Bladder Cancer (BC) is one of the most common cancers in the world. Recent studies show that non-coding RNAs such as lncRNAs and circRNAs play critical roles in the progression of this cancer, but their regulatory relationships and functions are still largely unknown. As a new regulatory process within the cell, the coding and non-coding RNAs compete with each other to sponge their target miRNAs. This mechanism is described as “the competing endogenous RNA (ceRNA) hypothesis” which provides a new perspective to understand the regulation of gene expression in health and diseases such as cancer. In this study, to investigate the role of non-coding RNAs in BC, a new approach was used to reconstruct the ceRNA network for Non-Muscle Invasive Bladder Cancer (NMIBC) based on the expression data of coding and non-coding genes. Analysis of ceRNA networks in the early stage of BC led to the detection of an important module containing the lncRNA MEG3 as the central gene. The results show that the lncRNAs CARMN, FENDRR and ADAMTS9-AS2 may regulate MEG3 in NMIBC through sponging some important miRNAs such as miR-143-3p, miR-106a-5p and miR-34a-3p. Also, the lncRNA AC007608.2 is shown to be a potential BC related lncRNA for the first time based on ceRNA stage-specific network analysis. Furthermore, hub and altered genes in stage-specific and between stage networks led to the detection of hsa_circ_0017586 and hsa_circ_0001741 as novel potential circRNAs related to NMIBC. Finally, the hub genes in the networks were shown to be valuable candidates as biomarkers for the early stage diagnosis of BC.

Biomarker detection is one of the most important and challenging problems in cancer studies. Recently, non-coding RNA based biomarkers such as miRNA expression levels have been used for early diagnosis of many cancer types. In this study, a systems biology approach was used to detect novel miRNA based biomarkers for CRC diagnosis in early stages. The mRNA expression data from three CRC stages (Low-grade Intraepithelial Neoplasia (LIN), High-grade Intraepithelial Neoplasia (HIN) and Adenocarcinoma) were used to reconstruct co-expression networks. The networks were clustered to extract co-expression modules and detected low preserved modules among CRC stages. Then, the experimentally validated mRNA-miRNA interaction data were applied to reconstruct three mRNA-miRNA bipartite networks. Twenty miRNAs with the highest degree (hub miRNAs) were selected in each bipartite network to reconstruct three bipartite subnetworks for further analysis. The analysis of these hub miRNAs in the bipartite subnetworks revealed 30 distinct important miRNAs as prognostic markers in CRC stages. There are two novel CRC related miRNAs (hsa-miR-190a-3p and hsa-miR-1277-5p) in these 30 hub miRNAs that have not been previously reported in CRC. Furthermore, a drug-gene interaction network was reconstructed to detect potential candidate drugs for CRC treatment. Our analysis shows that the hub miRNAs in the mRNA-miRNA bipartite network are very essential in CRC progression and should be investigated precisely in future studies. In addition, there are many important target genes in the results that may be critical in CRC progression and can be analyzed as therapeutic targets in future research.

Protein complexes are aggregates of protein molecules that play important roles in biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks is one of the most challenging problems in computational biology, and many computational methods have been developed to solve this problem. Generally, these methods yield high false positive rates. In this article, a semantic similarity measure between proteins, based on Gene Ontology (GO) structure, is applied to weigh PPI networks. Consequently, one of the well-known methods, COACH, has been improved to be compatible with weighted PPI networks for protein complex detection. The new method, WCOACH, is compared to the COACH, ClusterOne, IPCA, CORE, OH-PIN, HC-PIN and MCODE methods on several PPI networks such as DIP, Krogan, Gavin 2002 and MIPS. WCOACH can be applied as a fast and high-performance algorithm to predict protein complexes in weighted PPI networks. All data and programs are freely available at http://bioinformatics. aut.ac.ir/wcoach.

BackgroundSOX2 overlapping transcript (SOX2OT) is a long non-coding RNA, over-expressed in human tumor tissues and embryonic cells. Evidences support its function in the cell cycle; however there is no clear mechanism explaining its function in cell proliferation regulation. Here we investigated cancer cell response to SOX2OT knockdown by RNA sequencing.MethodsSOX2OT expression was inhibited by siRNA in two cancer cell lines (A549, U-87 MG), then the RNA of treated cells were used for the cDNA library synthesis and RNA sequencing. The differentially expressed genes were used for functional enrichment and the gene expression network was analyzed to find the most relevant biological process with SOX2OT function. Furthermore, the expression change of candidate genes was measured by qRT-PCR for more confirmation and the cell cycle was monitored by PI staining.ResultsOur findings showed that SOX2OT knockdown affects the cellular gene expression generally with enriched cell proliferation and development biological process. Particularly, the cell cycle and mitotic regulatory genes expression including: CDK2, CDK2AP2, ACTR3, and chromosome structure associated genes like SMC4, INCENP and GNL3L are changed in treated cancer cells.ConclusionOur results propound SOX2OT association with cell cycle and mitosis regulation in cancer cells.Electronic supplementary materialThe online version of this article (10.1186/s12935-018-0618-8) contains supplementary material, which is available to authorized users.

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