ALDH-1-positive cells exhibited a radioresistant phenotype that was enhanced with hypoxia in cervical cancer

Background We have previously found there was a small subpopulation of cells with cancer stem cell-like phenotype ALDH-1 in cervical cancer. Radiotherapy has been applied in most of the cervical cancer. However,the mechanisms underlying radioresistance still remained elusive. Our study is to explore whether ALDH+ cell promotes radioresistance by hypoxia. Methods Cells were respectively cultured in hypoxia and normoxia environment and analyzed for marker stability, and cell cycle distribution. Results: Cell growth, apoptosis, cell cycle, sphere formation were affected by hypoxia. ALDH-1 and CHK2 were upregulated after hypoxia. Conclusions Here we show that ALDH-1 positive cells contribute to cervical carcinoma radioresistance through preferential activation of the DNA damage checkpoint response and an increase in DNA repair capacity. The fraction of these cells is enriched after radiation in cervical carcinoma.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the ‘thorough’ removal of unwanted variations, the collective consideration of multiple criteria (‘intragroup variation’, ‘marker stability’ and ‘classification capability’) was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were ‘first’ discovered to be non-CWP. ‘Then’, 21 new strategies that combined the ‘sample’-based method with the ‘metabolite’-based one were found to be CWP. ‘Finally’, a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability

To secure the world’s food supply it is essential that we improve our knowledge of the genetic underpinnings of complex agronomic traits. In this paper, we report our findings from performing trait prediction and association mapping using marker stability in diverse rice landraces. We used the least absolute shrinkage and selection operator as our marker selection algorithm, and considered twelve real agronomic traits and a hundred simulated traits using a population with approximately a hundred thousand markers. For trait prediction, we considered several statistical/machine learning methods. We found that some of the methods considered performed best when preselected markers using marker stability were used. However, our results also show that one might need to make a trade-off between model size and performance for some learning methods. For association mapping, we compared marker stability to the genome-wide efficient mixed-model analysis (GEMMA), and for the simulated traits, we found that marker stability significantly outperforms GEMMA. For the real traits, marker stability successfully identifies multiple associated markers, which often entail those selected by GEMMA. Further analysis of the markers selected for the real traits using marker stability showed that they are located in known quantitative trait loci (QTL) using the QTL Annotation Rice Online database. Furthermore, co-functional network prediction of the selected markers using RiceNet v2 also showed association to known controlling genes. We argue that a wide adoption of the marker stability approach for the prediction of agronomic traits and association mapping could improve global rice breeding efforts.