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Background: ZEB2 is a protein-coding gene that is differentially expressed in tumors and can regulate the growth of tumor cells. This study investigated the specific regulatory mechanism of ZEB2 in COAD, a common cancer with high rates of morbidity and mortality.Methods: Multi-omics panoramic display of expression and function of ZEB2 in colon cancer. R software was used to study the expression of ZEB2 in 33 types of cancer. Furthermore, RT-PCR was used to detect the expression of ZEB2 in colon cancers and para-cancer tissues, as well as in colon cancer cells and normal cells. The ssGSEA was then used to explore the relationship between ZEB2 and immune cells, with UALCAN, EWAS and MEXPRESS applied to explore the methylation of ZEB2. The relationship between immunomodulators and chemokines (or receptors) based on expression data, copy number data, methylation data, and mutation data of ZEB2 was investigated using TISIDB. Finally, a protein interaction network of ZEB2 was constructed, and GO and KEGG analyses were performed on the differentially expressed genes.Results: ZEB2 is downregulated in most cancers, including COAD. The infiltration of the immune cells NK CD56 and Th17 cells was negatively correlated with ZEB2 expression, while the other 22 cells were positively correlated with ZEB2 expression. The DNA methylation of ZEB2 and the methylation of the ZEB2 protein on the EWAS website increased significantly. Analysis of the methylation levels and ZEB2 expression revealed that only the DNA methylation level and the expression of ZEB2 were significantly negatively correlated. The tumor-infiltrating lymphocytes positively correlated with the expression of ZEB2 but negatively correlated with the methylation of ZEB2. The same trend was observed for immunomodulators, chemokines, and receptors. The network showed that the protein performed certain biological functions, thereby affecting disease symptoms.Conclusion: These findings provide evidence that ZEB2-based therapy may represent a powerful treatment strategy for COAD.

Abstract Background Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNA-seq called variants, we found majority (> 95%) are likely germline variants, leading to C > T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Background: Colon adenocarcinoma (COAD) is still the main cause of cancer deaths worldwide. Although immunotherapy has made progress in recent years, there is still a need to improve diagnosis, prognosis, and treatment tools. UL-16 binding protein 1 (ULBP1) is a ligand that activates the receptor natural killer cell group 2 receptor D (NKG2D) and plays an important immunomodulatory role. We aimed to investigate the clinical significance of ULBP1 in COAD.Methods: We obtained the relevant data from The Cancer Genome Atlas (TCGA). A total of 438 patients with COAD were included in this study, with a mean age of 67.1 ± 13.03 years old, of which 234 (53.42%) were male. The diagnostic value of COAD tumor tissues and adjacent tissues was analyzed by ROC curve. Univariate and multivariate survival analysis investigated the prognostic value of ULBP1 gene, and Gene Set Enrichment Analysis (GSEA) curve was performed to analyze the biological process and enriched enrichment pathway of ULBP1 in COAD. Combination survival analysis investigated the combined prognostic effect of prognostic genes.Results:ULBP1 gene had a high diagnostic value in COAD [AUC (TCGA) = 0.959; AUC (Guangxi) = 0.898]. Up-regulated ULBP1 gene of patients with COAD predicted a worse prognosis compared to those patients with down-regulated ULBP1 gene (Adjusted HR = 1.544, 95% CI = 1.020–2.337, p = 0.040). The GSEA showed that ULBP1 was involved in the apoptotic pathway and biological process of T cell mediated cytotoxicity, regulation of natural killer cell activation, and T cell mediated immunity of COAD. The combination survival analysis showed that the combination of high expression of ULBP1, AARS1, and DDIT3 would increase the 2.2-fold death risk of COAD when compared with those of low expression genes.Conclusion: The immune-related ULBP1 gene had diagnostic and prognostic value in COAD. The combination of ULBP1, AARS1, and DDIT3 genes could improve the prognostic prediction performance in COAD.

Long non-coding RNAs (lncRNAs) and their N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis and cancer progression. This study was designed to explore the value of m6A-related lncRNAs in prognosis and therapeutic applications of immune infiltration of colon adenocarcinoma (COAD). We downloaded the COAD gene expression and clinical data from The Cancer Genome Atlas project. By co-expression analysis, Lasso Cox regression analysis, and univariate and multivariate Cox regression, we constructed an independent prognostic signature of seven m6A-related lncRNAs. The prognostic lncRNAs were divided into two clusters by consistent clustering analysis, as well as into two groups of low–high risk based on the signature. Then we identified the relationship between the different groups with clinical features and immune cell infiltration. Cluster 2 had a higher risk score with a lower survival rate. The risk score was higher in groups with advanced clinical features, such as stage III–IV, N1-3, and M1. The expression of AC156455.1 was increased in tumor tissues and cluster 2, and the lncRNA ZEB1−AS1 was notably higher in the high-risk group. Five types of immune cells showed differences in two clusters, and most were upregulated in type 2. The expression of memory B cells was positively correlated with the risk score. The prognostic model was verified by the Gene Expression Omnibus (GEO) dataset. Besides, we found that the expression of these seven lncRNAs in tumor tissues was significantly higher than that in normal tissues, which verified the feasibility of the model. Thus, the signature of seven m6A-related lncRNAs can independently predict the prognosis of COAD. This signature is also closely associated with immune cell infiltration, and new therapeutic targets can be explored from this field.

Colorectal cancer (CRC) is one of the most common cancers. Almost 80% of CRC cases are colon adenocarcinomas (COADs). Several studies have indicated the role of immunotherapy in the treatment of various cancers. Our study aimed to identify immune-related long non-coding RNAs (lncRNAs) and to use them to construct a risk assessment model for evaluating COAD prognosis. Using differential expression, correlation, and Cox regression analyses, we identified three immune-related differentially expressed lncRNAs (IR-DELs) and used them to construct a risk assessment model. The area under the curve (AUC) for each receiver operating characteristic (ROC) curve at 3-, 5-, and 10-years were greater than 0.6. In addition, the risk assessment model was correlated with several immune cells and factors. The three IR-DELs (AC124067.4, LINC02604, and MIR4435-2HG) identified in this study can be used to predict outcomes for patients with COAD and might help in identifying those who can benefit from anti-tumor immunotherapy.