Circular RNAs (circRNAs) are emerging as a new class of endogenous and regulatory noncoding RNAs in latest years. With the widespread application of RNA sequencing (RNA-seq) technology and bioinformatics prediction, large numbers of circRNAs have been identified. However, at present, we lack a comprehensive characterization of all these circRNAs in interested samples. In this study, we integrated 87 935 circRNAs sequences that cover most of circRNAs identified till now represented in circBase to design microarray probes targeting back-splice site of each circRNA to profile expression of those circRNAs. By comparing the circRNA detection efficiency of RNA-seq with this circRNA microarray, we revealed that microarray is more efficient than RNA-seq for circRNA profiling. Then, we found ∼80 000 circRNAs were expressed in cervical tumors and matched normal tissues, and ∼25 000 of them were differently expressed. Notably, many of these circRNAs detected by this microarray can be validated by quantitative reverse transcription polymerase chain reaction (RT-qPCR) or RNA-seq. Strikingly, as many as ∼18 000 circRNAs could be robustly detected in cell-free plasma samples, and the expression of ∼2700 of them differed after surgery for tumor removal. Our findings provided a comprehensive and genome-wide characterization of circRNAs in paired normal tissues and tumors and plasma samples from multiple individuals. In addition, we also provide a rich resource with 41 microarray data sets and 10 RNA-seq data sets and strong evidences for circRNA expression in cervical cancer. In conclusion, circRNAs could be efficiently profiled by circRNA microarray to target their reported back-splice sites in interested samples.

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The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug–drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug–drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).

Cancer cells evolve various mechanisms to overcome cellular stresses and maintain progression. Protein kinase R (PKR) and its protein activator (PACT) are the initial responders in monitoring diverse stress signals and lead to inhibition of cell proliferation and cell apoptosis in consequence. However, the regulation of PACT-PKR pathway in cancer cells remains largely unknown. Herein, we identify that the long non-coding RNA (lncRNA) aspartyl-tRNA synthetase antisense RNA 1 (DARS-AS1) is directly involved in the inhibition of the PACT-PKR pathway and promotes the proliferation of cancer cells. Using large-scale CRISPRi functional screening of 971 cancer-associated lncRNAs, we find that DARS-AS1 is associated with significantly enhanced proliferation of cancer cells. Accordingly, knocking down DARS-AS1 inhibits cell proliferation of multiple cancer cell lines and promotes cancer cell apoptosis in vitro and significantly reduces tumor growth in vivo. Mechanistically, DARS-AS1 directly binds to the activator domain of PACT and prevents PACT-PKR interaction, thereby decreasing PKR activation, eIF2α phosphorylation and inhibiting apoptotic cell death. Clinically, DARS-AS1 is broadly expressed across multiple cancers and the increased expression of this lncRNA indicates poor prognosis. This study elucidates the lncRNA DARS-AS1 directed cancer-specific modulation of the PACT-PKR pathway and provides another target for cancer prognosis and therapeutic treatment.

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13Tumor metastasis is the cause of death for 90% of cancer patients, and no 14 currently-available therapies target this multi-step process in which cancer cells 15 spread from the local tissue of a primary tumor to distant organs where they 16 establish secondary tumors 1 . Although epithelial-to-mesenchymal transition 2 , 17 tumor-secreted exosomes 3 , epigenetic regulators as well as other genes 4-8 have 18 been implicated in metastasis, little is known about how cells adapt to and 19 colonize new tissue environments. Here, we show that the epigenetics-mediated 20reprogramming of tissue-specific gene transcription in cancer cells promotes 21 metastasis. Using colorectal cancer (CRC) as a model, we found in both clinical 22 3 reprogramming in several cohorts of clinical CRC tumor samples and in multiple 33 other forms of metastatic cancers, indicate that this reprogramming may be a 34 common feature of metastasis in multiple cancers and suggest the targeted 35 disruption of this epigenetic reprogramming as a strategy for the development of 36 therapies to treat metastasis, the leading cause of cancer-related mortality. 37 MAIN 39Tumor metastasis refers to the movement of tumor cells from a primary site 40to distant organs that they progressively colonize 9 . More than 100 years ago, 41Paget suggested the idea of metastasis as the interaction of "seeds" and "soil" 10 , 42 but subsequent research has yielded only a limited understanding of the 43 mechanism(s) through which metastatic cancer cells ("seeds") adapt to and 44 colonize a new tissue environment ("soil"), the crucial steps of the metastasis 45 process 11 . 46It has been reported that the expression of tissue-specific or cell-lineage 47 7 the genome regions around loci encoding up-regulated liver-specific genes had 113 enriched H3K27ac (P value = 2.2e-16, by t-test) and H3K4me2 (P value = 2.2e-16, 114 by t-test) deposition (Fig. 2c). 115So-called super-enhancers are a small fraction of total enhancers and 116 encompass broad chromatin domains with H3K27ac deposition near genes 117 essential for defining cell identity 20,21 . By identifying super-enhancers in SW480 118 and SW620 cells based on our H3K27ac ChIP-seq datasets, we found that in 119 addition to the 264 super-enhancers common to both cell lines, there are 280 and 120 215 unique super-enhancers in SW620 and SW480 cells, respectively (Extended 121 Data Fig. 4d). Comparison between our ChIP-seq and RNA-seq data revealed a 122 high Pearson correlation coefficient (R = 0.807, P value < 2.2e-16) between the 123 genome-wide distribution of super-enhancers and the expression levels of the 124 nearest genes in these two cell lines ( Fig. 2d and Extended Data Fig. 4e). Notably, 125 many of the liver-specific genes in SW620 cells were found near SW620-unique 126 super-enhancers; while the colon-specific genes in SW480 cells were found close 127 to SW480-unique super-enhancers ( Fig. 2e and Extended Data Fig. 4f, g). Our 128 epigenetic and transcriptomics experiments demonstrate that reshaped enhancer 129 ...

Metastasis is the cause of death for 90% of cancer patients, but little is known about how cancer cells adapt to and colonize new tissue environments. Colorectal cancer (CRC) is the third most common cancer and the fourth most common cancer cause of death globally. Here, we investigated how metastatic cancer cells, such as CRC cells, with original-tissue specificity adapt to the environment of a distant tissue, like liver. By analyzing transcriptome data of multiple cohorts of clinical samples and primary/metastatic cell lines, we found metastatic CRC cells lose their colon-specific gene transcription program and gain a liver-specific gene transcription program as they metastasize in the liver. The elevated expression of a number of liver-specific genes was confirmed in human liver metastatic tumors by immunohistochemistry. Through epigenomic profiling, we revealed this transcription reprogramming is driven by a reshaped epigenetic landscape of both typical and super-enhancers, and chemical inhibition of enhancer activity disrupts the ability of cells to execute this altered transcription program and consequently inhibits metastasis. Further we identified liver-specific transcription factors, FOXA2 and HNF1A, bind to the gained enhancers in liver metastatic cells and activate the expression of liver-specific genes. We also found both FOXA2 and HNF1A are highly expressed in human liver metastatic tumors compared to primary CRC tumors. Loss of function of FOXA2 or HNF1A inhibits liver-specific transcription and impairs the ability of metastatic CRC cells to adapt to liver. Consistently, the gain of function of HNF1A activated liver-specific transcription and enhanced CRC liver metastasis. Our findings indicate the direct contribution of a reprogrammed tissue-specific transcription program, which is induced by reshaped epigenetic landscape as well as distant-tissue-specific transcription factors, to the adaption and colonization of metastatic CRC cells to a distal organ. Notably, in addition to CRC liver metastasis, this tissue-specific transcription reprogramming is also observed in other multiple distant organs and/or cancer types. In summary, our data suggest that epigenetically reprogrammed tissue-specific transcription promotes metastasis and might have implications for targeted anti-metastasis therapies. Citation Format: Shuaishuai Teng, Yang Li, Ming Yang, Rui Qi, Qianyu Wang, Zhi Lu, Dong Wang. Epigenetic reprogramming of tissue-specific transcription promotes metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4334.