Colorectal cancer risk based on extended family history and body mass index

Obesity and early-onset colorectal cancer risk: emerging clinical evidence and biological mechanisms

Early-onset colorectal cancer (EOCRC) is defined as diagnosed at younger than 50 years of age and indicates a health burden globally. Patients with EOCRC have distinct risk factors, clinical characteristics, and molecular pathogenesis compared with older patients with CRC. Further investigations have identified different roles of obesity between EOCRC and late-onset colorectal cancer (LOCRC). Most studies have focused on the clinical characteristics of obesity in EOCRC, therefore, the mechanism involved in the association between obesity and EOCRC remains inconclusive. This review further states that obesity affects the carcinogenesis of EOCRC as well as its development and progression, which may lead to obesity-related metabolic syndrome, intestinal dysbacteriosis, and intestinal inflammation.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND

Colorectal cancer is a common digestive cancer worldwide. As a comprehensive treatment for locally advanced rectal cancer (LARC), neoadjuvant therapy (NT) has been increasingly used as the standard treatment for clinical stage II/III rectal cancer. However, few patients achieve a complete pathological response, and most patients require surgical resection and adjuvant therapy. Therefore, identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.

AIM

To establish effective prognostic nomograms and risk score prediction models to predict overall survival (OS) and disease-free survival (DFS) for LARC treated with NT.

METHODS

Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017. The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors, which were validated by the Cox regression method. Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves, and that of the two nomograms was conducted by calculating the concordance index (C-index) and calibration curves. The results were validated in a cohort of 65 patients from 2015 to 2017.

RESULTS

Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model: Vascular_tumors_bolt, cancer nodules, yN, body mass index, matchmouth distance from the edge, nerve aggression and postoperative carcinoembryonic antigen. The nomogram showed good predictive value for OS, with a C-index of 0.91 (95%CI: 0.85, 0.97) and good calibration. In the validation cohort, the C-index was 0.69 (95%CI: 0.53, 0.84). The risk factor prediction model showed good predictive value. The areas under the curve for 3- and 5-year survival were 0.811 and 0.782. The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77 (95%CI: 0.69, 0.85). In the validation cohort, the C-index was 0.71 (95%CI: 0.61, 0.81). The prediction model for DFS also had good predictive value, with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.

CONCLUSION

We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.

A spatial database of colorectal cancer patients and potential nutritional risk factors in an urban area in the Middle East

OBJECTIVES

Colorectal cancer (CRC) is the third most common cancer across the world that multiple risk factors together contribute to CRC development. There is a limited research report on impact of nutritional risk factors and spatial variation of CRC risk. Geographical information system (GIS) can help researchers and policy makers to link the CRC incidence data with environmental risk factor and further spatial analysis generates new knowledge on spatial variation of CRC risk and explore the potential clusters in the pattern of incidence. This spatial analysis enables policymakers to develop tailored interventions. This study aims to release the datasets, which we have used to conduct a spatial analysis of CRC patients in the city of Mashhad, Iran between 2016 and 2017.

DATA DESCRIPTION

These data include five data files. The file CRCcases_Mashhad contains the geographical locations of 695 CRC cancer patients diagnosed between March 2016 and March 2017 in the city of Mashhad. The Mashhad_Neighborhoods file is the digital map of neighborhoods division of the city and their population by age groups. Furthermore, these files include contributor risk factors including average of daily red meat consumption, average of daily fiber intake, and average of body mass index for every of 142 neighborhoods of the city.