Browsing by Author "Huang, Sing-Han"

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A homologous mapping method for three-dimensional reconstruction of protein networks reveals disease-associated mutations
(2018-03-19)
Huang, Sing-Han
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Lo, Yu-Shu
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Luo, Yong-Chun
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Tseng, Yu-Yao
;
Yang, Jinn-Moon
Background: One of the crucial steps toward understanding the associations among molecular interactions, pathways, and diseases in a cell is to investigate detailed atomic protein-protein interactions (PPIs) in the structural interactome. Despite the availability of large-scale methods for analyzing PPI networks, these methods often focused on PPI networks using genome-scale data and/or known experimental PPIs. However, these methods are unable to provide structurally resolved interaction residues and their conservations in PPI networks. Results: Here, we reconstructed a human three-dimensional (3D) structural PPI network (hDiSNet) with the detailed atomic binding models and disease-associated mutations by enhancing our PPI families and 3D-domain interologs from 60,618 structural complexes and complete genome database with 6,352,363 protein sequences across 2274 species. hDiSNet is a scale-free network (gamma = 2.05), which consists of 5177 proteins and 19,239 PPIs with 5843 mutations. These 19,239 structurally resolved PPIs not only expanded the number of PPIs compared to present structural PPI network, but also achieved higher agreement with gene ontology similarities and higher co-expression correlation than the ones of 181,868 experimental PPIs recorded in public databases. Among 5843 mutations, 1653 and 790 mutations involved in interacting domains and contacting residues, respectively, are highly related to diseases. Our hDiSNet can provide detailed atomic interactions of human disease and their associated proteins with mutations. Our results show that the disease-related mutations are often located at the contacting residues forming the hydrogen bonds or conserved in the PPI family. In addition, hDiSNet provides the insights of the FGFR (EGFR)-MAPK pathway for interpreting the mechanisms of breast cancer and ErbB signaling pathway in brain cancer. Conclusions: Our results demonstrate that hDiSNet can explore structural-based interactions insights for understanding the mechanisms of disease-associated proteins and their mutations. We believe that our method is useful to reconstruct structurally resolved PPI networks for interpreting structural genomics and disease associations.
An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer
(2020-05-01)
Chuang, Yi-Hsuan
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Lee, Chia-Hwa
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Lin, Chun-Yu
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Liu, Chia-Lin
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Huang, Sing-Han
Although many studies have shown the association between smoking and the increased incidence and adverse prognosis of head and neck squamous cell carcinoma (HNSCC), the mechanisms and pharmaceutical targets involved remain unclear. Here, we integrated gene expression signatures, genetic alterations, and survival analyses to identify prognostic indicators and therapeutic targets for smoking HNSCC patients, and we discovered that the FDA-approved drug varenicline inhibits the target for cancer cell migration/invasion. We first identified 18 smoking-related and prognostic genes for HNSCC by using RNA-Seq and clinical follow-up data. One of these genes, CHRNB4 (neuronal acetylcholine receptor subunit beta-4), increased the risk of death by approximately threefold in CHRNB4-high expression smokers compared to CHRNB4-low expression smokers (log rank, p = 0.00042; hazard ratio, 2.82; 95% CI, 1.55-5.14), former smokers, and non-smokers. Furthermore, we examined the functional enrichment of co-regulated genes of CHRNB4 and its 246 frequently occurring copy number alterations (CNAs). We found that these genes were involved in promoting angiogenesis, resisting cell death, and sustaining proliferation, and contributed to much worse outcomes for CHRNB4-high patients. Finally, we performed CHRNB4 gene editing and drug inhibition assays, and the results validate these observations. In summary, our study suggests that CHRNB4 is a prognostic indicator for smoking HNSCC patients and provides a potential new therapeutic drug to prevent recurrence or distant metastasis.
Identification of the PCa28 gene signature as a predictor in prostate cancer
(2018-01-01)
Lee, Jung-Yu
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Lin, Si-Yu
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Chuang, Yi-Hsuan
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Huang, Sing-Han
;
Tseng, Yu-Yao
Prostate cancer (PCa) is the second-leading cause of cancer death among men in the worldwide. Most PCa is slowly growing and usually early symptomless. About 70% of PCa patients were diagnosed at later stage and metastasis has been observed. Additionally, the cure rate of PCa closely relies on the early diagnosis with biomarkers. Prostatic Specific Antigen (PSA) is currently the only clinical biomarker for PCa diagnosis. However, the PSA test has inherent limitations and has about 75% of false-positive results. The identification of a set of genes (as biomarkers) for diagnosis and prognosis is an urgent clinical issue for PCa. Here, we integrated genome-wide analysis and protein-protein interaction network to identify potential genes for early diagnostic biomarkers of PCa. First, we collected gene expression datasets of 145 PCa samples, consisting of both tumor and corresponding normal tissues, from two different sources in Gene Expression Omnibus (GEO). We found 158 and 268 significantly highly and lowly expressed genes, respectively, in tumor samples. Moreover, we proposed cluster score (CS) and predicting score (PS) to select 28 prostate cancer-related genes (called PCa28). The results indicate that PCa28 can discriminate between the normal/tumor tissues and are specific for prostate cancer. Finally, we examined 8 genes in PCa28 on four PCa cell lines by real time quantitative polymerase chain reaction (RT-qPCR). Experimental results show that up-regulated genes have higher expression level in tumor cells in comparison to normal cells, and down-regulated genes have lower expression level in tumor cells. We believe that our method is useful and PCa28 are potential biomarkers that provide the clues to develop targeting therapy for PCa.
Identification of the PCA29 gene signature as a predictor in prostate cancer
(2019-06-01)
Lee, Jung-Yu
;
Lin, Si-Yu
;
Lin, Chun-Yu
;
Chuang, Yi-Huan
;
Huang, Sing-Han
Prostate cancer (PCa) is the second leading cause of cancer death among men worldwide. About 70% of PCa patients were diagnosed at later stage, and metastasis has been observed. Additionally, the cure rate of PCa closely relies on the early diagnosis with biomarkers. The identification of biomarkers for diagnosis and prognosis is an urgent clinical issue for PCa. Here, we developed a novel scoring strategy, including cluster score (CS) and predicting score (PS), to identify 29 PCa genes (called PCa29) for early diagnostic biomarkers from two datasets in Gene Expression Omnibus. The result indicates that PCa29 can discriminate between normal and tumor tissues and are specific for prostate cancer. To validate PCa29, we found that 97% of PCa29 were consistently significant with these gene expressions in The Cancer Genome Atlas; furthermore, similar to 70% of PCa29 are consensus to the protein expression in The Human Protein Atlas. Finally, we examined 10 genes in PCa29 on three PCa cell lines by real-time quantitative polymerase chain reaction. The experimental results show that the trend of the differential PCa29 expression is consistent with the analyzed results from our novel scoring method. We believe that our method is useful and PCa29 are potential biomarkers that provide the clues to develop targeting therapy for PCa.
Omics-based Investigation of Diet-induced Obesity Synergized with HBx, Src, and p53 Mutation Accelerating Hepatocarcinogenesis in Zebrafish Model
(2019-12-01)
Yang, Wan-Yu
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Rao, Pei-Shu
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Luo, Yong-Chun
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Lin, Hua-Kuo
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Huang, Sing-Han
The primary type of liver cancer, hepatocellular carcinoma (HCC), has been associated with nonalcoholic steatohepatitis, diabetes, and obesity. Previous studies have identified some genetic risk factors, such as hepatitis B virus X antigens, overexpression of SRC oncogene, and mutation of the p53 tumor suppressor gene; however, the synergism between diet and genetic risk factors is still unclear. To investigate the synergism between diet and genetic risk factors in hepatocarcinogenesis, we used zebrafish with four genetic backgrounds and overfeeding or high-fat-diet-induced obesity with an omics-based expression of genes and histopathological changes. The results show that overfeeding and high-fat diet can induce obesity and nonalcoholic steatohepatitis in wild-type fish. In HBx, Src (p53-) triple transgenic zebrafish, diet-induced obesity accelerated HCC formation at five months of age and increased the cancer incidence threefold. We developed a global omics data analysis method to investigate genes, pathways, and biological systems based on microarray and next-generation sequencing (NGS, RNA-seq) omics data of zebrafish with four diet and genetic risk factors. The results show that two Kyoto Encyclopedia of Genes and Genomes (KEGG) systems, metabolism and genetic information processing, as well as the pathways of fatty acid metabolism, steroid biosynthesis, and ribosome biogenesis, are activated during hepatocarcinogenesis. This study provides a systematic view of the synergism between genetic and diet factors in the dynamic liver cancer formation process, and indicate that overfeeding or a high-fat diet and the risk genes have a synergistic effect in causing liver cancer by affecting fatty acid metabolism and ribosome biogenesis.
Reconstructing Genome-Wide Protein-Protein Interaction Networks Using Multiple Strategies with Homologous Mapping
(2015-01-20)
Lo, Yu-Shu
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Huang, Sing-Han
;
Luo, Yong-Chun
;
Lin, Chun-Yu
;
Yang, Jinn-Moon
Background One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein-protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms. Results Here, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value <= 1 x 10(-40)), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/ essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes. Conclusions Experimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.