Identification of sample-specific regulations using integrative network level analysis
Chengyu Liu1, Riku Louhimo2, Marko Laakso3
1Research Programs Unit, Genome-Scale Biology Research Program and Institute of Biomedicine, University of Helsinki, Haartmaninkatu 8, Helsinki, FI-00014, Finland. Chengyu.Liu@helsinki.fi.
Background:
Histologically similar tumors even from the same anatomical position may still show high variability at molecular level hindering analysis of genome-wide data. Leveling the analysis to a gene regulatory network instead of focusing on single genes has been suggested to overcome the heterogeneity issue although the majority of the network methods require large datasets. Network methods that are able to function at a single sample level are needed to overcome the heterogeneity and sample size issues.
Methods:
We present a novel network method, Differentially Expressed Regulation Analysis (DERA) that integrates expression data to biological network information at a single sample level. The sample-specific networks are subsequently used to discover samples with similar molecular functions by identification of regulations that are shared between samples or are specific for a subgroup.
Results:
We applied DERA to identify key regulations in triple negative breast cancer (TNBC), which is characterized by lack of estrogen receptor, progesterone receptor and HER2 expression and has poorer prognosis than the other breast cancer subtypes. DERA identified 110 core regulations consisting of 28 disconnected subnetworks for TNBC. These subnetworks are related to oncogenic activity, proliferation, cancer survival, invasiveness and metastasis. Our analysis further revealed 31 regulations specific for TNBC as compared to the other breast cancer subtypes and thus form a basis for understanding TNBC. We also applied DERA to high-grade serous ovarian cancer (HGS-OvCa) data and identified several common regulations between HGS-OvCa and TNBC. The performance of DERA was compared to two pathway analysis methods GSEA and SPIA and our results shows better reproducibility and higher sensitivity in a small sample set.
Conclusions:
We present a novel method called DERA to identify subnetworks that are similarly active for a group of samples. DERA was applied to breast cancer and ovarian cancer data showing our method is able to identify reliable and potentially important regulations with high reproducibility. R package is available at http://csbi.ltdk.helsinki.fi/pub/czliu/DERA/.
Insights
This study introduces Differentially Expressed Regulation Analysis (DERA), a novel network method for analyzing molecular functions in single cancer samples. DERA effectively identifies key gene regulations in triple-negative breast cancer and ovarian cancer, offering improved reproducibility and sensitivity.
Area of Science:
- Computational biology and bioinformatics
- Cancer genomics and systems biology
Background:
- Tumor heterogeneity poses challenges for genome-wide data analysis.
- Existing network methods often require large datasets, limiting their application.
- A need exists for network methods capable of single-sample analysis to address heterogeneity and sample size limitations.
Purpose of the Study:
- To introduce Differentially Expressed Regulation Analysis (DERA), a novel network method.
- To enable analysis of gene regulatory networks at a single sample level.
- To identify molecular functions and subgroups within cancer samples.
Main Methods:
- Developed DERA, a network method integrating gene expression data with biological network information.
- Constructed sample-specific networks to identify shared or subgroup-specific regulations.
- Applied DERA to triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGS-OvCa) datasets.
Main Results:
- DERA identified 110 core regulations and 28 subnetworks in TNBC, linked to oncogenic activity, proliferation, survival, invasion, and metastasis.
- Discovered 31 regulations specific to TNBC, aiding in understanding this subtype.
- Identified common regulations between TNBC and HGS-OvCa, and demonstrated superior reproducibility and sensitivity compared to GSEA and SPIA on small datasets.
Conclusions:
- DERA is a novel method for identifying similarly active subnetworks within sample groups.
- Application to breast and ovarian cancer data confirmed DERA's ability to identify reliable and significant regulations with high reproducibility.
- An R package for DERA is publicly available.
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