PANoptosis is a new cell death proposed by Malireddi et al in 2019, which is characterized by pyroptosis, apoptosis and necroptosis, but cannot be explained by any of them alone. The interaction between pyroptosis, apoptosis and necroptosis is involved in PANoptosis. In this review, from the perspective of PANoptosis, we focus on the relationship between pyroptosis, apoptosis and necroptosis, the key molecules in the process of PANoptosis and the formation of PANoptosome, as well as the role of PANoptosis in diseases. We aim to understand the mechanism of PANoptosis and provide a basis for targeted intervention of PANoptosis-related molecules to treat human diseases. 

PANoptosis: A Cell Death Characterized by Pyroptosis, Apoptosis, and Necroptosis

A novel potential edge weight method for identifying influential nodes in complex networks based on neighborhood and position

Abstract Pyroptosis, apoptosis, and necroptosis are mainly programmed cell death (PCD) pathways for host defense and homeostasis. PANoptosis is a newly distinct inflammatory PCD pathway that is uniquely regulated by multifaceted PANoptosome complexes and highlights significant crosstalk and coordination among pyroptosis (P), apoptosis (A), and/or necroptosis(N). Although some studies have focused on the possible role of PANpoptosis in diseases, the pathogenesis of PANoptosis is complex and underestimated. Furthermore, the progress of PANoptosis and related agonists or inhibitors in disorders has not yet been thoroughly discussed. In this perspective, we provide perspectives on PANoptosome and PANoptosis in the context of diverse pathological conditions and human diseases. The treatment targeting on PANoptosis is also summarized. In conclusion, PANoptosis is involved in plenty of disorders including but not limited to microbial infections, cancers, acute lung injury/acute respiratory distress syndrome (ALI/ARDS), ischemia-reperfusion, and organic failure. PANoptosis seems to be a double-edged sword in diverse conditions, as PANoptosis induces a negative impact on treatment and prognosis in disorders like COVID-19 and ALI/ARDS, while PANoptosis provides host protection from HSV1 or Francisella novicida infection, and kills cancer cells and suppresses tumor growth in colorectal cancer, adrenocortical carcinoma, and other cancers. Compounds and endogenous molecules focused on PANoptosis are promising therapeutic strategies, which can act on PANoptosomes-associated members to regulate PANoptosis. More researches on PANoptosis are needed to better understand the pathology of human conditions and develop better treatment. 

Mechanisms of PANoptosis and relevant small-molecule compounds for fighting diseases

CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy

Identifying influential nodes in complex networks is an essential research issue in network science since it may attribute to understand network structure and function. Majority of existing methods have been established by considering topological characteristics of networks. In this paper, we put forward a novel algorithm based on the Local Propagation Probability (LPP) model for identifying influential nodes in complex networks. The core idea of LPP algorithm is that the nodal influence is measured by total comprehensive scores of neighbor nodes within its three level neighborhood. Specially, the comprehensive score is calculated from three dimensions, namely the propagation influence score between different order neighbors, the propagation influence score in the same order neighbors and the hierarchical structure information of nodes. To validate the performance and applicability of the proposed algorithm, LPP is compared with eight state-of-the-art and competitive algorithms on nine real-world networks. Experimental results demonstrate that LPP performs better in terms of ranking accuracy, effectiveness, top-k nodes and distinguishing ability. The low time complexity allows LPP to be applied to large-scale sparse networks. 

A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model

PANoptosis is a newly-discovered cell death pathway that involves crosstalk and co-ordination between pyroptosis, apoptosis, and necroptosis processes. However, the roles of PANoptosis-related genes (PRGs) in prognosis and immune landscape of colon cancer remain widely unknown. Here, we performed a bioinformatics analysis of expression data of nineteen PRGs identified from previous studies and clinical data of colon cancer patients obtained from TCGA and GEO databases. Colon cancer cases were divided into two PRG clusters, and prognosis-related differentially expressed genes (PRDEGs) were identified. The patient data were then separated into two corresponding distinct gene clusters, and the relationship between the risk score, patient prognosis, and immune landscape was analyzed. The identified PRGs and gene clusters correlated with patient survival and immune system and cancer-related biological processes and pathways. A prognosis signature based on seven genes was identified, and patients were divided into high-risk and low-risk groups based on the calculated risk score. A nomogram model for prediction of patient survival was also developed based on the risk score and other clinical features. Accordingly, the high-risk group showed worse prognosis, and the risk score was related to immune cell abundance, cancer stem cell (CSC) index, checkpoint expression, and response to immunotherapy and chemotherapeutic drugs. Results of quantitative real-time polymerase chain reaction (qRT-PCR) showed that LGR5 and VSIG4 were differentially expressed between normal and colon cancer samples. In conclusion, we demonstrated the potential of PANoptosis-based molecular clustering and prognostic signatures for prediction of patient survival and tumor microenvironment (TME) in colon cancer. Our findings may improve our understanding of the role of PANoptosis in colon cancer, and enable the development of more effective treatment strategies.

/pdf/panoptosis-based-molecular-clustering-and-prognostic-2p3c0y8x.pdf

PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer

Identifying influential nodes in complex networks is one of the most significant and challenging issues, which may contribute to optimizing the network structure, controlling the process of epidemic spreading and accelerating information diffusion. The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity. Moreover, they do not take into account the impact of network topology evolution over time, resulting in limitations in some applications. Based on local information of networks, a local clustering H-index (LCH) centrality measure is proposed, which considers neighborhood topology, the quantity and quality of neighbor nodes simultaneously. The proposed measure only needs the information of first-order and second-order neighbor nodes of networks, thus it has nearly linear time complexity and can be applicable to large-scale networks. In order to test the proposed measure, we adopt the susceptible-infected-recovered (SIR) and susceptible-infected (SI) models to simulate the spreading process. A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks

The identification of influential nodes is one of the most significant and challenging research issues in network science. Many centrality indices have been established starting from topological fe...

GPN: A novel gravity model based on position and neighborhood to identify influential nodes in complex networks

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PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks

GPN: A novel gravity model based on position and neighborhood to identify influential nodes in complex networks