SySAP: a system-level predictor of deleterious single amino acid polymorphisms

Huang, Tao; Zhang, Guoqing; Xie, Lu; Li, Yixue

doi:10.1007/s13238-011-1130-2

Cited by 18 publications

(12 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It evaluates the prediction potential of each feature variable and then performs forward feature selection, also known as incremental feature selection (IFS). IFS has been widely used to solve high dimensional regression [ 65 ] and classification problems [ 66 – 69 ]. The VIF regression program was downloaded from http://cran.r-project.org/web/packages/VIF/ .…”

Section: Methodsmentioning

confidence: 99%

Novel Candidate Key Drivers in the Integrative Network of Genes, MicroRNAs, Methylations, and Copy Number Variations in Squamous Cell Lung Carcinoma

Huang

Jing

Cai

2015

BioMed Research International

Self Cite

View full text Add to dashboard Cite

The mechanisms of lung cancer are highly complex. Not only mRNA gene expression but also microRNAs, DNA methylation, and copy number variation (CNV) play roles in tumorigenesis. It is difficult to incorporate so much information into a single model that can comprehensively reflect all these lung cancer mechanisms. In this study, we analyzed the 129 TCGA (The Cancer Genome Atlas) squamous cell lung carcinoma samples with gene expression, microRNA expression, DNA methylation, and CNV data. First, we used variance inflation factor (VIF) regression to build the whole genome integrative network. Then, we isolated the lung cancer subnetwork by identifying the known lung cancer genes and their direct regulators. This subnetwork was refined by the Bayesian method, and the directed regulations among mRNA genes, microRNAs, methylations, and CNVs were obtained. The novel candidate key drivers in this refined subnetwork, such as the methylation of ARHGDIB and HOXD3, microRNA let-7a and miR-31, and the CNV of AGAP2, were identified and analyzed. On three large public available lung cancer datasets, the key drivers ARHGDIB and HOXD3 demonstrated significant associations with the overall survival of lung cancer patients. Our results provide new insights into lung cancer mechanisms.

show abstract

Section: Methodsmentioning

confidence: 99%

Novel Candidate Key Drivers in the Integrative Network of Genes, MicroRNAs, Methylations, and Copy Number Variations in Squamous Cell Lung Carcinoma

Huang

Jing

Cai

2015

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Matthews correlation coefficient (MCC) [29], [30] was calculated to determine the correlation of the TP cytological diagnosis with the tissue histological diagnosis. The MCC ranges between −1 and +1.…”

Section: Methodsmentioning

confidence: 99%

Application of ThinPrep Bronchial Brushing Cytology in the Early Diagnosis of Lung Cancer: A Retrospective Study

et al. 2014

View full text Add to dashboard Cite

The majority of lung cancer patients are diagnosed at advanced stages of disease. This study evaluated the diagnostic value of ThinPrep (TP) bronchial brushing cytology in lung cancer. A total of 595 patients with suspicious lung cancer were enrolled in this study. The bronchial brushing samples were prepared by TP. The data were then compared to histology of lung tissue samples. Histologically, 479 of these 595 patients were diagnosed with lung cancer, including 223 cases of lung squamous cell carcinoma (SCC), 77 cases of lung adenocarcinoma (ADC), and 152 cases of small cell lung carcinoma (SCLC). The TP cytology revealed a total of 460 cases of lung cancer (including 232 SCCs, 91 ADCs, and 108 SCLCs). The TP cytological technique had 87.06% sensitivity and 62.93% specificity in the diagnosis of lung cancer. Specifically, TP cytology confirmed 195 of 223 SCCs, 47 of 77 ADCs, and 94 of 152 SCLCs. The TP cytology showed 87.44% sensitivity and 90.05% specificity for the diagnosis of SCC, with a Matthew's correlation coefficient (MCC) of 0.820; while the sensitivity was reduced to 61.04% and the specificity was 90.93% for the diagnosis of ADC, with a MCC of 0.464. For the diagnosis of SCLC, the sensitivity was 61.84% and the specificity was 96.84%, with a MCC of 0.648. Thus, this study demonstrated the usefulness of TP bronchial brushing cytology in the early diagnosis of lung cancer, especially the early stage of lung SCC. A prospective clinical trial will verify these data before being translated into the clinic.

show abstract

“…During the IFS operation, the accuracies of all possible top gene sets were calculated and the gene set that had the highest prediction accuracy was chosen as the optimal gene set, that is, the biomarkers. The accuracy was examined by the jackknife test, also known as Leave-One-Out Cross Validation (LOOCV) [36–39] and the prediction model was Nearest Neighbor Algorithm (NNA) [40]. The prediction accuracy was defined as the number of correctly predicted samples divided by the number of total samples.…”

Section: Methodsmentioning

confidence: 99%

Signal Propagation in Protein Interaction Network during Colorectal Cancer Progression

Jiang

Huang

Chen

et al. 2013

BioMed Research International

Self Cite

View full text Add to dashboard Cite

Colorectal cancer is generally categorized into the following four stages according to its development or serious degree: Dukes A, B, C, and D. Since different stage of colorectal cancer actually corresponds to different activated region of the network, the transition of different network states may reflect its pathological changes. In view of this, we compared the gene expressions among the colorectal cancer patients in the aforementioned four stages and obtained the early and late stage biomarkers, respectively. Subsequently, the two kinds of biomarkers were both mapped onto the protein interaction network. If an early biomarker and a late biomarker were close in the network and also if their expression levels were correlated in the Dukes B and C patients, then a signal propagation path from the early stage biomarker to the late one was identified. Many transition genes in the signal propagation paths were involved with the signal transduction, cell communication, and cellular process regulation. Some transition hubs were known as colorectal cancer genes. The findings reported here may provide useful insights for revealing the mechanism of colorectal cancer progression at the cellular systems biology level.

show abstract

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

Cited by 18 publications

References 53 publications

Novel Candidate Key Drivers in the Integrative Network of Genes, MicroRNAs, Methylations, and Copy Number Variations in Squamous Cell Lung Carcinoma

Novel Candidate Key Drivers in the Integrative Network of Genes, MicroRNAs, Methylations, and Copy Number Variations in Squamous Cell Lung Carcinoma

Application of ThinPrep Bronchial Brushing Cytology in the Early Diagnosis of Lung Cancer: A Retrospective Study

Signal Propagation in Protein Interaction Network during Colorectal Cancer Progression

Contact Info

Product

Resources

About