Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

VARAdb: a comprehensive variation annotation database for human.

With the study of human diseases and biological processes increasing, a large number of non-coding variants have been identified and facilitated. The rapid accumulation of genetic and epigenomic information has resulted in an urgent need to collect and process data to explore the regulation of non-coding variants. Here, we developed a comprehensive variation annotation database for human (VARAdb, http://www.licpathway.net/VARAdb/), which specifically considers non-coding variants. VARAdb provides annotation information for 577,283,813 variations and novel variants, prioritizes variations based on scores using nine annotation categories, and supports pathway downstream analysis. Importantly, VARAdb integrates a large amount of genetic and epigenomic data into five annotation sections, which include 'Variation information', 'Regulatory information', 'Related genes', 'Chromatin accessibility' and 'Chromatin interaction'. The detailed annotation information consists of motif changes, risk SNPs, LD SNPs, eQTLs, clinical variant-drug-gene pairs, sequence conservation, somatic mutations, enhancers, super enhancers, promoters, transcription factors, chromatin states, histone modifications, chromatin accessibility regions and chromatin interactions. This database is a user-friendly interface to query, browse and visualize variations and related annotation information. VARAdb is a useful resource for selecting potential functional variations and interpreting their effects on human diseases and biological processes.

ATACdb: a comprehensive human chromatin accessibility database.

Accessible chromatin is a highly informative structural feature for identifying regulatory elements, which provides a large amount of information about transcriptional activity and gene regulatory mechanisms. Human ATAC-seq datasets are accumulating rapidly, prompting an urgent need to comprehensively collect and effectively process these data. We developed a comprehensive human chromatin accessibility database (ATACdb, http://www.licpathway.net/ATACdb), with the aim of providing a large amount of publicly available resources on human chromatin accessibility data, and to annotate and illustrate potential roles in a tissue/cell type-specific manner. The current version of ATACdb documented a total of 52 078 883 regions from over 1400 ATAC-seq samples. These samples have been manually curated from over 2200 chromatin accessibility samples from NCBI GEO/SRA. To make these datasets more accessible to the research community, ATACdb provides a quality assurance process including four quality control (QC) metrics. ATACdb provides detailed (epi)genetic annotations in chromatin accessibility regions, including super-enhancers, typical enhancers, transcription factors (TFs), common single-nucleotide polymorphisms (SNPs), risk SNPs, eQTLs, LD SNPs, methylations, chromatin interactions and TADs. Especially, ATACdb provides accurate inference of TF footprints within chromatin accessibility regions. ATACdb is a powerful platform that provides the most comprehensive accessible chromatin data, QC, TF footprint and various other annotations.

HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures.

With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.

KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors.

Transcription factors (TFs) and their target genes have important functions in human diseases and biological processes. Gene expression profile analysis before and after knockdown or knockout is one of the most important strategies for obtaining target genes of TFs and exploring TF functions. Human gene expression profile datasets with TF knockdown and knockout are accumulating rapidly. Based on the urgent need to comprehensively and effectively collect and process these data, we developed KnockTF (http://www.licpathway.net/KnockTF/index.html), a comprehensive human gene expression profile database of TF knockdown and knockout. KnockTF provides a number of resources for human gene expression profile datasets associated with TF knockdown and knockout and annotates TFs and their target genes in a tissue/cell type-specific manner. The current version of KnockTF has 570 manually curated RNA-seq and microarray datasets associated with 308 TFs disrupted by different knockdown and knockout techniques and across multiple tissue/cell types. KnockTF collects upstream pathway information of TFs and functional annotation results of downstream target genes. It provides details about TFs binding to promoters, super-enhancers and typical enhancers of target genes. KnockTF constructs a TF-differentially expressed gene network and performs network analyses for genes of interest. KnockTF will help elucidate TF-related functions and potential biological effects.

ENdb: a manually curated database of experimentally supported enhancers for human and mouse.

Enhancers are a class of cis-regulatory elements that can increase gene transcription by forming loops in intergenic regions, introns and exons. Enhancers, as well as their associated target genes, and transcription factors (TFs) that bind to them, are highly associated with human disease and biological processes. Although some enhancer databases have been published, most only focus on enhancers identified by high-throughput experimental techniques. Therefore, it is highly desirable to construct a comprehensive resource of manually curated enhancers and their related information based on low-throughput experimental evidences. Here, we established a comprehensive manually-curated enhancer database for human and mouse, which provides a resource for experimentally supported enhancers, and to annotate the detailed information of enhancers. The current release of ENdb documents 737 experimentally validated enhancers and their related information, including 384 target genes, 263 TFs, 110 diseases and 153 functions in human and mouse. Moreover, the enhancer-related information was supported by experimental evidences, such as RNAi, in vitro knockdown, western blotting, qRT-PCR, luciferase reporter assay, chromatin conformation capture (3C) and chromosome conformation capture-on-chip (4C) assays. ENdb provides a user-friendly interface to query, browse and visualize the detailed information of enhancers. The database is available at http://www.licpathway.net/ENdb.

SEdb: a comprehensive human super-enhancer database.

Super-enhancers are important for controlling and defining the expression of cell-specific genes. With research on human disease and biological processes, human H3K27ac ChIP-seq datasets are accumulating rapidly, creating the urgent need to collect and process these data comprehensively and efficiently. More importantly, many studies showed that super-enhancer-associated single nucleotide polymorphisms (SNPs) and transcription factors (TFs) strongly influence human disease and biological processes. Here, we developed a comprehensive human super-enhancer database (SEdb, http://www.licpathway.net/sedb) that aimed to provide a large number of available resources on human super-enhancers. The database was annotated with potential functions of super-enhancers in the gene regulation. The current version of SEdb documented a total of 331 601 super-enhancers from 542 samples. Especially, unlike existing super-enhancer databases, we manually curated and classified 410 available H3K27ac samples from >2000 ChIP-seq samples from NCBI GEO/SRA. Furthermore, SEdb provides detailed genetic and epigenetic annotation information on super-enhancers. Information includes common SNPs, motif changes, expression quantitative trait locus (eQTL), risk SNPs, transcription factor binding sites (TFBSs), CRISPR/Cas9 target sites and Dnase I hypersensitivity sites (DHSs) for in-depth analyses of super-enhancers. SEdb will help elucidate super-enhancer-related functions and find potential biological effects.

ce-Subpathway: Identification of ceRNA-mediated subpathways via joint power of ceRNAs and pathway topologies.

Competing endogenous RNAs (ceRNAs) represent a novel mechanism of gene regulation that may mediate key subpathway regions and contribute to the altered activities of pathways. However, the classical methods used to identify pathways fail to specifically consider ceRNAs within the pathways and key regions impacted by them. We proposed a powerful strategy named ce-Subpathway for the identification of ceRNA-mediated functional subpathways. It provided an effective level of pathway analysis via integrating ceRNAs, differentially expressed (DE) genes and their key regions within the given pathways. We respectively analysed one pulmonary arterial hypertension (PAH) and one myocardial infarction (MI) data sets and demonstrated that ce-Subpathway could identify many subpathways whose corresponding entire pathways were ignored by those non-ceRNA-mediated pathway identification methods. And these pathways have been well reported to be associated with PAH/MI-related cardiovascular diseases. Further evidence showed reliability of ceRNA interactions and robustness/reproducibility of the ce-Subpathway strategy by several data sets of different cancers, including breast cancer, oesophageal cancer and colon cancer. Survival analysis was finally applied to illustrate the clinical application value of the ceRNA-mediated functional subpathways using another data sets of pancreatic cancer. Comprehensive analyses have shown the power of a joint ceRNAs/DE genes and subpathway strategy based on their topologies.

Identification and analysis of a key long non-coding RNAs (lncRNAs)-associated module reveal functional lncRNAs in cardiac hypertrophy.

Cardiac hypertrophy (CH) is a common disease that originates from long-term heart pressure overload and finally leads to heart failure. Recently, long non-coding RNAs (lncRNAs) have attracted attention because they have broad and crucial functions in regulating complex biological processes. Some studies had found that lncRNAs play vital roles in complex cardiovascular diseases. However, the function and mechanism of lncRNAs in CH have not been elucidated. In our study, to investigate the potential roles of lncRNAs in CH, the Cardiac Hypertrophy-associated LncRNAs-Protein coding genes Network (CHLPN) was constructed by integrating gene microarray re-annotation and subpathway enrichment analyses. After performing random walking with restart in CHLPN, we predicted 21 significant risk lncRNAs, of which 7 (Kis2, 1700110K17Rik, Gm17501, E330017L17Rik, C630043F03Rik, Gm9866 and Ube4bos1) formed a close module with their co-expressed protein-coding genes (PCGs). We found that the module might play crucial roles in the development of CH. In particular, 44 PCGs that were co-expressed with six lncRNAs were enriched in CH-related biological processes and pathways. We also found that some lncRNAs participated in the competitive endogenous RNA cross-talk that might be involved in CH. These results indicate that the functional lncRNAs are related to post-transcriptional regulation and could shed light on a new molecular diagnostic target of CH.

Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways.

One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy.

Fluid-solid-heat coupling deformation analysis of the valve trims in a multistage pressure reducing valve.

A multistage pressure reducing valve with specially designed pressure reducing components is presented in this paper. As the deformation of the valve trims under fluid-solid-heat coupling has an important influence on the operation reliability of the valve, a numerical simulation is carried out to analyse the flow field characteristic in the valve and radial deformation of the valve trims using the ANSYS software. And a deformation experiment is designed to validate the deformations of the valve trims at high temperature of 693.15 K. The results indicate that the simulation results agree well with the experimental data. Moreover, it is found that the temperature field has the most significant influence on the deformation of the valve trims, the radial deformations of the matching surface vary from 0.439 to 0.442 mm. And the radial deformations caused by other factors vary from 0.005 to 0.015 mm. In addition, as a novel indicator, the clearance after deformation of the matching surface is used to evaluate the operation reliability of the valve. By using the GAP function in ANSYS static module, the clearances of the matching surface are obtained at different openings under the condition of fluid-solid-heat coupling, further indicating that the initial clearance between the valve plug and inner sleeve should be greater than 0.014 mm to ensure the operation reliability of the valve.

Evaluation the possibility of vortex-induced resonance for a multistage pressure reducing valve.

A multistage pressure reducing valve is presented in this paper. The pressure reducing components are specially designed to not only control the flow rate but also effectively prevent the cavitation vibration. However, when the fluid flows through the pressure reducing components, the divergence and shedding of the vortices in the flow field seriously affect the stability of the valve and cause vortex-induced vibration. Especially, the main frequency of the vortex shedding is in the same frequency range as the modal frequency of the valve, the vortex-induced resonance of the valve occurs. It seriously affects the safety of a control system. In this paper, by monitoring the lift coefficient of the vortex cross flow in the valve, the frequency spectrum information of the lift coefficient is used as the novelty indexes to indicate vortex-induced vibration of the fluid in the valve. The main frequency and amplitude of vortex-induced vibration are obtained. The factors affecting the vortex-induced vibration of the fluid are analyzed. The results indicate that vortex-induced vibration is the most serious when the valve is opened or closed. The variation of the flow velocity and the pressure difference have obvious effects on vortex-induced vibration of the valve. The intensity of the variation affects the main frequency and amplitude of vortex-induced vibration. Using thermal-fluid-solid coupling modal analysis instead of traditional modal analysis, the modal frequency under the working state of the valve is obtained. It is compared with the main frequency of vortex shedding, and vortex-induced resonance does not occur in the multistage pressure reducing valve.

TRmir: A Comprehensive Resource for Human Transcriptional Regulatory Information of MiRNAs.

MicroRNAs (miRNAs) are small non-coding RNAs, which play important roles in regulating various biological functions. Many available miRNA databases have provided a large number of valuable resources for miRNA investigation. However, not all existing databases provide comprehensive information regarding the transcriptional regulatory regions of miRNAs, especially typical enhancer, super-enhancer (SE), and chromatin accessibility regions. An increasing number of studies have shown that the transcriptional regulatory regions of miRNAs, as well as related single-nucleotide polymorphisms (SNPs) and transcription factors (TFs) have a strong influence on human diseases and biological processes. Here, we developed a comprehensive database for the human transcriptional regulation of miRNAs (TRmir), which is focused on providing a wealth of available resources regarding the transcriptional regulatory regions of miRNAs and annotating their potential roles in the regulation of miRNAs. TRmir contained a total of 5,754,414 typical enhancers/SEs and 1,733,966 chromatin accessibility regions associated with 1,684 human miRNAs. These regions were identified from over 900 human H3K27ac ChIP-seq, ATAC-seq, and DNase-seq samples. Furthermore, TRmir provided detailed (epi)genetic information about the transcriptional regulatory regions of miRNAs, including TFs, common SNPs, risk SNPs, linkage disequilibrium (LD) SNPs, expression quantitative trait loci (eQTLs), 3D chromatin interactions, and methylation sites, especially supporting the display of TF binding sites in the regulatory regions of over 7,000 TF ChIP-seq samples. In addition, TRmir integrated miRNA expression and related disease information, supporting extensive pathway analysis. TRmir is a powerful platform that offers comprehensive information about the transcriptional regulation of miRNAs for users and provides detailed annotations of regulatory regions. TRmir is free for academic users and can be accessed at http://bio.liclab.net/trmir/index.html.

MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer.

BACKGROUND: Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients. METHODS: We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules. RESULTS: We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets. CONCLUSIONS: Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.

Knockdown of LncRNA GHET1 suppresses prostate cancer cell proliferation by inhibiting HIF-1α/Notch-1 signaling pathway via KLF2.

Prostate cancer (PC) is one of the most common cancers in male groups worldwide. Long noncoding RNAs (LncRNAs) are reported to be dysregulated in a variety of cancers, including PC. This study aimed to explore the role of LncRNA GHET1 in the pathogenesis of PC. RT-qPCR was carried out to examine the relative expression level of GHET1 in PC patients. In vitro, GHET1 siRNA (si-GHET1) was used to investigate the biological role of GHET1 in PC cell lines. Cell proliferation was detected by CCK-8 and colony formation assay, while cell cycle and cell apoptosis were analyzed using flow cytometry. Moreover, western blot was carried out to measure the protein expression levels of KLF2 and HIF-1α/Notch-1 signal pathway. We found that GHET1 showed higher expression in PC tissues and had a negative correlation with KLF2 expression. Knockdown of GHET1 significantly suppressed the cell proliferation, induced cell cycle arrest at G0/G1 phase and promoted cell apoptosis. Additionally, si-GHET1 transfection induced KLF2 upregulation and HIF-1α/Notch-1 signal pathway suppression, which could be rescued by si-KLF2 transfection. These results suggest the key role of GHET1 in PC progression. Moreover, GHET1 might be explored to be a potential target for clinical treatment of PC. © 2019 BioFactors, 45(3):364-373, 2019.

Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk.

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA-mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA-mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA-mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway-based, miRNA-based, and gene-based classification methods. The high-frequency miRNA-mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.

RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method.

While gene fusions have been increasingly detected by next-generation sequencing (NGS) technologies based methods in human cancers, these methods have limitations in identifying driver fusions. In addition, the existing methods to identify driver gene fusions ignored the specificity among different cancers or only considered their local rather than global topology features in networks. Here, we proposed a novel network-based method, called RWCFusion, to identify phenotype-specific cancer driver gene fusions. To evaluate its performance, we used leave-one-out cross-validation in 35 cancers and achieved a high AUC value 0.925 for overall cancers and an average 0.929 for signal cancer. Furthermore, we classified 35 cancers into two classes: haematological and solid, of which the haematological got a highly AUC which is up to 0.968. Finally, we applied RWCFusion to breast cancer and found that top 13 gene fusions, such as BCAS3-BCAS4, NOTCH-NUP214, MED13-BCAS3 and CARM-SMARCA4, have been previously proved to be drivers for breast cancer. Additionally, 8 among the top 10 of the remaining candidate gene fusions, such as SULF2-ZNF217, MED1-ACSF2, and ACACA-STAC2, were inferred to be potential driver gene fusions of breast cancer by us.

Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics, and lacked integration and utilization of multiple 'omics' data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based method to evaluate the topological importance of each gene in a reconstructed gene-metabolite graph by integrating information from matched gene expression profiles and metabolomic profiles. The joint use of gene and metabolite information contributes to accurate evaluation of the topological importance of genes and reproducible pathway activities. We constructed classifiers using reproducible pathway activities for precise cancer classification and risk metabolic pathway identification. We applied the proposed method to the classification of prostate cancer. Within-dataset experiments and cross-dataset experiments on three independent datasets demonstrated that the proposed method achieved a more accurate and robust overall performance compared to several existing classification methods. The resulting risk pathways and topologically important differential genes and metabolites provide biologically informative models for prostate cancer prognosis and therapeutic strategies development.

Integration of pathway structure information into a reweighted partial Cox regression approach for survival analysis on high-dimensional gene expression data.

Accurately predicting the risk of cancer relapse or death is important for clinical utility. The emerging high-dimensional gene expression data provide the opportunity as well as the challenge to uncover the relationship between gene expression and censored survival outcome. While several Cox models have been proposed to deal with high-dimensional covariates and censored continuous survival data, they usually generalize poorly to independent datasets. Most methods build the Cox model exclusively on gene expression data, but ignore the molecular interaction relation among genes, which has been successfully integrated into molecular classification with categorical outcomes and improved predictive performance. Here, we integrate gene-interaction information into a Cox model and propose a reweighted partial Cox regression (RPCR) approach in order to accurately predict the risk of cancer events. RPCR improves the predictive accuracy and generalization of a Cox model by promoting genes with large topological importance, which is evaluated by a directed random walk in a reconstructed global pathway graph. We applied RPCR to the survival prediction of two cancer types and used two concordance statistic measures to assess the prediction performance. Both within-dataset experiments and cross-dataset experiments showed that RPCR could predict the risk of patients with higher accuracy and greater robustness.