Microarray analysis of spaceflown murine thymus tissue reveals changes in gene expression regulating stress and glucocorticoid receptors.

The detrimental effects of spaceflight and simulated microgravity on the immune system have been extensively documented. We report here microarray gene expression analysis, in concert with quantitative RT-PCR, in young adult C57BL/6NTac mice at 8 weeks of age after exposure to spaceflight aboard the space shuttle (STS-118) for a period of 13 days. Upon conclusion of the mission, thymus lobes were extracted from space flown mice (FLT) as well as age- and sex-matched ground control mice similarly housed in animal enclosure modules (AEM). mRNA was extracted and an automated array analysis for gene expression was performed. Examination of the microarray data revealed 970 individual probes that had a 1.5-fold or greater change. When these data were averaged (n = 4), we identified 12 genes that were significantly up- or down-regulated by at least 1.5-fold after spaceflight (P < or = 0.05). The genes that significantly differed from the AEM controls and that were also confirmed via QRT-PCR were as follows: Rbm3 (up-regulated) and Hsph110, Hsp90aa1, Cxcl10, Stip1, Fkbp4 (down-regulated). QRT-PCR confirmed the microarray results and demonstrated additional gene expression alteration in other T cell related genes, including: Ctla-4, IFN-alpha2a (up-regulated) and CD44 (down-regulated). Together, these data demonstrate that spaceflight induces significant changes in the thymic mRNA expression of genes that regulate stress, glucocorticoid receptor metabolism, and T cell signaling activity. These data explain, in part, the reported systemic compromise of the immune system after exposure to the microgravity of space.

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

BACKGROUND: Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges. METHODS: Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison. RESULTS: A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome. CONCLUSIONS: Stratification of cases based on clinical data, careful selection of two groups for comparison, and the application of logistic regression analysis substantially improved predictive accuracy in comparison to conventional KM approaches. B cell-related genes dominated the list of prognostic genes in early stage SQCC of the lung and triple negative breast cancer.