Retracted Article: Long non-coding RNA KCNQ1OT1 promotes osteosarcoma progression by increasing β-catenin activity

LDICDL: LncRNA-Disease Association Identification Based on Collaborative Deep Learning

It has been proved that long noncoding RNA (lncRNA) plays critical roles in many human diseases. Therefore, inferring associations between lncRNAs and diseases can contribute to disease diagnosis, prognosis and treatment. To overcome the limitation of traditional experimental methods such as expensive and time-consuming, several computational methods have been proposed to predict lncRNA-disease associations by fusing different biological data. However, the prediction performance of lncRNA-disease associations identification needs to be improved. In this study, we propose a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning. It uses an automatic encoder to denoise multiple lncRNA feature information and multiple disease feature information, respectively. Then, the matrix decomposition algorithm is employed to predict the potential lncRNA-disease associations. In addition, to overcome the limitation of matrix decomposition, the hybrid model is developed to predict associations between new lncRNA (or disease) and diseases (or lncRNA). The ten-fold cross validation and de novo test are applied to evaluate the performance of method. The experimental results show LDICDL outperforms than other state-of-the-art methods in prediction performance.

DMFLDA: A Deep Learning Framework for Predicting lncRNA-Disease Associations

A growing amount of evidence suggests that long non-coding RNAs (lncRNAs) play important roles in the regulation of biological processes in many human diseases. However, the number of experimentally verified lncRNA-disease associations is very limited. Thus, various computational approaches are proposed to predict lncRNA-disease associations. Current matrix factorization-based methods cannot capture the complex non-linear relationship between lncRNAs and diseases, and traditional machine learning-based methods are not sufficiently powerful to learn the representation of lncRNAs and diseases. Considering these limitations in existing computational methods, we propose a deep matrix factorization model to predict lncRNA-disease associations (DMFLDA in short). DMFLDA uses a cascade of non-linear hidden layers to learn latent representation to represent lncRNAs and diseases. By using non-linear hidden layers, DMFLDA captures the more complex non-linear relationship between lncRNAs and diseases than traditional matrix factorization-based methods. In addition, DMFLDA learns features directly from the lncRNA-disease interaction matrix and thus can obtain more accurate representation learning for lncRNAs and diseases than traditional machine learning methods. The low dimensional representations of the lncRNAs and diseases are fused to estimate the new interaction value. To evaluate the performance of DMFLDA, we perform leave-one-out cross-validation and 5-fold cross-validation on known experimentally verified lncRNA-disease associations. The experimental results show that DMFLDA performs better than the existing methods. The case studies show that many predicted interactions of colorectal cancer, prostate cancer, and renal cancer have been verified by recent biomedical literature. The source code and datasets can be obtained from https://github.com/CSUBioGroup/DMFLDA.

KCNQ1OT1: An Oncogenic Long Noncoding RNA

Long noncoding RNAs (lncRNAs) are transcripts greater than 200 nucleotides that do not code for proteins but regulate gene expression. Recent studies indicate that lncRNAs are involved in the modulation of biological functions in human disease. KCNQ1 Opposite Strand/Antisense Transcript 1 (KCNQ1OT1) encodes a lncRNA from the opposite strand of KCNQ1 in the CDKN1C/KCNQ1OT1 cluster that is reported to play a vital role in the development and progression of cancer. KCNQ1OT1 regulates cancer cell proliferation, cell cycle, migration and invasion, metastasis, glucose metabolism, and immune evasion. The aberrant expression of KCNQ1OT1 in cancer patients is associated with poor prognosis and decreased survival. This review summarizes recent literature related to the biological functions and molecular mechanisms of KCNQ1OT1 in various human cancers, including colorectal, bladder, breast, oral, melanoma, osteosarcoma, lung, glioma, ovarian, liver, acute myeloid leukemia, prostate, and gastric. We also discuss the role of KCNQ1OT1 as a promising diagnostic biomarker and a novel therapeutic target in human cancers.

Knockdown of KCNQ1OT1 Inhibits Proliferation, Invasion, and Drug Resistance by Regulating miR-129-5p-Mediated LARP1 in Osteosarcoma

KCNQ1OT1 exerts an important role in various cancers, but its role in osteosarcoma (OS) and the potential mechanism remain to be clarified. In the present research, we aimed to explore the effect of KCNQ1OT1 on osteosarcoma and further explore the special molecular mechanism. The expression of KCNQ1OT1 was analyzed in tumor and adjacent tissues of 30 patients with osteosarcoma by RT-PCR. Cell proliferation and invasion were explored using MTT and transwell assay, respectively. Luciferase reporter analysis and pull-down assay were performed to determine the binding activity of KCNQ1OT1 and miR-129-5p. The result revealed that KCNQ1OT1 was highly expressed in osteosarcoma tissues and cells. KCNQ1OT1-siRNA inhibited the proliferation, invasion, and drug resistance of osteosarcoma cells. The luciferase reporter assay and pull-down assay demonstrated that KCNQ1OT1 directly interact with miR-129-5p. In addition, miR-129-5p binds to LARP1 directly, and LARP1 promoted the proliferation, invasion, and drug resistance of osteosarcoma cells. What is more, KCNQ1OT1 promoted proliferation, invasion, and drug resistance via inhibiting the expression of miR-129-5p and further promoting the expression of miR-129-5p-mediated LARP1. Collectively, it suggests that downregulation of KCNQ1OT1 inhibits proliferation, invasion, and drug resistance by regulating miR-129-5p-mediated LARP1 in osteosarcoma cells.