy in the classification of metastatic vs non-metastatic breast tumors.Our findings indicate that certain biological networks underlying breast cancer metastasis differ in a proliferation-dependent manner.These networks, in combination, may form the basis of highly accurate prognostic classification models and may have clinical utility in guiding therapeutic options for patients.

View Article: PubMed Central - PubMed

Affiliation: 1] Division of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA. [2] School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, P R China.

ABSTRACT
Tumor proliferative capacity is a major biological correlate of breast tumor metastatic potential. In this paper, we developed a systems approach to investigate associations among gene expression patterns, representative protein-protein interactions, and the potential for clinical metastases, to uncover novel survival-related subnetwork signatures as a function of tumor proliferative potential. Based on the statistical associations between gene expression patterns and patient outcomes, we identified three groups of survival prognostic subnetwork signatures (SPNs) corresponding to three proliferation levels. We discovered 8 SPNs in the high proliferation group, 8 SPNs in the intermediate proliferation group, and 6 SPNs in the low proliferation group. We observed little overlap of SPNs between the three proliferation groups. The enrichment analysis revealed that most SPNs were enriched in distinct signaling pathways and biological processes. The SPNs were validated on other cohorts of patients, and delivered high accuracy in the classification of metastatic vs non-metastatic breast tumors. Our findings indicate that certain biological networks underlying breast cancer metastasis differ in a proliferation-dependent manner. These networks, in combination, may form the basis of highly accurate prognostic classification models and may have clinical utility in guiding therapeutic options for patients.

No MeSH data available.


Related in: MedlinePlus

Enrichment analysis of P-high SPNs in BP sets and KEGG pathway sets.(A) visualize the enriched categories of Biological Process (BP) sets. (B) visualize the enriched categories of KEGG pathway sets. Enriched biological process or pathway (i.e. enrichment) was indicated by yellow, whereas non-enrichment was indicated by blue.
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f4: Enrichment analysis of P-high SPNs in BP sets and KEGG pathway sets.(A) visualize the enriched categories of Biological Process (BP) sets. (B) visualize the enriched categories of KEGG pathway sets. Enriched biological process or pathway (i.e. enrichment) was indicated by yellow, whereas non-enrichment was indicated by blue.

Mentions: For the P-high SPNs, some biological processes, like the protein folding process, were enriched in 3 SPNs (i.e. SPN 1, 4 and 8). Nine KEGG pathways were enriched in 3 SPNs (i.e. SPN 3, 6, 7) (Fig. 4A,B). Of these pathways, SPN 3 enriched in the ECM receptor interaction pathway and the antigen processing and presentation pathway, which are implicated in tumor progression and breast cancer metastasis1617. Interestingly, SPN 6 enriched in prostate cancer pathway and general pathways in cancer. The cell cycle pathway was enriched in SPN 7.


Systems biology approach to studying proliferation-dependent prognostic subnetworks in breast cancer.

Song Q, Wang H, Bao J, Pullikuth AK, Li KC, Miller LD, Zhou X - Sci Rep (2015)

Enrichment analysis of P-high SPNs in BP sets and KEGG pathway sets.(A) visualize the enriched categories of Biological Process (BP) sets. (B) visualize the enriched categories of KEGG pathway sets. Enriched biological process or pathway (i.e. enrichment) was indicated by yellow, whereas non-enrichment was indicated by blue.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4530341&req=5

f4: Enrichment analysis of P-high SPNs in BP sets and KEGG pathway sets.(A) visualize the enriched categories of Biological Process (BP) sets. (B) visualize the enriched categories of KEGG pathway sets. Enriched biological process or pathway (i.e. enrichment) was indicated by yellow, whereas non-enrichment was indicated by blue.
Mentions: For the P-high SPNs, some biological processes, like the protein folding process, were enriched in 3 SPNs (i.e. SPN 1, 4 and 8). Nine KEGG pathways were enriched in 3 SPNs (i.e. SPN 3, 6, 7) (Fig. 4A,B). Of these pathways, SPN 3 enriched in the ECM receptor interaction pathway and the antigen processing and presentation pathway, which are implicated in tumor progression and breast cancer metastasis1617. Interestingly, SPN 6 enriched in prostate cancer pathway and general pathways in cancer. The cell cycle pathway was enriched in SPN 7.

Bottom Line: The SPNs were validated on other cohorts of patients, and delivered high accuracy in the classification of metastatic vs non-metastatic breast tumors.Our findings indicate that certain biological networks underlying breast cancer metastasis differ in a proliferation-dependent manner.These networks, in combination, may form the basis of highly accurate prognostic classification models and may have clinical utility in guiding therapeutic options for patients.

View Article: PubMed Central - PubMed

Affiliation: 1] Division of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA. [2] School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, P R China.

ABSTRACT
Tumor proliferative capacity is a major biological correlate of breast tumor metastatic potential. In this paper, we developed a systems approach to investigate associations among gene expression patterns, representative protein-protein interactions, and the potential for clinical metastases, to uncover novel survival-related subnetwork signatures as a function of tumor proliferative potential. Based on the statistical associations between gene expression patterns and patient outcomes, we identified three groups of survival prognostic subnetwork signatures (SPNs) corresponding to three proliferation levels. We discovered 8 SPNs in the high proliferation group, 8 SPNs in the intermediate proliferation group, and 6 SPNs in the low proliferation group. We observed little overlap of SPNs between the three proliferation groups. The enrichment analysis revealed that most SPNs were enriched in distinct signaling pathways and biological processes. The SPNs were validated on other cohorts of patients, and delivered high accuracy in the classification of metastatic vs non-metastatic breast tumors. Our findings indicate that certain biological networks underlying breast cancer metastasis differ in a proliferation-dependent manner. These networks, in combination, may form the basis of highly accurate prognostic classification models and may have clinical utility in guiding therapeutic options for patients.

No MeSH data available.


Related in: MedlinePlus