Prediction Model for Immunotherapy Efficacy in Hepatocellular Carcinoma Based on Alternative Splicing Sequencing Data.

Background: Integrating immune checkpoint inhibitors with multi-target tyrosine kinase inhibitors presents an innovative and hopeful strategy in liver cancer treatment. Nonetheless, a degree of resistance to this treatment is noticeable in certain patients. Alternative splicing (AS) represents a common biological process that controls the variety of life functions via isoforms. Purpose: Investigating how gene AS affects the effectiveness of combined immunotherapy in treating hepatocellular carcinoma (HCC). Methods: Our retrospective examination focused on AS's effect on immune therapy effectiveness, utilizing accessible tissue sequencing and clinical records for HCC. For corroborating our results, we gathered samples of drug-resistant HCC tissue, nearby tissues, HCC tissue with high drug responsiveness, and healthy liver tissue from clinical studies. Results: The study revealed a link between the frequency of AS occurrences, the expression levels of programmed cell death 1 ligand 1, and the resistance to tumor medications. Our study detailed the AS occurrences in HCC, leading to the creation of a risk-assessment function and a predictive model using AS data. The results of our study revealed that the risk score effectively distinguished between various immune subtypes and the effectiveness of immune therapy. Additional examination of the chosen AS occurrences uncovered their effects on both the immune microenvironment and cellular immunity. Our investigation also delved into the regulatory framework of AS, uncovering the role of stringently controlled splicing factors in the emergence of tumors and the modulation of the body's immune response. Conclusions: Increased AS in HCC diminishes the efficacy of immunotherapy; conversely, more AS in peritumoral tissue elevates the likelihood of tumor immune evasion.

Experimental demonstration of wavefront reconstruction and correction techniques for variable targets based on distorted grating and deep learning.

This research presents a practical approach for wavefront reconstruction and correction adaptable to variable targets, with the aim of constructing a high-precision, general extended target adaptive optical system. Firstly, we delve into the detailed design of a crucial component, the distorted grating, simplifying the optical system implementation while circumventing potential issues in traditional phase difference-based collection methods. Subsequently, normalized fine features (NFFs) and structure focus features (SFFs) which both are independent of the imaging target but corresponded precisely to the wavefront aberration are proposed. The two features provide a more accurate and robust characterization of the wavefront aberrations. Then, a Noise-to-Denoised Generative Adversarial Network (N2D-GAN) is employed for denoising real images. And a lightweight network, Attention Mechanism-based Efficient Network (AM-EffNet), is applied to achieve efficient and high-precision mapping between features and wavefronts. A prototype of object-independent adaptive optics system is demonstrated by experimental setup, and the effectiveness of this method in wavefront reconstruction for different imaging targets has been verified. This research holds significant relevance for engineering applications of adaptive optics, providing robust support for addressing challenges within practical systems.

Performance of the neural network-based prediction model in closed-loop adaptive optics.

Adaptive optics (AO) technology is an effective means to compensate for atmospheric turbulence, but the inherent delay error of an AO system will cause the compensation phase of the deformable mirror (DM) to lag behind the actual distortion, which limits the correction performance of the AO technology. Therefore, the feed-forward prediction of atmospheric turbulence has important research value and application significance to offset the inherent time delay and improve the correction bandwidth of the AO system. However, most prediction algorithms are limited to an open-loop system, and the deployment and the application in the actual AO system are rarely reported, so its correction performance improvement has not been verified in practice. We report, to our knowledge, the first successful test of a deep learning-based spatiotemporal prediction model in an actual 3 km laser atmospheric transport AO system and compare it with the traditional closed-loop control methods, demonstrating that the AO system with the prediction model has higher correction performance.

Enhanced NK cell activation via eEF2K-mediated potentiation of the cGAS-STING pathway in hepatocellular carcinoma.

BACKGROUND: Liver cancer, particularly hepatocellular carcinoma (HCC), is characterized by a high mortality rate, attributed primarily to the establishment of an immunosuppressive microenvironment. Within this context, we aimed to elucidate the pivotal role of eukaryotic elongation factor 2 kinase (eEF2K) in orchestrating the infiltration and activation of natural killer (NK) cells within the HCC tumor microenvironment. By shedding light on the immunomodulatory mechanisms at play, our findings should clarify HCC pathogenesis and help identify potential therapeutic intervention venues. METHODS: We performed a comprehensive bioinformatics analysis to determine the functions of eEF2K in the context of HCC. We initially used paired tumor and adjacent normal tissue samples from patients with HCC to measure eEF2K expression and its correlation with prognosis. Subsequently, we enrolled a cohort of patients with HCC undergoing immunotherapy to examine the ability of eEF2K to predict treatment efficacy. To delve deeper into the mechanistic aspects, we established an eEF2K-knockout cell line using CRISPR/Cas9 gene editing. This step was crucial for verifying activation of the cGAS-STING pathway and the subsequent secretion of cytokines. To further elucidate the role of eEF2K in NK cell function, we applied siRNA-based techniques to effectively suppress eEF2K expression in vitro. For in vivo validation, we developed a tumor-bearing mouse model that enabled us to compare the infiltration and activation of NK cells within the tumor microenvironment following various treatment strategies. RESULTS: We detected elevated eEF2K expression within HCC tissues, and this was correlated with an unfavorable prognosis (30.84 vs. 20.99 months, P = 0.033). In addition, co-culturing eEF2K-knockout HepG2 cells with dendritic cells led to activation of the cGAS-STING pathway and a subsequent increase in the secretion of IL-2 and CXCL9. Moreover, inhibiting eEF2K resulted in notable NK cell proliferation along with apoptosis reduction. Remarkably, after combining NH125 and PD-1 treatments, we found a significant increase in NK cell infiltration within HCC tumors in our murine model. Our flow cytometry analysis revealed reduced NKG2A expression and elevated NKG2D expression and secretion of granzyme B, TNF-α, and IFN-γ in NK cells. Immunohistochemical examination confirmed no evidence of damage to vital organs in the mice treated with the combination therapy. Additionally, we noted higher levels of glutathione peroxidase and lipid peroxidation in the peripheral blood serum of the treated mice. CONCLUSION: Targeted eEF2K blockade may result in cGAS-STING pathway activation, leading to enhanced infiltration and activity of NK cells within HCC tumors. The synergistic effect achieved by combining an eEF2K inhibitor with PD-1 antibody therapy represents a novel and promising approach for the treatment of HCC.