IntroductionLong noncoding RNAs (lncRNAs) are emerging as key players in the development and progression of cancer. However, the biological role and clinical significance of most lncRNAs in lung carcinogenesis remain unclear. In this study, we identified and explored the role of a novel lncRNA, lung cancer associated transcript 1 (LCAT1), in lung cancer.MethodsWe predicted and validated LCAT1 from RNA-sequencing (RNA-seq) data of lung cancer tissues. The LCAT1–miR-4715-5p–RAC1 axis was assessed by dual-luciferase reporter and RNA immunoprecipitation (RIP) assays. Signaling pathways altered by LCAT1 knockdown were identified using RNA-seq. Furthermore, the mechanism of LCAT1 was investigated using loss-of-function and gain-of-function assays in vivo and in vitro.ResultsLCAT1 is an oncogene that is significantly upregulated in lung cancer tissues and associated with poor prognosis. LCAT1 knockdown caused growth arrest and cell invasion in lung cancer cells in vitro, and inhibited tumorigenesis and metastasis in the mouse xenografts. Mechanistically, LCAT1 functions as a competing endogenous RNA for miR-4715-5p, thereby leading to the upregulation of the activity of its endogenous target, Rac family small GTPase 1 (RAC1). Moreover, EHop-016, a small molecule inhibitor of RAC1, as an adjuvant could improve the Taxol monotherapy against lung cancer cells in vitro.ConclusionsLCAT1–miR-4715-5p–RAC1/PAK1 axis plays an important role in the progression of lung cancer. Our findings may provide valuable drug targets for treating lung cancer. The novel combination therapy of Taxol and EHop-016 for lung cancer warrants further investigation, especially in lung cancer patients with high LCAT1 expression.
Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predicted from these computational tools lack consistency and are prone to false positives. Here, we developed an approach (DriverML) integrating Rao’s score test and supervised machine learning to identify cancer driver genes. The weight parameters in the score statistics quantified the functional impacts of mutations on the protein. To obtain optimized weight parameters, the score statistics of prior driver genes were maximized on pan-cancer training data. We conducted rigorous and unbiased benchmark analysis and comparisons of DriverML with 20 other existing tools in 31 independent datasets from The Cancer Genome Atlas (TCGA). Our comprehensive evaluations demonstrated that DriverML was robust and powerful among various datasets and outperformed the other tools with a better balance of precision and sensitivity. In vitro cell-based assays further proved the validity of the DriverML prediction of novel driver genes. In summary, DriverML uses an innovative, machine learning-based approach to prioritize cancer driver genes and provides dramatic improvements over currently existing methods. Its source code is available at https://github.com/HelloYiHan/DriverML .
Abstract:The potential biodegradation of crude oil was assessed based on the development of a fermentative process with a strain of Pseudomonas aeruginosa which produced 15.4 g/L rhamnolipids when cultured in a basal mineral medium using glycerol as a sole carbon source. However, neither cell growth nor rhamnolipid production was observed in the comparative culture system using crude oil as the sole carbon source instead. As rhamnolipid, an effective biosurfactant, has been reported to stimulate the biodegradation of hydrocarbons, 1 g/L glycerol or 0.22 g/L rhamnolipid was initially added into the medium to facilitate the biodegradation of crude oil. In both situations, more than 58% of crude oil was degraded and further converted into accumulated cell biomass and rhamnolipids. These results suggest that Pseudomonas aeruginosa could degrade most of crude oil with direct or indirect addition of rhamnolipid. And this conclusion was further supported by another adsorption experiment, where the adsorption capacity of crude oil by killed cell biomass was negligible in comparison with the biologic activities of live cell biomass.
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