The Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung Cancer

End-to-End Representation Learning for Chemical-Chemical Interaction Prediction

Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a learning-based approach. In other drug analyses, deep learning has been actively used in recent years. However, in most cases, deep learning has been used only for classification even though it has feature extraction capabilities. Thus, in this paper, we propose an end-to-end representation learning method for CCI, named DeepCCI, which includes feature extraction and a learning-based approach. Our proposed architecture is based on the Siamese network. Hidden representations are extracted from a simplified molecular input line entry system (SMILES), which is a string notation representing the chemical structure using weight-shared convolutional neural networks. Subsequently, L1 element-wise distances between the two extracted hidden representations are measured. The performance of DeepCCI is compared with those of 12 fingerprint-method combinations. The proposed DeepCCI shows the best performance in most of the evaluation metrics used. In addition, DeepCCI was experimentally validated to guarantee the commutative property. The automatically extracted features can alleviate the efforts required for manual feature engineering and improve prediction performance.

An α-quaternary chiral latam derivative, YH-304 as a novel broad-spectrum anticancer agent

Previously, we reported that α-quaternary chiral lactam derivatives have broad spectrum anticancer activity. However, the underlying molecular mechanisms and its relevance are largely unknown. In the present study, we report progress on α-quaternary chiral lactam analogues that address this, focusing on the novel analogue YH-304 as a candidate to broadly target human cancer cells. The effect of YH-304 on cell transformation was assessed by clonogenic assay in non-small cell lung cancer cells (NSCLCs) A549 and 226B. Proapoptotic activity of YH-304 was determined by TUNEL assay and cleaved PARP, cleaved caspase-9, and Bax as markers for apoptosis. The p53-dependency and therapeutic spectrum of YH-304 was assessed by western blot analysis, real-time PCR, and cell viability assays in cells expressing endogenous wild or mutant p53. The effect of YH-304 on angiogenesis in vivo was examined by bFGF-mediated angiogenesis assay in zebrafish. Finally, the effect of YH-304 on AKT and ERK activation (phosphorylation) as a putative mechanism underlying the effect of YH-304 on bFGF-mediated angiogenesis was assessed using western blotting. We found that YH-304 significantly decreases the colony-forming activities of both A549 and 226B cells, inducing cellular apoptosis. Unlike nutlin-3 (p53 pathway activator), YH-304 did not affect the expression levels of p53 and its target gene such as p21 and thus showed p53-independent anticancer activity with broad spectrum. In addition, YH-304 inhibited bFGF-induced angiogenesis in vivo through mediating AKT and ERK signaling pathway, which plays an important role in bFGF activation and angiogenesis. Taken together, our data indicate that YH-304 may represent a novel therapeutic option for the treatment of cancer in a p53-independent manner.

Investigation of the roles of trace elements during hepatitis C virus infection using protein-protein interactions and a shortest path algorithm

BACKGROUND

Hepatitis is a type of infectious disease that induces inflammation of the liver without pinpointing a particular pathogen or pathogenesis. Type C hepatitis, as a type of hepatitis, has been reported to induce cirrhosis and hepatocellular carcinoma within a very short amount of time. It is a great threat to human health. Some studies have revealed that trace elements are associated with infection with and immune rejection against hepatitis C virus (HCV). However, the mechanism underlying this phenomenon is still unclear.

METHODS

In this study, we aimed to expand our knowledge of this phenomenon by designing a computational method to identify genes that may be related to both HCV and trace element metabolic processes. The searching procedure included three stages. First, a shortest path algorithm was applied to a large network, constructed by protein-protein interactions, to identify potential genes of interest. Second, a permutation test was executed to exclude false discoveries. Finally, some rules based on the betweenness and associations between candidate genes and HCV and trace elements were built to select core genes among the remaining genes.

RESULTS

12 lists of genes, corresponding to 12 types of trace elements, were obtained. These genes are deemed to be associated with HCV infection and trace elements metabolism.

CONCLUSIONS

The analyses indicate that some genes may be related to both HCV and trace element metabolic processes, further confirming the associations between HCV and trace elements. The method was further tested on another set of HCV genes, the results indicate that this method is quite robustness.

GENERAL SIGNIFICANCE

The newly found genes may partially reveal unknown mechanisms between HCV infection and trace element metabolism. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Identifying novel genes and chemicals related to nasopharyngeal cancer in a heterogeneous network

Nasopharyngeal cancer or nasopharyngeal carcinoma (NPC) is the most common cancer originating in the nasopharynx. The factors that induce nasopharyngeal cancer are still not clear. Additional information about the chemicals or genes related to nasopharyngeal cancer will promote a better understanding of the pathogenesis of this cancer and the factors that induce it. Thus, a computational method NPC-RGCP was proposed in this study to identify the possible relevant chemicals and genes based on the presently known chemicals and genes related to nasopharyngeal cancer. To extensively utilize the functional associations between proteins and chemicals, a heterogeneous network was constructed based on interactions of proteins and chemicals. The NPC-RGCP included two stages: the searching stage and the screening stage. The former stage is for finding new possible genes and chemicals in the heterogeneous network, while the latter stage is for screening and removing false discoveries and selecting the core genes and chemicals. As a result, five putative genes, CXCR3, IRF1, CDK1, GSTP1, and CDH2, and seven putative chemicals, iron, propionic acid, dimethyl sulfoxide, isopropanol, erythrose 4-phosphate, β-D-Fructose 6-phosphate, and flavin adenine dinucleotide, were identified by NPC-RGCP. Extensive analyses provided confirmation that the putative genes and chemicals have significant associations with nasopharyngeal cancer.