Background: Colorectal cancer (CRC) is a kind of gastrointestinal tumor with serious high morbidity and mortality. Several reports have implicated the disorder of RNA-binding proteins (RBPs) in plenty of tumors, associating it to tumorigenesis and disease progression. The study is intended to construct novel prognostic biomarkers associated with CRC patients. Methods: Data of gene expression was acquired from the TCGA database, prognosis-related genes were selected. Besides, we analyzed GO and KEGG pathways. Univariate and multivariate Cox analyses were performed to generate a prognostic-related gene signature, which was evaluated by the Kaplan-Meier (K-M) and the Receiver Operating Characteristic (ROC) curve. The independent prognostic factor was established by survival analysis. GSE38832 dataset was used to validate the signature. Finally, expression of 8 genes was further confirmed by qRT-PCR in SW480 and SW620 cell lines. Results: We obtained 224 differentially expressed RBPS in total, of which 78 were downregulated and 146 were upregulated. Univariate COX analysis was conducted in the TCGA cohort to select 13 RBPs with P < 0.005, stepwise multivariate COX regression analysis was used to construct an 8—RBP signature (TERT, PPARGC1A, BRCA1, CELF4, TDRD7, LUZP4, PNLDC1, ZC3H12C). Based on the model, systematic analysis illustrated that a high risk score was obviously connected to a poor prognosis. The prognostic value of the risk score was validated in GSE38832 dataset, indicating that the risk model was accurate and effective. The prognostic signature-based risk score was identified as an independent prognostic indicator for CRC. The expression results of qRT-PCR were consistent with the results of differential expression analysis. Conclusions: The eight-RBP signature can predict the survival of CRC patients and potentially act as CRC prognostic biomarker.
Background: Colorectalcancer (CRC) is a prevalent gastrointestinal tumor with high incidence and mortality. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. This study aimed to develop and validate new biomarkers related to CRC prognosis by a series of bioinformatics analysis.Methods: We mined the gene expression data of 510 CRC samples from The Cancer Genome Atlas (TCGA) database, differentially expressed genes were screened and prognosis-related genes were identified. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out. A prognosis-related gene signature was constructed by univariate and multivariate Cox analysis. Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves were utilized to evaluate the signature,The test set was used to validate the RBPs risk score model.Survival analysis was carried out to determine the independent prognostic significance of the signature. A nomogram combined with the gene signature was constructed.Results: A total of 224 aberrantly expressed RBPs were obtained, comprising 78 downregulated and 146 upregulatedRBPs. 13 RBPs with p < 0.005 were revealed in univariateCox regression analysis of train group, then stepwise multivariate Cox regression was applied for constituting an eight- RBP (BRCA1, TERT, TDRD7, PPARGC1A, LUZP4, CELF4, ZC3H12C, PNLDC1) signature prognostic biomarkers. Further analysis demonstrated that high risk score for patients was significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.730 at 5 years. The signature-based risk score was an independent prognostic factor in CRC patients. We also established a nomogram based on eight RBPs and internal validation in the train set, which displayed a favorable discriminating ability for Colorectal cancer.Conclusions: The established eight-RBP signature may serve as a novel independent prognostic factor that could be an important tool to predict the prognostic outcome of CRC patients. However, the specific biological mechanism needs further verification.
Background: Immunotherapy is a new strategy for Colorectal cancer (CRC) treatment. Tumor mutation burden (TMB) may act as an emerging biomarker for predicting responses to immunotherapy. Nevertheless, no studies investigate if these gene mutations correlate to TMB and tumor-infiltrating immune cells. Methods: Somatic mutation data for CRC samples were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Then, we investigated the relationship between these mutant genes, TMB and overall survival outcomes. GSEA analysis was performed to explore the underlying mechanism of mutant gene. Finally, we further verified the connection between gene mutations and immune response.Results: We identified 17 common mutant genes shared by both two cohorts. Further analysis found that MUC4 mutation was strongly related to higher TMB and predicted a poorer prognosis. Moreover, functional enrichment analysis of samples with MUC4 mutation revealed that they were involved in regulating the natural killer cell mediated cytotoxicity signaling pathway. Significant changes in the proportion of the immune cells of CD8 T cells, activated NK cells, M1 macrophages and resting memory CD4 T cells were observed using the CIBERSORT algorithm. Conclusions: Our research revealed that MUC4 mutation significantly correlated with increased TMB, a worse prognosis and modulating the immune microenvironment, which may be considered a biomarker to predict the outcome of the immune response in colorectal cancer.
Background: Ulcerative colitis (UC) is a chronic nonspecific intestinal inflammatory disorder associated with continuous, diffuse inflammatory alterations in the colonic mucosa of unknown etiology, Increasing evidence has showed aberrant expression of gene plays a vital function in the pathophysiological mechanisms of ulcerative colitis, Herein, we employed bioinformatics to investigate the core of the pathogenesis and provide potential markers for UC. Results: We downloaded the GSE36807, GSE65114, GSE59071 datasets from the Gene Expression Omnibus(GEO), then the differentially expressed genes (DEGs) were determined using adjusted P<0. 05 and |log2FC>2 | between normal samples and UC samples. Intersection analysis among three datasets showed 12 DEGs were found to be significantly dysregulated in UC. Results indicated that the DEGs were primarily associated with functions like the humoral immune response, antimicrobial humoral response, and CXCR chemokine receptor binding, and they were primarily enriched in KEGG pathways, including the IL−17 signaling pathway, and Toll−like receptor signaling pathway . Cytoscape software calculated that CXCL8, DMBT1, REG3A, S100A8, DUOX2, and MMP1 were hub genes of UC. In addition, We collected samples of 8 UC tissues and 8 normal Colonic tissues to validate the selected genes by mRNA microarray.Conclusions: These results may provide potential biomarkers for UC, and our data and methodology provides new ideas that may be helpful in the understanding of the vital mechanisms underlying UC development.
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