Motif-Based Contrastive Learning for Community Detection.

Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis. However, most of the existing higher order approaches are based on shallow methods, failing to capture the intricate nonlinear relationships between nodes. In order to better fuse higher order and lower order structural information, a novel deep learning framework called motif-based contrastive learning for community detection (MotifCC) is proposed. First, a higher order network is constructed based on motifs. Subnetworks are then obtained by removing isolated nodes, addressing the fragmentation issue in the higher order network. Next, the concept of contrastive learning is applied to effectively fuse various kinds of information from nodes, edges, and higher order and lower order structures. This aims to maximize the similarity of corresponding node information, while distinguishing different nodes and different communities. Finally, based on the community structure of subnetworks, the community labels of all nodes are obtained by using the idea of label propagation. Extensive experiments on real-world datasets validate the effectiveness of MotifCC.

CRP inhibits the osteoblastic differentiation of OPCs via the up-regulation of primary cilia and repression of the Hedgehog signaling pathway.

Inflammation disrupts bone metabolism and leads to bone damage. C-reactive protein (CRP) is a typical inflammation marker. Although CRP measurement has been conducted for many decades, how osteoblastic differentiation influences molecular mechanisms remains largely unknown. The present study attempted to investigate the effects of CRP on primary cultured osteoblast precursor cells (OPCs) while elucidating the underlying molecular mechanisms. OPCs were isolated from suckling Sprague-Dawleyrats. Fewer OPCs were observed after recombinant C-reactive protein treatment. In a series of experiments, CRP inhibited OPC proliferation, osteoblastic differentiation, and the OPC gene expression of the hedgehog (Hh) signaling pathway. The inhibitory effect of CRP on OPC proliferation occurred via blockade of the G1-S transition of the cell cycle. In addition, the regulation effect of proto cilium on osteoblastic differentiation was analyzed using the bioinformatics p. This revealed the primary cilia activation of recombinant CRP effect on OPCs through in vitro experiments. A specific Sonic Hedgehog signaling agonist (SAG) rescued osteoblastic differentiation inhibited by recombinant CRP. Moreover, chloral hydrate, which removes primary cilia, inhibited the Suppressor of Fused (SUFU) formation and blocked Gli2 degradation. This counteracted osteogenesis inhibition caused by CRP. Therefore, these data depict that CRP can inhibit the proliferation and osteoblastic differentiation of OPCs. The underlying mechanism could be associated with primary cilia activation and Hh pathway repression.

Gray Learning From Non-IID Data With Out-of-Distribution Samples.

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-independent and identically distributed (non-IID) datasets comprising both in-and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in-and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed gray learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics.

PDGF-BB/PDGFRß induces tumour angiogenesis via enhancing PKM2 mediated by the PI3K/AKT pathway in Wilms' tumour.

Platelet-derived growth factor receptor-ß (PDGFRß) is a critical type III receptor tyrosine kinase family member, which is involved in Wilms' tumour (WT) metastasis and aerobic glycolysis. The role of PDGFRß in tumour angiogenesis has not been fully elucidated. Here, we examined the effect of PDGFRß on angiogenesis in WT. First, the NCBI database integrated three datasets, GSE2712, GSE11151, and GSE73209, to screen differentially expressed genes. The R language was used to analyse the correlation between PDGFRB and vascular endothelial growth factor (VEGF). The results showed that PDGFRB, encoding PDGFRß, was upregulated in WT, and its level was correlated with VEGFA expression. Next, PDGFRß expression was inhibited by small interfering RNA (siRNA) or activated with the exogenous ligand PDGF-BB. The expression and secretion of the angiogenesis elated factor VEGFA in WT G401 cells were detected using Western blotting and ELISA, respectively. The effects of conditioned medium from G401 cells on endothelial cell viability, migration, invasion, the total length of the tube, and the number of fulcrums were investigated. To further explore the mechanism of PDGFRß in the angiogenesis of WT, the expression of VEGFA was detected after blocking the phosphatidylinositol-3-kinase (PI3K) pathway and inhibiting the expression of PKM2, a key enzyme of glycolysis. The results indicated that PDGFRß regulated the process of tumour angiogenesis through the PI3K/AKT/PKM2 pathway. Therefore, this study provides a novel therapeutic strategy to target PDGFRß and PKM2 to inhibit glycolysis and anti-angiogenesis, thus, developing a new anti-vascular therapy.

Bifidobacterium infantis-Mediated Herpes Simplex Virus-TK/Ganciclovir Treatment Inhibits Cancer Metastasis in Mouse Model.

Previous studies have found that Bifidobacterium infantis-mediated herpes simplex virus-TK/ganciclovir (BF-TK/GCV) reduces the expression of VEGF and CD146, implying tumor metastasis inhibition. However, the mechanism by which BF-TK/GCV inhibits tumor metastasis is not fully studied. Here, we comprehensively identified and quantified protein expression profiling for the first time in gastric cancer (GC) cells MKN-45 upon BF-TK/GCV treatment using quantitative proteomics. A total of 159 and 72 differential expression proteins (DEPs) were significantly changed in the BF-TK/GCV/BF-TK and BF-TK/GCV/BF/GCV comparative analysis. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis enriched some metastasis-related pathways such as gap junction and cell adhesion molecules pathways. Moreover, the transwell assay proved that BF-TK/GCV inhibited the invasion and migration of tumor cells. Furthermore, immunohistochemistry (IHC) demonstrated that BF-TK/GCV reduced the expression of HIF-1α, mTOR, NF-κB1-p105, VCAM1, MMP13, CXCL12, ATG16, and CEBPB, which were associated with tumor metastasis. In summary, BF-TK/GCV inhibited tumor metastasis, which deepened and expanded the understanding of the antitumor mechanism of BF-TK/GCV.

Consensus Seeking in Large-Scale Multiagent Systems With Hierarchical Switching-Backbone Topology.

Recent developments in multiagent consensus problems have heightened the role of network topology when the agent number increases largely. The existing works assume that the convergence evolution typically proceeds over a peer-to-peer architecture where agents are treated equally and communicate directly with perceived one-hop neighbors, thus resulting in slower convergence speed. In this article, we first extract the backbone network topology to provide a hierarchical organization over the original multiagent system (MAS). Second, we introduce a geometric convergence method based on the constraint set (CS) under periodically extracted switching-backbone topologies. Finally, we derive a fully decentralized framework named hierarchical switching-backbone MAS (HSBMAS) that is designed to conduct agents converge to a common stable equilibrium. Provable connectivity and convergence guarantees of the framework are provided when the initial topology is connected. Extensive simulation results on different-type and varying-density topologies have shown the superiority of the proposed framework.

Multiparty Secure Broad Learning System for Privacy Preserving.

Multiparty learning is an indispensable technique to improve the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML), a key research task in multiparty learning. Despite this, the existing PPML methods generally cannot simultaneously meet multiple requirements, such as security, accuracy, efficiency, and application scope. To deal with the aforementioned problems, in this article, we present a new PPML method based on the secure multiparty interactive protocol, namely, the multiparty secure broad learning system (MSBLS) and derive its security analysis. To be specific, the proposed method employs the interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train the neural network classifier. To the best of our knowledge, this is the first attempt for privacy computing method that jointly combines secure multiparty computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. Three classical datasets are adopted to verify our conclusion.

Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning.

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via unified and discrete bipartite graph learning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning.

Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL). In this study, we collect resting-state EEG data from 187 tinnitus patients and 80 healthy subjects to generate a high-quality large-scale EEG dataset on tinnitus diagnosis, and then apply the MECRL framework on the generated dataset to obtain a deep neural network model which can distinguish tinnitus patients from the healthy controls accurately. Subject-independent tinnitus diagnosis experiments are conducted and the result shows that the proposed MECRL method is significantly superior to other state-of-the-art baselines and can be well generalized to unseen topics. Meanwhile, visual experiments on key parameters of the model indicate that the high-classification weight electrodes of tinnitus' EEG signals are mainly distributed in the frontal, parietal and temporal regions. In conclusion, this study facilitates our understanding of the relationship between electrophysiology and pathophysiology changes of tinnitus and provides a new deep learning method (MECRL) to identify the neuronal biomarkers in tinnitus.

HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.

G3SR: Global Graph Guided Session-Based Recommendation.

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.

Higher Order Connection Enhanced Community Detection in Adversarial Multiview Networks.

Community detection in multiview networks has drawn an increasing amount of attention in recent years. Many approaches have been developed from different perspectives. Despite the success, the problem of community detection in adversarial multiview networks remains largely unsolved. An adversarial multiview network is a multiview network that suffers an adversarial attack on community detection in which the attackers may deliberately remove some critical edges so as to hide the underlying community structure, leading to the performance degeneration of the existing approaches. To address this problem, we propose a novel approach, called higher order connection enhanced multiview modularity (HCEMM). The main idea lies in enhancing the intracommunity connection of each view by means of utilizing the higher order connection structure. The first step is to discover the view-specific higher order Microcommunities (VHM-communities) from the higher order connection structure. Then, for each view of the original multiview network, additional edges are added to make the nodes in each of its VHM-communities fully connected like a clique, by which the intracommunity connection of the multiview network can be enhanced. Therefore, the proposed approach is able to discover the underlying community structure in a multiview network while recovering the missing edges. Extensive experiments conducted on 16 real-world datasets confirm the effectiveness of the proposed approach.

Multiview Clustering via Proximity Learning in Latent Representation Space.

Most existing multiview clustering methods are based on the original feature space. However, the feature redundancy and noise in the original feature space limit their clustering performance. Aiming at addressing this problem, some multiview clustering methods learn the latent data representation linearly, while performance may decline if the relation between the latent data representation and the original data is nonlinear. The other methods which nonlinearly learn the latent data representation usually conduct the latent representation learning and clustering separately, resulting in that the latent data representation might be not well adapted to clustering. Furthermore, none of them model the intercluster relation and intracluster correlation of data points, which limits the quality of the learned latent data representation and therefore influences the clustering performance. To solve these problems, this article proposes a novel multiview clustering method via proximity learning in latent representation space, named multiview latent proximity learning (MLPL). For one thing, MLPL learns the latent data representation in a nonlinear manner which takes the intercluster relation and intracluster correlation into consideration simultaneously. For another, through conducting the latent representation learning and consensus proximity learning simultaneously, MLPL learns a consensus proximity matrix with k connected components to output the clustering result directly. Extensive experiments are conducted on seven real-world datasets to demonstrate the effectiveness and superiority of the MLPL method compared with the state-of-the-art multiview clustering methods.

Incomplete Data Meets Uncoupled Case: A Challenging Task of Multiview Clustering.

Incomplete multiview clustering (IMC) methods have achieved remarkable progress by exploring the complementary information and consensus representation of incomplete multiview data. However, to our best knowledge, none of the existing methods attempts to handle the uncoupled and incomplete data simultaneously, which affects their generalization ability in real-world scenarios. For uncoupled incomplete data, the unclear and partial cross-view correlation introduces the difficulty to explore the complementary information between views, which results in the unpromising clustering performance for the existing multiview clustering methods. Besides, the presence of hyperparameters limits their applications. To fill these gaps, a novel uncoupled IMC (UIMC) method is proposed in this article. Specifically, UIMC develops a joint framework for feature inferring and recoupling. The high-order correlations of all views are explored by performing a tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on recoupled and inferred self-representation matrices. Moreover, all hyperparameters of the UIMC method are updated in an exploratory manner. Extensive experiments on six widely used real-world datasets have confirmed the superiority of the proposed method in handling the uncoupled incomplete multiview data compared with the state-of-the-art methods.

Icariin Treatment Rescues Diabetes Induced Bone Loss via Scavenging ROS and Activating Primary Cilia/Gli2/Osteocalcin Signaling Pathway.

Diabetes-associated bone complications lead to fragile bone mechanical strength and osteoporosis, aggravating the disease burden of patients. Advanced evidence shows that chronic hyperglycemia and metabolic intermediates, such as inflammatory factor, reactive oxygen species (ROS), and advanced glycation end products (AGEs), are regarded as dominant hazardous factors of bone complications, whereas the pathophysiological mechanisms are complex and controversial. By establishing a diabetic Sprague-Dawley (SD) rat model and diabetic bone loss cell model in vitro, we confirmed that diabetes impaired primary cilia and led to bone loss, while adding Icariin (ICA) could relieve the inhibitions. Mechanistically, ICA could scavenge ROS to maintain the mitochondrial and primary cilia homeostasis of osteoblasts. Intact primary cilia acted as anchoring and modifying sites of Gli2, thereby activating the primary cilia/Gli2/osteocalcin signaling pathway to promote osteoblast differentiation. All results suggest that ICA has potential as a therapeutic drug targeting bone loss induced by diabetes.

A Tensor Approach for Uncoupled Multiview Clustering.

Multiview clustering plays an important part in unsupervised learning. Although the existing methods have shown promising clustering performances, most of them assume that the data is completely coupled between different views, which is unfortunately not always ensured in real-world applications. The clustering performance of these methods drops dramatically when handling the uncoupled data. The main reason is that: 1) cross-view correlation of uncoupled data is unclear, which limits the existing multiview clustering methods to explore the complementary information between views and 2) features from different views are uncoupled with each other, which may mislead the multiview clustering methods to partition data into wrong clusters. To address these limitations, we propose a tensor approach for uncoupled multiview clustering (T-UMC) in this article. Instead of pairwise correlation, T-UMC chooses a most reliable view by view-specific silhouette coefficient (VSSC) at first, and then couples the self-representation matrix of each view with it by pairwise cross-view coupling learning. After that, by integrating recoupled self-representation matrices into a third-order tensor, the high-order correlations of all views are explored with tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN). And the view-specific local structures of each individual view are also preserved with the local structure learning scheme with manifold learning. Besides, the physical meaning of view-specific coupling matrix is also discussed in this article. Extensive experiments on six commonly used benchmark datasets have demonstrated the superiority of the proposed method compared with the state-of-the-art multiview clustering methods.

Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency.

Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on 12 multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. The code is available at https://github.com/youweiliang/Multi-view_Graph_Learning.

Refining Graph Structure for Incomplete Multi-View Clustering.

As a challenging problem, incomplete multi-view clustering (MVC) has drawn much attention in recent years. Most of the existing methods contain the feature recovering step inevitably to obtain the clustering result of incomplete multi-view datasets. The extra target of recovering the missing feature in the original data space or common subspace is difficult for unsupervised clustering tasks and could accumulate mistakes during the optimization. Moreover, the biased error is not taken into consideration in the previous graph-based methods. The biased error represents the unexpected change of incomplete graph structure, such as the increase in the intra-class relation density and the missing local graph structure of boundary instances. It would mislead those graph-based methods and degrade their final performance. In order to overcome these drawbacks, we propose a new graph-based method named Graph Structure Refining for Incomplete MVC (GSRIMC). GSRIMC avoids recovering feature steps and just fully explores the existing subgraphs of each view to produce superior clustering results. To handle the biased error, the biased error separation is the core step of GSRIMC. In detail, GSRIMC first extracts basic information from the precomputed subgraph of each view and then separates refined graph structure from biased error with the help of tensor nuclear norm. Besides, cross-view graph learning is proposed to capture the missing local graph structure and complete the refined graph structure based on the complementary principle. Extensive experiments show that our method achieves better performance than other state-of-the-art baselines.

Bacillus subtilis HG-15, a Halotolerant Rhizoplane Bacterium, Promotes Growth and Salinity Tolerance in Wheat (Triticum aestivum).

Certain plant growth-promoting bacteria (PGPB) reduce salt stress damage in plants. Bacillus subtilis HG-15 is a halotolerant bacterium (able to withstand NaCl concentrations as high as 30%) isolated from the wheat rhizoplane in the Yellow River delta. A qualitative and quantitative investigation of the plant growth-promoting characteristics of this strain confirmed nitrogen fixation, potassium dissolution, ammonia, plant hormone, ACC deaminase, and proline production abilities. B. subtilis HG-15 colonization of wheat roots, stems, and leaves was examined via scanning electron microscopy, rep-PCR, and double antibiotic screening. After inoculation with the B. subtilis HG-15 strain, the pH (1.08-2.69%), electrical conductivity (3.17-11.48%), and Na+ (12.98-15.55%) concentrations of rhizosphere soil significantly decreased (p < 0.05). Under no-salt stress (0.15% NaCl), low-salt stress (0.25% NaCl), and high-salt stress (0.35% NaCl) conditions, this strain also significantly increased (p < 0.05) the dry weight (17.76%, 24.46%, and 9.31%), fresh weight (12.80%, 20.48%, and 7.43%), plant height (7.79%, 5.86%, and 13.13%), and root length (10.28%, 17.87%, and 48.95%). Our results indicated that B. subtilis HG-15 can effectively improve the growth of wheat and elicit induced systemic tolerance in these plants, thus showing its potential as a microbial inoculant that can protect wheat under salt stress conditions.

PDGF-BB/PDGFRß promotes epithelial-mesenchymal transition by affecting PI3K/AKT/mTOR-driven aerobic glycolysis in Wilms' tumor G401 cells.

Wilms' tumor (WT) is the most common pediatric renal malignancy. PDGFRß belongs to the type III receptor tyrosine kinase family and is known to be involved in tumor metastasis and angiogenesis. Here, we studied the effect and underlying mechanism of PDGFRß on WT G401 cells. Transwell assay and wound-healing assay were used to detect the effect of PDGFRß on G401 cells invasion and migration. Western blot and immunofluorescence were used to detect the expression of EMT-related genes. The expression of PI3K/AKT/mTOR pathway proteins was detected by Western blot. The relationship between PDGFRß and aerobic glycolysis was studied by assessing the expression of glycolysis-related enzymes detected by qRT-PCR and Western blot. The activity of HK, PK, and LDH was detected by corresponding enzyme activity kits. The concentration of lactic acid and glucose was detected by Lactic Acid Assay Kit and Glucose Assay Kit-glucose oxidase method separately. To investigate the mechanism of PDGFRß in the development of WT, the changes of glucose and lactic acid were analyzed after blocking PI3K pathway, aerobic glycolysis, or PDGFRß. The key enzyme was screened by Western blot and glucose metabolism experiment after HK2, PKM2, and PDK1 were inhibited. The results showed that PDGFRß promoted the EMT process by modulating aerobic glycolysis through PI3K/AKT/mTOR pathway in which PKM2 plays a key role. Therefore, our study of the mechanism of PDGFRß in G401 cells provides a new target for the treatment of WT.