Reconstruction and Analysis of Cancer-specific Gene Regulatory Networks from Gene Expression Profiles (1305.5750v2)

Abstract: The main goal of Systems Biology research is to reconstruct biological networks for its topological analysis so that reconstructed networks can be used for the identification of various kinds of disease. The availability of high-throughput data generated by microarray experiments fueled researchers to use whole-genome gene expression profiles to understand cancer and to reconstruct key cancer-specific gene regulatory network. Now, the researchers are taking a keen interest in the development of algorithm for the reconstruction of gene regulatory network from whole genome expression profiles. In this study, a cancer-specific gene regulatory network (prostate cancer) has been constructed using a simple and novel statistics based approach. First, significant genes differentially expressing them self in the disease condition has been identified using a two-stage filtering approach t-test and fold-change measure. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. The obtained results has been validated with the available databases and literature. We obtained a cancer-specific regulatory network of 29 genes with a total of 55 regulatory relations in which some of the genes has been identified as hub genes that can act as drug target for the cancer diagnosis.

• The paper presents a novel method that reconstructs a cancer-specific gene regulatory network from high-throughput microarray data.
• It employs T-test filtering (p ≤ 0.001) and a five-fold change criterion to reduce 27,575 genes to 101 significantly expressed ones.
• Analysis reveals key hub genes with predominantly activating regulatory relationships, offering promising biomarkers and therapeutic targets for prostate cancer.

Reconstruction and Analysis of Cancer-Specific Gene Regulatory Networks from Gene Expression Profiles

This paper explores the reconstruction and topological analysis of a cancer-specific gene regulatory network (GRN) with a focus on prostate cancer. The authors employ a statistical methodology to extract differentially expressed genes from high-throughput microarray data and analyze their regulatory relationships.

Methodology Overview

The paper outlines a multi-step process in constructing the GRN. Preprocessing involved cleaning the gene expression dataset consisting of 27,575 genes, with missing values and duplicates addressed. A meticulous two-stage filtering process was employed to identify significant genes:

1. T-test Filtering: Genes demonstrating a p-value less than or equal to 0.001 were retained.
2. Fold-change Filtering: Only genes exhibiting a minimum five-fold alteration in expression levels were considered significant.

This approach reduced the dataset from an initial 27,575 genes to 101 differentially expressed genes. Subsequent analysis using Pearson correlation coefficients identified 55 regulatory relationships among 29 genes, allowing for targeted focus on strongly correlated gene pairs.

Results and Validation

The reconstructed GRN revealed several hub genes, including KRT5, BNIP3, GJB5, and KCNE2, each characterized by high connectivity within the network. From the regulatory interactions identified, 52 were activating and only three were inhibiting. The findings were validated against existing databases and literature, affirming the involvement of these genes in prostate cancer.

Implications and Future Research

The construction of GRNs provides insights into the molecular dynamics of prostate cancer, potentially informing the development of targeted therapies. The identification of hub genes offers prospective drug targets and biomarkers for cancer diagnostics. The authors suggest expanding this approach to other cancers, such as colon and breast cancer, utilizing artificial intelligence techniques to refine network inference from microarray data.

The paper recognizes the challenge of validating GRN relationships given the scarcity of accessible databases and highlights the need for further experimental corroboration. Advanced computational methods such as fuzzy logic, neural networks, and evolutionary computation are proposed for enhancing network reconstruction accuracy in complex and noisy datasets.

In conclusion, the paper serves as a valuable foundation for understanding cancer-specific molecular networks, heralding future explorations and innovations in systems biology and bioinformatics.