TRIM50 inhibits glycolysis and the malignant progression of gastric cancer by ubiquitinating PGK1.

Ubiquitination plays a pivotal regulatory role in tumor progression. Among the components of the ubiquitin-proteasome system (UPS), ubiquitin-protein ligase E3 has emerged as a key molecule. Nevertheless, the biological functions of E3 ubiquitin ligases and their potential mechanisms orchestrating glycolysis in gastric cancer (GC) remain to be elucidated. In this study, we conducted a comprehensive transcriptomic analysis to identify the core E3 ubiquitin ligases in GC, followed by extensive validation of the expression patterns and clinical significance of Tripartite motif-containing 50 (TRIM50) both in vitro and in vivo. Remarkably, we found that TRIM50 was downregulated in GC tissues, associated with malignant progression and poor patient survival. Functionally, overexpression of TRIM50 suppressed GC cell proliferation and indirectly mitigated the invasion and migration of GC cells by inhibiting the M2 polarization of tumor-associated macrophages (TAMs). Mechanistically, TRIM50 inhibited the glycolytic pathway by ubiquitinating Phosphoglycerate Kinase 1 (PGK1), thereby directly suppressing GC cell proliferation. Simultaneously, the reduction in lactate led to diminished M2 polarization of TAMs, indirectly inhibiting the invasion and migration of GC cells. Notably, the downregulation of TRIM50 in GC was mediated by the METTL3/YTHDF2 axis in an m6A-dependent manner. In our study, we definitively identified TRIM50 as a tumor suppressor gene (TSG) that effectively inhibits glycolysis and the malignant progression of GC by ubiquitinating PGK1, thus offering novel insights and promising targets for the diagnosis and treatment of GC.

Probing the Atomic Erosion Wear Characteristics of TiC-Coated Fe in Oil and Gas Drilling Environments.

The titanium carbide (TiC) coating is considered one of the key coating materials to resist erosion wear in oil and gas drilling environments due to its excellent impact and wear resistance. Based on molecular dynamics, the erosion wear resistance of TiC coatings and the pure Fe system in the simulation of nanoindentation, scratch, and particle impact was studied at the microscale. The results indicate that TiC coatings can effectively enhance the load-bearing capacity of the Fe substrate within the critical load range and exhibit low friction characteristics and erosion resistance. However, the protection is lost after the TiC coating cracks, leading to an increase in the tangential force. In addition, when TiC coatings and pure Fe systems exhibit typical erosion characteristics of brittle materials, the TiC coating will significantly reduce the erosion wear of the substrate.

What Determines the Low-Friction Mechanism of the Silicon-Doped Diamond-like Carbon Film in a Water Environment: An Atomic-Level Understanding.

It is widely acknowledged that doping silicon can significantly enhance the friction performance of diamond-like carbon (DLC) films in a water environment. However, the mechanism of low friction caused by doped silicon is still highly controversial. Therefore, this article compares the interface interaction between DLC and Si-DLC films in a water environment through first-principles calculations of physisorption and chemisorption effects. The results indicate that water molecules are predominantly chemically adsorbed rather than physically adsorbed on the Si-DLC surface. Further study reveals that when OH-termination is formed on the Si-DLC surface, water molecules are predominantly physically adsorbed rather than chemically adsorbed on the Si-DLC hydroxylation surface. Consequently, a more stable hydration layer is formed on the surface through the hydrogen bond network formed by Si-OH groups, ultimately leading to lower friction. Moreover, molecular dynamics simulations further suggest that the lower friction coefficient of Si-DLC films in a water environment may be due to more water molecules at the friction interface and fewer interface covalent bonds. In short, the low-friction coefficient of the Si-DLC film in a water environment may be caused not only by the chemisorption of water molecules on its surface but also by the physisorption of water molecules on the Si-DLC film after surface hydroxylation.

circVAPA-rich small extracellular vesicles derived from gastric cancer promote neural invasion by inhibiting SLIT2 expression in neuronal cells.

Gastric cancer (GC) is one of the most common cancer worldwide. Neural invasion (NI) is considered as the symbiotic interaction between nerves and cancers, which strongly affects the prognosis of GC patients. Small extracellular vesicles (sEVs) play a key role in intercellular communication. However, whether sEVs mediate GC-NI remains unexplored. In this study, sEVs release inhibitor reduces the NI potential of GC cells. Muscarinic receptor M3 on GC-derived sEVs regulates their absorption by neuronal cells. The enrichment of sEV-circVAPA in NI-positive patients' serum is validated by serum high throughput sEV-circRNA sequencing and clinical samples. sEV-circVAPA promotes GC-NI in vitro and in vivo. Mechanistically, sEV-circVAPA decreases SLIT2 transcription by miR-548p/TGIF2 and inhibits SLIT2 translation via binding to eIF4G1, thereby downregulates SLIT2 expression in neuronal cells and finally induces GC-NI. Together, this work identifies the preferential absorption mechanism of GC-derived sEVs by neuronal cells and demonstrates a previously undefined role of GC-derived sEV-circRNA in GC-NI, which provides new insight into sEV-circRNA based diagnostic and therapeutic strategies for NI-positive GC patients.

A machine learning-based approach to predict energy layer for each field in spot-scanning proton arc therapy for lung cancer: A feasibility study.

BACKGROUND: Determining the optimal energy layer (EL) for each field, under considering both dose constraints and delivery efficiency, is crucial to promoting the development of proton arc therapy (PAT) technology. PURPOSE: This study aimed to explore the feasibility and potential clinical benefits of utilizing machine learning (ML) technique to automatically select EL for each field in PAT plans of lung cancer. METHODS: Proton Bragg peak position (BPP) was employed to characterize EL. The ground truth BPPs for each field were determined using the modified ELO-SPAT framework. Features in geometric, water-equivalent thicknesses (WET) and beamlet were defined and extracted. By analyzing the relationship between the extracted features and ground truth, a polynomial regression model with L2-norm regularization (Ridge regression) was constructed and trained. The performance of the regression model was reported as an error between the predictions and the ground truth. Besides, the predictions were used to make PAT plans (PAT_PRED). These plans were compared with those using the ground truth BPPs (PAT_TRUTH) and the mid-WET of the target volumes (PAT_MID) in terms of relative biological effectiveness-weighted dose (RWD) distributions. One hundred ten patients with lung cancer, a total of 7920 samples, were enrolled retrospectively, with 5940 cases randomly selected as the training set and the remaining 1980 cases as the testing set. Nine patients (648 samples) were collected additionally to evaluate the regression model in terms of plan quality and robustness. RESULTS: With regard to the prediction errors, the root mean squared errors and mean absolute errors between the ML-predicted and ground truth BPPs for the testing set were 9.165 and 6.572 mm, respectively, indicating differences of approximately two to three ELs. As for plan quality, the PAT_TRUTH and PAT_PRED plans performed similarly in terms of plan robustness, target coverage and organs at risk (OARs) protection, with differences smaller than 0.5 Gy(RBE). This trend was also observed for dose conformity and uniformity. The PAT_MID plans produced the lowest robustness index and lowest doses to OARs, along with the highest heterogeneity index, indicating better protection for OARs, improved plan robustness, but compromised dose homogeneity. Additionally, for relatively small tumor sizes, the PAT_MID plan demonstrated a notably poor dose conformity index. CONCLUSIONS: Within this cohort under investigation, our study demonstrated the feasibility of using ML technique to predict ELs for each field, offering a fast (within 2 s) and memory-efficient reduced way to select ELs for PAT plan.

Associations of dietary factors with gastric cancer risk: insights from NHANES 2003-2016 and mendelian randomization analyses.

Background: Gastric cancer (GC) continues to be one of the leading causes of cancer-related deaths globally. Diet significantly influences the incidence and progression of GC. However, the relationship between dietary intake and GC is inconsistent. Methods: A study was conducted with adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2016 to investigate possible associations between 32 dietary factors and GC. To further detect potential causal relationships between these dietary factors and the risk of GC, a two-sample Mendelian randomization (MR) analysis was conducted. The primary method employed was the inverse variance weighted (IVW) analysis, and its results were further validated by four other methods. Results: Of the 35,098 participants surveyed, 20 had a history of GC. Based on the results of weighted logistic multivariate analysis, it was observed that there was a positive correlation between total fat intake [odds ratio (OR) = 1.09, 95% confidence interval (CI): (1.01-1.17), p = 0.03] and GC as well as negative association of dietary monounsaturated fatty acids (MUFAs) intake [OR = 0.83, 95% CI: (0.76-0.92), p < 0.001]. Further evaluations of the odds of GC across the quartiles of dietary MUFAs showed that the top quartile of total MUFA intake was associated with a lower likelihood of GC in three different models [model1: OR = 0.03, 95% CI: (0.00-0.25), p < 0.01; model2: OR = 0.04, 95% CI: (0.00-0.38), p = 0.01; model3: OR = 0.04, 95% CI: (0.00-0.40), p = 0.01]. For the MR analyses, genetic instruments were selected from the IEU Open GWAS project; IVW analysis showed that GC risk was not associated with MUFAs [OR = 0.82, 95% CI: (0.59-1.14), p = 0.23] or the ratio of MUFAs to total fatty acids [OR = 1.00, 95% CI: (0.75-1.35), p = 0.98]. Similar results were observed when using the other MR methods. Conclusion: The NHANES study revealed that consuming MUFAs was linked to a lower risk of GC, although the results of MR analyses do not provide evidence of a causal relationship. Additional research is therefore necessary to clarify these findings.

Zeb1-controlled metabolic plasticity enables remodeling of chromatin accessibility in the development of neuroendocrine prostate cancer.

Cell plasticity has been found to play a critical role in tumor progression and therapy resistance. However, our understanding of the characteristics and markers of plastic cellular states during cancer cell lineage transition remains limited. In this study, multi-omics analyses show that prostate cancer cells undergo an intermediate state marked by Zeb1 expression with epithelial-mesenchymal transition (EMT), stemness, and neuroendocrine features during the development of neuroendocrine prostate cancer (NEPC). Organoid-formation assays and in vivo lineage tracing experiments demonstrate that Zeb1+ epithelioid cells are putative cells of origin for NEPC. Mechanistically, Zeb1 transcriptionally regulates the expression of several key glycolytic enzymes, thereby predisposing tumor cells to utilize glycolysis for energy metabolism. During this process, lactate accumulation-mediated histone lactylation enhances chromatin accessibility and cellular plasticity including induction of neuro-gene expression, which promotes NEPC development. Collectively, Zeb1-driven metabolic rewiring enables the epigenetic reprogramming of prostate cancer cells to license the adeno-to-neuroendocrine lineage transition.

ADORA2A-driven proline synthesis triggers epigenetic reprogramming in neuroendocrine prostate and lung cancers.

Cell lineage plasticity is one of the major causes for the failure of targeted therapies in various cancers. However, the driver and actionable drug targets in promoting cancer cell lineage plasticity are scarcely identified. Here, we found that a G protein-coupled receptor, ADORA2A, is specifically upregulated during neuroendocrine differentiation, a common form of lineage plasticity in prostate cancer and lung cancer following targeted therapies. Activation of the ADORA2A signaling rewires the proline metabolism via an ERK/MYC/PYCR cascade. Increased proline synthesis promotes deacetylases SIRT6/7-mediated deacetylation of histone H3 at lysine 27 (H3K27), and thereby biases a global transcriptional output toward a neuroendocrine lineage profile. Ablation of Adora2a in genetically engineered mouse models inhibits the development and progression of neuroendocrine prostate and lung cancers, and, intriguingly, prevents the adenocarcinoma-to-neuroendocrine phenotypic transition. Importantly, pharmacological blockade of ADORA2A profoundly represses neuroendocrine prostate and lung cancer growth in vivo. Therefore, we believe that ADORA2A can be used as a promising therapeutic target to govern the epigenetic reprogramming in neuroendocrine malignancies.

Direct and indirect effects of health expenditure on economic growth in China

Background: The main social contradictions in China have changed: the core concept is high quality development. Health care investment improves the health of residents and promotes regional economic growth. Aims: To analyse the direct and indirect economic effects of health expenditure during 2012–2018 and to test whether China’s investment in health care meets the requirements for high quality development. Method: We selected spatial panel data reflecting the input and output of health resources. We used the knowledge production function and a model of spatial economics to conduct empirical analysis of 31 provinces to show the effects of health expenditure on economic growth. Results: Economic development (LnGDP) was the dependent variable; explanatory variables included health financial input (LnHI), health personnel input (LnHR), health assets (LnCW) and health insurance expenditure (LnHIE). The regression coefficients for indirect, direct and total effects of LnHI were 0.4346, 0.0623 and 0.4970 respectively (all statistically significant). The direct effect coefficient of LnHR (0.3343) was statistically significant. The regression coefficients for the indirect and total effects were –0.6779 and –0.3436, respectively. The direct, indirect and total effect regression coefficients for LnCW and LnHIE were all statistically significant. Conclusion: Both LnHI and LnHIE positively promote economic growth within provinces and in neighbouring provinces, i.e. there are direct and indirect positive effects from investing in health care. Increasing the input of health care personnel can promote the economic growth of a province but not that of neighbouring provinces. Overall planning and coordinated development will facilitate high quality development and economic advancement.