BACKGROUNDCarcinoembryonic antigen (CEA) is a commonly used biomarker in colorectal cancer. However, controversy exists regarding the insufficient prognostic value of preoperative serum CEA alone in rectal cancer. Here, we combined preoperative serum CEA and the maximum tumor diameter to correct the CEA level, which may better reflect the malignancy of rectal cancer.AIMTo assess the prognostic impact of preoperative CEA/tumor size in rectal cancer.METHODSWe retrospectively reviewed 696 stage I to III rectal cancer patients who underwent curative tumor resection from 2007 to 2012. These patients were randomly divided into two cohorts for cross-validation: training cohort and validation cohort. The training cohort was used to generate an optimal cutoff point and the validation cohort was used to further validate the model. Maximally selected rank statistics were used to identify the optimum cutoff for CEA/tumor size. The Kaplan-Meier method and log-rank test were used to plot the survival curve and to compare the survival data. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of CEA/tumor size. The primary and secondary outcomes were overall survival (OS) and disease-free survival (DFS), respectively.RESULTSIn all, 556 patients who satisfied both the inclusion and exclusion criteria were included and randomly divided into the training cohort (2/3 of 556, n = 371) and the validation cohort (1/3 of 556, n = 185). The cutoff was 2.429 ng/mL per cm. Comparison of the baseline data showed that high CEA/tumor size was correlated with older age, high TNM stage, the presence of perineural invasion, high CEA, and high carbohydrate antigen 19-9 (CA 19-9). Kaplan-Meier curves showed a manifest reduction in 5-year OS (training cohort: 56.7% vs 81.1%, P < 0.001; validation cohort: 58.8% vs 85.6%, P < 0.001) and DFS (training cohort: 52.5% vs 71.9%, P = 0.02; validation cohort: 50.3% vs 79.3%, P = 0.002) in the high CEA/tumor size group compared with the low CEA/tumor size group. Univariate and multivariate analyses identified CEA/tumor size as an independent prognostic factor for OS (training cohort: hazard ratio (HR) = 2.18, 95% confidence interval (CI): 1.28-3.73, P = 0.004; validation cohort: HR = 4.83, 95%CI: 2.21-10.52, P < 0.001) as well as DFS (training cohort: HR = 1.47, 95%CI: 0.93-2.33, P = 0.096; validation cohort: HR = 2.61, 95%CI: 1.38-4.95, P = 0.003).CONCLUSIONPreoperative CEA/tumor size is an independent prognostic factor for patients with stage I-III rectal cancer. Higher CEA/tumor size is associated with worse OS and DFS.

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DNA methylation contributes to malignant transformation, but little is known about how the methylation drives colorectal cancer evolution at the early stages. Here we identify aberrant INA (a-internexin) gene methylation in colon adenoma and adenocarcinoma by filtering data obtained from a genome-wide screen of methylated genes. The gene encoding INA, a type IV intermediate filament, was frequently hypermethylated in CpG islands located in the promoter region. This hypermethylation preferentially occurred in large tumors and was a prognostic marker for poor overall survival in patients with colorectal cancer. This type of epigenetic alteration silenced INA expression in both adenoma and adenocarcinoma tissues. Gene silencing of INA in colorectal cancer cells increased cell proliferation, migration, and invasion. Restored INA expression blocked migration and invasion in vitro and reduced lung metastasis in vivo.Mechanistically, INA directly inhibited microtubule polymerization in vitro and decreased intracellular microtubule plus-end assembly rates. A peptide array screen surveying the tubulinbinding sites in INA identified a tubulin-binding motif located in the N-terminal head domain that plays a tumor-suppressive role by binding to unpolymerized tubulins and impeding microtubule polymerization. Thus, epigenetic inactivation of INA is an intermediate filament reorganization event that is essential to accelerate microtubule polymerization in the early stages of colorectal cancer.Significance: This work provides insight into the epigenetic inactivation of INA, a novel identified tumor suppressor, which increases microtubule polymerization during colorectal cancer progression.

Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC).Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator algorithm (LASSO). A nomogram was constructed by combining the radiomics score with clinicopathological risk factors for predicting the prognosis of CRC patients. The performance of the nomogram was evaluated by the calibration curve, receiver operating characteristic (ROC) curve and C-index statistics. Functional analysis and correlation analysis were used to explore the underlying association between radiomic feature and the gene-expression patterns.Results: Five radiomic features were selected to calculate the radiomics score by using the LASSO regression model. The Kaplan-Meier analysis showed that radiomics score was significantly associated with disease-free survival (DFS) [primary cohort: hazard ratio (HR): 5.65, 95% CI: 2.26–14.13, P &lt; 0.001; validation cohort: HR: 8.49, 95% CI: 2.05–35.17, P &lt; 0.001]. Multivariable analysis confirmed the independent prognostic value of radiomics score (primary cohort: HR: 5.35, 95% CI: 2.14–13.39, P &lt; 0.001; validation cohort: HR: 5.19, 95% CI: 1.22–22.00, P = 0.026). We incorporated radiomics signature with the TNM stage to build a nomogram, which performed better than TNM stage alone. The C-index of the nomogram achieved 0.74 (0.69–0.80) in the primary cohort and 0.82 (0.77–0.87) in the validation cohort. Functional analysis and correlation analysis found that the radiomic signatures were mainly associated with metabolism related pathways.Conclusions: The radiomics score derived from the preoperative CT image was an independent prognostic factor and could be a complement to the current staging strategies of colorectal cancer.

Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations.Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging.Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4–90.6, p &lt; 0.001; validation cohort: HR: 2.04, 95% CI: 1.44–2.89, p &lt; 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8–69.3, p &lt; 0.001; validation cohort: HR: 1.83, 95% CI: 1.19–2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways.Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

Background This study aimed to develop a radiogenomic prognostic prediction model for colorectal cancer (CRC) by investigating the biological and clinical relevance of intratumoural heterogeneity. Methods This retrospective multi-cohort study was conducted in three steps. First, we identified genomic subclones using unsupervised deconvolution analysis. Second, we established radiogenomic signatures to link radiomic features with prognostic subclone compositions in an independent radiogenomic dataset containing matched imaging and gene expression data. Finally, the prognostic value of the identified radiogenomic signatures was validated using two testing datasets containing imaging and survival information collected from separate medical centres. Results This multi-institutional retrospective study included 1601 patients (714 females and 887 males; mean age, 65 years ± 14 [standard deviation]) with CRC from 5 datasets. Molecular heterogeneity was identified using unsupervised deconvolution analysis of gene expression data. The relative prevalence of the two subclones associated with cell cycle and extracellular matrix pathways identified patients with significantly different survival outcomes. A radiogenomic signature-based predictive model significantly stratified patients into high- and low-risk groups with disparate disease-free survival (HR = 1.74, P = 0.003). Radiogenomic signatures were revealed as an independent predictive factor for CRC by multivariable analysis (HR = 1.59, 95% CI:1.03–2.45, P = 0.034). Functional analysis demonstrated that the 11 radiogenomic signatures were predominantly associated with extracellular matrix and immune-related pathways. Conclusions The identified radiogenomic signatures might be a surrogate for genomic signatures and could complement the current prognostic strategies.

Background DNA methylation is one of the most promising biomarkers in predicting the prognosis of colorectal cancer (CRC). We aimed to develop a DNA methylation biomarker that could evaluate the prognosis of CRC. Methods A promising DNA methylation biomarker was developed by hypermethylated genes in cancer tissue that were identified from Illumina EPIC methylation arrays. A cohort comprising 30 pairs of snap-frozen tumor tissue and adjacent normal tissue was used for correlation analysis between the methylation and expression status of the marker. The other cohort comprising 254 formalin-fixed paraffin-embedded (FFPE) tumor tissue from 254 CRC patients was used for prognosis analysis. Results Regulating synaptic membrane exocytosis 2 (RIMS2) was hypermethylated and lowly expressed in CRC comparing to adjacent normal tissue. Hypermethylation of RIMS2 in CRC was correlated with less frequent KRAS mutant and high differentiation. RIMS2 promoter methylation showed independent predictive value for survival outcome (P = 0.015, HR 1.992, 95% CI [(1.140–3.48)]), and a combination of RIMS2 methylation with KRAS status could predict prognosis better. Conclusions RIMS2 is frequently hypermethylated in CRC, which can silence the expression of RIMS2. RIMS2 methylation is a novel biomarker for predicting the prognosis of CRC.

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