Data mining of The Cancer Genome Atlas (TCGA) data has significantly facilitated cancer genome research and provided unprecedented opportunities for cancer researchers. However, existing web applications for DNA methylation analysis does not adequately address the need of experimental biologists, and many additional functions are often required. To facilitate DNA methylation analysis, we present the SMART (Shiny Methylation Analysis Resource Tool) App, a user-friendly and easy-to-use web application for comprehensively analyzing the DNA methylation data of TCGA project. The SMART App integrates multi-omics and clinical data with DNA methylation and provides key interactive and customized functions including CpG visualization, pan-cancer methylation profile, differential methylation analysis, correlation analysis and survival analysis for users to analyze the DNA methylation in diverse cancer types in a multi-dimensional manner. The SMART App serves as a new approach for users, especially wet-bench scientists with no programming background, to analyze the scientific big data and facilitate data mining. The SMART App is available at http://www.bioinfo-zs.com/smartapp.

/pdf/the-smart-app-an-interactive-web-application-for-4gec7bdvop.pdf

The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization

Rationale: The incidence of hepatocellular carcinoma is rising worldwide. It is predicted that nearly half of the early-stage hepatocellular carcinoma (E-HCC) patients will develop recurrence. Dysregulated pH, a hallmark of E-HCC, is correlated with poor prognosis. The acidic microenvironment has been shown to promote the release of exosomes, the membrane vesicles recognized as intercellular communicators associated with tumor progression, recurrence, and metastasis. We, therefore, aimed to identify exosomes induced by acidic microenvironment that may regulate E-HCC progression and to explore their mechanisms and clinical significance in E-HCCs. Methods: miRNA microarray analysis and LASSO logistic statistic model were used to identify the main functional exosomal miRNAs. Invasion and scratch assays were performed to examine the migration and invasion of HCC cells. Immunoblotting and immunofluorescence were employed to detect the epithelial-to-mesenchymal transition (EMT) in HCC cells. Chromatin immunoprecipitation (ChIP) was used to analyze the binding of HIF-1α and HIF-2α to promoter regions of miR-21 and miR-10b. Results: The acidic microenvironment in HCC was correlated with poor prognosis of patients. Exosomes from HCC cells cultured in the acidic medium could promote cell proliferation, migration, and invasion of recipient HCC cells. We identified miR-21 and miR-10b as the most important functional miRNAs in acidic HCC-derived exosomes. Also, the acidic microenvironment triggered the activation of HIF-1α and HIF-2α and stimulated exosomal miR-21 and miR-10b expression substantially promoting HCC cell proliferation, migration, and invasion both in vivo and in vitro. In E-HCC patients, serum exosomal miR-21 and miR-10b levels were associated with advanced tumor stage and HIF-1α and HIF-2α expression and were independent prognostic factors for disease-free survival of E-HCC patients. Most importantly, we developed a nano-drug to target exosomal miR-21 and/or miR-10b and examined its therapeutic effects against HCC in vivo. Conclusion: Our findings suggested that the exosomal miR-21 and miR-10b induced by acidic microenvironment in HCC promote cancer cell proliferation and metastasis and may serve as prognostic molecular markers and therapeutic targets for HCC.

Acidic microenvironment up-regulates exosomal mir-21 and mir-10b in early-stage hepatocellular carcinoma to promote cancer cell proliferation and metastasis

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

Summary Background Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p Interpretation Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study.

Molecular characterization of hepatocellular carcinoma (HCC) has greatly improved our understanding of disease pathogenesis. Mutational analysis, RNA and microRNA expression profiling, and epigenetic characterization have revealed common aberrations in oncogenes and tumor suppressors that correlate with disease biology and serve as a guide for the rational design of targeted therapies. These approaches have also led to the discovery of novel targets, including mutations in isocitrate dehydrogenase and chromatin remodeling enzymes. With the advent of immunotherapy, RNA expression profiling of the tumor microenvironment has identified a subset of HCC with high lymphocyte infiltration that may benefit from checkpoint inhibitor therapy. Molecular signatures thus capture the biology of a tumor, providing a supplement to current staging schema, which are based on tumor size and number, for more accurate prognostication of recurrence risk and survival. Molecular signatures may also be used to guide interventional therapy by defining those most suitable for transplantation or locoregional therapy rather than surgical resection. Finally, a multiomics approach involves the aggregation and analysis of multiple signatures for a more comprehensive characterization of pathogenic mechanisms. This broader approach attempts to address issues with signaling pathway cross-talk and redundancy, which have greatly limited the potential value of targeted therapies to date. Cancer 2018. © 2018 American Cancer Society.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cncr.31257

Molecular signatures in hepatocellular carcinoma: A step toward rationally designed cancer therapy

Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three-CpG-based signature, is useful in predicting recurrence for patients with E-HCC.

CpG methylation signature predicts recurrence in early-stage hepatocellular carcinoma: Results from a multicenter study

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CpG methylation signature predicts recurrence in early-stage hepatocellular carcinoma: Results from a multicenter study