Background LncRNAs have been found to be involved in various aspects of biological processes. In this study, we aimed to uncover the molecular mechanisms of lncRNA EPB41L4A-AS1 in regulating glycolysis and glutaminolysis in cancer cells. Methods The expression of EPB41L4A-AS1 in cancer patients was analyzed in TCGA and GEO datasets. The level of cellular metabolism was determined by extracellular flux analyzer. The relationship between p53 and EPB41L4A-AS1 was explored by qRT-PCR, luciferase assay and ChIP assay. The interactions between EPB41L4A-AS1 and HDAC2 or NPM1 were determined by RNA immunoprecipitation, RNA pull-down assay and RNA-FISH- immunofluorescence. Findings EPB41L4A-AS1 was a p53-regulated gene. Low expression and deletion of lncRNA EPB41L4A-AS1 were found in a variety of human cancers and associated with poor prognosis of cancer patients. Knock down EPB41L4A-AS1 expression triggered Warburg effect, demonstrated as increased aerobic glycolysis and glutaminolysis. EPB41L4A-AS1 interacted and colocalized with HDAC2 and NPM1 in nucleolus. Silencing EPB41L4A-AS1 reduced the interaction between HDAC2 and NPM1, released HDAC2 from nucleolus and increased its distribution in nucleoplasm, enhanced HDAC2 occupation on VHL and VDAC1 promoter regions, and finally accelerated glycolysis and glutaminolysis. Depletion of EPB41L4A-AS1 increased the sensitivity of tumor to glutaminase inhibitor in tumor therapy. Interpretation EPB41L4A-AS1 functions as a repressor of the Warburg effect and plays important roles in metabolic reprogramming of cancer.

Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using label-free cellular images obtained from an optical microscope. Although these studies showed promising results, such classifiers were not able to reflect the biological diversity of different types of cell. While in terms of malignant cell, it is well-known that intracellular actin filaments are altered substantially. This is thought to be closely related to the abnormal growth features of tumor cells, their ability to invade surrounding tissues and also to metastasize. Therefore, being able to classify different types of cell based on their biological behaviors using automated technique is more advantageous. This article reveals the difference in the actin cytoskeleton structures between breast normal and cancer cells, which may provide new information regarding malignant changes and be used as additional diagnostic marker. Since the features cannot be well detected by human eyes, we proposed the application of convolutional neural network (CNN) in cell classification based on actin-labeled fluorescence microscopy images. The CNN was evaluated on a large number of actin-labeled fluorescence microscopy images of one human normal breast epithelial cell line and two types of human breast cancer cell line with different levels of aggressiveness. The study revealed that the CNN performed better in the cell classification task compared to a human expert.

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