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Abstract The clinical phenomenon whereby diffuse large B-cell lymphoma (DLBCL) occurs in patients with a history of autoimmune disease (AD) has been noted, but it remains controversial. This study aimed to evaluate the causal associations between nine ADs and DLBCL via a Mendelian randomization (MR) study. Single-nucleotide polymorphism (SNP) obtained from published genome-wide association studies (GWAS) was chosen as instrumental variable (IV). A total of nine ADs of European ancestry including asthma (56,167 cases and 352,255 controls), psoriasis (4,510 cases and 212,242 controls), autoimmune hyperthyroidism (962 cases and 172,976 controls), inflammatory bowel disease (31,665 cases and 33,977 controls), type 1 diabetes (6,683 cases and 12,173 controls), multiple sclerosis (14,498 cases and 24,091 controls), sarcoidosis (2,046 cases and 215,712 controls), ankylosing spondylitis (9,069 cases and 1,550 controls), and celiac disease (12,041 cases and 12,228 controls), were set as the exposure and DLBCL (209 cases and 218,583 controls) of European ancestry as the outcome. Inverse-variance weighted (IVW) was used as the primary analysis method, and the weighted median and MR-Egger method were used as supplementary methods. The sensitivity analyses employed in this study include the MR-Egger intercept, MR-PRESSO global test, Cochran’s Q test, leave-one-out analysis, and funnel plot. IVW showed that inflammatory bowel disease (OR = 1.241, 95% CI 1.009–1.526, P = 0.040) and autoimmune hyperthyroidism (OR = 1.464, 95% CI 1.103–1.942, P = 0.008) increased the risk of DLBCL without significant heterogeneity or horizontal pleiotropy, and the results remained stable according to the leave-one-out analysis. The IVW results revealed no associations between the other seven ADs and DLBCL: asthma (OR = 0.782, 95% CI 0.395–1.546, P = 0.159), psoriasis (OR = 0.842, 95% CI 0.669–1.060, P = 0.143), type 1 diabetes (OR = 1.071, 95% CI 0.860–1.334, P = 0.537), multiple sclerosis (OR = 1.331, 95% CI 0.941–1.883, P = 0.105), sarcoidosis (OR = 1.324, 95% CI 0.861–2.038, P = 0.200), ankylosing spondylitis (OR = 1.884, 95% CI 0.776–4.573, P = 0.161), and celiac disease (OR = 1.003, 95% CI 0.854–1.178, P = 0.969). Although no significant heterogeneity or horizontal pleiotropy was detected in these seven ADs and DLBCL, these results did not pass the leave-one-out analysis; therefore, the results need to be interpreted with caution. Inflammatory bowel disease and autoimmune hyperthyroidism may increase the onset of DLBCL. The risk of DLBCL should be considered in specific types of ADs.

Acute myeloid leukemia (AML), as the most common malignancy of the hematopoietic system, poses challenges in treatment efficacy, relapse, and drug resistance. In this study, we have utilized 151 RNA sequencing datasets, 194 DNA methylation datasets, and 200 somatic mutation datasets from the AML cohort in the TCGA database to develop a multi-omics stratification model. This model enables comparison of prognosis, clinical features, gene mutations, immune microenvironment and drug sensitivity across subgroups. External validation datasets have been sourced from the GEO database, which includes 562 mRNA datasets and 136 miRNA datasets from 984 adult AML patients. Through multi-omics-based stratification model, we classified 126 AML patients into 4 clusters (CS). CS4 had the best prognosis, with the youngest age, highest M3 subtype proportion, fewest copy number alterations, and common mutations in WT1, FLT3, and KIT genes. It showed sensitivity to HDAC inhibitors and BCL-2 inhibitors. Both the M3 subtype and CS4 were identified as independent protective factors for survival. Conversely, CS3 had the worst prognosis due to older age, high copy number alterations, and frequent mutations in RUNX1, DNMT3A, and TP53 genes. Additionally, it showed higher proportions of cytotoxic cells and Tregs, suggesting potential sensitivity to mTOR inhibitors. CS1 had a better prognosis than CS2, with more copy number alterations, while CS2 had higher monocyte proportions. CS1 showed good sensitivity to cytarabine, while CS2 was sensitive to RXR agonists. Both CS1 and CS2, which predominantly featured mutations in FLT3, NPM1, and DNMT3A genes, benefited from FLT3 inhibitors. Using the Kappa test, our stratification model underwent robust validation in the miRNA and mRNA external validation datasets. With advancements in sequencing technology and machine learning algorithms, AML is poised to transition towards multi-omics precision medicine in the future. We aspire for our study to offer new perspectives on multi-drug combination clinical trials and multi-targeted precision medicine for AML.

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma.Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents.Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity.Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as “Min exchange energy for a C-N bond” (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities.Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.