BACKGROUND: Long non-coding RNA testis-specific transcript, Y-linked 15 (TTTY15) is oncogenic in prostate cancer, however its expression and function in colorectal cancer remain largely unknown. METHODS: Paired colorectal cancer samples/adjacent tissues were collected, and the expression levels of TTTY15, miR-29a-3p and disheveled segment polarity protein 3 (DVL3) were examined by quantitative real-time polymerase chain reaction (qRT-PCR); TTTY15 shRNA and overexpression plasmids were transfected into HT29 and HCT-116 cell lines using lipofectamine reagent, respectively; the proliferation and colony formation were detected by CCK-8 assay and plate colony formation assay; qRT-PCR and Western blot were used to analyze the changes of miR-29a-3p and DVL3; dual-luciferase reporter gene assay was used to determine the regulatory relationships between miR-29a-3p and TTTY15, miR-29a-3p and DVL3. RESULTS: TTTY15 was significantly up-regulated in cancerous tissues of colorectal cancer samples, positively correlated with the expression of DVL3, while negatively correlated with the expression of miR-29a-3p. After TTTY15 shRNAs were transfected into colorectal cancer cells, the proliferation and metastasis of cancer cells were significantly inhibited, while TTTY15 overexpression had opposite biological effects. TTTY15 shRNA could reduce the expression of DVL3 on both mRNA and protein levels, and the luciferase activity of TTTY15 sequence was also inhibited by miR-29a-3p. DVL3 was also validated as a target gene of miR-29a-3p, and it could be repressed by miR-29a-3p mimics or TTTY15 shRNA. CONCLUSION: TTTY15 is abnormally upregulated in colorectal cancer tissues, and it can modulate the proliferation and metastasis of colorectal cancer cells. It functions as the ceRNA to regulate the expression of DVL3 by sponging miR-29a-3p.

Background: Colon cancer is a common malignant tumor with poor prognosis. The aim of this study is to explore the immune-related prognostic signatures and the tumor immune microenvironment of colon cancer.Methods: The mRNA expression data of TCGA-COAD from the UCSC Xena platform and the list of immune-related genes (IRGs) from the ImmPort database were used to identify immune-related differentially expressed genes (DEGs). Then, we constructed an immune-related risk score prognostic model and validated its predictive performance in the test dataset, the whole dataset, and two independent GEO datasets. In addition, we explored the differences in tumor-infiltrating immune cell types, tumor mutation burden (TMB), microsatellite status, and expression levels of immune checkpoints and their ligands between the high-risk and low-risk score groups. Moreover, the potential value of the identified immune-related signature with respect to immunotherapy was investigated based on an immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent.Results: Seven immune-related DEGs were identified as prognostic signatures. The areas under the curves (AUCs) of the constructed risk score model for overall survival (OS) were calculated (training dataset: 0.780 at 3 years, 0.801 at 4 years, and 0.766 at 5 years; test dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years; and the whole dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years). In the high-risk score group of the whole dataset, patients had worse OS, higher TMN stages, advanced pathological stages, and a higher TP53 mutation rate (p < 0.05). In addition, a high level of resting NK cells or M0 macrophages, and high TMB were significantly related to poor OS (p < 0.05). Also, we observed that high-risk score patients had a high expression level of PD-L1, PD-1, and CTLA-4 (p < 0.05). The patients with high-risk scores demonstrated worse prognosis than those with low-risk scores in multiple datasets (GSE39582: p = 0.0023; GSE17536: p = 0.0008; immunotherapeutic cohort without platinum treatment: p = 0.0014; immunotherapeutic cohort with platinum treatment: p = 0.0027).Conclusion: We developed a robust immune-related prognostic signature that performed great in multiple cohorts and explored the characteristics of the tumor immune microenvironment of colon cancer patients, which may give suggestions for the prognosis and immunotherapy in the future.

The present study aimed to construct a novel methylation‐related prognostic model based on microsatellite status that may enhance the prognosis of colorectal cancer (CRC) from methylation and microsatellite status perspective. DNA methylation and mRNA expression data with clinical information were downloaded from The Cancer Genome Atlas (TCGA) data set. The samples were divided into microsatellite stability and microsatellite instability group, and CIBERSORT was used to assess the immune cell infiltration characteristics. After identifying the differentially methylated genes and differentially expression genes using R packages, the methylation‐driven genes were further identified. Prognostic genes that were used to establish the methylation‐related risk score model were generated by the univariate and multivariate Cox regression model. Finally, we established and evaluated the methylation‐related prognostic model for CRC patients. A total of 69 MDGs were obtained and three of these genes (MIOX, TH, DKFZP434K028) were selected to construct the prognostic model. Patients in the low‐risk score group had a conspicuously better overall survival than those in the high‐risk score group (p < .0001). The area under the receiver operating characteristic curve for this model was 0.689 at 3 years, 0.674 at 4 years, and 0.658 at 5 years. The Wilcoxon test showed that higher risk score was associated with higher T stage (p = .01), N stages (p = .0028), metastasis (p = .013), and advanced pathological stage (p = .0013). However, the more instability of microsatellite status, the lower risk score of CRC patients (p = .0048). Our constructed methylation‐related prognostic model based on microsatellite status presents potential significance in assessing recurrence risk stratification, tumor staging, and immunotherapy for CRC patients.

The present study focused on identifying the immune-related signatures and exploring their performance in predicting the prognosis, immunotherapeutic responsiveness, and diagnosis of patients with colon cancer. Firstly, the immunotherapeutic response-related differential expressed genes (DEGs) were identified by comparing responders and non-responders from an anti-PD-L1 cohort using the edgeR R package. Then, the immunotherapeutic response related DEGs was intersected with immune-related genes (IRGs) to obtain the immunotherapeutic response and immune-related genes (IRIGs). Then, an immunotherapeutic response and immune-related risk score (IRIRScore) model consisting of 6 IRIGs was constructed using the univariable Cox regression analysis and multivariate Cox regression analysis based on the COAD cohort from the cancer genome atlas (TCGA) database, which was further validated in two independent gene expression omnibus database (GEO) datasets (GSE39582 and GSE17536) and anti-PD-L1 cohort. A nomogram with good accuracy was established based on the immune-related signatures and clinical factors (C-index = 0.75). In the training dataset and GSE39582, higher IRIRScore was significantly associated with higher TMN and advanced pathological stages. Based on the anti-PD-L1 cohort, patients who were sensitive to immunotherapy had significantly lower risk score than non-responders. Furthermore, we explored the immunotherapy-related signatures based on the training dataset. Kaplan-Meier curve revealed a high level of T cells regulatory (Tregs) was significantly related to poor overall survival (OS), while a high level of T cells CD4 memory resting was significantly related to better OS. Besides, the TMB value of patients in the high-risk group was significantly higher than those in a low-risk group. Moreover, patients in the high-risk group had significantly higher expression levels of immune checkpoint inhibitors. In addition, the immune-related signatures were applied to establish prediction models using the random forest algorithm. Among them, TDGF1 and NRG1 revealed excellent diagnostic predictive performance (AUC >0.8). In conclusion, the current findings provide new insights into immune-related immunotherapeutic responsiveness, prognosis, and diagnosis of colon cancer.

Background: This work investigates the use of methylation driven biomarkers for diagnosis and prognosis in colorectal cancer (CRC) by mining DNA methylation and gene expression data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), and the Gene Expression Omnibus (GEO). Methods: The differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were screened using mRNA expression and DNA methylation data from TCGA, respectively. The methylation driven genes (MDGs) of CRC were further identified using the MethylMix R package. Subsequently, the MDGs were analyzed with Random Forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms to establish diagnosis prediction models as independent indicators using mRNA expression data from TCGA and GTEx. The RF algorithm was determined to be the most suitable and used to construct the diagnostic model with the combined MDGs, which was then validated by GSE39582 from GEO. Prognostic biomarkers were used to establish the risk score model, which was generated by univariate and multivariate Cox regression analyses. Moreover, we constructed and validated a nomogram that integrated the risk score and clinical information, including age, gender, and tumor stage. Results: 9 out of 10 MDGs performed well as independent diagnostic predictors, and STK33 and EPHX4 were also found to be associated with overall survival (OS). The results of the nomogram suggest that it is a better predictive model for prognosis than the risk score model. Conclusion: Our findings suggest that the identified MDGs could be biomarkers for diagnosis and prognosis of CRC.