Derivation and validation of a nomogram incorporating modifiable lifestyle factors to predict development of colorectal adenomas after negative index colonoscopy.

This retrospective cohort study aimed to identify baseline patient characteristics involving modifiable lifestyle factors that are associated with the development of colorectal adenomas, and establish and validate a nomogram for risk predictions among high-risk populations with negative index colonoscopy. A total of 83,076 participants who underwent an index colonoscopy at the Tianjin Union Medical Center between 2004 and 2019 were collected. According to meticulous inclusion and exclusion criteria, 249 subjects were enrolled and categorized into the primary and validation cohorts. Based on the primary cohort, we utilized the LASSO-Cox regression and the univariate/multivariate Cox proportional hazards (Cox-PH) regression parallelly to select variables, and incorporated selected variables into two nomogram models established using the multivariate Cox-PH regression. Comparison of the Akaike information criterion and the area under the receiver operating characteristic curve of the two models demonstrated that the nomogram model constituted by four covariates retained by the LASSO-Cox regression, including baseline age, body mass index, physical activity and family history of colorectal cancer (CRC) in first-degree relatives, performed better at predicting adenoma-free survival probabilities. Further validation including the concordance index, calibration plots, decision curve analysis and Kaplan-Meier survival curves also revealed good predictive accuracy, discriminating ability, clinical utility and risk stratification capacity of the nomogram model. Our nomogram will assist high-risk individuals with negative index colonoscopy to prevent colorectal adenoma occurrence and CRC morbidity with improved cost-effectiveness.

An Activated Dendritic-Cell-Related Gene Signature Indicative of Disease Prognosis and Chemotherapy and Immunotherapy Response in Colon Cancer Patients.

Accumulating evidence has underscored the prognostic value of tumor-infiltrating immune cells in the tumor microenvironment of colon cancer (CC). In this retrospective study, based on publicly available transcriptome profiles and clinical data from the Gene Expression Omnibus and The Cancer Genome Atlas databases, we derived and verified an activated dendritic cell (aDC)-related gene signature (aDCRS) for predicting the survival outcomes and chemotherapy and immunotherapy response of CC patients. We quantified the infiltration abundance of 22 immune cell subtypes via the "CIBERSORT" R script. Univariate Cox proportional hazards (PHs) regression was used to identify aDC as the most robust protective cell type for CC prognosis. After selecting differentially expressed genes (DEGs) significantly correlated with aDC infiltration, we performed univariate Cox-PH regression, LASSO regression, and stepwise multivariate Cox-PH regression successively to screen out prognosis-related genes from selected DEGs for constructing the aDCRS. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analysis were employed to assess the discriminatory ability and risk-stratification capacity. The "oncoPredict" package, Cancer Treatment Response gene signature DataBase, and Tumor Immune Dysfunction and Exclusion algorithm were utilized to estimate the practicability of the aDCRS in predicting response to chemotherapy and immune checkpoint blockade. Gene set enrichment analysis and single-cell RNA sequencing analysis were also implemented. Furthermore, an aDCRS-based nomogram was constructed and validated via ROC curves, calibration plots and decision curve analysis. In conclusion, aDCRS and an aDCRS-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions, thus improving the survival outcomes of CC patients in the future.