LncRNAs are involved in a wide range of biological processes, such as chromatin remodeling, mRNA splicing, mRNA editing and translation. They can either upregulate or downregulate gene expression, and play key roles in the progression of various human cancers. However, the functional mechanisms of most lncRNAs still remain unknown at present. This paper aims to provide space for the understanding of lncRNAs by proposing a new method to obtain protein-coding genes (PCGs) regulated by lncRNAs, thus identifying candidate cancer-related lncRNAs using bioinformatics approaches. This study presents a method based on sample correlation, which is applied to the expression profiles of lncRNAs and PCGs in prostate cancer in combination with protein interaction data to build a lncRNA–PCG bipartite network. Candidate cancer-related lncRNAs were extracted from the bipartite network by using a random walk. 14 prostate cancer-related lncRNAs were acquired from the LncRNADisease database and MNDR, of which 6 lncRNAs were present in our network. As one of the seed nodes, ENSG00000234741 achieved the highest score among them. The other two cancer-related lncRNAs (ENSG00000225937 and ENSG00000236830) were ranked within the top 30. In addition, the top candidate lncRNA ENSG00000261777 shares an intron with DDX19, and interacts with IGF2 P1, indicating its involvement in prostate cancer. In this paper, we described a new method for predicting candidate lncRNA targets, and obtained candidate therapeutic targets using this method. We hope that this study will bring a new perspective in future lncRNA studies.

Identification of potential cancer-related pseudogenes in lung adenocarcinoma based on ceRNA hypothesis Identification of potential cancer-related pseudogenes in lung adenocarcinoma based on ceRNA hypothesis

Background: Lung cancer, especially non-small cell lung cancer, is a leading cause of malignant tumor death worldwide. Understanding the mechanisms employed by the main regulators, such as microRNAs (miRNAs) and transcription factors (TFs), still remains elusive. The patterns of their cooperation and biological functions in the synergistic regulatory network have rarely been studied. Results: Here, we describe the first miRNA-TF synergistic regulation network in human lung cancer. We identified important regulators (MYC, NFKB1, miR-590, and miR-570) and significant miRNA-TF synergistic regulatory motifs by random simulations. The two most significant motifs were the co-regulation of miRNAs and TFs, and TF-mediated cascade regulation. We also developed an algorithm to uncover the biological functions of the human lung cancer miRNA-TF synergistic regulatory network (regulation of apoptosis, cellular protein metabolic process, and cell cycle), and the specific functions of each miRNA-TF synergistic subnetwork. We found that the miR-17 family exerted important effects in the regulation of non-small cell lung cancer, such as in proliferation and cell cycle regulation by targeting the retinoblastoma protein (RB1) and forming a feed forward loop with the E2F1 TF. We proposed a model for the miR-17 family, E2F1, and RB1 to demonstrate their potential roles in the occurrence and development of non-small cell lung cancer. Conclusions: This work will provide a framework for constructing miRNA-TF synergistic regulatory networks, function analysis in diseases, and identification of the main regulators and regulatory motifs, which will be useful for understanding the putative regulatory motifs involving miRNAs and TFs, and for predicting new targets for cancer studies.

RNA-binding proteins (RBPs) are key regulators of gene expression. Some long non-coding RNAs (lncRNAs) affect gene expression by interacting with RBPs. However, whether this influences the biological characteristics of lncRNAs in diseases still remains unknown. Here, we classify lncRNAs into two categories, using the interaction information between lncRNAs and RBPs: the lncRNAs that interact with RBPs (Rlncs) and the lncRNAs that do not interact with RBPs (NRlncs). Then we systematically analyze the basic attributes and functions of the two categories of lncRNAs across 10 cancers. By comparing the two categories, we find that the attributes of Rlncs are significantly higher than those of NRlncs in different aspects such as expression level, protein-coding potential, and evolutionary conservation. Furthermore, functional enrichment analysis reveals that the two categories of lncRNAs are involved in different functions and biological pathways. Finally, the prognostic analysis results suggest that the two categories of lncRNAs affect the overall survival of patients through participating in different functions. Our systematic characterization of Rlncs and NRlncs provides a new perspective for understanding the role of lncRNAs, and improves knowledge of cancer biology.

Accumulating evidences indicate that cancer-related lncRNAs occur frequent somatic copy number alternation (SCNA). Although individual SCNA lncRNAs have been implicated in tumor biology, their regulatory mechanism has not been assessed in a systematic way. In order to explore the expression characteristics and biological functions of SCNA lncRNAs in cancer, we built a computational framework based on lncRNA expression profiles, lncRNA copy numbers and dosage sensitivity score (DSS). First, we found that the lncRNAs with different DSS were involved in distinct biological processes, while those with the same DSS had similar functions. Second, some of the lncRNAs participated in the progression and metastasis of lung adenocarcinoma (LUAD) through cis-acting regulation. In lncRNA-TF-mRNA network, lncRNAs interacted with 4 TFs and affected the immune system, and further influenced LUAD progression. Third, competing endogenous RNA network analysis inferred that lncRNA ENSG00000240990 competed with HOXA10 to absorb hsa-let-7a/b/f/g-5p and affected patient prognosis in LUAD. Last but not least, by integrating target information of miRNA we also provided a new perspective for the discovery of potential small molecule drugs. In summary, we systematically analyzed the regulatory role of SCNA lncRNAs. This work may facilitate cancer research and serve as the basis for future efforts to understand the role of SCNA lncRNAs, develop novel biomarkers and improve knowledge of tumor biology.