In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population

Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence

BACKGROUND

In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc. However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical applications. Gene regulatory networks, in which genes are represented by nodes and the associations between genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically, the present study sought to measure gene-gene associations by using the coefficient of intrinsic dependence (CID) to capture more nonlinear as well as cause-effect gene relationships.

RESULTS

A stepwise procedure using the CID along with the partial coefficient of intrinsic dependence (pCID) was demonstrated for the rebuilding of simulated networks and the well-known CBF-COR pathway under cold stress using Arabidopsis microarray data. The procedure was also applied to the construction of bHLH gene regulatory pathways under abiotic stresses using rice microarray data, in which OsbHLH104, a putative phytochrome-interacting factor (OsPIF14), and OsbHLH060, a positive regulator of iron homeostasis (OsPRI1) were inferred as the most affiliated genes. The inferred regulatory pathways were verified through literature reviews.

CONCLUSIONS

The proposed method can efficiently decipher gene regulatory pathways and may assist in achieving higher predictive power in practical applications. The lack of any mention in the literature of some of the regulatory event may have been due to the high complexity of the regulatory systems in the plant transcription, a possibility which could potentially be confirmed in the near future given ongoing rapid developments in bio-technology.

HEB silencing induces anti-proliferative effects on U87MG cells cultured as neurospheres and monolayers

Glioblastoma multiforme (GBM) is a lethal tumor and novel strategies are required to overcome resistance. Transcription factor 12 (HEB) has been associated with neural and stem cell proliferation, is overexpressed in certain tumor types and is induced in irradiated U87MG cells. The present study aimed to determine whether HEB knockdown, with or without irradiation, may sensitize GBM cells. U87MG GBM and ACBRI‑371 primary human astrocytes were cultured in monolayers or neurospheres. Cell proliferation and death, cell cycle and sub‑G1 detection, and cluster of differentiation (CD) 133 immunofluorescence were analyzed by flow cytometry, whereas HEB protein expression was analyzed by immunocytochemistry and western blotting. Greater HEB protein expression was observed in U87MG neurospheres compared with ACBRI‑371, and the two cell lines exhibited nuclear HEB expression. HEB silencing in cells grown in monolayers induced a significant reduction in proliferation and decreased the proportion of cells in G0/G1 phase. In addition, HEB silencing reduced (two‑fold) the number of neurospheres compared with control scrambled (SCR) cells. HEB silencing combined with irradiation reduced U87MG cell proliferation when cultured in monolayers and reduced neurosphere cell number compared with the SCR irradiated group; however, not significantly. Differentiation of U87MG cells from neurospheres was reduced in HEB‑silenced cells, whereas in irradiated cells the proportion of CD133+ cells was similar in HEB‑silenced cells compared with the SCR control. These results suggest that HEB may contribute to the proliferation and maintenance of GBM cells. However, only limited effects were exerted by irradiation in HEB‑silenced cells. HEB may be a potential target to decrease proliferation in U87MG GBM cells, grown as monolayers or neurospheres, and may provide important information for the development of novel strategies for cancer therapy.

A Stepwise Approach of Finding Dependent Variables via Coefficient of Intrinsic Dependence

The coefficient of intrinsic dependence (CID) is capable of determining associations among variables without making distributional or functional assumptions regarding random variables. In this study, we developed the partial coefficient of intrinsic dependence (pCID) to facilitate the step-by-step selection of variables that are relevant to a target variable. The strategy of selecting relevant variables using the CID along with the pCID can eliminate interference from other relevant variables. From simulation results, we observed that the proposed method is more sensitive to curvilinearity and more specific to linearity than the combination of Pearsons correlation coefficient and the partial correlation coefficient (PCC/pPCC). This property may provide the opportunity to index different levels of curvilinearity according to CID/pCID outcomes. In practice trials conducted using publicly available microarray data, the CID/pCID procedure successfully identified cold-responsive genes related to three C-repeat binding factors, and was especially effective at identifying some sample-specific gene-gene interactions. Therefore, the proposed strategy may be beneficial in meta-analysis to distinguish general forms of relationships from the noise.

The Major Prognostic Features of Nuclear Receptor NR5A2 in Infiltrating Ductal Breast Carcinomas

Background. Gene expression profiles of 181 breast cancer samples were analyzed to identify prognostic features of nuclear receptors NR5A1 and NR5A2 based upon their associated transcriptional networks. Methods. A supervised network analysis approach was used to build the NR5A-mediated transcriptional regulatory network. Other bioinformatic tools and statistical methods were utilized to confirm and extend results from the network analysis methodology. Results. NR5A2 expression is a negative factor in breast cancer prognosis in both ER(-) and ER(-)/ER(+) mixed cohorts. The clinical and cohort significance of NR5A2-mediated transcriptional activities indicates that it may have a significant role in attenuating grade development and cancer related signal transduction pathways. NR5A2 signature that conditions poor prognosis was identified based upon results from 15 distinct probes. Alternatively, the expression of NR5A1 predicts favorable prognosis when concurrent NR5A2 expression is low. A favorable signature of eight transcription factors mediated by NR5A1 was also identified. Conclusions. Correlation of poor prognosis and NR5A2 activity is identified by NR5A2-mediated 15-gene signature. NR5A2 may be a potential drug target for treating a subset of breast cancer tumors across breast cancer subtypes, especially ER(-) breast tumors. The favorable prognostic feature of NR5A1 is predicted by NR5A1-mediated 8-gene signature.

A supervised network analysis on gene expression profiles of breast tumors predicts a 41-gene prognostic signature of the transcription factor MYB across molecular subtypes

Background. MYB is predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features of MYB via a supervised network analysis. Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson's correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included. Results. ARNT2 is predicted to be the essential gene partner of MYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature of MYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members of MYB are elevated to promote poor prognosis when both levels of MYB and ARNT2 decline. Both MYBL1 and MYBL2 may partially decrease the tumor suppressive activities that are predicted to be up-regulated by MYB and ARNT2. Conclusions. The major prognostic feature of MYB is predicted to be determined by the MYB subnetwork (41 probes) that is relevant across subtypes.

Prognostic features of signal transducer and activator of transcription 3 in an ER(+) breast cancer model system

The aberrantly expressed signal transducer and activator of transcription 3 (STAT3) predicts poor prognosis, primarily in estrogen receptor positive (ER(+)) breast cancers. Activated STAT3 is overexpressed in luminal A subtype cells. The mechanisms contributing to the prognosis and/or subtype relevant features of STAT3 in ER(+) breast cancers are through multiple interacting regulatory pathways, including STAT3-MYC, STAT3-ERα, and STAT3-MYC-ERα interactions, as well as the direct action of activated STAT3. These data predict malignant events, treatment responses and a novel enhancer of tamoxifen resistance. The inferred crosstalk between ERα and STAT3 in regulating their shared target gene-METAP2 is partially validated in the luminal B breast cancer cell line-MCF7. Taken together, we identify a poor prognosis relevant gene set within the STAT3 network and a robust one in a subset of patients. VEGFA, ABL1, LYN, IGF2R and STAT3 are suggested therapeutic targets for further study based upon the degree of differential expression in our model.

Identifying gene set association enrichment using the coefficient of intrinsic dependence

Gene set testing problem has become the focus of microarray data analysis. A gene set is a group of genes that are defined by a priori biological knowledge. Several statistical methods have been proposed to determine whether functional gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to analyzing the dependence structure among gene sets. In this study, we have proposed a novel statistical method of gene set association analysis to identify significantly associated gene sets using the coefficient of intrinsic dependence. The simulation studies show that the proposed method outperforms the conventional methods to detect general forms of association in terms of control of type I error and power. The correlation of intrinsic dependence has been applied to a breast cancer microarray dataset to quantify the un-supervised relationship between two sets of genes in the tumor and non-tumor samples. It was observed that the existence of gene-set association differed across various clinical cohorts. In addition, a supervised learning was employed to illustrate how gene sets, in signaling transduction pathways or subnetworks regulated by a set of transcription factors, can be discovered using microarray data. In conclusion, the coefficient of intrinsic dependence provides a powerful tool for detecting general types of association. Hence, it can be useful to associate gene sets using microarray expression data. Through connecting relevant gene sets, our approach has the potential to reveal underlying associations by drawing a statistically relevant network in a given population, and it can also be used to complement the conventional gene set analysis.