Colorectal cancer (CRC) is one of the most leading causes of cancer deaths worldwide. In the study, we aimed to identify key long non-coding RNAs (lncRNAs) significantly associated with prognosis of CRC and develop an expression-based lncRNA signature to provide survival risk prediction for CRC patients. LncRNA expression profiles and clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Six hundred and eighteen differentially expressed lncRNAs were identified between CRC and normal tissues. After univariate and multivariate Cox regression analysis for these differentially expressed lncRNAs and overall survival of CRC patients, six predictive lncRNAs (RP1-170O19.17, RP11-785D18.3, RP11-798K3.2, XXbac-B476C20.9, RP11-481J13.1, and RP11-167H9.4) were finally screened out to construct a six-lncRNA signature, based on which patients in the training dataset were divided into the high-risk and low-risk group with significantly different overall survival. ROC curve analysis demonstrated competitive performance of the six-lncRNA signature. The prognostic power of the six-lncRNA signature was successfully validated in the testing and entire dataset. Multivariate Cox regression analysis and stratification analysis further suggested that the six-lncRNA signature was independent of other conventional clinical variables for survival prediction in CRC patients. Functional enrichment analysis revealed the possible roles of these predictive lncRNAs in some cancer-related biological processes and pathways. Our study demonstrated the promising potential of the novel six-lncRNA signature as an independent biomarker for survival prediction of CRC patients.

BackgroundAccumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer.Material/MethodsThe RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs.ResultsA total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism.ConclusionsOur study developed a novel candidate model providing additional and more powerful prognostic information beyond conventional clinicopathological factors for survival prediction of esophageal cancer patients. Moreover, it also brings us new insights into the molecular mechanisms underlying esophageal cancer.

PurposeThis study was aimed to develop a lncRNA-associated competing endogenous RNA (ceRNA) network to provide further understanding of the ceRNA regulatory mechanism and pathogenesis in colorectal cancer (CRC).Patients and methodsExpression profiles of mRNAs, lncRNAs, and miRNAs, and clinical information for CRC patients were obtained from The Cancer Genome Atlas. The differentially expressed mRNAs, lncRNAs, and miRNAs (referred to as “DEmRNAs”, “DElncRNAs”, and “DEmiRNAs”, respectively) were screened out between 539 CRC samples and 11 normal samples. The interactions between DElncRNAs and DEmiRNAs were predicted by miRcode. The DEmRNAs targeted by the DEmiRNAs were retrieved according to TargetScan, miRTar-Base, and miRDB. The lncRNA–miRNA–mRNA ceRNA network was constructed based on the DEmiRNA–DElncRNA and DEmiRNA–DEmRNA interactions. Functional enrichment analysis revealed the biological processes and pathways of DEmRNAs involved in the development of CRC. Key lncRNAs were further analyzed for their associations with overall survival and clinical features of CRC patients.ResultsA total of 1,767 DEmRNAs, 608 DElncRNAs, and 283 DEmiRNAs were identified as CRC-specific RNAs. Three hundred eighty-two DEmiRNA–DElncRNA interactions and 68 DEmiRNA–DEmRNA interactions were recognized according to the relevant databases. The lncRNA–miRNA–mRNA ceRNA network was constructed using 25 DEmiRNAs, 52 DEmRNAs, and 64 DElncRNAs. Two DElncRNAs, five DEmiRNAs, and six DEmRNAs were demonstrated to be related to the prognosis of CRC patients. Four DElncRNAs were found to be associated with clinical features. Twenty-eight Gene Ontology terms and 10 Kyoto Encyclopedia of Genes and Genomes pathways were found to be significantly enriched by the DEmRNAs in the ceRNA network.ConclusionOur results showed cancer-specific mRNA, lncRNA, and miRNA expression patterns and enabled us to construct an lncRNA-associated ceRNA network that provided new insights into the molecular mechanisms of CRC. Key RNA transcripts related to the overall survival and clinical features were also found with promising potential as biomarkers for diagnosis, survival prediction, and classification of CRC.

PurposeThe study aimed to explore the anticancer effects of a novel proteasome inhibitor, ixazomib, on colorectal cancer (CRC) using a combined method of microarray and bioinformatics analysis.Materials and methodsCell proliferation was tested by Cell Counting Kit-8 (CCK-8) assay for SW620 cells treated with different concentrations of ixazomib and different treatment times. The microarray analysis was conducted for six samples, including three samples of SW620 cells untreated with ixazomib and three samples of SW620 cells treated with ixazomib. The differentially expressed genes (DEGs) between untreated and treated samples were identified by the Linear Models for Microarray data (LIMMA) package in R language. The Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the DEGs using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and KEGG Orthology-Based Annotation System (KOBAS) online tool. The protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and module analysis was performed for the PPI network.ResultsIxazomib could inhibit the proliferation of SW620 cells in a dose-dependent and time-dependent manner. A total of 743 DEGs, including 203 upregulated DEGs such as HSPA6 and 540 downregulated DEGs such as APCDD1, were identified. Eighty-three GO terms were enriched for DEGs, which were mainly related to protein folding, apoptotic process, transcription factor activity, and proteasome. Thirty-seven KEGG pathways were perturbed, including pathway of apoptosis and cell cycle. Forty-six hub genes, such as TP53, JUN, and ITGA2, were screened out, and three modules with important functions were mined from the PPI network.ConclusionThe novel proteasome inhibitor ixazomib significantly inhibited the proliferation of human CRC SW620 cells. It exerted anticancer effects through targeting the expression of DEGs, such as HSPA6, APCDD1, TP53, and JUN, and affecting the signaling pathways including apoptosis and cell cycle pathway, which demonstrated the promising potential of ixazomib for CRC therapy.