High expression of CDKN2A is associated with poor prognosis in colorectal cancer and may guide PD-1-mediated immunotherapy.

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignancies worldwide. Immunotherapy targeting the programmed death protein 1(PD-1) and its ligand (PD-L1), is a promising treatment option for many cancers, but has exhibited poor therapeutic efficacy in CRC. This study aimed to identify and validate the prognostic value of immune-related genes and PD-1-associated genes for immunotherapy treatment of CRC. METHODS: An extensive analysis of prognostic immune-related DEGs and PD-1-related genes has highlighted CDKN2A as a vital overlapping gene. To further explore its expression in CRC and its prognostic value, we conducted qRT-PCR, Western blot experiments, and consulted various databases. Subsequently, we conducted gene expression analysis, survival and prognostic analysis, enrichment analysis, immune infiltration assessment, and TIDE analysis to assess the significance of CDKN2A. RESULTS: In CRC, CDKN2A was highly expressed compared to normal tissue. It was found that CDKN2A expression was related to clinicopathological features such as inflammation and tumor stage. Furthermore, a significant correlation was identified between CDKN2A and immune infiltration, specifically involving CD4 T cells, CD8 T cells, and macrophages. The analysis of the GSEA of CRC samples with high CDKN2A expression identified enrichment of genes involved in MYC target-v2 and metabolism pathways. Furthermore, UBE2I, CDK4, CDK6, TP53, and CCND1 were found to be significantly coexpressed with CDKN2A, suggesting a potential role that these gene play in CRC and immunotherapy. CONCLUSIONS: Our study revealed that high CDKN2A expression in CRC is a potentially valuable prognostic biomarker, which may guide PD-1-mediated immunotherapy.

Prognostic nomogram in patients with right-sided colon cancer after colectomy: a surveillance, epidemiology, and end results-based study.

Objective: This study aimed to develop and validate a nomogram for predicting overall survival (OS) in patients undergoing surgery for right-sided colon cancer (RCC). Methods: We collected 25,203 patients with RCC from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided them into 7:3 training and internal validation set. Utilizing the Cox proportional hazards regression model, we constructed a nomogram based on prognostic risk factors. Furthermore, for external validation, we retrospectively followed up with 228 patients from Jiaxing First Hospital and assessed and calibrated the nomogram using the C-index and calibration curves. Results: After identifying independent prognostic factors through univariate and multivariate analyses, a nomogram was developed. The c-index values of this nomogram differed as follows: 0.851 (95% CI: 0.845-0.857) in the training set, 0.860 (95% CI: 0.850-0.870) in the internal validation set, and 0.834 (95% CI: 0.780-0.888) in the external validation set, indicating the model's strong discriminative ability. Calibration curves for 1-year, 3-year, and 5-year overall survival (OS) probabilities exhibited a high level of consistency between predicted and actual survival rates. Furthermore, Decision Curve Analysis (DCA) demonstrated that the new model consistently outperformed the TNM staging system in terms of net benefit. Conclusion: We developed and validated a survival prediction model for patients with RCC. This novel nomogram outperforms the traditional TNM staging system and can guide clinical practitioners in making optimal clinical decisions.

Determination and Modeling on Ultraviolet Light Degradation of Pyridaben Based on Fluorescence Spectrum.

AIMS AND OBJECTIVE: Pesticide residues seriously affect human health, so it is very important to study the degradation of pesticide residues for food safety. The degradation of pyridaben by ultraviolet (UV) irradiation was studied, the degradation characteristics and modeling were analyzed in this paper. This study was undertaken to fully reveal the degradation mechanism of UV irradiation for pyridaben residue and provided the evaluation method of degradation effect. MATERIALS AND METHODS: Firstly, the fluorescence spectra of pyridaben samples were measured by LS55 fluorescence photometer, and the relationship between pyridaben concentration and the fluorescence intensity of characteristic peak was established. Then, using UV irradiation approach, the pyridaben was degraded to different degrees by controlling the irradiation time. The degradation process was characterized according to the change of fluorescence characteristic peak intensity before and after degradation. The relationship between degradation time and fluorescence intensity was established at last. RESULTS: The results showed that the fluorescence characteristic peak of pyridaben was located at 356 nm. The pyridaben content prediction model function was obtained with the correlation coefficient of 0.9989 and the average recovery of 99.70%. The relative standard deviation (RSD%), the limit of detection (LOD) and the limit of quantity (LOQ) was 1.71%, 0.0058 ug/ml and 0.0193 ug/ml, respectively. The exponential function model between UV degradation time and fluorescence intensity was obtained, the corresponding correlation coefficient was 0.9991, and the average recovery was 100.49%. CONCLUSION: UV light irradiation can effectively degrade pyridaben, degradation process can be characterized by the change of fluorescence intensity, and the degradation model was tested to be accurate.