Background: Colorectal cancer (CRC) is the third most common cancer worldwide. The pathogenic mechanisms of this disorder are still unclear. Long noncoding RNAs (lncRNAs) play critical roles in gene expression regulation. This study aimed to introduce two novel lncRNAs as biomarkers of CRC diagnosis and treatment. Method: In the first step, we investigated the differentially expressed proteins (DEPs) in CRC by bioinformatic study and constructed a regulatory multi-layer network (DEPs-miRNAs-lncRNAs) using Cytoscape software. In the last step, tissues ware collected from thirty patients after informed consent. Then, we evaluated the expression of LINC00943 and SLC9A3-AS1 lncRNAs in the 30 tumor tissues and 30 adjacent non-tumor tissues of the CRC patients by qRT-PCR. Result: According to the evaluation of the constructed multi-layer, SLC9A3-AS1, LINC00943, and miR-1301-3p were considered important nodes that the association of the SLC9A3-AS1 and LINC00943 with CRC had not been investigated until now. We demonstrated that the expression of SLC9A3-AS1 and LINC00943 was up-regulated in the tumor tissues compared to adjacent non-tumor tissues in the CRC patients. Conclusion: Considering the constructed interacting map not only provides a deeper understanding of the underlying mechanisms like an immunological aspect of CRC, which placed a premium on other pathways but also indicated several hub central molecules involved in CRC pathogenesis. In addition, the increase in LINC00943 and SLC9A3-AS1 gene expression in tumor tissues compared to adjacent healthy tissues in each patient confirms bioinformatical predictions and the role of these two genes in colorectal cancer. According to the results obtained, the LINC00943 gene is suggested as a biomarker in colorectal cancer. In previous studies as well, expression changes of LINC00943 and SLC9A3-AS1 genes were reported in gastric cancer and nasopharyngeal carcinoma, respectively. MiR-1301-3p expression changes were reported in colorectal cancer too.

BackgroundColorectal Cancer (CRC) is a high prevalence malignant of the digestive system. Notwithstanding huge investigation, the precise involved mechanisms are still blunted. It emerges that more research is needed to understand the underlying mechanisms of CRC better. In this investigation, a systems biology method was applied to provide a comprehensive insight through making a multilayer network to recognize novel biomarkers and potential drug targets.MethodTo identify the affected proteins in this disease, an existing protein expression profile on CRC was attained from ProteomeXchange. In the first step, the quality of the dataset was assessed, and then differentially expressed proteins (DEPs) were recognized based on FDR<0.05 as the statistically significant threshold. Subsequently, all identified DEPs were subjected to further enrichment analysis for finding the involved biomedical phenomena. In the following action, lncRNAs and miRNAs related to DEPs were predicted as regulatory layers via Cytoscape to make an integrative network.ResultThe empirical findings in this study indicated that the identified DEPs were mainly associated with two biomedical terms, such as “Mitochondrial dysfunction” and “Extracellular matrix organization.” Additionally, several hub molecules were introduced in the constructed multilayer network, hub-DEPs (ITGB1, ACTG1, ...), hub-miRs (miR-16-5p, miR-26b-5p, ...), and hub-lncRNAs (KCNQ1OT1, NEAT1, …) that Can be deemed as a potential biomarker in the pathogenesis of CRC, further research should concentrate on the investigation of their role in CRC development.ConclusionThe attained results of this study may be paving a way to provide a deeper understanding of the vaguely involved mechanisms through an integrative insight into the interaction of proteins with regulatory layers.