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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.

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.

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.