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Puberty onset through hypothalamic-pituitary-gonad (HPG) axis as an important reproductive event in postnatal development is initiated from hypothalamic arcuate nucleus (ARC). The growing evidence indicates that translational control also plays an essential role in the final expression of gonadotropin genes. To investigate the role of protein translation and behavior of ribosomes in pubertal onset, the global profiles of transcriptome, single ribosome (monosome), polysome, and tandem mass tag proteome were comprehensively investigated in rat hypothalamic ARCs of different pubertal stages using RNA sequencing, polyribo sequencing, and mass spectrum. Transcriptome-wide enrichments of N6-methyladenosine and IGF2BP2 were investigated using meRIP and RIP sequencing. Monosome was robustly enriched on a large proportion of mRNA in early puberty rats (postnatal day (PND)-25) compared to late puberty (PND-35 and PND-45). Monosome-enriched mRNAs, including HPG axis-related genes, had a large number of upstream ORFs (uORF, < 100 nt) and displayed translational repression in early puberty. Furthermore, insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) could particularly interact with and facilitate monosome to bind with mRNA in early puberty. Finally, ectopic over-expression of IGF2BP2 in hypothalamic ARC via lateral ventricle injection in vivo could recruit monosome to aggregate on mRNA and delay puberty onset. We uncovered a novel regulatory mechanism of IGF2BP2 and monosome for translational control in puberty onset, which shed light on the neuroendocrine regulatory network involved in HPG axis activation.

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Background: Gastric cancer (GC) is a gastric malignant tumor with over 1 million new cases globally each year. There are many diagnostic methods for GC, but due to the hidden early symptoms of GC, early GC is easy to be missed and misdiagnosed, which affects the follow-up treatment of patients. The early and accurate diagnosis of GC is of great significance for the treatment and survival of GC patients. Our laboratory study found that gamma-glutamyl transferase (GGT) was highly expressed in GC patients, but the mechanism of GGT family genes in the occurrence and development of GC remained to be further studied. Therefore, this study aimed to explore the mechanism of GGT family functional gene GGT5 regulating the proliferation and migration of GC cells, and provide a possible new biomarker for the early diagnosis of GC. Methods: The value of serum GGT in GC patients was first statistically analyzed. Then, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to analyze the mRNA expression of GGT5 in GC, and its clinical relationship and function. Furthermore, expression of GGT5 was reduced by lentivirus RNA interference and verified by polymerase chain reaction (PCR), Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) assays were used to detect cell proliferation after GGT5 knockdown. Scratch and Transwell assays were applied to observe cell migration after knockdown of GGT5. Finally, Western blot assays were observed to demonstrate PI3K/AKT-MAPK and MMPs expression levels after knockdown of GGT5. Results: Serum GGT was expressed at a high level in GC patients. GGT5 was highly expressed in GC tissues, and was associated with poor prognosis and clinical stage of GC. GGT5 might be involved in the regulation of vascular development and angiogenesis, as well as in the mechanisms of cell motility and migration, and it was positively correlated with the PI3K/AKT pathway. The proliferation and migration capacity of GC cells was dampened by downregulation of GGT5. GGT5 mediated proliferation and migration of GC cells by directly targeting PI3K/AKT-MAPK-MMPs pathways. Conclusions: Low expression of GGT5 reduced proliferation and migration in GC cells by modulating the PI3K/AKT-MAPK-MMPs pathway, and GGT5 might be a new target for GC.

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The healing of large skin defects remains a significant challenge in clinical settings. The lack of epidermal sources, such as autologous skin grafting, limits full-thickness skin defect repair and leads to excessive scar formation. Skin organoids have the potential to generate a complete skin layer, supporting in-situ skin regeneration in the defect area. In this study, skin organoid spheres, created with human keratinocytes, fibroblasts, and endothelial cells, showed a specific structure with a stromal core surrounded by surface keratinocytes. We selected an appropriate bioink and innovatively combined an extrusion-based bioprinting technique with dual-photo source cross-linking technology to ensure the overall mechanical properties of the 3D bioprinted skin organoid. Moreover, the 3D bioprinted skin organoid was customized to match the size and shape of the wound site, facilitating convenient implantation. When applied to full-thickness skin defects in immunodeficient mice, the 3D bioprinted human-derived skin organoid significantly accelerated wound healing through in-situ regeneration, epithelialization, vascularization, and inhibition of excessive inflammation. The combination of skin organoid and 3D bioprinting technology can overcome the limitations of current skin substitutes, offering a novel treatment strategy to address large-area skin defects.

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This article focuses on solving the secure control problem by developing a novel resilient hybrid learning scheme for discrete-time Markov jump cyber-physical systems with malicious attacks. Within the zero-sum game framework, the secure control problem is converted into solving a set of game coupled algebraic Riccati equations. However, it contains the coupling terms arising from the Markov jump parameters, which are difficult to solve. To address this issue, we propose a framework for parallel reinforcement learning. Thereafter, a model-based resilient hybrid learning scheme is first designed to obtain the optimal policies, where the system dynamics are required during the learning process. Furthermore, a novel online model-free resilient hybrid learning scheme combining the advantages of value iteration and policy iteration is proposed without using the system dynamics. Besides, the convergence of the proposed hybrid learning schemes is discussed. Eventually, the effectiveness of the designed algorithms is demonstrated with the inverted pendulum model.

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The simulation of nano-scratching on metallic substrates using smooth particle hydrodynamics (SPH) has been attempted by researchers in recent years. From a review of the existing SPH simulations of nano-scratching processes, it was found that mainly two different material constitutive models (i.e., the Johnson-Cook model and the elasto-plastic model) were employed to describe the material flow. In the majority of these investigations, the Johnson-Cook model was employed to characterise the stress flow of the material subjected to scratching. A natural question remains as to which material constitutive model is preferable for the SPH modelling of nano-scratching when quantitatively predicting the process outcomes. In this paper, a quantitative comparison of material responses during the nano-scratching of copper is reported when the process is simulated using SPH with two different constitutive material models, namely the Johnson-Cook and the elasto-plastic models. In particular, the simulated cutting and normal forces as well as the machined topography using both approaches are compared with the experimental work reported in the literature. The SPH-based simulation results in this paper are investigated based on the following three aspects: (a) cutting and normal forces with different material models and depths of the cut, (b) the effect of the cutting speed on forces and its dependence on adopted material models, and (c) the effect of adopted material models on the surface topography of machined nano-grooves. The SPH simulation results showed that using the Johnson-Cook material model, cutting and normal forces were closer to the experimental data compared to the results obtained with the elasto-plastic model. The results also showed that the cross-sectional profile of simulated nano-grooves using the Johnson-Cook model was closer to the experimental results. Overall, this paper shows that the selection of the Johnson-Cook model is preferable for the SPH modelling of the nano-scratching process.

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The Competitor, Stress Tolerator, and Ruderal (CSR) theory delineates the ecological strategies of plant species. Nevertheless, how these ecological strategies shift at the levels of individuals, functional groups and plant communities to cope with increasing nitrogen deposition remains unclear. In this study, simulated nitrogen deposition experiments were performed in high-altitude grasslands of alpine meadows and alpine steppe on the Qinghai-Tibetan Plateau (QTP) by employing the strategy and functional type framework (StrateFy) methodology to evaluate plant CSR strategies. Our results indicated that the dominant ecological strategy of the high-altitude grassland on the QTP were predominantly aligned with the R-strategy. In both alpine meadow and alpine steppe grasslands, the community-weighted mean (CWM) of C scores were increased with nitrogen addition, while CWM of R and S scores were not significantly correlated with nitrogen addition. Remarkably, the increase in C scores due to nitrogen enrichment was observed solely in non-legumes, suggesting an enhanced competitive capability of non-legumes in anticipation of future nitrogen deposition. Leymus secalinus was dominated in both alpine meadow and alpine steppe grasslands across all levels of nitrogen deposition, with increasing C scores along the nitrogen gradients. Furthermore, the sensitivity of C scores of individual plant, functional group and plant community to nitrogen deposition rates was more pronounced in alpine steppe grassland than in alpine meadow grassland. These findings furnish novel insights into the alterations of ecological strategies in high-altitude alpine grasslands on the QTP and similar regions worldwide in cope with escalating nitrogen deposition.

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Breast cancer, the most common cancer among women worldwide, lacks specific tumor markers for accurate diagnosis. Recent advances have highlighted tumor-derived exosomes as a promising non-invasive biomarker for cancer detection. Continuous monitoring of surface protein markers on exosomes in the blood could offer valuable insights for breast cancer diagnosis. However, integrating the isolation and detection of exosomes from whole blood is bulky, time-consuming, and requires professional operations. To address this difficulty, we developed a method of integrated centrifugal disk chip (CD chip) exosome enrichment directly from whole blood followed by a colorimetric visualization strategy for multiplex analysis. The disc consists of multi-chambers and multi-microchannels with immediate smartphone-enabled processing of colorimetric results. The combination of CEA + CA125 + EGFR on-chip detection could significantly differentiate the different stages of cancer in tumor-bearing mice and successfully distinguish between breast cancer patients and healthy individuals. Crucially, small volumes (100 µL) of blood samples were adequate. In addition, the chip was simple and fast, with results within 10 min, which provides immediate exosomal information through consecutive blood sampling, which could potentially result in a more timely and well-informed clinical breast cancer diagnosis.

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Introduction: In recent years, nitrogen deposition has constantly continued to rise globally. However, the impact of nitrogen deposition on the soil physicochemical properties and microbial community structure in northern Guangxi is still unclear. Methods: Along these lines, in this work, to investigate the impact of atmospheric nitrogen deposition on soil nutrient status and bacterial community in subtropical regions, four different nitrogen treatments (CK: 0 gN m-2 a-1, II: 50 gN m-2 a-1, III: 100 gN m-2 a-1, IV: 150 gNm- 2 a-1) were established. The focus was on analyzing the soil physical and chemical properties, as well as bacterial community characteristics across varying nitrogen application levels. Results and discussion: From the acquired results, it was demonstrated that nitrogen application led to a significant decrease in soil pH. Compared with CK, the pH of treatment IV decreased by 4.23%, which corresponded to an increase in soil organic carbon and total nitrogen. Moreover, compared with CK, the soil organic carbon of treatment IV increased by 9.28%, and the total nitrogen of treatment IV increased by 19.69%. However, no significant impact on the available nitrogen and phosphorus was detected. The bacterial diversity index first increased and then decreased with the increase of the nitrogen application level. The dominant phylum in the soil was Acidobacteria (34.63-40.67%), Proteobacteria, and Chloroflexi. Interestingly, the abundance of Acidobacteria notably increased with higher nitrogen application levels, particularly evident in the IV treatment group where it surpassed the control group. Considering that nitrogen addition first changes soil nutrients and then lowers soil pH, the abundance of certain oligotrophic bacteria like Acidobacteria can be caused, which showed a first decreasing and then increasing trend. On the contrary, eutrophic bacteria, such as Actinobacteria and Proteobacteria, displayed a decline. From the redundancy analysis, it was highlighted that total nitrogen and pH were the primary driving forces affecting the bacterial community composition.

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The utilization of extracellular vesicles (EVs) in wound healing has been well-documented. However, the direct administration of free EVs via subcutaneous injection at wound sites may result in the rapid dissipation of bioactive components and diminished therapeutic efficacy. Functionalized hydrogels provide effective protection, as well as ensure the sustained release and bioactivity of EVs during the wound healing process, making them an ideal candidate material for delivering EVs. In this review, we introduce the mechanisms by which EVs accelerate wound healing, and then elaborate on the construction strategies for engineered EVs. Subsequently, we discuss the synthesis strategies and application of hydrogels as delivery systems for the sustained release of EVs to enhance complicated wound healing. Furthermore, in the face of complicated wounds, functionalized hydrogels with specific wound microenvironment regulation capabilities, such as antimicrobial, anti-inflammatory, and immune regulation, used for loading engineered EVs, provide potential approaches to addressing these healing challenges. Ultimately, we deliberate on potential future trajectories and outlooks, offering a fresh viewpoint on the advancement of artificial intelligence (AI)-energized materials and 3D bio-printed multifunctional hydrogel-based engineered EVs delivery dressings for biomedical applications.

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BACKGROUND: Little is known about the effect of postnatal exposure to heavy metals on children's behavior problems. This study aimed to investigate the association between metal exposure during different stages of postnatal life and neurobehavioral outcomes in preschool children. METHODS: Urinary concentrations of six metals (arsenic, cadmium, chromium, lead, manganese, and vanadium) were measured using inductively coupled plasma mass spectrometry in 220 participants at two time points: before 1 year and at 5 years of age. Mothers completed the Child Behavior Checklist when the children were 5 years old. Multivariable linear and logistic regression analyses were used to evaluate the association between metal concentrations and behavioral outcomes. We employed Bayesian kernel machine regression (BKMR) to assess possible joint effects and potential interactions between metal mixtures and behavioral outcomes. RESULTS: Concentrations of urinary arsenic (As) in infants were associated with higher scores for anxious/shy behavior problems (ß ranging from 0.03 to 0.23). Further analyses showed that As exposure increased the odds of scores falling into the borderline or clinical range on anxious/depressed, affective, and pervasive developmental problems (ORs: 2.45-3.40). Stratification by sex indicated significance in girls but not in boys. BKMR analysis showed that, among the metal mixtures, As displayed a major effect on behavior scores. Concentrations of urinary cadmium in infants were also associated with higher behavioral scores but did not increase the risk of clinical problems. A cross-sectional survey in 5-year-olds did not show a significant association between concurrent metal exposure and behavioral outcome. CONCLUSION: Our results showed that exposure to As and Cd during infancy was associated with emotional problems in children. The effect of arsenic exposure was more pronounced among female infants. We suggest reducing exposure to toxic metals during early postnatal life to prevent behavioral problems in children."

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The behavioral sensitization, characterized by escalated behavioral responses triggered by recurrent exposure to psychostimulants, involves neurobiological mechanisms that are brain-region and cell-type specific. Enduring neuroadaptive changes have been observed in response to methamphetamine (METH) within the orbitofrontal cortex (OFC), the cell-type specific transcriptional alterations in response to METH sensitization remain understudied. In this study, we utilized Single-nucleus RNA-sequencing (snRNA-seq) to profile the gene expression changes in the OFC of a rat METH sensitization model. The analyses of differentially expressed genes (DEGs) unveiled cell-type specific transcriptional reactions associated with METH sensitization, with the most significant alterations documented in microglial cells. Bioinformatic investigations revealed that distinct functional and signaling pathways enriched in microglia-specific DEGs majorly involved in macroautophagy processes and the activation of N-methyl-D-aspartate ionotropic glutamate receptors (NMDAR). To validate the translational relevance of our findings, we analyzed our snRNA-seq data in conjunction with a transcriptomic study of individuals with opioid use disorder (OUD) and a large-scale Genome-Wide Association Studies (GWAS) from multiple externalizing phenotypes related to drug addiction. The validation analysis confirmed the consistent expression changes of key microglial DEGs in human METH addiction. Moreover, the integration with GWAS data revealed associations between addiction risk genes and the DEGs observed in specific cell types, particularly microglia and excitatory neurons. Our study highlights the importance of cell-type specific transcriptional alterations in the OFC in the context of METH sensitization and their potential translational relevance to human drug addiction.

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Synovitis is characterized by a distinctmetabolic profile featuring the accumulation of lactate, a byproduct of cellular metabolism within inflamed joints. This study reveals that the activation of the CD31 signal by lactate instigates a metabolic shift, specifically initiating endothelial cell autophagy. This adaptive process plays a pivotal role in fulfilling the augmented energy and biomolecule demands associated with the formation of new blood vessels in the synovium of Rheumatoid Arthritis (RA). Additionally, the amino acid substitutions in the CD31 cytoplasmic tail at the Y663F and Y686F sites of the immunoreceptor tyrosine-based inhibitory motifs (ITIM) alleviate RA. Mechanistically, this results in the downregulation of glycolysis and autophagy pathways. These findings significantly advance our understanding of potential therapeutic strategies for modulating these processes in synovitis and, potentially, other autoimmune diseases.

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In confronting the global prevalence of bone-related disorders, bone tissue engineering (BTE) has developed into a critical discipline, seeking innovative materials to revolutionize treatment paradigms. Quantum dots (QDs), nanoscale semiconductor particles with tunable optical properties, are at the cutting edge of improving bone regeneration. This comprehensive review delves into the multifaceted roles that QDs play within the realm of BTE, emphasizing their potential to not only revolutionize imaging but also to osteogenesis, drug delivery, antimicrobial strategies and phototherapy. The customizable nature of QDs, attributed to their size-dependent optical and electronic properties, has been leveraged to develop precise imaging modalities, enabling the visualization of bone growth and scaffold integration at an unprecedented resolution. Their nanoscopic scale facilitates targeted drug delivery systems, ensuring the localized release of therapeutics. QDs also possess the potential to combat infections at bone defect sites, preventing and improving bacterial infections. Additionally, they can be used in phototherapy to stimulate important bone repair processes and work well with the immune system to improve the overall healing environment. In combination with current trendy artificial intelligence (AI) technology, the development of bone organoids can also be combined with QDs. While QDs demonstrate considerable promise in BTE, the transition from laboratory research to clinical application is fraught with challenges. Concerns regarding the biocompatibility, long-term stability of QDs within the biological environment, and the cost-effectiveness of their production pose significant hurdles to their clinical adoption. This review summarizes the potential of QDs in BTE and highlights the challenges that lie ahead. By overcoming these obstacles, more effective, efficient, and personalized bone regeneration strategies will emerge, offering new hope for patients suffering from debilitating bone diseases.

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Acrolein is a widespread contaminant found in both diet and environment, entering the human body through food, alcohol, smoking, and exposure to fuel combustion fumes. While prior studies have highlighted acrolein's harmful impact on oocyte quality and early embryonic development in vitro, the specific mechanisms by which acrolein affects the female reproductive system in vivo remain poorly understood. This study first confirmed that in vitro acrolein exposure disrupts spindle morphology and chromosome alignment during the mid-MI stage of oocyte development, thus hindering oocyte maturation. Besides, exposure to acrolein not only stunts growth in mice but also impairs ovarian development, decreases the ovarian coefficient, disrupts follicular development, and increases the count of atretic follicles in vivo. Additional research has shown that acrolein exposure reduces the activity of key enzymes in glycolysis, pyruvate metabolism, and the tricarboxylic acid cycle within the ovaries. It also suppresses mitochondrial complex expression and disturbs the balance between mitochondrial fission and fusion, as confirmed by metabolomic analyses. Moreover, acrolein exposure in vivo induced granulosa cell apoptosis and reduced oocyte number. In summary, acrolein exposure impairs glucose metabolism and induces mitochondrial dysfunction in the ovaries.

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Patients with diabetes often experience fragile fractures despite normal or higher bone mineral density (BMD), a phenomenon termed the diabetic bone paradox (DBP). The pathogenesis and therapeutics opinions for diabetic bone disease (DBD) are not fully explored. In this study, we utilize two preclinical diabetic models, the leptin receptor-deficient db/db mice (DB) mouse model and the streptozotocin-induced diabetes (STZ) mouse model. These models demonstrate higher BMD and lower mechanical strength, mirroring clinical observations in diabetic patients. Advanced glycation end products (AGEs) accumulate in diabetic bones, causing higher non-enzymatic crosslinking within collagen fibrils. This inhibits intrafibrillar mineralization and leads to disordered mineral deposition on collagen fibrils, ultimately reducing bone strength. Guanidines, inhibiting AGE formation, significantly improve the microstructure and biomechanical strength of diabetic bone and enhance bone fracture healing. Therefore, targeting AGEs may offer a strategy to regulate bone mineralization and microstructure, potentially preventing the onset of DBD.

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Background: In some patients, persistent gastrointestinal symptoms like abdominal pain, nausea, and diarrhea occur as part of long COVID-19 syndrome following acute respiratory symptoms caused by SARS-CoV-2. However, the characteristics of immune cells in the gastrointestinal tract of COVID-19 patients and their association with these symptoms remain unclear. Methodology: Data were collected from 95 COVID-19 patients. Among this cohort, 11 patients who exhibited gastrointestinal symptoms and underwent gastroscopy were selected. Using imaging mass cytometry, the gastrointestinal tissues of these patients were thoroughly analyzed to identify immune cell subgroups and investigate their spatial distribution. Results: Significant acute inflammatory responses were found in the gastrointestinal tissues, particularly in the duodenum, of COVID-19 patients. These alterations included an increase in the levels of CD68+ macrophages and CD3+CD4+ T-cells, which was more pronounced in tissues with nucleocapsid protein (NP). The amount of CD68+ macrophages positively correlates with the number of CD3+CD4+ T-cells (R = 0.783, p < 0.001), additionally, spatial neighborhood analysis uncovered decreased interactions between CD68+ macrophages and multiple immune cells were noted in NP-positive tissues. Furthermore, weighted gene coexpression network analysis was employed to extract gene signatures related to clinical features and immune responses from the RNA-seq data derived from gastrointestinal tissues from COVID-19 patients, and we validated that the MEgreen module shown positive correlation with clinical parameter (i.e., Total bilirubin, ALT, AST) and macrophages (R = 0.84, p = 0.001), but negatively correlated with CD4+ T cells (R = -0.62, p = 0.004). By contrast, the MEblue module was inversely associated with macrophages and positively related with CD4+ T cells. Gene function enrichment analyses revealed that the MEgreen module is closely associated with biological processes such as immune response activation, signal transduction, and chemotaxis regulation, indicating its role in the gastrointestinal inflammatory response. Conclusion: The findings of this study highlight the role of specific immune cell groups in the gastrointestinal inflammatory response in COVID-19 patients. Gene coexpression network analysis further emphasized the importance of the gene modules in gastrointestinal immune responses, providing potential molecular targets for the treatment of COVID-19-related gastrointestinal symptoms.

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A novel reinforcement learning-based predefined-time tracking control scheme with prescribed performance is presented in this article for nonlinear systems in the presence of external disturbances. First, by employing the backstepping strategy, an adaptive optimized controller is developed under the identifier-critic-actor framework. Therein, the unknown nonlinear dynamics and the system control behavior can be learned effectively through neural networks. Moreover, aiming at obtaining the preset tracking performance, the prescribed performance control is integrated with the predefined-time control. In contrast to previous studies, the proposed scheme can not only constrain the tracking error rapidly to a prearranged vicinity of origin, but also ensure that the upper bound of convergence time can be adjusted in advance via a separate control parameter. In terms of the predefined-time stability theory, the boundedness of all system states can be proven within a predefined time. Finally, the availability and improved performances of the proposed control scheme are demonstrated by a numerical example and a single-link manipulator example.

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PURPOSE: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT). MATERIALS AND METHODS: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy. RESULTS: In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD. CONCLUSION: Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

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TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels.

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