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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.

Copy number alteration (CNA) represents an important class of genetic variations that may contribute to tumorigenesis, tumor growth and metastatic spread. CNA can directly affect the expression of genes within the CNA regions; however, genes within the CNA regions exhibit heterogeneity in gene dosage sensitivity. In this study, a computational framework was built to identify 1170 dosage-sensitive genes (DSGs) and 1215 dosage-resistant genes (DRGs) that were related to ovarian serous cystadenocarcinoma (OV) through the association between CNA and gene expression. To analyze the different functions of the genes within the two groups, the functional annotation results indicated that DRGs were involved in cancer-related processes like immune response, cell death and apoptosis, while DSGs were enriched in essential processes like the cell cycle and the DNA metabolic process. Meanwhile, two three-dimensional regulatory networks for differentially expressed miRNAs, differentially expressed transcription factors (TFs) and DSGs or DRGs were constructed based on feed-forward loops. We identified key regulators (such as miR-16-5p, miR-98-5p, MYB and HOXA5) and cancer prognosis-related network motifs (such as miR-98-5p-HOXA5-TP53 and miR-16-5p-MYB-IGF1R) after the analysis of network topological features. Our results lead us to speculate that these genes and associated regulators may be potential mechanistic biomarkers for tumorigenesis and progression of cancer. Research on the network characteristics and the role of feed-forward loops in OV tumorigenesis and development could lead to feasible suggestions for the prevention and early diagnosis of OV, which will shed light on understanding the functional mechanism of CNA in cancer.

Although the key roles of long non-coding RNAs (lncRNAs) in multiple diseases are well documented, the relationship between the lncRNA copy number and expression is unknown. Here, we present a comprehensive study that demonstrates the impact of miRNA-TF co-regulatory motifs on the dosage effects of protein-coding genes (PCGs) and lncRNAs in prostate cancer. By integrating copy number profiles, expression profiles and regulatory relationships with miRNAs and transcription factors, we reveal that lncRNAs and PCGs correlate with distinct dosage sensitivity and regulatory pattern characteristics. We also show that lncRNAs from different genomic regions have different features. Using a custom-built framework, we identified 24 candidate prostate cancer-related lncRNAs based on the known properties of established prostate-related lncRNAs. Our method will enable the identification of cancer-related lncRNAs, which will provide new insights into cancer lncRNA biology.