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1.
Breast Cancer Res Treat ; 178(1): 63-73, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31364001

RESUMO

BACKGROUND: Chromosomal instabilities (CIN) of plasma cell-free DNA (cfDNA) are common in breast cancer. We aimed to investigate the value of cfDNA CIN in monitoring the breast cancer relapse and additionally to compare it with the traditional biomarkers (CA15-3 and CEA). METHODS: Overall 62 recurrent breast cancer patients and 20 healthy controls were recruited. Low-pass whole-genome sequencing (LPWGS) was performed to detect cfDNA CIN. A CIN score was calculated. The performance of CA15-3, CEA, and CIN score in monitoring the recurrence was investigated with receiver operating characteristic (ROC) curve and the area under curve (AUC). Multivariable Cox proportional hazard model was established to analyze the correlations between copy number gain/loss and disease-free survival (DFS). RESULTS: cfDNA CIN achieved the positive rate of 77.6% [(95% confidence interval (CI) 73.4-95.3%)] among recurrent breast cancer patients, with an AUC value of 0.933, superior to CA15-3 (positive rate: 38.7%; AUC: 0.864) and CEA (positive rate: 41.93%; AUC: 0.878) (P < 0.01). The combination of cfDNA CIN with two biomarkers further increased the positive rate to 88.7% (95% confidence interval 77.5-95.0%). cfDNA CIN achieved better performance in patients with shorter DFS (≤ 41 months), with an AUC value of 0.975. CONCLUSIONS: cfDNA CIN yields a higher accuracy in monitoring breast cancer recurrence compared to traditional biomarkers (CA15-3 and CEA), especially for biomarker-negative patients. The combination of cfDNA CIN to traditional biomarkers further improved the detection rate of recurrence, which may provide a new method for monitoring the early relapse of breast cancer, though further investigations are warranted.


Assuntos
Neoplasias da Mama/diagnóstico , Ácidos Nucleicos Livres/genética , Recidiva Local de Neoplasia/diagnóstico , Sequenciamento Completo do Genoma/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Neoplasias da Mama/genética , Antígeno Carcinoembrionário/metabolismo , Instabilidade Cromossômica , Feminino , Humanos , Pessoa de Meia-Idade , Mucina-1/metabolismo , Recidiva Local de Neoplasia/genética , Curva ROC
2.
Oncotarget ; 7(5): 5461-9, 2016 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-26701727

RESUMO

Hepatocellular carcinoma (HCC) is the fifth most common type of cancers worldwide. However, current therapeutic approaches for this epidemic disease are limited, and its 5-year survival rate hasn't been improved in the past decades. Patient-derived xenograft (PDX) tumor models have become an excellent in vivo system for understanding of disease biology and drug discovery. In order to identify new therapeutic targets for HCC, whole-exome sequencing (WES) was performed on more than 60 HCC PDX models. Among them, four models exhibited protein-altering mutations in JAK1 (Janus Kinase 1) gene. To explore the transforming capability, these mutations were then introduced into HEK293FT and Ba/F3 cells. The results demonstrated that JAK1S703I mutation was able to activate JAK-STAT (Signal Transducer and Activator of Transcription) signaling pathway and drive cell proliferation in the absence of cytokine stimulation in vitro. Furthermore,the sensitivity to the treatment of a JAK1/2 inhibitor, ruxolitinib, was observed in JAK1S703I mutant PDX model, but not in other non-activating mutant or wild type models. Pharmacodynamic analysis showed that phosphorylation of STAT3 in the Ruxolitinib-treated tumor tissues was significantly suppressed. Collectively, our results suggested that JAK1S703I is an activating mutation for JAK-STAT signaling pathway in vitro and in vivo, and JAK-STAT pathway might represent a new therapeutic approach for HCC treatment. Monotherapy using a more potent and specific JAK1 inhibitor and combinatory therapy should be further explored in JAK1 mutant PDX models.


Assuntos
Carcinoma Hepatocelular/tratamento farmacológico , Janus Quinase 1/genética , Neoplasias Hepáticas/tratamento farmacológico , Mutação/genética , Pirazóis/farmacologia , Fator de Transcrição STAT3/antagonistas & inibidores , Animais , Apoptose , Western Blotting , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/patologia , Proliferação de Células , Feminino , Humanos , Janus Quinase 1/antagonistas & inibidores , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patologia , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Nus , Fosforilação , RNA Mensageiro/genética , Reação em Cadeia da Polimerase em Tempo Real , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Fator de Transcrição STAT3/genética , Transdução de Sinais , Células Tumorais Cultivadas , Ensaios Antitumorais Modelo de Xenoenxerto
3.
Gastric Cancer ; 19(1): 116-27, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-25618371

RESUMO

BACKGROUND: Gastric cancer (GC) is an aggressive malignancy whose mechanisms of development and progression are poorly understood. The identification of prognosis-related genomic loci and genes may suffer from the relatively small case numbers and a lack of systematic validation in previous studies. METHODS: Array-based comparative genomic hybridization (aCGH) coupled with patient clinical information was applied to identify prognosis-related loci and genes with high-frequency recurrent gains in 129 GC patients. The candidate loci and genes were then validated using an independent cohort of 384 patients through branched DNA signal amplification analysis (QuantiGene assays). RESULTS: In the 129 patients, a copy number gain of three chromosome regions-namely, 8q22 (including ESRP1 and CCNE2), 8q24 (including MYC and TNFRSF11B), and 20q11-q13 (including SRC, MMP9, and CSE1L)--conferred poor survival for patients. In addition, the correlation between the branched DNA signal amplification analysis results and the aCGH results was analyzed in 73 of these 129 patients, and MYC, TNFRSF11B, ESRP1, CSE1L, and MMP9 were found to be well correlated. Further validation using an independent cohort (n = 384) verified that only MYC and TNFRSF11B within 8q24 are related to survival. Patients with gains in both MYC and TNFRSF11B had poorer survival than those with no gains, particularly those with noncardia GC. Gains in both of these genes were also a significant independent prognostic indicator. CONCLUSIONS: Our results revealed that copy number gains in MYC and TNFRSF11B located at 8q24 are associated with survival in GC, particularly noncardia GC.


Assuntos
Cromossomos Humanos Par 8 , Genes myc , Osteoprotegerina/genética , Neoplasias Gástricas/genética , Neoplasias Gástricas/mortalidade , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Hibridização Genômica Comparativa/métodos , Feminino , Amplificação de Genes , Dosagem de Genes , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Neoplasias Gástricas/patologia , Análise de Sobrevida
4.
Genom Data ; 6: 1-3, 2015 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26697315

RESUMO

Interaction between HBV and host genome integrations in hepatocellular carcinoma (HCC) development is a complex process and the mechanism is still unclear. Here we described in details the quality controls and data mining of aCGH and transcriptome sequencing data on 50 HCC samples from the Chinese patients, published by Dong et al. (2015) (GEO#: GSE65486). In additional to the HBV-MLL4 integration discovered, we also investigated the genetic aberrations of HBV and host genes as well as their genetic interactions. We reported human genome copy number changes and frequent transcriptome variations (e.g. TP53, CTNNB1 mutation, especially MLL family mutations) in this cohort of the patients. For HBV genotype C, we identified a novel linkage disequilibrium region covering HBV replication regulatory elements, including basal core promoter, DR1, epsilon and poly-A regions, which is associated with HBV core antigen over-expression and almost exclusive to HBV-MLL4 integration.

5.
BMC Cancer ; 15: 454, 2015 Jun 04.
Artigo em Inglês | MEDLINE | ID: mdl-26040563

RESUMO

BACKGROUND: MAPK7/ERK5 (extracellular-signal-regulated kinase 5) functions within a canonical three-tiered MAPK (mitogen activated protein kinase) signaling cascade comprising MEK (MAPK/ERK kinase) 5, MEKK(MEK kinase) 2/3 and ERK5 itself. Despite being the least well studied of the MAPK-modules, evidence supports a role for MAPK7-signaling in the pathology of several cancer types. METHODS AND RESULTS: Fluorescence in situ hybridization (FISH) analysis identified MAPK7 gene amplification in 4% (3/74) of non-small cell lung cancers (NSCLC) (enriched to 6% (3/49) in squamous cell carcinoma) and 2% (2/95) of squamous esophageal cancers (sqEC). Immunohistochemical (IHC) analysis revealed a good correlation between MAPK7 gene amplification and protein expression. MAPK7 was validated as a proliferative oncogenic driver by performing in vitro siRNA knockdown of MAPK7 in tumor cell lines. Finally, a novel MEK5/MAPK7 co-transfected HEK293 cell line was developed and used for routine cell-based pharmacodynamic screening. Phosphorylation antibody microarray analysis also identified novel downstream pharmacodynamic (PD) biomarkers of MAPK7 kinase inhibition in tumor cells (pMEF2A and pMEF2D). CONCLUSIONS: Together, these data highlight a broader role for dysregulated MAPK7 in driving tumorigenesis within niche populations of highly prevalent tumor types, and describe current efforts in establishing a robust drug discovery screening cascade.


Assuntos
Carcinoma de Células Escamosas/genética , Ensaios de Seleção de Medicamentos Antitumorais , Neoplasias Esofágicas/genética , Neoplasias Pulmonares/genética , Proteína Quinase 7 Ativada por Mitógeno/genética , Inibidores de Proteínas Quinases/farmacologia , Biomarcadores Tumorais/metabolismo , Carcinoma de Células Escamosas/química , Proliferação de Células/genética , Neoplasias Esofágicas/química , Regulação Neoplásica da Expressão Gênica , Técnicas de Silenciamento de Genes , Células HEK293 , Humanos , Neoplasias Pulmonares/química , Fatores de Transcrição MEF2/metabolismo , Proteína Quinase 7 Ativada por Mitógeno/análise , Proteína Quinase 7 Ativada por Mitógeno/antagonistas & inibidores , Fosforilação , Transdução de Sinais
6.
PLoS One ; 10(4): e0123175, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25901726

RESUMO

To gain molecular insights of HBV integration that may contribute to HCC tumorigenesis, we performed whole transcriptome sequencing and whole genome copy number profiling of hepatocellular carcinoma (HCC) samples from 50 Chinese patients. We identified a total of 33 HBV-human integration sites in 16 of 44 HBV-positive HCC tissues, which were enriched in HBV genotype C-infected patients. In addition, significantly recurrent HBV-MLL4 integration (18%; 8/44) was found in this cohort of patients. Using long-range PCR and Sanger sequencing, we comprehensively characterized gDNA and cDNA sequences that encode for the HBV-MLL4 transcripts, and we revealed that HBV integration into MLL4 exons led to much higher mRNA expression of MLL4 than the integration into MLL4 introns due to an alternative splicing mechanism. Moreover, the HBV-MLL4 integration occurred almost exclusively in CTNNB1 and TP53 wild-type patients. The integration was also associated with a distinct gene expression profile. In conclusion, this is the first report on the molecular basis of the MLL4 integration driving MLL4 over-expression. HBV-MLL4 integration occurred frequently in Chinese HCC patients, representing a unique molecular segment for HCC with HBV infection.


Assuntos
Grupo com Ancestrais do Continente Asiático/genética , Carcinoma Hepatocelular/virologia , Proteínas de Ligação a DNA/metabolismo , Vírus da Hepatite B/fisiologia , Neoplasias Hepáticas/virologia , Integração Viral , Processamento Alternativo , Carcinoma Hepatocelular/genética , Proteínas de Ligação a DNA/genética , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Genoma Humano/genética , Genótipo , Humanos , Neoplasias Hepáticas/genética , Transcrição Genética
7.
J Transl Med ; 13: 116, 2015 Apr 11.
Artigo em Inglês | MEDLINE | ID: mdl-25889497

RESUMO

BACKGROUND: Genetic amplification of HER2 drives tumorigenesis and cancer progression in a subset of patients with gastric cancer (GC), and treatment with trastuzumab, a humanized HER2-neutralizing antibody, improves the overall survival rate of HER2-positive patients. However, a considerable portion of the patients does not respond to trastuzumab and the molecular mechanisms underlying the intrinsic resistance to anti-HER2 therapy in GC is not fully understood. METHODS: We performed whole-transcriptome sequencing on 21 HER2-positive tumor specimens from Chinese GC patients. Whole genome sequencing was performed on the three samples with HER2 fusion to discover the DNA integration structure. A multicolor FISH assay for HER2 split screening was conducted to confirm HER2 fusion and IHC (HercepTest™) was used to detect the membranous expression of HER2. Fusion cDNA were transfected into NIH/3T3 cells and generate stable cell line by lentivirus. The expression of exogenous HER2 fusion proteins and pHER2 were examined by western blot analysis. In vitro efficacy studies were also conducted by PD assay and softagar assay in cell line expression wild type and fusion HER2. T-DM1 was used to assess its binding to NIH/3T3 cells ectopically expressing wild-type and fusion HER2. Finally, the anti-tumor efficacy of trastuzumab was tested in NIH/3 T3 xenografts expressing the HER2 fusion variants. RESULTS: We identified three new HER2 fusions with ZNF207, MDK, or NOS2 in 21 HER2-amplified GC samples (14%; 3/21). Two of the fusions, ZNF207-HER2, and MDK-HER2, which are oncogenic, lead to aberrant activation of HER2 kinase. Treatment with trastuzumab inhibited tumor growth significantly in xenografts expressing MDK-HER2 fusion. In contrast, trastuzumab had no effect on the growth of xenografts expressing ZNF207-HER2 fusion, due to its inability to bind to trastuzumab. CONCLUSIONS: Our results provide the molecular basis of a novel resistance mechanism to trastuzumab-based anti-HER2 therapy, supporting additional molecule stratification within HER2-positive GC patients for more effective therapy options.


Assuntos
Genes erbB-2 , Oncogenes , Neoplasias Gástricas/genética , Animais , Sequência de Bases , Primers do DNA , Fusão Gênica , Humanos , Hibridização in Situ Fluorescente , Camundongos , Células NIH 3T3 , Reação em Cadeia da Polimerase Via Transcriptase Reversa
8.
Genes Chromosomes Cancer ; 53(11): 883-94, 2014 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-24935174

RESUMO

Gastric cancer is the second leading cause of death from cancer worldwide, with an approximately 20% 5-year survival rate. To identify molecular subtypes associated with the clinical prognosis, in addition to genetic aberrations for potential targeted therapeutics, we conducted a comprehensive whole-genome analysis of 131 Chinese gastric cancer tissue specimens using whole-genome array comparative genomic hybridization. The analyses revealed gene focal amplifications, including CTSB, PRKCI, PAK1, STARD13, KRAS, and ABCC4, in addition to ERBB2, FGFR2, and MET. The growth of PAK1-amplified gastric cancer cells in vitro and in vivo was inhibited when the corresponding mRNA was knocked down. Furthermore, both KRAS amplification and KRAS mutation were identified in the gastric cancer specimens. KRAS amplification was associated with worse clinical outcomes, and the KRAS gene mutation predicted sensitivity to the MEK1/2 inhibitor AZD6244 in gastric cancer cell lines. In summary, amplified PAK1, as well as KRAS amplification/mutation, may represent unique opportunities for developing targeted therapeutics for the treatment of gastric cancer.


Assuntos
Dosagem de Genes , Genoma Humano , Proteínas Proto-Oncogênicas/genética , RNA Mensageiro/metabolismo , Neoplasias Gástricas/genética , Quinases Ativadas por p21/genética , Proteínas ras/genética , Benzimidazóis/farmacologia , Instabilidade Cromossômica , Estudos de Coortes , Feminino , Amplificação de Genes , Perfilação da Expressão Gênica , Humanos , MAP Quinase Quinase 1/antagonistas & inibidores , MAP Quinase Quinase 2/antagonistas & inibidores , Masculino , Pessoa de Meia-Idade , Mutação , Proteínas Proto-Oncogênicas/metabolismo , Proteínas Proto-Oncogênicas p21(ras) , Neoplasias Gástricas/metabolismo , Neoplasias Gástricas/mortalidade , Taxa de Sobrevida , Quinases Ativadas por p21/metabolismo , Proteínas ras/metabolismo
9.
Cancer Biol Ther ; 15(1): 128-34, 2014 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-24253382

RESUMO

Protein phosphatase methylesterase 1 (PPME1) is a protein phosphatase 2A (PP2A)-specific methyl esterase that negatively regulates PP2A through demethylation at its carboxy terminal leucine 309 residue. Emerging evidence shows that the upregulation of PPME1 is associated with poor prognosis in glioblastoma patients. By performing an array comparative genomic hybridization analysis to detect copy number changes, we have been the first to identify PPME1 gene amplification in 3.8% (5/131) of Chinese gastric cancer (GC) samples and 3.1% (4/124) of Chinese lung cancer (LC) samples. This PPME1 gene amplification was confirmed by fluorescence in situ hybridization analysis and is correlated with elevated protein expression, as determined by immunohistochemistry analysis. To further investigate the role of PPME1 amplification in tumor growth, short-hairpin RNA-mediated gene silencing was employed. A knockdown of PPME1 expression resulted in a significant inhibition of cell proliferation and induction of cell apoptosis in PPME1-amplified human cancer cell lines SNU668 (GC) and Oka-C1 (LC), but not in nonamplified MKN1 (GC) and HCC95 (LC) cells. The PPME1 gene knockdown also led to a consistent decrease in PP2A demethylation at leucine 309, which was correlated with the downregulation of cellular Erk and AKT phosphorylation. Our data indicate that PPME1 could be an attractive therapeutic target for a subset of GCs and LCs.


Assuntos
Hidrolases de Éster Carboxílico/genética , Neoplasias Pulmonares/genética , Neoplasias Gástricas/genética , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Apoptose , Hidrolases de Éster Carboxílico/metabolismo , Hidrolases de Éster Carboxílico/uso terapêutico , Linhagem Celular Tumoral , Feminino , Dosagem de Genes , Perfilação da Expressão Gênica , Humanos , Neoplasias Pulmonares/metabolismo , Masculino , Pessoa de Meia-Idade , RNA Interferente Pequeno/genética , Transdução de Sinais , Neoplasias Gástricas/metabolismo , Adulto Jovem
10.
Sci Signal ; 5(249): ra80, 2012 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-23131847

RESUMO

Enhanced activation of phosphoinositide 3-kinase (PI3K) is a hallmark of many human tumors because it promotes cell proliferation and survival through several mechanisms. One of these mechanisms is the phosphorylation of the serine and threonine kinase Akt at the cytosolic side of the plasma membrane by phosphoinositide-dependent protein kinase 1 (PDK1), which is recruited and activated by binding to the phosphoinositides produced by PI3K. We previously demonstrated increased nuclear accumulation of PDK1 in cells with enhanced PI3K activity. We report that nuclear PDK1 promoted cell proliferation by suppressing FOXO3A-dependent transcription of the gene encoding p27Kip1 (an inhibitor of cell cycle progression), whereas it enhanced cell survival by inhibiting the activation of c-Jun amino-terminal kinase. Cells with nuclear-localized PDK1 showed anchorage-independent growth, and when injected into mice, these cells induced the formation of solid tumors. In human prostate tumors, cytoplasmic localization of PDK1 correlated only with early-stage, low-risk tumors, whereas nuclear PDK1 localization correlated with high-risk tumors. Together, our findings suggest a role for nuclear-translocated PDK1 in oncogenic cellular transformation and tumor progression in mice and humans.


Assuntos
Apoptose , Núcleo Celular/enzimologia , Proliferação de Células , Transformação Celular Neoplásica/metabolismo , Fosfoglicerato Quinase/metabolismo , Neoplasias da Próstata/enzimologia , Proteínas Serina-Treonina Quinases/metabolismo , Transporte Ativo do Núcleo Celular/genética , Animais , Núcleo Celular/genética , Núcleo Celular/patologia , Sobrevivência Celular , Transformação Celular Neoplásica/genética , Transformação Celular Neoplásica/patologia , Humanos , Masculino , Camundongos , Camundongos Knockout , Fosfatidilinositol 3-Quinases/genética , Fosfatidilinositol 3-Quinases/metabolismo , Fosfoglicerato Quinase/genética , Neoplasias da Próstata/genética , Neoplasias da Próstata/patologia , Ligação Proteica , Proteínas Serina-Treonina Quinases/genética , Proteínas Proto-Oncogênicas c-akt/genética , Proteínas Proto-Oncogênicas c-akt/metabolismo
11.
Clin Cancer Res ; 18(24): 6658-67, 2012 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-23082000

RESUMO

PURPOSE: To investigate the incidence of FGFR1 amplification in Chinese non-small cell lung cancer (NSCLC) and to preclinically test the hypothesis that the novel, potent, and selective fibroblast growth factor receptor (FGFR) small-molecule inhibitor AZD4547 will deliver potent antitumor activity in NSCLC FGFR1-amplified patient-derived tumor xenograft (PDTX) models. EXPERIMENTAL DESIGN: A range of assays was used to assess the translational relevance of FGFR1 amplification and AZD4547 treatment including in vitro lung cell line panel screening and pharmacodynamic (PD) analysis, FGFR1 FISH tissue microarray (TMA) analysis of Chinese NSCLC (n = 127), and, importantly, antitumor efficacy testing and PD analysis of lung PDTX models using AZD4547. RESULTS: The incidence of FGFR1 amplification within Chinese patient NSCLC tumors was 12.5% of squamous origin (6 of 48) and 7% of adenocarcinoma (5 of 76). AZD4547 displayed a highly selective profile across a lung cell line panel, potently inhibiting cell growth only in those lines harboring amplified FGFR1 (GI(50) = 0.003-0.111 µmol/L). AZD4547 induced potent tumor stasis or regressive effects in four of five FGFR1-amplified squamous NSCLC PDTX models. Pharmacodynamic modulation was observed in vivo, and antitumor efficacy correlated well with FGFR1 FISH score and protein expression level. CONCLUSIONS: This study provides novel epidemiologic data through identification of FGFR1 gene amplification in Chinese NSCLC specimens (particularly squamous) and, importantly, extends the clinical significance of this finding by using multiple FGFR1-amplified squamous lung cancer PDTX models to show tumor stasis or regression effects using a specific FGFR inhibitor (AZD4547). Thus, the translational science presented here provides a strong rationale for investigation of AZD4547 as a therapeutic option for patients with squamous NSCLC tumors harboring amplification of FGFR1.


Assuntos
Antineoplásicos/farmacologia , Benzamidas/farmacologia , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Neoplasias Pulmonares/tratamento farmacológico , Piperazinas/farmacologia , Pirazóis/farmacologia , Receptor Tipo 1 de Fator de Crescimento de Fibroblastos/genética , Animais , Antineoplásicos/uso terapêutico , Benzamidas/uso terapêutico , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Feminino , Amplificação de Genes , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Masculino , Camundongos , Camundongos Nus , Piperazinas/uso terapêutico , Pirazóis/uso terapêutico , Receptor Tipo 1 de Fator de Crescimento de Fibroblastos/metabolismo , Transdução de Sinais/efeitos dos fármacos , Ensaios Antitumorais Modelo de Xenoenxerto
12.
BMC Res Notes ; 3(1): 142, 2010 May 25.
Artigo em Inglês | MEDLINE | ID: mdl-20500822

RESUMO

BACKGROUND: Understanding gene expression and regulation is essential for understanding biological mechanisms. Because gene expression profiling has been widely used in basic biological research, especially in transcription regulation studies, we have developed GeneReg, an easy-to-use R package, to construct gene regulatory networks from time course gene expression profiling data; More importantly, this package can provide information about time delays between expression change in a regulator and that of its target genes. FINDINGS: The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases. CONCLUSIONS: GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.

13.
Protein Pept Lett ; 17(7): 899-908, 2010 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-20394581

RESUMO

The transcription factor (TF) is a protein that binds DNA at specific site to help regulate the transcription from DNA to RNA. The mechanism of transcriptional regulatory can be much better understood if the category of transcription factors is known. We introduce a system which can automatically categorize transcription factors using their primary structures. A feature analysis strategy called "mRMR" (Minimum Redundancy, Maximum Relevance) is used to analyze the contribution of the TF properties towards the TF classification. mRMR is coupled with forward feature selection to choose an optimized feature subset for the classification. TF properties are composed of the amino acid composition and the physiochemical characters of the proteins. These properties will generate over a hundred features/parameters. We put all the features/parameters into a classifier, called NNA (nearest neighbor algorithm), for the classification. The classification accuracy is 93.81%, evaluated by a Jackknife test. Feature analysis using mRMR algorithm shows that secondary structure, amino acid composition and hydrophobicity are the most relevant features for classification. A free online classifier is available at http://app3.biosino.org/132dvc/tf/.


Assuntos
Algoritmos , Sequência de Aminoácidos , Reconhecimento Automatizado de Padrão/métodos , Fatores de Transcrição , Aminoácidos/química , Cisteína/química , Interações Hidrofóbicas e Hidrofílicas , Dados de Sequência Molecular , Software , Fatores de Transcrição/química , Fatores de Transcrição/classificação , Triptofano/química
14.
J Comput Chem ; 31(8): 1766-76, 2010 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-20033913

RESUMO

Determination of whether a small organic molecule interacts with an enzyme can help to understand the molecular and cellular functions of organisms, and the metabolic pathways. In this research, we present a prediction model, by combining compound similarity and enzyme similarity, to predict the interactiveness between small molecules and enzymes. A dataset consisting of 2859 positive couples of small molecule and enzyme and 286,056 negative couples was employed. Compound similarity is a measurement of how similar two small molecules are, proposed by Hattori et al., J Am Chem Soc 2003, 125, 11853 which can be availed at http://www.genome.jp/ligand-bin/search_compound, while enzyme similarity was obtained by three ways, they are blast method, using gene ontology items and functional domain composition. Then a new distance between a pair of couples was established and nearest neighbor algorithm (NNA) was employed to predict the interactiveness of enzymes and small molecules. A data distribution strategy was adopted to get a better data balance between the positive samples and the negative samples during training the prediction model, by singling out one-fourth couples as testing samples and dividing the rest data into seven training datasets-the rest positive samples were added into each training dataset while only the negative samples were divided. In this way, seven NNAs were built. Finally, simple majority voting system was applied to integrate these seven models to predict the testing dataset, which was demonstrated to have better prediction results than using any single prediction model. As a result, the highest overall prediction accuracy achieved 97.30%.


Assuntos
Enzimas/genética , Enzimas/metabolismo , Compostos Orgânicos/química , Compostos Orgânicos/metabolismo , Algoritmos , Bases de Dados Genéticas , Enzimas/química , Conformação Molecular , Peso Molecular
15.
Mol Divers ; 14(4): 815-9, 2010 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-19590970

RESUMO

Identifying the cooperation between transcription factors is crucial and challenging to uncover the mystery behind the complex gene expression patterns. Computational methods aimed to infer transcription factor cooperation are expected to get good results if we can integrate the knowledge (existed functional/structural annotations) of proteins. In this contribution, we proposed an information integrative computational framework to infer the cooperation between transcription factors, which relies on the hybridization-space method that can integrate the annotation information of proteins. In our computational experiments, by using function domain annotations only, on our testing dataset, the overall prediction accuracy and the specificity reaches 84.3% and 76.9%, respectively, which is a fairly good result and outperforms the prediction by both amino acid composition-based method and BLAST-based approach. The corresponding online service TFIPS (Transcription Factor Interaction Prediction System) is available on http://pcal.biosino.org/cgi-bin/TFIPS/TFIPS.pl.


Assuntos
Biologia Computacional/métodos , Fatores de Transcrição/química , Fatores de Transcrição/farmacologia , Algoritmos , Sequência de Aminoácidos/fisiologia , Inteligência Artificial , Sítios de Ligação/genética , Ligação Competitiva/fisiologia , Sinergismo Farmacológico , Previsões , Bases de Conhecimento , Ligação Proteica , Relação Estrutura-Atividade , Fatores de Transcrição/metabolismo , Fatores de Transcrição/fisiologia , Ativação Transcricional/efeitos dos fármacos , Ativação Transcricional/fisiologia
16.
Mol Divers ; 14(4): 719-29, 2010 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-20041294

RESUMO

We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets. The NNA was then applied, learning from these data to produce a predictor. We implemented minimum redundancy maximum relevance (mRMR) and properties forward selection (PFS) to reduce the redundancy of our encoding system, obtaining 91 most efficient properties. Finally, via the Jackknife cross-validation test, we got a positive accuracy of 69.2% and an overall accuracy of 96.0% with all the 253 properties. Besides, we got a positive accuracy of 83.8% and an overall accuracy of 97.2% with the 91 most efficient properties. A web-server for predictions is also made available at http://app3.biosino.org:8080/miRTP/index.jsp.


Assuntos
Algoritmos , Sequência de Bases/fisiologia , Biologia Computacional/métodos , MicroRNAs/metabolismo , Homologia de Sequência , Sequência de Aminoácidos , Inteligência Artificial , Sítios de Ligação/genética , Previsões , MicroRNAs/fisiologia , Anotação de Sequência Molecular/métodos , Dados de Sequência Molecular , Interferência de RNA/fisiologia
17.
J Proteome Res ; 7(10): 4521-4, 2008 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-18763822

RESUMO

Peptidases play pivotal regulatory roles in conception, birth, digestion, growth, maturation, aging, and death of all organisms. These regulatory roles include activation, synthesis and turnover of proteins. In the proteomics era, computational methods to identify peptidases and catalog the peptidases into six different major classes-aspartic peptidases, cysteine peptidases, glutamic peptidases, metallo peptidases, serine peptidases and threonine peptidases can give an instant glance at the biological functions of a newly identified protein. In this contribution, by combining the nearest neighbor algorithm and the functional domain composition, we introduce both an automatic peptidase identifier and an automatic peptidase classier. The successful identification and classification rates are 93.7% and 96.5% for our peptidase identifier and peptidase classifier, respectively. Free online peptidase identifier and peptidase classifier are provided on our Web page http://pcal.biosino.org/protease_classification.html.


Assuntos
Reconhecimento Automatizado de Padrão/métodos , Peptídeo Hidrolases/química , Peptídeo Hidrolases/classificação , Análise de Sequência de Proteína/métodos , Algoritmos , Bases de Dados de Proteínas , Peptídeo Hidrolases/genética , Peptídeo Hidrolases/metabolismo
18.
Mol Divers ; 12(2): 131-7, 2008 May.
Artigo em Inglês | MEDLINE | ID: mdl-18704735

RESUMO

Efficient in silico screening approaches may provide valuable hints on biological functions of the compound-candidates, which could help to screen functional compounds either in basic researches on metabolic pathways or drug discovery. Here, we introduce a machine learning method (Nearest Neighbor Algorithm) based on functional group composition of compounds to the analysis of metabolic pathways. This method can quickly map small chemical molecules to the metabolic pathway that they likely belong to. A set of 2,764 compounds from 11 major classes of metabolic pathways were selected for study. The overall prediction rate reached 73.3%, indicating that functional group composition of compounds was really related to their biological metabolic functions.


Assuntos
Redes e Vias Metabólicas , Bibliotecas de Moléculas Pequenas/metabolismo , Algoritmos , Glicólise , Reprodutibilidade dos Testes
19.
BMC Bioinformatics ; 9: 282, 2008 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-18554421

RESUMO

BACKGROUND: Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand. RESULTS: The support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL). CONCLUSION: The SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.


Assuntos
Algoritmos , Inteligência Artificial , Fases de Leitura Aberta/genética , Reconhecimento Automatizado de Padrão/métodos , Análise de Sequência de DNA/métodos , Fatores de Transcrição/química , Fatores de Transcrição/genética , Sequência de Bases , Dados de Sequência Molecular
20.
J Proteome Res ; 7(3): 1131-7, 2008 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-18260610

RESUMO

Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins' functions. We describe a new strategy for the prediction of the types of membrane proteins using the Nearest Neighbor Algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins' domain profile and proteins' physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called "mRMR" (Minimum Redundancy, Maximum Relevance) ( IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 ( 8), 1226- 1238 ). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://pcal.biosino.org/TransmembraneProteinClassification.html.


Assuntos
Proteínas de Membrana/química , Algoritmos
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