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Abstract. Circular RNAs (circRNAs) have stable structures, being a covalently closed loop without 5′ and 3′ free ends. They can function as “miRNA sponges” in regulating the expression of their target genes. It was thought that circRNAs are involved in the development of the secondary hair follicle (SHF) in cashmere goats. In our previous investigation, a new circRNA named circRNA-0100 was identified from the SHF of cashmere goats, but its function is unknown. In this work, we found that circRNA-0100 exhibited significantly higher expression at anagen SHF bulge than its counterpart at telogen in cashmere goats. Based on the use of both overexpression and siRNA interference assays, our data indicated that circRNA-0100 promoted the differentiation of cashmere goat SHF stem cells (SHF-SCs) into hair follicle lineage, which was evaluated by analyzing the transcriptional level changes of six indicator genes in SHF-SCs of cashmere goats. Using the RNA pull-down technique, we showed that circRNA-0100 served as “molecular sponges” of miR-153-3p in SHF-SCs. Through the use of dual-luciferase reporter assays, our data indicated that circRNA-0100 positively regulated the transcriptional expression of the KLF5 gene via the miR-153-3p-mediated pathway. Ultimately, we showed that circRNA-0100 promoted the differentiation of SHF-SCs into hair lineage, which might be achieved via sequestering miR-153-3p to heighten the KLF5 expression in SHF-SCs of cashmere goats. Our results provide novel scientific evidence for revealing the potential molecular regulatory mechanisms on the differentiation of SHF-SCs into hair lineage in cashmere goats.

N6-methyladenosine (m6A) is the most abundant modification in linear RNA molecules. Over the last few years, interestingly, many circRNA molecules are also found to have extensive m6A modification sites with temporal and spatial specific expression patterns. To date, however, little information is available concerning the expression profiling and functional regulatory characteristics of m6A modified circRNAs (m6A-circRNAs) in secondary hair follicles (SHFs) of cashmere goats. In this study, a total of fifteen m6A-circRNAs were identified and characterized in the skin tissue of cashmere goats. Of these, six m6A-circRNAs were revealed to have significantly higher expression in skin at anagen compared with those at telogen. The constructed ceRNA network indicated a complicated regulatory relationship of the six anagen up-regulated m6A-circRNAs through miRNA mediated pathways. Several signaling pathways implicated in the physiological processes of hair follicles were enriched based on the potential regulatory genes of the six anagen up-regulated m6A-circRNAs, such as TGF-beta, axon guidance, ribosome, and stem cell pluripotency regulatory pathways, suggesting the analyzed m6A-circRNAs might be essentially involved in SHF development and cashmere growth in cashmere goats. Further, we showed that four m6A-circRNAs had highly similar expression trends to their host genes in SHFs of cashmere goats including m6A-circRNA-ZNF638, -TULP4, -DNAJB6, and -CAT. However, the expression patterns of two m6A-circRNAs (m6A-circRNA-STAM2 and -CAAP1) were inconsistent with the linear RNAs from their host genes in the SHFs of cashmere goats. These results provide novel information for eluci-dating the biological function and regulatory characteristics of the m6A-circRNAs in SHF development and cashmere growth in goats.

Recently time series prediction based on network analysis has become a hot research topic. However, how to more accurately forecast time series with good efficiency is still an open question. To address this issue, we propose an efficient time series forecasting method based on the DC algorithm and visibility relations on the vertexes set. Firstly, the time series is mapped into the network by the DC algorithm, which is a more efficient approach to generate the visibility graph. Then, we use the variation trends (slope) of those nodes that have visibility relation with the last node to get the preliminary predictive values. Afterward, the value of the last node is adopted to obtain the revised predictive values, which are assigned different weights according to node degree and time distance to get the final weighted result. To better demonstrate the prediction performance and applicability of the proposed method, the proposed method is applied to different time series data sets. The empirical results show that the proposed method could provide a higher level of forecasting accuracy than many methods with relatively lower time complexity. INDEX TERMS Complex network, time series forecasting, node degree, visibility graph, variation trend.