Identification of an m6A-related lncRNA prognostic signature in colorectal cancer.

Data sets of colorectal cancer (CRC) were obtained from The Cancer Genome Atlas (TCGA), three N6-methyladenosine (m6A) subtypes were identified using 21 m6A-related long noncoding RNAs (lncRNAs) and differential m6A subtypes of different CRC tumors were determined in this study to evaluate the m6A expression and the prognosis of patients with CRC. Subsequently, eight key lncRNAs were identified based on co-expression with 21 m6A-related genes in CRC tumors using the single-factor Cox and least absolute shrinkage and selection operator. Finally, an m6A-related lncRNA risk score model of CRC tumor was established using multifactor Cox regression based on the eight important lncRNAs and found to have a better performance in evaluating the prognosis of patients in the TCGA-CRC data set. TCGA-CRC tumor samples were divided based on the risk scores: high and low. Then, the clinical characteristics, tumor mutation load, and tumor immune cell infiltration difference between the high- and low-risk-score groups were explored, and the predictive ability of the risk score was assessed for immunotherapeutic benefits. We found that the risk score model can determine the overall survival, be a relatively independent prognostic indicator, and better evaluate the immunotherapeutic benefits for patients with CRC. This study provides data support for accurate immunotherapy in CRC.

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis.

Background: Knowledge about the prognostic role of long noncoding RNA (lncRNA) in colorectal cancer (CRC) is limited. Therefore, we constructed a lncRNA-related prognostic model based on data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Materials and Methods: CRC transcriptome and clinical data were downloaded from the GSE20916 dataset and the TCGA database, respectively. R software was used for data processing and analysis. The differential lncRNA expression within the two datasets was first screened, and then intersections were measured. Cox regression and the Kaplan-Meier method were used to evaluate the effects of various factors on prognosis. The area under the curve (AUC) of the receiver operating characteristic curve and a nomogram based on multivariate Cox analysis were used to estimate the prognostic value of the lncRNA-related model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to elucidate the significantly involved biological functions and pathways. Results: A total of 11 lncRNAs were crossed. The univariate Cox analysis screened out two lncRNAs, which were analyzed in the multivariate Cox analysis. A nomogram based on the two lncRNAs and other clinicopathological risk factors was constructed. The AUC of the nomogram was 0.56 at 3 years and 0.71 at 5 years. The 3-year nomogram model was compared with the ideal model, which showed that some indices of the 3-year model were consistent with the ideal model, suggesting that our model was highly accurate. The GO and KEGG enrichment analyses showed that positive regulation of secretion by cells, positive regulation of secretion, positive regulation of exocytosis, endocytosis, and the calcium signaling pathway were differentially enriched in the two-lncRNA-associated phenotype. Conclusions: A two-lncRNA prognostic model of CRC was constructed by bioinformatics analysis. The model had moderate prediction accuracy. LncRNA BBOX1-AS1 and lncRNA FOXP4-AS1 were identified as prognostic biomarkers.

miR-133a-3p Targets SUMO-Specific Protease 1 to Inhibit Cell Proliferation and Cell Cycle Progress in Colorectal Cancer.

Dysregulation of SUMO-specific protease 1 (SENP1) expression has been reported in several kinds of cancer, including human colorectal and prostate cancers, proposing SENP1 as an oncogene with a critical role in cancer progression. miR-133a-3p has been reported as a tumor suppressor in several malignant neoplasias. However, the precise molecular mechanisms underlying its role in colorectal cancer remain largely unknown. The aim of this work was to investigate the relationship between miR-133a-3p and SENP1 in colorectal cancer cells. We found that miR-133a-3p expression was downregulated in colorectal cancer tissues. In silico analyses indicated that SENP1 is one of the target genes of miR-133a-3p. Overexpression of miR-133a-3p mimics was able to inhibit cell growth with G1 arrest of colorectal cancer cells. Overexpression of miR-133a-3p antisense promoted cell growth of colorectal cancer cells. The luciferase reporter experiments showed that miR-133a-3p regulated the expression of SENP1 by combining with its 3'-UTR and resulted in downregulation of SENP1 and upregulation of CDK inhibitors such as p16, p19, p21, and p27. These results suggest that the miR-133a-3p-SENP1 axis might play a role in cell proliferation and cell cycle regulation of colorectal cancer cells.