• Colorectal cancer
• Predictive factors
• Recurrence
• Stage II

• Humans
• Neoplasm Staging
• Prognosis
• Retrospective Studies
• Colorectal Neoplasms/pathology
• Risk Factors
• Neoplasm Recurrence, Local/pathology

• 2020B1515020005 Guangdong Natural Science Fund for Outstanding Youth Scholars
• 81770557 National Natural Science Foundation of China
• 82070684 National Natural Science Foundation of China

IL-38, an anti-inflammatory cytokine, is a key regulator of homeostasis in host immunity. Intestinal immunity plays a critical role in defence against pathogenic invasion, as it is the largest surface organ and the most common entry point for micro-organisms. Dysregulated IL-38 activity is observed in several autoimmune diseases including systemic lupus erythematosus and atherosclerosis. The protective role of IL-38 is well illustrated in experimental colitis models, showing significantly worse colitis in IL-38 deficient mice, compared to wildtype mice. Moreover, exogenous IL-38 has been shown to ameliorate experimental colitis. Surprisingly, upregulated IL-38 is detected in inflamed tissue from inflammatory bowel disease patients, consistent with increased circulating cytokine levels, demonstrating the complex nature of host immunity in vivo. However, colonic IL-38 is significantly reduced in malignant tissues from patients with colorectal cancer (CRC), compared to adjacent non-cancerous tissue. Additionally, IL-38 expression in CRC correlates with 5-year survival, tumour size and differentiation, suggesting IL-38 plays a protective role during the development of CRC. IL-38 is also an independent biomarker for the prognosis of CRC, offering useful information in the management of CRC. Taken together, these data demonstrate the role of IL-38 in the maintenance of normal intestinal mucosal homeostasis, but that dysregulation of IL-38 contributes to initiation of chronic inflammatory bowel disease (resulting from persistent local inflammation), and that IL-38 provides protection during the development of colorectal cancer. Such data provide useful information for the development of novel therapeutic targets in the management of intestinal diseases for more precise medicine.

• Mice
• Animals
• Colitis
• Inflammatory Bowel Diseases
• Cytokines
• Colorectal Neoplasms/pathology

Background: The increasing number of young colorectal cancer (CRC) survivors has led to ongoing concerns about the risk of secondary primary malignancies (SPMs). Here, we intended to comprehensively explore the pooled standardized incidence rates (SIRs) for total and site-specific SPMs in CRC survivors with different restriction to lag period.

Methods: Pubmed, Embase, Cochrane Library, and Web of science databases were searched to identify any studies reporting the SIRs of SPM following CRC until August 2021. Total and site-specific SIRs with different restriction to lag period were pooled using fixed/random effect models.

Results: A total of 42 full-text publications with more than 1, 524, 236 CRC survivors and 166, 210 SPM patients were included in the meta-analysis. Pooled data showed an increased SIRs for all SPMs in CRC survivors with different restriction to lag period (no restriction to lag period, SIR = 1.15, 95% CI = [1.08–1.23]; 1-year lag, 1.16 [1.10–1.23]; 5-year lag, 1.18 [1.09–1.28]; 10-year lag, 1.24 [1.11–1.39]). The conclusions were consistent for neoplasms of colorectum, corpus uteri, and small intestine with different restriction to lag period. However, limited evidence was presented for associations between CRC survivors and SPM for prostate, breast (female), ovarian, stomach, urinary bladder, kidney, thyroid, bone and soft tissue.

Conclusion: CRC survivors are associated with an increased risk of SPMs, especially neoplasms of colorectum, corpus uteri, and small intestine. Further studies should explore the risks for these neoplasms in CRC survivors, thus providing the reference for future follow-up care.

• colorectal cancer
• primary tumor location
• prognosis
• tumor size
• tumor length times width (tltw)
• tumor width

• Adenocarcinoma/pathology
• Carcinoma, Renal Cell/pathology
• Colonic Neoplasms/pathology
• Colorectal Neoplasms/pathology
• Humans
• Kidney Neoplasms/pathology
• Neoplasm Staging
• Rectal Neoplasms/pathology
• Retrospective Studies

• IL-37
• IL-38
• TAM
• TAN
• colorectal cancer

• Colonoscopic surveillance
• Colorectal cancer
• Hazard function

Signet ring cell carcinoma (SRCC) is an uncommon subtype in colorectal cancer (CRC), with a short survival time. Therefore, it is imperative to establish a useful prognostic model. As a simple visual predictive tool, nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years. Until now, there has been no nomogram to predict the outcome of CRC patients with SRCC.

To build effective nomograms for predicting overall survival (OS) and cause-specific survival (CSS) of CRC patients with SRCC.

Data were extracted from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms. Performance of the nomograms was assessed by concordance index, calibration curves, and receiver operating characteristic (ROC) curves. ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis (TNM) staging system. Patients were classified as high-risk, moderate-risk, and low-risk groups using the novel nomograms. Kaplan-Meier curves were plotted to compare survival differences.

In total, 1230 patients were included. The concordance index of the nomograms for OS and CSS were 0.737 (95% confidence interval: 0.728-0.747) and 0.758 (95% confidence interval: 0.738-0.778), respectively. The calibration curves and ROC curves demonstrated good predictive accuracy. The 1-, 3-, and 5-year area under the curve values of the nomogram for predicting OS were 0.796, 0.825 and 0.819, in comparison to 0.743, 0.798, and 0.803 for the TNM staging system. In addition, the 1-, 3-, and 5-year area under the curve values of the nomogram for predicting CSS were 0.805, 0.847 and 0.863, in comparison to 0.740, 0.794, and 0.800 for the TNM staging system. Based on the novel nomograms, stratified analysis showed that the 5-year probability of survival in the high-risk, moderate-risk, and low-risk groups was 6.8%, 37.7%, and 67.0% for OS (P < 0.001), as well as 9.6%, 38.5%, and 67.6% for CSS (P < 0.001), respectively.

Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC, which are superior to the conventional TNM staging system.

• Colorectal carcinoma
• Signet ring cell carcinoma
• Nomogram
• Overall survival
• Cause-specific survival
• Prognosis

• Colorectal cancer
• Multiple cancers
• Prognosis
• Synchronous cancers

• Colonic Neoplasms
• Colorectal Neoplasms/pathology
• Colorectal Neoplasms/therapy
• Endoscopy
• Humans
• Neoplasm Staging
• Prognosis
• Retrospective Studies

• colorectal cancer
• nomograms
• predictive tool
• survival

The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.

• TNM staging
• artificial intelligence
• colon cancer
• disease-free survival
• machine learning
• prediction

• Colorectal cancer
• IL-38
• Potential target
• Prognosis

• Cancer-specific survival
• Colon cancer
• Disease-free survival
• T1 stage
• Tumor size

• China
• Colonic Neoplasms/pathology
• Humans
• Kaplan-Meier Estimate
• Neoplasm Recurrence, Local
• Neoplasm Staging
• Prognosis

• chemotherapy
• metastatic colorectal cancer
• tumor size

• colorectal cancer
• primary tumor location
• prognosis
• propensity score matching
• tumor size

• clonality
• intertumor heterogeneity
• metachronous colorectal cancer
• somatic single nucleotide variation

• Colon stenosis
• Colorectal cancer
• Multiple colorectal cancers
• Surgical resection
• Synchronous colorectal cancer

• Age Factors
• Aged
• Colonoscopy
• Colorectal Neoplasms/epidemiology
• Colorectal Neoplasms/mortality
• Colorectal Neoplasms/pathology
• Colorectal Neoplasms/surgery
• Epidemiological Monitoring
• Female
• Follow-Up Studies
• Humans
• Incidence
• Japan/epidemiology
• Logistic Models
• Male
• Middle Aged
• Neoplasm Staging
• Neoplasms, Multiple Primary/epidemiology
• Neoplasms, Multiple Primary/mortality
• Neoplasms, Multiple Primary/pathology
• Neoplasms, Multiple Primary/surgery
• Perioperative Care/methods
• Prognosis
• Retrospective Studies
• Risk Factors
• Sex Factors
• Survival Rate