PMC:3475481 / 1448-3683
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/3475481","sourcedb":"PMC","sourceid":"3475481","source_url":"https://www.ncbi.nlm.nih.gov/pmc/3475481","text":"Introduction\nDespite recent advances in surgical, radiation, and chemotherapeutic treatment protocols, the prognosis of oral squamous cell carcinoma (OSCC) remains mournful, with an approximate 50% 5-year mortality rate from disease or associated complications [1]. Therefore, the identification of biological markers is essential to make progress in detecting malignancy at an early stage and developing novel therapies [2].\nMicroarray datasets that are created for the same research purposes in different laboratories have accumulated rapidly. The results from different datasets are often inconsistent due to the utilization of different platforms, sample preparations, or various technical variations. If we could combine such datasets by adjusting for systematic biases that exist among different datasets derived from different experimental conditions, the power of statistical tests would be improved by the increase in sample size [3].\nIn OSCC, although lots of microarray-based studies have been conducted to provide insights into gene expression changes, most of these studies have contained insufficient samples for detecting reliable information using statistical analysis [4, 5]. Therefore, this study attempted to combine several datasets in the public database for detecting significant genes.\nWe used two small microarray datasets of OSCC for this study, which were based on the same platform but had different expression scales. These two datasets were combined after discretization, because a previous study showed that classification could be improved using combined datasets after discretization [3]. After combining datasets, we used chi-square test for identifying the significant genes. Chi-square test has been used commonly to detect differentially expressed genes after discretization of expression intensities in the microarray experiment.\nIn this study, gene expression ratios of two datasets were transformed with their ranks for each dataset. Next, the transformed datasets were combined, and a nonparametric statistical method was applied to the combined dataset to detect informative genes. Finally, we showed that most of the selected genes were known to be involved in various cancers, including OSCC.","divisions":[{"label":"Title","span":{"begin":0,"end":12}}],"tracks":[{"project":"2_test","denotations":[{"id":"23105925-16652080-44845464","span":{"begin":262,"end":263},"obj":"16652080"},{"id":"23105925-19633365-44845465","span":{"begin":422,"end":423},"obj":"19633365"},{"id":"23105925-18554423-44845466","span":{"begin":940,"end":941},"obj":"18554423"},{"id":"23105925-15381369-44845467","span":{"begin":1186,"end":1187},"obj":"15381369"},{"id":"23105925-15558013-44845468","span":{"begin":1189,"end":1190},"obj":"15558013"},{"id":"23105925-18554423-44845469","span":{"begin":1617,"end":1618},"obj":"18554423"}],"attributes":[{"subj":"23105925-16652080-44845464","pred":"source","obj":"2_test"},{"subj":"23105925-19633365-44845465","pred":"source","obj":"2_test"},{"subj":"23105925-18554423-44845466","pred":"source","obj":"2_test"},{"subj":"23105925-15381369-44845467","pred":"source","obj":"2_test"},{"subj":"23105925-15558013-44845468","pred":"source","obj":"2_test"},{"subj":"23105925-18554423-44845469","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#93b5ec","default":true}]}]}}