Cancer and alterations in the endocytic pathway

Knockdown of HIP1 expression promotes ligand‑induced endocytosis of EGFR in HeLa cells

Huntington-interacting protein 1 (HIP1) is associated with various tumor types; however, its precise functions in tumor cells are unclear. In this study, the effects of HIP1 on the degradation of EGFR, which have important roles in carcinogenesis after EGF stimulation, were examined. After screening 17 cell lines, the coexpression of HIP1 and EGFR was detected in HeLa cells. Accordingly, the expression of HIP1 was knocked down in HeLa cells using various HIP1 siRNA sequences. The endocytosis of EGFR and localization of clathrin in HeLa cells were examined after stimulation by EGF at various concentrations (i.e., 1.5 and 100 ng/ml). After HIP1 expression was blocked by siRNAs, EGFR endocytosis was accelerated and this effect was dependent on the EGF concentration. This endocytosis was colocalized with clathrin expression. These findings indicate that the inhibition of HIP1 can accelerate the endocytosis and degradation of EGFR. Furthermore, they suggest that HIP1 is a potential therapeutic target for various cancer types, particularly those with high EGFR expression, but further research is needed to examine this hypothesis.

BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis

Comparing the gene-expression profiles between biological conditions is useful for understanding gene regulation underlying complex phenotypes. Along this line, analysis of differential co-expression (DC) has gained attention in the recent years, where genes under one condition have different co-expression patterns compared with another. We developed an R package Bayes Factor approach for Differential Co-expression Analysis (BFDCA) for DC analysis. BFDCA is unique in integrating various aspects of DC patterns (including Shift, Cross, and Re-wiring) into one uniform Bayes factor. We tested BFDCA using simulation data and experimental data. Simulation results indicate that BFDCA outperforms existing methods in accuracy and robustness of detecting DC pairs and DC modules. Results of using experimental data suggest that BFDCA can cluster disease-related genes into functional DC subunits and estimate the regulatory impact of disease-related genes well. BFDCA also achieves high accuracy in predicting case-control phenotypes by using significant DC gene pairs as markers. BFDCA is publicly available at http://dx.doi.org/10.17632/jdz4vtvnm3.1.