Dietary risk has always been a major risk factor for colorectal cancer (CRC). However, the contribution of dietary risk factors to CRC at the level of region, gender, and age has not been fully characterized. Based on the Global Burden of Disease (GBD) 2019 study, the death rates, age-standardized mortality rates (ASDRs), and estimated annual percentage changes (EAPCs) were calculated to assess the trends of CRC attributable to dietary risk factors over the past 30 years. Globally, the death cases of CRC increased to 1,085,797 in 2019, and the number of deaths attributed to dietary risk factors increased to 365,752 in 2019, representing approximately one-third of all CRC-related fatalities. Overall, the ASDR attributable to dietary risks was 4.61 per 100,000 in 2019, with a slight downward trend (EAPC = −0.29). Notably, there is a rising trend in early-onset colorectal cancer mortality associated with dietary factors. To alleviate CRC burdens, it is recommended to elevate the intake of whole grains, milk, calcium, and fiber while reducing consumption of red and processed meats. The results will improve the understanding, and provide guidance on the diet of CRC in different regions, gender, and age groups worldwide.

Objective: Acute myeloid leukemia (AML) is a malignant clonal disorder. Despite enormous progress in its diagnosis and treatment, the mortality rate of AML remains high. The aim of this study was to identify prognostic biomarkers by using the gene expression profile dataset from public database, and to improve the risk-stratification criteria of survival for patients with AML. Materials and methods: The gene expression data and clinical parameter were acquired from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. A total of 856 differentially expressed genes (DEGs) were obtained from the childhood AML patients classified into first complete remission (CR1) group (n=791) and not CR group (n=249). We performed a series of bioinformatics analysis to screen key genes and pathways, further comprehending these DEGs through Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Results: Six genes (SLC17A7, MSX2, CDC26, MSLN, CTSZ and DEFA3) identified by univariate, Kaplan-Meier survival and multivariate Cox regression analyses were used to develop the prognostic model. Further analysis showed that the survival estimations in the high-risk group had an increased risk of death compared with the low-risk group based on the model. The area under the curve of the receiver operator characteristic curve in the prognostic model for predicting the overall survival was 0.729, confirming good prognostic model. We also performed a nomogram to provide an individual patient with the overall probability, and internal validation in the TARGET cohort. Conclusion: We identified a six-gene prognostic signature for risk-stratifying in patients with childhood AML. The risk classification model can be used to predict CR markers and may assist clinicians in providing realize the individualized treatment in this patient population.

Purpose: Acute myeloid leukaemia (AML) is a common haematological disease in adults. The overall survival (OS) remains unsatisfactory. It is critical to identify potential prognostic biomarkers and develop a nomogram that predicts overall survival in patients with AML. Patients and Methods: We used gene expression dataset and clinical data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify differential expression analysis, survival analysis, and prognostic value of IGHD gene family (IGHDs) in AML patients. A risk score model was built through Lasso analysis and multivariate Cox regression. We also developed a nomogram and evaluated its accuracy with Harrell’s Harmony Index (C-index) and calibration curve. Last, the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database was used for external validation. Results: IGHD1-20 mRNA expression level was an independent prognostic factor for patients with AML by multivariate analysis. After Lasso analysis and multivariate Cox regression, we constructed a 3-gene model (IGHD1-1, IGHD1-20, IGHD3-16) associated with OS in AML. Risk score and age were validated as independent risk factors for prognosis and were used to build a nomogram. The C index and calibration curve results show that its ability to predict 1-year, 3-year and 5-year overall survival is accurate. Conclusion: The mRNA level of IGHDs was increased in AML patients. IGHD1-20 was an independent risk factor for OS in AML patients. The IGHDs risk model (IGHD1-1, IGHD1-20, IGHD3-16) relates to the OS of AML patients. The nomogram, including risk score and age, can conveniently and effectively predict the overall survival rate of patients.

Acute myeloid leukemia (AML) is the most common hematological malignancy. Leukemia stem cells exhibit high levels of oxidative stress, with ROS being the primary products of this stress, inducing the expression of c-JUN activation domain-binding protein 1 (Jab1). Previous studies have demonstrated that Jab1, as a transcriptional coactivator of c-JUN, promotes the malignant progression of AML under oxidative stress. However, its role in immune evasion is still under investigation. Here, we observed that knocking out Jab1 reduced the expression of immune checkpoints in vivo, effectively overcoming the immune evasion of AML. Interestingly, the deletion of Jab1 had no impact on the maturation of normal hematopoietic cells in mice. Mechanistically, Jab1 directly activated IGF2BP3 by driving the transcription factor c-JUN, consequently modulated the m ⁶ A modification of LILRB4 mRNA, and promoted immune evasion in AML. Finally, CSN5i-3 effectively disrupted the signaling pathway mediated by Jab1, thereby restoring cellular immune surveillance and halting the progression of AML. Thus, our results highlight the functional role of Jab1 in supporting AML survival and support the development of targeted therapeutic strategies.