Impact of Hashimoto's thyroiditis on the tumor microenvironment in papillary thyroid cancer: insights from single-cell analysis.

This study investigates the impact of Hashimoto's thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune-stromal communication through the MIF-(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K-means clustering coefficients in copy number variation inference from bulk RNA-seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT-PTC interactions, deepening our understanding of HT's modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity-carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research.

Enhance the Proportion of Fe3+ in NiFe-Layered Double Hydroxides by utilizing Citric Acid to Improve the Efficiency and Durability of the Oxygen Evolution Reaction.

NiFe-layered double hydroxides (NiFe-LDH) are a type of catalyst known for their exceptional catalytic performance during the oxygen evolution reaction (OER). In this study, citric acid was incorporated into the synthesis process of NiFe-LDH, resulting in the NiFe-LDH-CA catalyst with superior OER performance. The catalytic efficacy is evaluated using linear sweep voltammetry (LSV), which demonstrates a significant reduction in the OER overpotential from 320 mV to 240 mV at a current density of 100 mA cm-2. X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectrum (XAS) indicate that the distribution of nickel valence states showed no significant difference between two samples, yet the NiFe-LDH-CA has a significantly higher proportion of Fe3+ ions in its iron content. In-situ Raman spectroscopes reveal that Fe3+ broadens the redox potential of nickel and Pourbaix diagrams indicate that higher Fe3+ levels could facilitate the interaction with oxygen active sites. Based on the analysis of test data, we propose a hypothesis that the high proportion of Fe3+ in catalysts may accelerate the oxygen evolution process by modulating the redox potential of nickel and engaging with reactive oxygen species. This provides valuable insights into how to improve the reaction rate of nickel-based catalysts.

Network model with internal complexity bridges artificial intelligence and neuroscience.

Artificial intelligence (AI) researchers currently believe that the main approach to building more general model problems is the big AI model, where existing neural networks are becoming deeper, larger and wider. We term this the big model with external complexity approach. In this work we argue that there is another approach called small model with internal complexity, which can be used to find a suitable path of incorporating rich properties into neurons to construct larger and more efficient AI models. We uncover that one has to increase the scale of the network externally to stimulate the same dynamical properties. To illustrate this, we build a Hodgkin-Huxley (HH) network with rich internal complexity, where each neuron is an HH model, and prove that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity.

SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT.

Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike-driven nature. Current SNNs employ "repeat coding" that re-enter all input tokens at each timestep, which fails to fully exploit temporal relationships between the tokens and introduces memory overhead. In this work, we align the number of input tokens with the timestep and refer to this input coding as "individual coding". To cope with the increase in training time for individual encoded SNNs due to the dramatic increase in timesteps, we design a Bidirectional Parallel Spiking Neuron (BPSN) with following features: First, BPSN supports spike parallel computing and effectively avoids the issue of uninterrupted firing; Second, BPSN excels in handling adaptive sequence length tasks, which is a capability that existing work does not have; Third, the fusion of bidirectional information enhances the temporal information modeling capabilities of SNNs; To validate the effectiveness of our BPSN, we present the SNN-BERT, a deep direct training SNN architecture based on the BERT model in NLP. Compared to prior repeat 4-timestep coding baseline, our method achieves a 6.46× reduction in energy consumption and a significant 16.1% improvement, raising the performance upper bound of the SNN domain on the GLUE dataset to 74.4%. Additionally, our method achieves 3.5× training acceleration and 3.8× training memory optimization. Compared with artificial neural networks of similar architecture, we obtain comparable performance but up to 22.5× energy efficiency. We would provide the codes.

ATF3 coordinates the survival and proliferation of cardiac macrophages and protects against ischemia-reperfusion injury.

Cardiac resident MerTK+ macrophages exert multiple protective roles after ischemic injury; however, the mechanisms regulating their fate are not fully understood. In the present study, we show that the GAS6-inducible transcription factor, activating transcription factor 3 (ATF3), prevents apoptosis of MerTK+ macrophages after ischemia-reperfusion (IR) injury by repressing the transcription of multiple genes involved in type I interferon expression (Ifih1 and Ifnb1) and apoptosis (Apaf1). Mice lacking ATF3 in cardiac macrophages or myeloid cells showed excessive loss of MerTK+ cardiac macrophages, poor angiogenesis and worse heart dysfunction after IR, which were rescued by the transfer of MerTK+ cardiac macrophages. GAS6 administration improved cardiac repair in an ATF3-dependent manner. Finally, we showed a negative association of GAS6 and ATF3 expression with the risk of major adverse cardiac events in patients with ischemic heart disease. These results indicate that the GAS6-ATF3 axis has a protective role against IR injury by regulating MerTK+ cardiac macrophage survival and/or proliferation.

IL-33 Facilitates Fibro-Adipogenic Progenitors to Establish the Pro-Regenerative Niche after Muscle Injury.

During the process of muscle regeneration post-injury in adults, muscle stem cells (MuSCs) function is facilitated by neighboring cells within the pro-regenerative niche. However, the precise mechanism triggering the initiation of signaling in the pro-regenerative niche remains unknown. Using single-cell RNA sequencing, 14 different muscle cells are comprehensively mapped during the initial stage following injury. Among these, macrophages and fibro-adipogenic progenitor cells (FAPs) exhibit the most pronounced intercellular communication with other cells. In the FAP subclusters, the study identifies an activated FAP phenotype that secretes chemokines, such as CXCL1, CXCL5, CCL2, and CCL7, to recruit macrophages after injury. Il1rl1, encoding the protein of the interleukin-33 (IL-33) receptor, is identified as a highly expressed signature surface marker of the FAP phenotype. Following muscle injury, autocrine IL-33, an alarmin, has been observed to activate quiescent FAPs toward this inflammatory phenotype through the IL1RL1-MAPK/NF-κB signaling pathway. Il1rl1 deficiency results in decreased chemokine expression and recruitment of macrophages, accompanied by impaired muscle regeneration. These findings elucidate a novel mechanism involving the IL-33/IL1RL1 signaling pathway in promoting the activation of FAPs and facilitating muscle regeneration, which can aid the development of therapeutic strategies for muscle-related disorders and injuries.

Embedded prompt tuning: Towards enhanced calibration of pretrained models for medical images.

Foundation models pre-trained on large-scale data have been widely witnessed to achieve success in various natural imaging downstream tasks. Parameter-efficient fine-tuning (PEFT) methods aim to adapt foundation models to new domains by updating only a small portion of parameters in order to reduce computational overhead. However, the effectiveness of these PEFT methods, especially in cross-domain few-shot scenarios, e.g., medical image analysis, has not been fully explored. In this work, we facilitate the study of the performance of PEFT when adapting foundation models to medical image classification tasks. Furthermore, to alleviate the limitations of prompt introducing ways and approximation capabilities on Transformer architectures of mainstream prompt tuning methods, we propose the Embedded Prompt Tuning (EPT) method by embedding prompt tokens into the expanded channels. We also find that there are anomalies in the feature space distribution of foundation models during pre-training process, and prompt tuning can help mitigate this negative impact. To explain this phenomenon, we also introduce a novel perspective to understand prompt tuning: Prompt tuning is a distribution calibrator. And we support it by analysing patch-wise scaling and feature separation operations contained in EPT. Our experiments show that EPT outperforms several state-of-the-art fine-tuning methods by a significant margin on few-shot medical image classification tasks, and completes the fine-tuning process within highly competitive time, indicating EPT is an effective PEFT method. The source code is available at github.com/zuwenqiang/EPT.

The target of rapamycin signaling pathway regulates vegetative development, aflatoxin biosynthesis, and pathogenicity in Aspergillus flavus.

The target of rapamycin (TOR) signaling pathway is highly conserved and plays a crucial role in diverse biological processes in eukaryotes. Despite its significance, the underlying mechanism of the TOR pathway in Aspergillus flavus remains elusive. In this study, we comprehensively analyzed the TOR signaling pathway in A. flavus by identifying and characterizing nine genes that encode distinct components of this pathway. The FK506-binding protein Fkbp3 and its lysine succinylation are important for aflatoxin production and rapamycin resistance. The TorA kinase plays a pivotal role in the regulation of growth, spore production, aflatoxin biosynthesis, and responses to rapamycin and cell membrane stress. As a significant downstream effector molecule of the TorA kinase, the Sch9 kinase regulates aflatoxin B1 (AFB1) synthesis, osmotic and calcium stress response in A. flavus, and this regulation is mediated through its S_TKc, S_TK_X domains, and the ATP-binding site at K340. We also showed that the Sch9 kinase may have a regulatory impact on the high osmolarity glycerol (HOG) signaling pathway. TapA and TipA, the other downstream components of the TorA kinase, play a significant role in regulating cell wall stress response in A. flavus. Moreover, the members of the TapA-phosphatase complexes, SitA and Ppg1, are important for various biological processes in A. flavus, including vegetative growth, sclerotia formation, AFB1 biosynthesis, and pathogenicity. We also demonstrated that SitA and Ppg1 are involved in regulating lipid droplets (LDs) biogenesis and cell wall integrity (CWI) signaling pathways. In addition, another phosphatase complex, Nem1/Spo7, plays critical roles in hyphal development, conidiation, aflatoxin production, and LDs biogenesis. Collectively, our study has provided important insight into the regulatory network of the TOR signaling pathway and has elucidated the underlying molecular mechanisms of aflatoxin biosynthesis in A. flavus.

Early matrix softening contributes to vascular smooth muscle cell phenotype switching and aortic dissection through down-regulation of microRNA-143/145.

Thoracic aortic dissection (TAD) is characterized by extracellular matrix (ECM) dysregulation. Aberrations in the ECM stiffness can lead to changes in cellular functions. However, the mechanism by which ECM softening regulates vascular smooth muscle cell (VSMCs) phenotype switching remains unclear. To understand this mechanism, we cultured VSMCs in a soft extracellular matrix and discovered that the expression of microRNA (miR)-143/145, mediated by activation of the AKT signalling pathway, decreased significantly. Furthermore, overexpression of miR-143/145 reduced BAPN-induced aortic softening, switching the VSMC synthetic phenotype and the incidence of TAD in mice. Additionally, high-throughput sequencing of immunoprecipitated RNA indicated that the TEA domain transcription factor 1 (TEAD1) is a common target gene of miR-143/145, which was subsequently verified using a luciferase reporter assay. TEAD1 is upregulated in soft ECM hydrogels in vitro, whereas the switch to a synthetic phenotype in VSMCs decreases after TEAD1 knockdown. Finally, we verified that miR-143/145 levels are associated with disease severity and prognosis in patients with thoracic aortic dissection. ECM softening, as a result of promoting the VSMCs switch to a synthetic phenotype by downregulating miR-143/145, is an early trigger of TAD and provides a therapeutic target for this fatal disease. miR-143/145 plays a role in the early detection of aortic dissection and its severity and prognosis, which can offer information for future risk stratification of patients with dissection.

Corrigendum: The role of microtubules in pancreatic cancer: therapeutic progress.

[This corrects the article DOI: 10.3389/fonc.2021.640863.].

Chitin Deacetylase Homologous Gene cda Contributes to Development and Aflatoxin Synthesis in Aspergillus flavus.

The fungal cell wall serves as the primary interface between fungi and their external environment, providing protection and facilitating interactions with the surroundings. Chitin is a vital structural element in fungal cell wall. Chitin deacetylase (CDA) can transform chitin into chitosan through deacetylation, providing various biological functions across fungal species. Although this modification is widespread in fungi, the biological functions of CDA enzymes in Aspergillus flavus remain largely unexplored. In this study, we aimed to investigate the biofunctions of the CDA family in A. flavus. The A. flavus genome contains six annotated putative chitin deacetylases. We constructed knockout strains targeting each member of the CDA family, including Δcda1, Δcda2, Δcda3, Δcda4, Δcda5, and Δcda6. Functional analyses revealed that the deletion of CDA family members neither significantly affects the chitin content nor exhibits the expected chitin deacetylation function in A. flavus. However, the Δcda6 strain displayed distinct phenotypic characteristics compared to the wild-type (WT), including an increased conidia count, decreased mycelium production, heightened aflatoxin production, and impaired seed colonization. Subcellular localization experiments indicated the cellular localization of CDA6 protein within the cell wall of A. flavus filaments. Moreover, our findings highlight the significance of the CBD1 and CBD2 structural domains in mediating the functional role of the CDA6 protein. Overall, we analyzed the gene functions of CDA family in A. flavus, which contribute to a deeper understanding of the mechanisms underlying aflatoxin contamination and lay the groundwork for potential biocontrol strategies targeting A. flavus.

Surface-Pore-Modified N-Doped Amorphous Carbon Nanospheres Tailored with Toluene as Anode Materials for Lithium-Ion Batteries.

The surface modification of amorphous carbon nanospheres (ACNs) through templates has attracted great attention due to its great success in improving the electrochemical properties of lithium storage materials. Herein, a safe methodology with toluene as a soft template is employed to tailor the nanostructure, resulting in ACNs with tunable surface pores. Extensive characterizations through transmission electron microscopy (TEM), scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption/desorption isotherms elucidate the impact of surface pore modifications on the external structure, morphology, and surface area. Electrochemical assessments reveal the enhanced performance of the surface-pore-modified carbon nanospheres, particularly ACNs-100 synthesized with the addition of 100 µL toluene, in terms of the initial discharge capacity, rate performance, and cycling stability. The interesting phenomenon of persistent capacity increase is ascribed to lithium ion movement within the graphite-like interlayer, resulting in ACNs-100 experiencing a capacity upswing from an initial 320 mAh g-1 to a zenith of 655 mAh g-1 over a thousand cycles at a rate of 2 C. The findings in this study highlight the pivotal role of tailored nanostructure engineering in optimizing energy storage materials.

Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.

By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called "Speck", a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the "dynamic imbalance" in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature.

Connectional-style-guided contextual representation learning for brain disease diagnosis.

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

Multimodal fall detection for solitary individuals based on audio-video decision fusion processing.

Falls often pose significant safety risks to solitary individuals, especially the elderly. Implementing a fast and efficient fall detection system is an effective strategy to address this hidden danger. We propose a multimodal method based on audio and video. On the basis of using non-intrusive equipment, it reduces to a certain extent the false negative situation that the most commonly used video-based methods may face due to insufficient lighting conditions, exceeding the monitoring range, etc. Therefore, in the foreseeable future, methods based on audio and video fusion are expected to become the best solution for fall detection. Specifically, this article outlines the following methodology: the video-based model utilizes YOLOv7-Pose to extract key skeleton joints, which are then fed into a two stream Spatial Temporal Graph Convolutional Network (ST-GCN) for classification. Meanwhile, the audio-based model employs log-scaled mel spectrograms to capture different features, which are processed through the MobileNetV2 architecture for detection. The final decision fusion of the two results is achieved through linear weighting and Dempster-Shafer (D-S) theory. After evaluation, our multimodal fall detection method significantly outperforms the single modality method, especially the evaluation metric sensitivity increased from 81.67% in single video modality to 96.67% (linear weighting) and 97.50% (D-S theory), which emphasizing the effectiveness of integrating video and audio data to achieve more powerful and reliable fall detection in complex and diverse daily life environments.

Multi-scale full spike pattern for semantic segmentation.

Spiking neural networks (SNNs), as the brain-inspired neural networks, encode information in spatio-temporal dynamics. They have the potential to serve as low-power alternatives to artificial neural networks (ANNs) due to their sparse and event-driven nature. However, existing SNN-based models for pixel-level semantic segmentation tasks suffer from poor performance and high memory overhead, failing to fully exploit the computational effectiveness and efficiency of SNNs. To address these challenges, we propose the multi-scale and full spike segmentation network (MFS-Seg), which is based on the deep direct trained SNN and represents the first attempt to train a deep SNN with surrogate gradients for semantic segmentation. Specifically, we design an efficient fully-spike residual block (EFS-Res) to alleviate representation issues caused by spiking noise on different channels. EFS-Res utilizes depthwise separable convolution to improve the distributions of spiking feature maps. The visualization shows that our model can effectively extract the edge features of segmented objects. Furthermore, it can significantly reduce the memory overhead and energy consumption of the network. In addition, we theoretically analyze and prove that EFS-Res can avoid the degradation problem based on block dynamical isometry theory. Experimental results on the Camvid dataset, the DDD17 dataset, and the DSEC-Semantic dataset show that our model achieves comparable performance to the mainstream UNet network with up to 31× fewer parameters, while significantly reducing power consumption by over 13×. Overall, our MFS-Seg model demonstrates promising results in terms of performance, memory efficiency, and energy consumption, showcasing the potential of deep SNNs for semantic segmentation tasks. Our code is available in https://github.com/BICLab/MFS-Seg.

One Endothelium-Targeted Combined Nucleic Acid Delivery System for Myocardial Infarction Therapy.

Acute myocardial infarction (MI) and ischemic heart disease are the leading causes of heart failure and mortality. Currently, research on MI treatment is focused on angiogenic and anti-inflammatory therapies. Although endothelial cells (ECs) are critical for triggering inflammation and angiogenesis, no approach has targeted them for the treatment of MI. In this study, we proposed a nonviral combined nucleic acid delivery system consisting of an EC-specific polycation (CRPPR-grafted ethanolamine-modified poly(glycidyl methacrylate), CPC) that can efficiently codeliver siR-ICAM1 and pCXCL12 for the treatment of MI. Animals treated with the combination therapy exhibited better cardiac function than those treated with each nucleic acid alone. In particular, the combination therapy of CPC/siR-ICAM1 and CPC/pCXCL12 significantly improved cardiac systolic function, anti-inflammatory responses, and angiogenesis compared to the control group. In conclusion, CPC-based combined gene delivery systems show impressive performance in the treatment of MI and provide a programmed strategy for the development of codelivery systems for various EC-related diseases.

Impact of Lysine Succinylation on the Biology of Fungi.

Post-translational modifications (PTMs) play a crucial role in protein functionality and the control of various cellular processes and secondary metabolites (SMs) in fungi. Lysine succinylation (Ksuc) is an emerging protein PTM characterized by the addition of a succinyl group to a lysine residue, which induces substantial alteration in the chemical and structural properties of the affected protein. This chemical alteration is reversible, dynamic in nature, and evolutionarily conserved. Recent investigations of numerous proteins that undergo significant succinylation have underscored the potential significance of Ksuc in various biological processes, encompassing normal physiological functions and the development of certain pathological processes and metabolites. This review aims to elucidate the molecular mechanisms underlying Ksuc and its diverse functions in fungi. Both conventional investigation techniques and predictive tools for identifying Ksuc sites were also considered. A more profound comprehension of Ksuc and its impact on the biology of fungi have the potential to unveil new insights into post-translational modification and may pave the way for innovative approaches that can be applied across various clinical contexts in the management of mycotoxins.

Advancing Spiking Neural Networks Toward Deep Residual Learning.

Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice. Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks, but rarely did previous work assessed their applicability to the specifics of SNNs. In this article, we first identify that this negligence leads to impeded information flow and the accompanying degradation problem in a spiking version of vanilla ResNet. To address this issue, we propose a novel SNN-oriented residual architecture termed MS-ResNet, which establishes membrane-based shortcut pathways, and further proves that the gradient norm equality can be achieved in MS-ResNet by introducing block dynamical isometry theory, which ensures the network can be well-behaved in a depth-insensitive way. Thus, we are able to significantly extend the depth of directly trained SNNs, e.g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem. To validate the effectiveness of MS-ResNet, experiments on both frame-based and neuromorphic datasets are conducted. MS-ResNet104 achieves a superior result of 76.02% accuracy on ImageNet, which is the highest to the best of our knowledge in the domain of directly trained SNNs. Great energy efficiency is also observed, with an average of only one spike per neuron needed to classify an input sample. We believe our powerful and scalable models will provide strong support for further exploration of SNNs.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC.

BACKGROUND: Long noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC). METHODS: HCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types. RESULTS: With this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients. CONCLUSION: We built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.