Objective: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. Background: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. Methods: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker—DeepCT-PDAC—by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. Results: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50–2.75; HR: 2.47, CI: 1.35–4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89–3.28; HR: 2.15, CI: 1.14–4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19–0.64), but did not affect OS in the subgroup with high risk. Conclusions: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.
Background Pseudogene has emerged as key regulators of important biological processes involved in human cancers. However, the PTTG3P biological function in colorectal cancer (CRC) needs further to be clarified. Methods qRT-PCR was adopted to measure the PTTG3P expression in different cell line and CRC tissues. Cellular localization of PTTG3P was detected by subcellular fractionation assay. In vitro and in vivo experiments were carried out to explore the bioeffect of PTTG3P in CRC cells. Chromatin immunoprecipitation (ChIP), luciferase assay and RIP were explored to certify the direct binding of PTTG3P and microRNA. Results PTTG3P was upregulated in CRC and closely related to poor prognosis. Through gain and loss of function approaches, PTTG3P facilitated proliferation and glycolysis through YAP1, and glycolysis inhibitor (2-DG,3-BG) and LDHA knockdown could rescue cell proliferation and tumorigenesis. Mechanically, miR-1271-5p modulates PTTG3P expression and miR-1271-5p mimics could recover the PTTG3P function. Also HIF1A increased PTTG3P expression in both normoxia and hypoxia conditions by ameliorating miR-1271-5p expression. In addition, silencing PTTG3P decreases the level of inflammatory cytokines TNF-α, IL-1β and IL-6, and low PTTG3P expression relates with CD8+ T, NK and TFH cells infiltration. Conclusions Our findings verified the oncogenic function of PTTG3P in assisting the cell proliferation and glycolysis, demonstrating the pivotal roles of HIF1A/miR-1271-5p/lncRNA PTTG3P/YAP1 in CRC progression, which proposes a novel approach for clinical treatment.
Backgrounds: Genome-wide association studies (GWAS) have identified multiple common CRC-related (colorectal cancer) SNPs (single nucleotide polymorphisms) including the Cadherin 1(CDH1) rs9929218 may act by increasing the risk of colorectal cancer, colorectal adenoma, or both. These studies, however, reported inconsistent associations. Methods: To derive a more accurate approximation of the connection, we carried out a meta-analysis of 12 published pieces of research including 11,590 controls and 8,192 cases. We used odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the associations' strength.Results: Meta-analysis implied considerable association between CRC and rs9929218 (OR = 1.21, 95%CI 1.04-1.42 for GG versus AA; OR = 1.22, 95%CI 1.05-1.42 for GG/AG versus AA).In the subgroup analyses, significantly increased risks were found among Europeans.Conclusions: In summary, our meta-analysis studies in different populations confirmed that SNP rs9929218 is significantly associated with CRC risk and that this variant may have a greater impact on Europeans.
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.