Belonging to the tether factors family, USO1 (vesicle transport factor) plays a critical role in endoplasmic reticulum-Golgi trafficking and vesicular transport which is important to tumorigenesis. However, the mechanism of USO1 in colon cancer was still unknown. In our research, the expression of USO1 was knockdown in colon cancer cells (HCT116 and HT-29) by using a special lentivirus shRNA approach. A series of experiments were carried out to evaluate the effect of deregulation of USO1, including cell proliferation, apoptosis, cell migration and cell cycle. Knockdown of USO1 inhibits the ability of cell proliferation and migration. Furthermore, the deregulation of USO1 induces early apoptosis and decreased cells in G2-M phase. We demonstrate for the first time that USO1 gene has a critical role in human colon cancer. Our finding represents that USO1 gene may be a promising target for therapy and diagnosis in treatment of human colon cancer.
Background Early detection of colorectal carcinoma (CRC) would help to identify tumors when curative treatments are available and beneficial. However, current screening methods for CRC, e.g., colonoscopy, may affect patients’ compliance due to the uncomfortable, invasive and time-consuming process. In recent decades, methylation profiles of blood-based circulating tumor DNA (ctDNA) have shown promising results in the early detection of multiple tumors. Here we conducted a study to investigate the performance of ctDNA methylation markers in early detection of CRC. Results In total, 742 participants were enrolled in the study including CRC (n = 332), healthy control (n = 333), benign colorectal disease (n = 65) and advanced adenoma (n = 12). After age-matched and randomization, 298 participants (149 cancer and 149 healthy control) were included in training set and 141 (67 cancer and 74 healthy control) were in test set. In the training set, the specificity was 89.3% (83.2–93.7%) and the sensitivity was 88.6% (82.4–93.2%). In terms of different stages, the sensitivities were 79.4% (62.1–91.2%) in patients with stage I, 88.9% (77.3–95.8%) in patients with stage II, 91.4% (76.9–98.2%) in patients with stage III and 96.2% (80.3–99.9%) in patients with stage IV. Similar results were validated in the test set with the specificity of 91.9% (83.1–97.0%) and sensitivity of 83.6% (72.5–91.6%). Sensitivities for stage I-III were 87.0% (79.7–92.4%) in the training set and 82.5% (70.2–91.3%) in the test set, respectively. In the unmatched total population, the positive ratios were 7.8% (5.2–11.2%) in healthy control, 30.8% (19.9–43.5%) in benign colorectal disease and 58.3% (27.5–84.7%) in advanced adenoma, while the sensitivities of stage I–IV were similar with training and test sets. Compared with methylated SEPT9 model, the present model had higher sensitivity (87.0% [81.8–91.2%] versus 41.2% [34.6–48.1%], P < 0.001) under comparable specificity (90.1% [85.4–93.7%] versus 90.6% [86.0–94.1%]). Conclusions Together our findings showed that ctDNA methylation markers were promising in the early detection of CRC. Further validation of this model is warranted in prospective studies.
BackgroundMost prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.MethodsPrognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan–Meier (K–M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.ResultsA total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.ConclusionA credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.
Background: Simultaneous resection for patients with synchronous colorectal cancer liver metastases (CRLM) remains an optimal option for the sake of curability. However, few studies so far focus on outcome of this subgroup of patients (who receive simultaneous resection for CRLM). Substantial heterogeneity exists among such patients and more precise categorization is needed preoperatively to identify those who may benefit more from surgery. In this study, we formulated this internally validated scoring system as an option.Methods: Clinicopathological and follow-up data of 234 eligible CRLM patients undergoing simultaneous resection from January 2010 to March 2019 in our center were included for analysis. Patients were randomized to either a training or validation cohort. We performed multivariable Cox regression analysis to determine preoperative factors with prognostic significance using data in training cohort, and a nomogram scoring system was thus established. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to evaluate the predictive power of our risk model. Results:In the multivariable Cox regression analysis, five factors including presence of node-positive primary defined by enhanced CT/MR, preoperative CEA level, primary tumor location, tumor grade and number of liver metastases were identified as independent prognostic indicators of overall survival (OS) and adopted to formulate the nomogram. In the training cohort, calibration plot graphically showed good fitness between estimated and actual 1-and 3-year OS. Time-dependent ROC curve by Kaplan-Meier method showed that our nomogram model was superior to widely used Fong's score in prediction of 1-and 3-year OS (AUC 0.702 vs. 0.591 and 0.848 vs. 0.801 for 1-and 3-year prediction in validation cohort, respectively). Kaplan-Meier curves for patients stratified by the assessment of nomogram showed great discriminability (P<0.001). Conclusions:In this retrospective analysis we identified several preoperative factors affecting survival of synchronous CRLM patients undergoing simultaneous resection. We also constructed and validated a risk model which showed high accuracy in predicting 1-and 3-year survival after surgery. Our risk model is expected to serve as a predictive tool for CRLM patients receiving simultaneous resection and assist physicians to make treatment decision.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.