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The majority of lung cancer patients are diagnosed with metastatic disease. This study identified a set of 73 microRNAs (miRNAs) that classified lung cancer tumors from normal lung tissues with an overall accuracy of 96.3% in the training patient cohort (n = 109) and 91.7% in unsupervised classification and 92.3% in supervised classification in the validation set (n = 375). Based on association with patient survival (n = 1016), 10 miRNAs were identified as potential tumor suppressors (hsa-miR-144, hsa-miR-195, hsa-miR-223, hsa-miR-30a, hsa-miR-30b, hsa-miR-30d, hsa-miR-335, hsa-miR-363, hsa-miR-451, and hsa-miR-99a), and 4 were identified as potential oncogenes (hsa-miR-21, hsa-miR-31, hsa-miR-411, and hsa-miR-494) in lung cancer. Experimentally confirmed target genes were identified for the 73 diagnostic miRNAs, from which proliferation genes were selected from CRISPR-Cas9/RNA interference (RNAi) screening assays. Pansensitive and panresistant genes to 21 NCCN-recommended drugs with concordant mRNA and protein expression were identified. DGKE and WDR47 were found with significant associations with responses to both systemic therapies and radiotherapy in lung cancer. Based on our identified miRNA-regulated molecular machinery, an inhibitor of PDK1/Akt BX-912, an anthracycline antibiotic daunorubicin, and a multi-targeted protein kinase inhibitor midostaurin were discovered as potential repositioning drugs for treating lung cancer. These findings have implications for improving lung cancer diagnosis, optimizing treatment selection, and discovering new drug options for better patient outcomes.

BACKGROUND Reduced estimated glomerular filtration rate (eGFR) in older adults is common and may reflect normal aging or significant kidney disease. Our objective was to develop a predictive model to better triage these individuals using routine laboratory data. MATERIALS AND METHODS Using a large US laboratory data set, we calculated individual eGFR regression slopes for 43,523 individuals aged 60 - 75 years with baseline eGFRs between 30 and 59 mL/min/1.73m2. We developed general linear models to predict the eGFR regression slope using urine protein measurements and other routinely available laboratory data as dependent variables. We validated these models on a similar data set comprised of 11,979 individuals. RESULTS In a model utilizing log10 urine albumin/creatinine (UACR), the variables that significantly predicted the eGFR regression slope were log10 UACR, initial eGFR, serum albumin, chloride, glucose, and aspartate aminotransferase (AST). In an otherwise identical model substituting log10 urine protein/creatinine (UPCR) for UACR, results were similar except that serum calcium was significant and AST was not. We analyzed the correspondence between actual eGFR regression slopes and those predicted by our models using receiver operator characteristic (ROC) statistics to calculate areas under the curves (AUC) for four eGFR slope cut points: -2, -3, -4, and -5 mL/min/year. AUCs using the UACR and UPCR models ranged from 0.716 to 0.900 and 0.751 to 0.868, respectively, for the training data set. Results were nearly identical for the validation data set. CONCLUSION Use of a laboratory-based predictive model of eGFR decline for older adults with eGFR 30 - 59 mL/min/1.73m2 may help distinguish between individuals with and without risk for further decline in kidney function.

Background: NHANES data (2009-2014) suggests that 25% of U.S. adults with diabetes have CKD (eGFR < 60 ml/min/1.73m2 or ACR ≥ 30 mg/g). However, prevalence may be different among patients evaluated in clinical practice or in adults with T1D compared to T2D. Methods: We utilized a clinical laboratory database maintained by LabCorp® to assess CKD prevalence and classified risk (low, moderate, high or very high) based on KDIGO criteria. The population included 48,036 adults with T1D and 1,461,915 with T2D who had both an ACR and a CKD-EPI eGFR between August 2014 and August 2017. Rates of eGFR decline were calculated for patients with ≥ 3 eGFR results over at least a 1 year period. Results: CKD prevalence was 40% higher in T2D than T1D (44.3% vs. 31.6%, p 300, was uncommon (T1D: 7.8%, T2D: 8.3%); the majority with MA had an eGFR ≥ 60 (T1D: 60%, T2D: 53%). Median eGFR decline (ml/min/year) was low in the entire population (T1D: -0.6, T2D: -0.8), and in patients with ACR < 30 (T1D: -0.34, T2D: -0.47) or ACR 30-300 (T1D: -0.97, T2D: - 1.06). However, median annual rates of decline in patients with MA were -3.80 in T1D and -3.58 in T2D. Conclusions: These findings suggest that CKD prevalence and risk are higher in T2D than T1D among adult patients recently evaluated in U.S. clinical practices. Although MA is uncommon and most frequently observed in patients with normal or only mildly reduced eGFR, it was a potent predictor of eGFR decline in both T1D and T2D. Availability of a large clinical laboratory database may add value when eligibility criteria, enrichment strategies, study endpoints or feasibility are assessed in clinical trials of patients with diabetes and CKD. Disclosure M. Cressman: Employee; Self; Covance Inc. J.L. Ennis: Employee; Self; LabCorp. Stock/Shareholder; Self; LabCorp. B.J. Goldstein: Employee; Self; Covance Inc.. L. Gourgiotis: None. D. Luo: None. M. Puri: Employee; Self; Covance Inc.. B. Gillespie: None.

Objectives: To describe the application of a data-mining statistical algorithm for calculation of clinical laboratory tests reference intervals. Methods: Reference intervals for eight different analytes and different age and sex groups (a total of 11 separate reference intervals) for tests that are unlikely to be ordered during routine screening of disease-free populations were calculated using the modified algorithm for data mining of test results stored in the laboratory database and compared with published peer-reviewed studies that used direct sampling. The selection of analytes was based on the predefined criteria that include comparability of analytical methods with a statistically significant number of observations. Results: Of the 11 calculated reference intervals, having upper and lower limits for each, 21 of 22 reference interval limits were not statistically different from the reference studies. Conclusions: The presented statistical algorithm is shown to be an accurate and practical tool for reference interval calculations.

Lung cancer remains the leading cause of cancer-related mortality for both men and women. Tumor recurrence and metastasis is the major cause of lung cancer treatment failure and death. The microRNA‑200 (miR-200) family is a powerful regulator of the epithelial-mesenchymal transition (EMT) process, which is essential in tumor metastasis. Nevertheless, miR-200 family target genes that promote metastasis in non-small cell lung cancer (NSCLC) remain largely unknown. Here, we sought to investigate whether the microRNA-200 family regulates our previously identified NSCLC prognostic marker genes associated with metastasis, as potential molecular targets. Novel miRNA targets were predicted using bioinformatics tools based on correlation analyses of miRNA and mRNA expression in 57 squamous cell lung cancer tumor samples. The predicted target genes were validated with quantitative RT-PCR assays and western blot analysis following re-expression of miR-200a, -200b and -200c in the metastatic NSCLC H1299 cell line. The results show that restoring miR-200a or miR-200c in H1299 cells induces downregulation of DLC1, ATRX and HFE. Reinforced miR-200b expression results in downregulation of DLC1, HNRNPA3 and HFE. Additionally, miR-200 family downregulates HNRNPR3, HFE and ATRX in BEAS-2B immortalized lung epithelial cells in quantitative RT-PCR and western blot assays. The miR-200 family and these potential targets are functionally involved in canonical pathways of immune response, molecular mechanisms of cancer, metastasis signaling, cell-cell communication, proliferation and DNA repair in Ingenuity pathway analysis (IPA). These results indicate that re-expression of miR-200 downregulates our previously identified NSCLC prognostic biomarkers in metastatic NSCLC cells. These results provide new insights into miR-200 regulation in lung cancer metastasis and consequent clinical outcome, and may provide a potential basis for innovative therapeutic approaches for the treatment of this deadly disease.

Background: Pre-processing, including normalization of raw microarray data is crucial to microarray-related data analysis. It takes time and effort to build newly-developed algorithms into commercial software or locally developed systems. While most new algorithms emerge in the form of sharable R packages, it can be difficult for many biologists to apply them as soon as they are available. Currently, we rely on statisticians and experienced programmers to develop and implement code to access those R packages. Therefore, we need a robust procedure to quickly implement pre-processing methods as they appear. The newly emerging cloud computing concept has directed us toward a new way for providing an easily accessible service to the biologists without requiring them to have any programming knowledge in R. Results: Based on our earlier Java-based software tool JavaStat, we developed an internet based application prototype to upload data and carry out pre-processing applications that include normalization, statistical analyses and plots. More im-portantly, R packages, e. g., for newly-developed normalization methods, and GC-robust multichip algorithm (RMA) for exon arrays, can be easily incorporated into the system with limited inputs from a biologist or a programmer. The data are stored in the cloud and the R code runs on server. Conclusion: The newly emerged cloud computing concept provides us a new way to provide an easily accessible and up-to-date service to biologists, as evidenced by our JavaStat system to incorporate new pre-processing package as they ap-pear. Users can access the application with a newly incorporated module through the Web. We expect this and other simi-lar systems greatly decrease turn-around time, improve accessibility of newly developed R model for pre-processing algo-rithms.

Due to the fibrous shape and durability of multi-walled carbon nanotubes (MWCNT), concerns regarding their potential for producing environmental and human health risks, including carcinogenesis, have been raised. This study sought to investigate how previously identified lung cancer prognostic biomarkers and the related cancer signaling pathways are affected in the mouse lung following pharyngeal aspiration of well-dispersed MWCNT. A total of 63 identified lung cancer prognostic biomarker genes and major signaling biomarker genes were analyzed in mouse lungs (n = 80) exposed to 0, 10, 20, 40, or 80 μg of MWCNT by pharyngeal aspiration at 7 and 56 days post-exposure using quantitative PCR assays. At 7 and 56 days post-exposure, a set of 7 genes and a set of 11 genes, respectively, showed differential expression in the lungs of mice exposed to MWCNT vs. the control group. Additionally, these significant genes could separate the control group from the treated group over the time series in a hierarchical gene clustering analysis. Furthermore, 4 genes from these two sets of significant genes, coiled-coil domain containing-99 (Ccdc99), muscle segment homeobox gene-2 (Msx2), nitric oxide synthase-2 (Nos2), and wingless-type inhibitory factor-1 (Wif1), showed significant mRNA expression perturbations at both time points. It was also found that the expression changes of these 4 overlapping genes at 7 days post-exposure were attenuated at 56 days post-exposure. Ingenuity Pathway Analysis (IPA) found that several carcinogenic-related signaling pathways and carcinogenesis itself were associated with both the 7 and 11 gene signatures. Taken together, this study identifies that MWCNT exposure affects a subset of lung cancer biomarkers in mouse lungs.

Architectures are described which allow a Java-based front-end to run R code on a server. The front-end described here is a Java application called JavaStat (http://javastat.stat.wvu.edu). JavaStat is a highly-interactive program for data analysis and dynamic visualization with data management capabilities. The objective is to bring the high-level functions of R to JavaStat, without excessive duplicative development work. Results returned from R are wrapped and then displayed using linked, dynamic plots in JavaStat. The principal idea is to use Remote Method Invocation to communicate with a Java server program (JRIServer), which in turn communicates with R using Java/R Interface (JRI). Two versions have been implemented. The first (basic) architecture maintains a connection between the client and server in order to return the results from R. This is suitable for small to moderate data sets in which relatively simple models are run. The second (enhanced) architecture queues the requests and uses polling to fetch the results. It is suitable for large data sets and complex models, e.g., those encountered in genomic studies. JavaStat supports the basic architecture out of the box, but a user account is required to enable the enhanced architectures. The enhanced architecture supports workflows, e.g., genomic and modeling workflows.

To the Editor We applaud the work by Katayev and colleagues1 reflected in their article on data mining for reference intervals. The development of robust automated tools to perform data mining in a standardized manner represents a significant step forward in the field. We would like to point out that the authors’ application of the Hoffmann2 method seems to deviate from the original in one important aspect. The original Hoffmann method involved manually plotting data on normal probability paper. When the cumulative frequency distribution of a Gaussian data set is plotted on this type of paper, a straight line is obtained. As Hoffmann pointed out, even when a data set includes a significant minority of data from a …

Smoking is responsible for 90% of lung cancer cases. There is currently no clinically available gene test for early detection of lung cancer in smokers, or an effective patient selection strategy for adjuvant chemotherapy in lung cancer treatment. In this study, concurrent coexpression with multiple signaling pathways was modeled among a set of genes associated with smoking and lung cancer survival. This approach identified and validated a 7-gene signature for lung cancer diagnosis and prognosis in smokers using patient transcriptional profiles (n=847). The smoking-associated gene coexpression networks in lung adenocarcinoma tumors (n=442) were highly significant in terms of biological relevance (network precision = 0.91, FDR

Background This paper describes the development of a web-based tool, GenDrux, which extracts and presents (over the Internet) information related to the disease-gene-drug nexus. This information is archived from the relevant biomedical literature using automated methods. GenDrux is designed to alleviate the difficulties of manually processing the vast biomedical literature to identify disease-gene-drug relationships. GenDrux will evolve with the literature without additional algorithmic modifications. Results GenDrux, a pilot system, is developed in the domain of breast cancer and can be accessed at http://www.microarray.uab.edu/drug_gene.pl. GenDrux can be queried based on drug, gene and/or disease name. From over 8,000 relevant abstracts from the biomedical literature related to breast cancer, we have archived a corpus of more than 4,000 articles that depict gene expression-drug activity relationships for breast cancer and related cancers. The archiving process has been automated. Conclusions The successful development, implementation, and evaluation of this and similar systems when created may provide clinicians with a tool for literature management, clinical decision making, thus setting the platform for personalized therapy in the future.

Background: Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment. Methodology/Principal Findings: From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naive Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log- rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P,0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: (2.08, 8.46)) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P,0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples. Conclusions/Significance: The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.

Statistical methods have been intensively applied in genomic signal processing (Dougherty et al. 2005). For budding yeastSaccharomyces cerevisiaewith around 6000 proteins, genome-wide protein-protein-interaction (PPI) (Fromont-Racine et al. 2000, Ito et al. 2001, Newman et al. 2000, and Uetz et al. 2000 among others) and protein subcellular localization (PSL) (Huh et al. 2003) data recently became available and for the latter the presence of 4152 proteins is experimentally tested in each of the 22 subcellular compartments. Recent work shows that multiple biological sources are helpful for both PSL and PPI predictions, and this paper studies statistical feasibility of modeling PPI from PSL since PSLs may play different marginal or joint roles in the complex regulatory network. However, our results indicate that PSL may be controversial for this purpose as an independent source.