The objective of the study was to investigate the expression and functions of CASC9 in esophageal squamous cell carcinoma (ESCC). Long noncoding RNAs (lncRNAs) upregulated in ESCC tissues were detected by RNA sequencing. Expression of CASC9 was determined from clinical samples and cell lines by qRT‐PCR. The effects of CASC9 knockdown on migration and invasion were evaluated by wound healing assay, cell migration and invasion assays in vitro. We found that the lncRNA, CASC9, was markedly upregulated in ESCC tissues. Furthermore, knockdown of CASC9 significantly suppressed cell migration and invasion in vitro. Furthermore, enhanced CASC9 expression level was correlated with differentiation. The results indicated that CASC9 is significantly upregulated in ESCC tissues and may represent a new marker of poor prognosis and a potential therapeutic target for esophageal cancer intervention.

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Recovering a large matrix from limited measurements is a challenging task arising in many real applications, such as image inpainting, compressive sensing and medical imaging, and this kind of problems are mostly formulated as low-rank matrix approximation problems. Due to the rank operator being non-convex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex relaxation and the low-rank matrix recovery problem is solved through minimization of the nuclear norm regularized problem. However, a major limitation of nuclear norm minimization is that all the singular values are simultaneously minimized and the rank may not be well approximated [1]. Correspondingly, in this paper, we propose a new multi-stage algorithm, which makes use of the concept of Truncated Nuclear Norm Regularization (TNNR) proposed in [1] and Iterative Support Detection (ISD) proposed in [2] to overcome the above limitation. Besides matrix completion problems considered in [1], the proposed method can be also extended to the general lowrank matrix recovery problems. Extensive experiments well validate the superiority of our new algorithms over other state-of-the-art methods.

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