MicroRNAs (miRNAs) regulate gene expression. It has been suggested that obtaining miRNA expression profiles can improve classification, diagnostic, and prognostic information in oncology. Here, we sought to comprehensively identify the miRNAs that are overexpressed in lung cancer by conducting miRNA microarray expression profiling on normal lung versus adjacent lung cancers from transgenic mice. We found that miR-136, miR-376a, and miR-31 were each prominently overexpressed in murine lung cancers. Real-time RT-PCR and in situ hybridization (ISH) assays confirmed these miRNA expression profiles in paired normalmalignant lung tissues from mice and humans. Engineered knockdown of miR-31, but not other highlighted miRNAs, substantially repressed lung cancer cell growth and tumorigenicity in a dose-dependent manner. Using a bioinformatics approach, we identified miR-31 target mRNAs and independently confirmed them as direct targets in human and mouse lung cancer cell lines. These targets included the tumor-suppressive genes large tumor suppressor 2 (LATS2) and PP2A regulatory subunit B alpha isoform (PPP2R2A), and expression of each was augmented by miR-31 knockdown. Their engineered repression antagonized miR-31-mediated growth inhibition. Notably, miR-31 and these target mRNAs were inversely expressed in mouse and human lung cancers, underscoring their biologic relevance. The clinical relevance of miR-31 expression was further independently and comprehensively validated using an array containing normal and malignant human lung tissues. Together, these findings revealed that miR-31 acts as an oncogenic miRNA (oncomir) in lung cancer by targeting specific tumor suppressors for repression.

There is no consensus on the approach to compute the power and sample size with logistic regression. Some authors use the likelihood ratio test; some use the test on proportions; some suggest various approximations to handle the multivariate case. We advocate the use of the Wald test since the Z-score is routinely used for statistical significance testing of regression coefficients. The null-variance formula became popular from early studies, which contradicts modern software, which utilizes the method of maximum likelihood estimation (MLE), when the variance of the MLE is estimated at the MLE, not at the null. We derive general Wald-based power and sample size formulas for logistic regression and then apply them to binary exposure and confounder to obtain a closed-form expression. These formulas are applied to minimize the total sample size in a case-control study to achieve a given power by optimizing the ratio of controls to cases. Approximately, the optimal number of controls to cases is equal to the square root of the alternative odds ratio. Our sample size and power calculations can be carried out online at www.dartmouth.edu/ approximately eugened.

BACKGROUND. Although access to cancer care is known to influence patient outcomes, to the authors' knowledge, little is known regarding geographic access to cancer care, and how it may vary by population characteristics. This study estimated travel time to specialized cancer care settings for the continental U.S. population and calculated per capita oncologist supply.METHODS. The closest travel times were estimated using a network analysis of CONCLUSIONS. There are population groups with limited access to the most specialized cancer care settings.

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These, and other data, suggest that ingestion of low to moderate arsenic levels may affect bladder cancer incidence, and that cigarette smoking may act as a co-carcinogen.

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There is no consensus on what test to use as the basis for sample size determination and power analysis. Some authors advocate the Wald test and some the likelihood-ratio test. We argue that the Wald test should be used because the Z-score is commonly applied for regression coefficient significance testing and therefore the same statistic should be used in the power function. We correct a widespread mistake on sample size determination when the variance of the maximum likelihood estimate (MLE) is estimated at null value. In our previous paper, we developed a correct sample size formula for logistic regression with single exposure (Statist. Med. 2007; 26(18):3385-3397). In the present paper, closed-form formulas are derived for interaction studies with binary exposure and covariate in logistic regression. The formula for the optimal control-case ratio is derived such that it maximizes the power function given other parameters. Our sample size and power calculations with interaction can be carried out online at www.dartmouth.edu/ approximately eugened.

Cyclin E is a critical G1-S cell cycle regulator aberrantly expressed in bronchial premalignancy and lung cancer. Cyclin E expression negatively affects lung cancer prognosis. Its role in lung carcinogenesis was explored. Retroviral cyclin E transduction promoted pulmonary epithelial cell growth, and small interfering RNA targeting of cyclin E repressed this growth. Murine transgenic lines were engineered to mimic aberrant cyclin E expression in the lung. Wild-type and proteasome degradation-resistant human cyclin E transgenic lines were independently driven by the human surfactant C (SP-C) promoter. Chromosome instability (CIN), pulmonary dysplasia, sonic hedgehog (Shh) pathway activation, adenocarcinomas, and metastases occurred. Notably, high expression of degradation-resistant cyclin E frequently caused dysplasia and multiple lung adenocarcinomas. Thus, recapitulation of aberrant cyclin E expression as seen in human premalignant and malignant lung lesions reproduces in the mouse frequent features of lung carcinogenesis, including CIN, Shh pathway activation, dysplasia, single or multiple lung cancers, or presence of metastases. This article reports unique mouse lung cancer models that replicate many carcinogenic changes found in patients. These models provide insights into the carcinogenesis process and implicate cyclin E as a therapeutic target in the lung.lung carcinogenesis ͉ lung cancer ͉ sonic hedgehog C yclin E binds to and activates cyclin-dependent kinase 2 (Cdk2) and promotes G 1 cell cycle transition (1, 2). Cyclin E overexpression shortens the G 1 cell cycle, alters S-phase progression, and causes chromosomal instability (CIN) (3,4). Cyclin E has oncogenic potential. Transgenic cyclin E expression in the mammary gland causes hyperplasia and carcinoma (5). Aberrant cyclin E expression occurs in premalignant lung lesions (6). Cyclin E expression has a negative prognostic impact in lung cancers (7-9). Tobacco carcinogens can transform immortalized human bronchial epithelial (HBE) cells and augment cyclin E expression (10). All-trans-retinoic acid (RA) chemoprevention represses cyclin E and associated Cdk2 kinase activity, causing G 1 arrest (10). This would permit repair of genomic DNA damage by carcinogens and was proposed as a chemoprevention mechanism (10, 11).Regulation of cyclin E is critical for cell cycle progression. Cyclin E accumulates late in G 1 and declines through S phase (1, 2). Cyclin E is regulated by the ubiquitin-proteasome pathway (12). The ubiquitin ligase Cullin 3 promotes ubiquitination of free cyclin E not bound to Cdk2 (13). Ubiquitination of Cdk2-bound cyclin E depends on phosphorylation of threonines Thr-62 and -380 as well as Ser-372 and -384 (14-16). Phosphorylation of these residues allows cyclin E to be recognized by Fbw7 (hCdc4) (15, 17, 18), a phosphoepitope-specific substrate recognition component of the Skp1-Cullin1 F-box protein (SCF) ubiquitin ligase. Fbw7 mutations occur in malignancies and contribute to cell cycle deregulation (15,(18)(19)(20)(21). Mutations of ...