Characterization of PANoptosis patterns predicts survival and immunotherapy response in gastric cancer

PANoptosis signaling enables broad immune response in psoriasis: From pathogenesis to new therapeutic strategies

Background

Accumulating evidence suggests that regulated cell death, such as pyroptosis, apoptosis, and necroptosis, is deeply involved in the pathogenesis of psoriasis. As a newly recognized form of systematic cell death, PANoptosis is involved in a variety of inflammatory disorders through amplifying inflammatory and immune cascades, but its role in psoriasis remains elusive.

Objectives

To reveal the role of PANoptosis in psoriasis for a potential therapeutic strategy.

Methods

Multitranscriptomic analysis and experimental validation were used to identify PANoptosis signaling in psoriasis. RNA-seq and scRNA-seq analyses were performed to establish a PANoptosis-mediated immune response in psoriasis, which revealed hub genes through WGCNA and predicted disulfiram as a potential drug. The effect and mechanism of disulfiram were verified in imiquimod (IMQ)-induced psoriasis.

Results

Here, we found a highlighted PANoptosis signature in psoriasis patients through multitranscriptomic analysis and experimental validation. Based on this, two distinct PANoptosis patterns (non/high) were identified, which were the options for clinical classification. The high-PANoptosis-related group had a higher response rate to immune cell infiltration (such as M1 macrophages and keratinocytes). Subsequently, WGCNA showed the hub genes (e.g., S100A12, CYCS, NOD2, STAT1, HSPA4, AIM2, MAPK7), which were significantly associated with clinical phenotype, PANoptosis signature, and identified immune response in psoriasis. Finally, we explored disulfiram (DSF) as a candidate drug for psoriasis through network pharmacology, which ameliorated IMQ-mediated psoriatic symptoms through antipyroptosis-mediated inflammation and enhanced apoptotic progression. By analyzing the specific ligand-receptor interaction pairs within and between cell lineages, we speculated that DSF might exert its effects by targeting keratinocytes directly or targeting M1 macrophages to downregulate the proliferation of keratinocytes.

Conclusions

PANoptosis with its mediated immune cell infiltration provides a roadmap for research on the pathogenesis and therapeutic strategies of psoriasis.

RCDdb: A manually curated database and analysis platform for regulated cell death

Regulated cell death is a pivotal regulatory mechanism governing the development and homeostasis of multicellular organisms. A comprehensive understanding of RCD's regulatory mechanisms is crucial for developing novel therapeutic strategies against diseases associated with cell death, such as cancer and neurodegenerative diseases. However, existing data repositories support limited types of cell death data and lack comprehensive annotation and analytical functionalities. Thus, establishing an extensive cell death database is an urgent imperative. To address this gap, we developed the Regulated Cell Death Database (RCDdb, chenyclab.com/RCDdb), the first comprehensively manually annotated database designed to support annotations and analytical capabilities across all RCD types. We compiled 3090 marker gene annotations associated with 15 RCD types from 2180 relevant articles. The RCDdb includes annotation data on these marker genes concerning diseases, drugs, pathways, proteins, and gene expressions. Furthermore, it provides 49 diverse visualization methods to present this information. More importantly, the RCDdb features three online analysis tools for identifying and analyzing RCD-related features within user-submitted data. Furthermore, the RCDdb offers a user-friendly interface for querying, browsing, analysis, and visualization of detailed information associated with each RCD category. This resource promises to significantly aid researchers in better understanding the mechanisms of cell death, thereby accelerating progress in research and therapeutic strategies aimed at combating RCD-related diseases.

NLRC4, inflammation and colorectal cancer (Review)

Chronic inflammation is recognized as a major risk factor for cancer and is involved in every phase of the disease. Inflammasomes are central to the inflammatory response and play a crucial role in cancer development. The present review summarizes the role of Nod‑like receptor C4 (NLRC4) in inflammation and colorectal cancer (CRC). Reviews of the literature were conducted using Web of Science, PubMed and CNKI, with search terms including 'NLRC4', 'colorectal cancer', 'auto‑inflammatory diseases' and 'prognosis'. Variants of NLRC4 can cause recessive immune dysregulation and autoinflammation or lead to ulcerative colitis as a heterozygous risk factor. Additionally, genetic mutations in inflammasome components may increase susceptibility to cancer. NLRC4 is considered a tumor suppressor in CRC. The role of NLRC4 in CRC signaling pathways is currently understood to involve five key aspects (caspase 1, NLRP3/IL‑8, IL‑1β/IL‑1, NAIP and p53). The mechanisms by which NLRC4 is involved in CRC are considered to be threefold (through pyroptosis, apoptosis, necroptosis and PANoptosis; regulating the immune response; and protecting intestinal epithelial cells to prevent CRC). However, the impact of NLRC4 mutations on CRC remains unclear. In conclusion, NLRC4 is a significant inflammasome that protects against CRC through various signaling pathways and mechanisms. The association between NLRC4 mutations and CRC warrants further investigation.

Identification of a novel PANoptosis-related gene signature for predicting the prognosis in clear cell renal cell carcinoma

PANoptosis has been shown to play an important role in tumorigenesis and gain more attention. Yet, the prognostic significance of PANoptosis-related genes has not been investigated more in clear cell renal cell carcinoma (ccRCC). The aim of this research was designed to identify and create a signature of PANoptosis-related genes which was expected to predict prognosis of ccRCC more effectively. The transcriptome data and clinical information were collected from The Cancer Genome Atlas database and the Gene Expression Omnibus database. Optimal differentially expressed PANoptosis-related genes, which were closely associated with prognosis and employed to construct a risk score, were extracted by univariate Cox analysis, least absolute shrinkage and selection operator Cox regression and multivariate Cox analysis. We performed Kaplan-Meier survival analysis and time-dependent receiver operating characteristic curves to complete this process. By adopting univariate and multivariate analysis, the constructed risk score was assessed to verify whether it could be taken as an independent contributor for prognosis. Moreover, we created a nomogram in order to predict overall survival (OS) of ccRCC. Five differentially expressed PANoptosis-related genes were screened out and used to construct a risk score. Our results showed that ccRCC patients with high risk score had a poor prognosis and shorter OS. The results of Kaplan-Meier curves and the area under the receiver operating characteristic curves of 1-, 3-, and 5-year OS indicated that the prediction performance was satisfactory. Additionally, the risk model could be taken as an independent prognostic factor in training and validation cohorts. The nomogram exhibited excellent reliability in predicting OS, which was validated by calibration curves. We identified 5 PANoptosis-related genes, which were used to construct a risk score and a nomogram for prognostic prediction with reliable predictive capability. The present study may provide new potential therapeutic targets and precise treatment strategies for ccRCC.

Significance of novel PANoptosis genes to predict prognosis and therapy effect in the lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the dominant histotype of non-small cell lung cancer. Panoptosis, a comprehensive form of programmed cell death, is central to carcinogenesis. In this study, the expression of PANoptosis-related genes (PRGs) and their impact on the development, prognosis, tumor microenvironment, and treatment response of patients with lung adenocarcinoma (LUAD) were systematically evaluated. PRGs were selected from The Cancer Genome Atlas database and Genecards dataset using differential expression analysis. The signature of included PRGs was identified using univariate Cox regression analysis and LASSO regression analysis. Additionally, a nomogram was developed that includes signature and clinical information. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to assess the predictive validity of these risk models. Finally, functional analysis of the selected PRGs in signature and analysis of immune landscape were also performed. Preliminary identification of 10 genes related to PANoptosis has significant implications for prognosis. Subsequently, seven related genes were integrated to classify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and the TNM stage were as independent prognostic factors and were utilized in creating a nomogram plot. Both the features and the nomogram plot showed accurate performance in predicting the overall survival of LUAD patients. The PRGs were enriched in several biological functions and pathways, and stratified studies were conducted on the differences in immune landscape between high-risk and low-risk groups based on their characteristics. Ultimately, our evaluation focused on the differences in drug treatment efficacy between the high-risk and low-risk groups, providing a foundation for future research directions. Potential associations between PRGs and patient prognosis in LUAD have been identified in this study. Potential biomarkers for clinical application could be considered for the prognostic predictors identified in this study.

Ultrasmall Enzyodynamic PANoptosis Nano-Inducers for Ultrasound-Amplified Hepatocellular Carcinoma Therapy and Lung Metastasis Inhibition

Addressing the inefficiency of current therapeutic approaches for hepatocellular carcinoma is an urgent and pressing challenge. PANoptosis, a form of inflammatory programmed cell death, presents a dependable strategy for combating cancer by engaging multiple cell death pathways (apoptosis, pyroptosis, and necroptosis). In this study, an ultrasmall Bi2Sn2O7 nanozyme with ultrasound-magnified multienzyme-mimicking properties is designed and engineered as a PANoptosis inducer through destroying the mitochondrial function of tumor cells and enhancing the intracellular accumulation of toxic reactive oxygen species, finally triggering the activation of PANoptosis process. The role of PANoptosis inducer has been verified by the expression of related proteins, including cleaved Caspase 3, NLRP3, N-GSDMD, cleaved Caspase 1, p-MLKL, and RIPK3. The inclusion of external ultrasonic irradiation significantly augments the enzyodynamic therapeutic efficiency. In vitro and in vivo antineoplastic efficacy, along with inhibition of lung metastasis, validate the benefits of the Bi2Sn2O7-mediated PANoptosis pathway. This study not only elucidates the intricate mechanisms underlying Bi2Sn2O7 as a PANoptosis inducer, but also offers a novel perspective for the treatment of hepatocellular carcinoma.

Constructing and validating pan-apoptosis-related features for predicting prognosis and immunotherapy response in hepatocellular carcinoma

The study aimed to develop a prognostic model for Hepatocellular Carcinoma (HCC) based on pan-apoptosis-related genes, a novel inflammatory programmed cell death form intricately linked to HCC progression. Utilizing transcriptome sequencing and clinical data from the TCGA database, we identified six crucial pan-apoptosis-related genes through statistical analyses. These genes were then employed to construct a prognostic model that accurately predicts overall survival rates in HCC patients. Our findings revealed a strong correlation between the model's risk scores and tumor microenvironment (TME) status, immune cell infiltration, and immune checkpoint expression. Furthermore, we screened for drugs with potential therapeutic efficacy in high- and low-risk HCC groups. Notably, PPP2R5B gene knockdown was found to inhibit HCC cell proliferation and clonogenic capacity, suggesting its role in HCC progression. In conclusion, this study presents a novel pan-apoptosis gene-based prognostic risk model for HCC, providing valuable insights into patient TME status and guiding the selection of targeted therapies and immunotherapies.

Identification of signature genes and immune infiltration analysis in thyroid cancer based on PANoptosis related genes

Background

Thyroid cancer is the most common malignancy of the endocrine system. PANoptosis is a specific form of inflammatory cell death. It mainly includes pyroptosis, apoptosis and necrotic apoptosis. There is increasing evidence that PANoptosis plays a crucial role in tumour development. However, no pathogenic mechanism associated with PANoptosis in thyroid cancer has been identified.

Methods

Based on the currently identified PANoptosis genes, a dataset of thyroid cancer patients from the GEO database was analysed. To screen the common differentially expressed genes of thyroid cancer and PANoptosis. To analyse the functional characteristics of PANoptosis-related genes (PRGs) and screen key expression pathways. The prognostic model was established by LASSO regression and key genes were identified. The association between hub genes and immune cells was evaluated based on the CIBERSORT algorithm. Predictive models were validated by validation datasets, immunohistochemistry as well as drug-gene interactions were explored.

Results

The results showed that eight key genes (NUAK2, TNFRSF10B, TNFRSF10C, TNFRSF12A, UNC5B, and PMAIP1) exhibited good diagnostic performance in differentiating between thyroid cancer patients and controls. These key genes were associated with macrophages, CD4+ T cells and neutrophils. In addition, PRGs were mainly enriched in the immunomodulatory pathway and TNF signalling pathway. The predictive performance of the model was confirmed in the validation dataset. The DGIdb database reveals 36 potential therapeutic target drugs for thyroid cancer.

Conclusion

Our study suggests that PANoptosis may be involved in immune dysregulation in thyroid cancer by regulating macrophages, CD4+ T cells and activated T and B cells and TNF signalling pathways. This study suggests potential targets and mechanisms for thyroid cancer development.

PANoptosis: bridging apoptosis, pyroptosis, and necroptosis in cancer progression and treatment

This comprehensive review explores the intricate mechanisms of PANoptosis and its implications in cancer. PANoptosis, a convergence of apoptosis, pyroptosis, and necroptosis, plays a crucial role in cell death and immune response regulation. The study delves into the molecular pathways of each cell death mechanism and their crosstalk within PANoptosis, emphasizing the shared components like caspases and the PANoptosome complex. It highlights the significant role of PANoptosis in various cancers, including respiratory, digestive, genitourinary, gliomas, and breast cancers, showing its impact on tumorigenesis and patient survival rates. We further discuss the interwoven relationship between PANoptosis and the tumor microenvironment (TME), illustrating how PANoptosis influences immune cell behavior and tumor progression. It underscores the dynamic interplay between tumors and their microenvironments, focusing on the roles of different immune cells and their interactions with cancer cells. Moreover, the review presents new breakthroughs in cancer therapy, emphasizing the potential of targeting PANoptosis to enhance anti-tumor immunity. It outlines various strategies to manipulate PANoptosis pathways for therapeutic purposes, such as targeting key signaling molecules like caspases, NLRP3, RIPK1, and RIPK3. The potential of novel treatments like immunogenic PANoptosis-initiated therapies and nanoparticle-based strategies is also explored.

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

Background

Thyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet.

Methods

We identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas.

Results

A LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue.

Conclusion

A potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Cell death shapes cancer immunity: spotlighting PANoptosis

PANoptosis represents a novel type of programmed cell death (PCD) with distinctive features that incorporate elements of pyroptosis, apoptosis, and necroptosis. PANoptosis is governed by a newly discovered cytoplasmic multimeric protein complex known as the PANoptosome. Unlike each of these PCD types individually, PANoptosis is still in the early stages of research and warrants further exploration of its specific regulatory mechanisms and primary targets. In this review, we provide a brief overview of the conceptual framework and molecular components of PANoptosis. In addition, we highlight recent advances in the understanding of the molecular mechanisms and therapeutic applications of PANoptosis. By elucidating the complex crosstalk between pyroptosis, apoptosis and necroptosis and summarizing the functional consequences of PANoptosis with a special focus on the tumor immune microenvironment, this review aims to provide a theoretical basis for the potential application of PANoptosis in cancer therapy.

Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update

Many studies on gastric cancer treatment have identified predictors of immunotherapy benefits. This article provides an update on the major developments in research related to predictive factors of immunotherapy for gastric cancer. We used the search term "predictive factors, immunotherapy, gastric cancer" to find the most current publications in the PubMed database related to predictive factors of immunotherapy in gastric cancer. Programmed cell death, genetic, and immunological factors are the main study topics of immunotherapy's predictive factors in gastric cancer. Other preventive factors for immunotherapy in gastric cancer were also found, including clinical factors, tumor microenvironment factors, imaging factors, and extracellular factors. Since there is currently no effective treatment for gastric cancer, we strongly propose that these studies be prioritized.

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

Targeting regulated cell death (RCD) in hematological malignancies: Recent advances and therapeutic potential

Regulated cell death (RCD) is a form of cell death that can be regulated by numerous biomacromolecules. Accumulating evidence suggests that dysregulated expression and altered localization of related proteins in RCD promote the development of cancer. Targeting subroutines of RCD with pharmacological small-molecule compounds is becoming a promising therapeutic avenue for anti-tumor treatment, especially in hematological malignancies. Herein, we summarize the aberrant mechanisms of apoptosis, necroptosis, pyroptosis, PANoptosis, and ferroptosis in hematological malignancies. In particular, we focus on the relationship between cell death and tumorigenesis, anti-tumor immunotherapy, and drug resistance in hematological malignancies. Furthermore, we discuss the emerging therapeutic strategies targeting different RCD subroutines. This review aims to summarize the significance and potential mechanisms of RCD in hematological malignancies, along with the development and utilization of pertinent therapeutic strategies.

Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine

Background

Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC.

Methods

Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation.

Results

Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts.

Conclusion

This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00362-8.

Utilizing a novel model of PANoptosis-related genes for enhanced prognosis and immune status prediction in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is the most common histopathologic type of renal cell carcinoma. PANoptosis, a cell death pathway that involves an interplay between pyroptosis, apoptosis and necroptosis, is associated with cancer immunity and development. However, the prognostic significance of PANoptosis in KIRC remains unclear. RNA-sequencing expression and mutational profiles from 532 KIRC samples and 72 normal samples with sufficient clinical data were retrieved from the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using differentially expressed genes (DEGs) related to PANoptosis in the TCGA cohort and was validated in a Gene Expression Omnibus (GEO) cohorts. Incorporating various clinical features, the risk model remained an independent prognostic factor in multivariate analysis, and it demonstrated superior performance compared to unsupervised clustering of the 21 PANoptosis-related genes alone. Further mutational analysis showed fewer VHL and more BAP1 alterations in the high-risk group, with alterations in both genes also associated with patient prognosis. The high-risk group was characterized by an unfavorable immune microenvironment, marked by reduced levels of CD4 + T cells and natural killer cells, but increased M2 macrophages and regulatory T cells. Finally, the risk model was predictive of response to immune checkpoint blockade, as well as sensitivity to sunitinib and paclitaxel. The PANoptosis-related risk model developed in this study enables accurate prognostic prediction in KIRC patients. Its associations with the tumor immune microenvironment and drug efficacy may offer potential therapeutic targets and inform clinical decisions.

Characterization of PANoptosis-related genes in Crohn's disease by integrated bioinformatics, machine learning and experiments

Currently, the biological understanding of Crohn's disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to participate in numerous diseases, including CD. In our study, we aimed to uncover the roles of PANoptosis in CD. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified by overlapping PANoptosis-related genes and differentially expressed genes between CD and normal samples in a combined microarray dataset. Three machine learning algorithms were adopted to detect hub DE-PRGs. To stratify the heterogeneity within CD patients, nonnegative matrix factorization clustering was conducted. In terms of immune landscape analysis, the "ssGSEA" method was applied. qRT-PCR was performed to examine the expression levels of the hub DE-PRGs in CD patients and colitis model mice. Ten hub DE-PRGs with satisfactory diagnostic performance were identified and validated: CD44, CIDEC, NDRG1, NUMA1, PEA15, RAG1, S100A8, S100A9, TIMP1 and XBP1. These genes displayed significant associations with certain immune cell types and CD-related genes. We also constructed gene‒microRNA, gene‒transcription factor and drug‒gene interaction networks. CD samples were classified into two PANoptosis patterns according to the expression levels of the hub DE-PRGs. Our results suggest that PANoptosis plays a nonnegligible role in CD by modulating the immune system and interacting with CD-related genes.

PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication

Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.

Identification and verification of a PANoptosis-related long noncoding ribonucleic acid signature for predicting the clinical outcomes and immune landscape in lung adenocarcinoma

PANoptosis is a type of programmed cell death (PCD) characterised by apoptosis, necroptosis and pyroptosis. Long non-coding ribonucleic acids (lncRNAs) are participating in the malignant behaviour of tumours regulated by PCD. Nevertheless, the function of PANoptosis-associated lncRNAs in lung adenocarcinoma remains to be investigated. In this work, a PANoptosis-related lncRNA signature (PRLSig) was developed based on the least absolute shrinkage and selection operator algorithm. The stability and fitness of PRLSig were confirmed by systematic evaluation of Kaplan-Meier, Cox analysis algorithm, receiver operating characteristic analysis, stratification analysis. In addition, ESTIMATE, single sample gene set enrichment analysis, immune checkpoints and the cancer immunome database confirmed the predictive value of the PRLSig in immune microenvironment and helped to identify populations for which immunotherapy is advantageous. The present research provides novel insights to facilitate risk stratification and optimise personalised treatment for LUAD.

Identification of PANoptosis-related biomarkers and analysis of prognostic values in head and neck squamous cell carcinoma

PANoptosis plays a crucial role in cancer initiation and progression. However, the roles of PANoptosis-related genes (PARGs) in the prognosis and immune landscape of head and neck squamous cell carcinoma (HNSCC) remain unclear. Integrated bioinformatics analyses based on the data of HNSCC patients in the TCGA database were conducted. We extracted 48 PARGs expression profile and then conducted differentially expressed analysis, following building a Cox model to predict the survival of HNSCC patients. Subsequently, the relationships between the risk score, immune landscape, chemo-, and immune-therapy responses were analyzed, respectively. Moreover, we investigated the prognostic value, and further predicted the pathways influenced by PARGs. Finally, we identified the biological function of crucial PARGs. A total of 18 differentially expressed PARGs were identified in HNSCC, and a Cox model including CASP8, FADD, NLRP1, TNF, and ZBP1 was constructed, which showed that the risk score was associated with the prognosis as well as immune infiltration of HNSCC patients, and the risk score could be regarded as an independent biomarker. Additionally, patients with high-risk score might be an indicator of lymph node metastasis and advanced clinical stage. High-risk scores also contributed to the chemotherapy resistance and immune escape of HNSCC patients. In addition, FADD and ZBP1 played a crucial role in various cancer-related pathways, such as the MAPK, WNT, and MTOR signaling pathways. On the other hand, we suggested that FADD facilitated the progression and 5-fluorouracil (5-FU) resistance of HNSCC cells. A signature based on PANoptosis showed great predictive power for lymph node metastasis and advanced stage, suggesting that the risk score might be an independent prognostic biomarker for HNSCC. Meanwhile, FADD, identified as a prognostic biomarker, may represent an effective therapeutic target for HNSCC.

Prognostic analysis of related factors of adverse reactions to immunotherapy in advanced gastric cancer and establishment of a nomogram model

BACKGROUND

Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years. However, the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study. In order to determine the association between adverse reaction factors and prognosis, the aim of this study was to conduct a systematic prognostic analysis. By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy, a nomogram model will be established to predict the survival status of patients more accurately.

AIM

To explore the characteristics and predictors of immune-related adverse reactions (irAEs) in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1 (PD-1) inhibitors and to analyze the correlation between irAEs and patient prognosis.

METHODS

A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected. Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred. Clinical features, manifestations, and prognosis of irAEs in the two groups were collected and analyzed. A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs, and the prediction model of irAEs was established. The receiver operating characteristic (ROC) curve was used to evaluate the ability of different indicators to predict irAEs. A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis. The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.

RESULTS

A total of 132 patients were followed up, of whom 63 (47.7%) developed irAEs. We looked at the two groups' clinical features and found that the two groups were statistically different in age ≥ 65 years, Ki-67 index, white blood cell count, neutrophil count, and regulatory T cell (Treg) count (all P < 0.05). Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence (P = 0.030). The ROC curve indicated that Treg + Ki-67 + age (≥ 65 years) combined could predict irAEs well (area under the curve = 0.753, 95% confidence interval: 0.623-0.848, P = 0.001). Results of the Kaplan-Meier survival curve showed that progression-free survival (PFS) was longer in the irAEs group than in the non-irAEs group (P = 0.001). Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS (P = 0.006).

CONCLUSION

The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy. irAEs can affect the patients' PFS and result in longer PFS. Treg + Ki-67 + age (≥ 65 years old) combined can better predict the occurrence of adverse reactions.

PLA inhibits TNF-α-induced PANoptosis of prostate cancer cells through metabolic reprogramming

Previous studies have shown that phenyllactic acid (alpha-Hydroxyhydrocinnamic acid, 2-Hydroxy-3-phenylpropionic acid, PLA), a type of organic acid metabolite, has excellent diagnostic efficacy when used to differentiate between prostate cancer, benign prostatic hyperplasia, and prostatitis. This research aims to explore the molecular mechanism by which PLA influences the PANoptosis of prostate cancer (PCa) cell lines. First, we found that PLA was detected in all prostate cancer cell lines (PC-3, PC-3 M, DU145, LNCAP). Further experiments showed that the addition of PLA to prostate cancer cells could promote ATP generation, enhance cysteine desulfurase (NFS1) expression, and reduce tumor necrosis factor alpha (TNF-α) levels, thereby inhibiting apoptosis in prostate cancer cells. Notably, overexpression of NFS1 can inhibit the binding of TNF-α to serpin mRNA binding protein 1 (SERBP1), suggesting that NFS1 competes with TNF-α for binding to SERBP1. Knockdown of SERBP1 significantly reduced the level of small ubiquity-related modifier (SUMO) modification of TNF-α. This suggests that NFS1 reduces the SUMO modification of TNF-α by competing with SERBP1, thereby reducing the expression and stability of TNF-α and ultimately inhibiting apoptosis in prostate cancer cell lines. In conclusion, PLA inhibits TNF-α induced panapoptosis of prostate cancer cells through metabolic reprogramming, providing a new idea for targeted treatment of prostate cancer.

A PANoptosis pattern to predict prognosis and immunotherapy response in head and neck squamous cell carcinoma

Individuals diagnosed with head and neck squamous cell carcinoma (HNSCC) experience a significant occurrence rate and are susceptible to premature spreading, resulting in a bleak outlook. Therapeutic approaches, such as chemotherapy, targeted therapy, and immunotherapy, may exhibit primary and acquired resistance during the advanced phases of HNSCC. There is currently no viable solution to tackle this issue. PANoptosis-a type of non-apoptotic cell death-is a recently identified mechanism of cellular demise that entails communication and synchronization among thermal apoptosis, apoptosis, and necrosis mechanisms. However, the extent to which PANoptosis-associated genes (PRG) contribute to the forecast and immune reaction of HNSCC remains mostly undisclosed. The present study aimed to thoroughly analyze the potential importance of PRG in HNSCC and report our discoveries. We systematically analyzed 19 PRG from previous studies and clinical data from HNSCC patients to establish a PAN-related signature and assess its prognostic, predictive potential. Afterward, the patient information was separated into two gene patterns that corresponded to each other, and the analysis focused on the connection between patient prognosis, immune status, and cancer immunotherapy. The PAN score was found to correlate with survival rates, immune systems, and cancer-related pathways. We then validated the malignant role of CD27 among them in HNSCC. In summary, we demonstrated the effectiveness of PAN.Score-based molecular clustering and prognostic features in predicting the outcome of HNSCC. The discovery we made could enhance our comprehension of the significance of PAN.Score in HNSCC and facilitate the development of more effective treatment approaches.

PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma

Background

Cancer growth is significantly influenced by processes such as pyroptosis, apoptosis, and necroptosis that underlie PANoptosis, a proinflammatory programmed cell death. Several studies have examined the long non-coding RNAs (lncRNAs) associated with pancreatic adenocarcinoma (PAAD). However, the predictive value of lncRNAs related to PANoptosis for PAAD has not been established.

Methods

The Clinical Genome Atlas database was used to obtain the transcriptome 、clinical data and the corresponding mutation data of the patients with PAAD in this study. The least absolute shrinkage and selection operator regression analysis was employed to obtain prognosis-related lncRNAs for constructing a risk signature. According to the median risk score of the signature, patients with PAAD were grouped into low- and high-risk groups to further compare the survival prognosis of different risk groups. Time-dependent receiver operating characteristic curves, c-index analysis, nomograms, principal component analysis and univariate Cox and multivariate Cox regression were performed for the internal validation of the signature. In addition, enrichment analysis of different genes was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Lastly, differences in tumor mutation burden (TMB), immune function, tumor immune dysfunction and rejection (TIDE), and drug response were determined for the two risk groups.

Results

The signature was constructed with six PANoptosis-related lncRNAs (AC067817.2、LINC02004、AC243829.1、AC092171.5、AP005233.2、AC004687.1) that predicted the prognosis of the patients with PAAD. Survival curves showed that patients in the two risk groups had statistically significant differences in prognosis (P < 0.05), and multi-cox regression analysis identified risk score as an independent risk factor for PAAD prognosis, and internal validation of nomograms showed high confidence in the signature. GO and KEGG enrichment analysis showed functional and pathway differences between the high- and low-risk groups. TMB evaluation demonstrated that patients in the high-risk group had a higher frequency of mutations. The TIDE score indicated that the high-risk group had a lower risk of immunotherapy escape and better immunotherapy outcomes. Additionally, the two risk groups revealed significantly different responses to 11 anticancer drugs.

Conclusion

We identified a novel risk signature for PANoptosis-related lncRNAs, which is a standalone prognostic indicator for PAAD. The PANoptosis-related lncRNA risk signature may be relevant for immunotherapy and a therapeutic target for PAAD.

Exploration of the prognostic prediction value of the PANoptosis-based risk score and its correlation with tumor immunity in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common cancer with high mortality worldwide. PANoptosis is a novel inflammatory programmed cell death modality with the characteristics of pyroptosis, apoptosis and necroptosis. It is necessary to explore PANoptosis-related genes in LUAD patients and offer evidence for prognosis prediction and therapeutic strategies. Single-cell RNA sequencing data and RNA expression profiles of LUAD patients from The Cancer Genome Atlas and Gene Expression Omnibus databases are used to screen PANoptosis-related differential genes for the construction of a risk model. Fifteen PANoptosis-related markers with prognostic value were identified by Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis. Kaplan-Meier analysis and receiver operating characteristic curve analysis further demonstrated the significant predictive capability. Immune infiltration, Single Nucleotide Variants (SNV) mutations, and clinical drug susceptibility were analyzed. In conclusion, a risk model of 15 PANoptosis-related genes has significant value in prognostic prediction for LUAD and has potential to direct clinical therapeutic strategies during the treatment.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma

Disulfidoptosis and ferroptosis are two distinct programmed cell death pathways that have garnered considerable attention due to their potential as therapeutic targets. However, despite their significance of these pathways, the role of disulfidoptosis-related ferroptosis genes in hepatocellular carcinoma (HCC) remains unclear. In this study, we employed a comprehensive approach that utilized various sophisticated techniques such as Pearson analysis, differential analysis, uniCox regression, lasso, ranger, and multivariable Cox regression to develop the disulfidoptosis-related ferroptosis (DRF) score. We then classified patients with HCC into high- and low-score groups to examine the association between the DRF score and various outcomes, including prognosis, functional enrichment, immune infiltration, immunotherapy, TACE sensitivity, drug sensitivity, and single-cell level function. Finally, we conducted in vitro experiments to validate the function of KIF20A. Our analysis revealed that KIF20A, G6PD, SLC7A11, and SLC2A1 were integral to constructing the DRF score. Our findings showed that patients with low DRF scores had significantly better prognoses and were more responsive to immunotherapy, TACE, and chemotherapy than those with high DRF scores. Based on our results obtained from bulk RNA-seq, single-cell RNA-seq, and in vitro experiments, we identified the cell cycle pathway as the primary distinguished factor between high-score and low-score groups. This study sheds light on the contribution of disulfidoptosis-related ferroptosis genes to the development and progression of HCC. The information gleaned from this study can be leveraged to improve our understanding of their potential as therapeutic targets for HCC treatment.

A novel PANoptosis-related long non-coding RNA index to predict prognosis, immune microenvironment and personalised treatment in hepatocellular carcinoma

BACKGROUND

PANoptosis is involved in the interaction of apoptosis, necroptosis and pyroptosis, playing a role in programmed cell death. Moreover, long non-coding RNAs (lncRNAs) regulate the PCD. This work aims to explore the role of PANoptosis-associated lncRNAs in hepatocellular carcinoma (HCC).

METHODS

Co-expression analysis identified PANoptosis-associated lncRNAs in HCC. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were utilised to filter lncRNAs and establish a PANoptosis-related lncRNA index (PANRI). Additionally, Cox, Kaplan-Meier and receiver operating characteristic (ROC) curves were utilised to systematically evaluate the PANRI. Furthermore, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), single sample gene set enrichment analysis (ssGSEA) and immune checkpoints were performed to analyse the potential of the PANRI in differentiating different tumour immune microenvironment (TIME) populations. The consensus clustering algorithm was used to distinguish individuals with HCC having different TIME subtypes. Finally, HCC cell lines HepG2 were utilised for further validation in in vitro experiments.

RESULTS

The PANRI differentiates patients according to risk. Notably, ESTIMATE and ssGSEA algorithms revealed a high immune infiltration status in high-risk patients. Additionally, consensus clustering divided the patients into three clusters to identify different subtypes of TIME. Moreover, in vitro results showed that siRNA-mediated silencing of AL049840.4 inhibited the viability and migration of HepG2 cells and promoted apoptosis.

CONCLUSIONS

This is the first PANoptosis-related, lncRNA-based risk index in HCC to assess patient prognosis, TIME and response to immunotherapy. This study offers novel perspectives on the role of PANoptosis-associated lncRNAs in HCC.

Machine learning reveals PANoptosis as a potential reporter and prognostic revealer of tumour microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD), a prominent lung cancer subtype, has an underexplored relationship with PANoptosis, a recently discovered mode of tumour cell death. This study incorporated iron death, copper death, scorch death, necrotizing apoptosis and bisulfide death into a pan-death gene set (PANoptosis) and conducted single-cell analysis of scRNA-seq data from 11 LUAD samples. Differentially expressed genes were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. Univariate COX regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen PANoptosis key genes for constructing an LUAD risk model. The model's prognostic performance was evaluated using survival curves, risk scores and validation in the Gene Expression Omnibus database. The study also explored the correlation between risk scores, tumour biological function, immunotherapy, drug sensitivity and immune infiltration. The SMS gene in the PANoptosis model was silenced in two LUAD cell lines for cellular validation. Single-cell analysis revealed eight major cell types and several PANoptosis genes significantly associated with LUAD survival. The risk model demonstrated strong prognostic performance and association with immune infiltration, suggesting PANoptosis involvement in LUAD tumour immunity. Cellular validation further supported these findings. The PANoptosis key risk genes are believed to be closely related to the tumour microenvironment and immune regulation of LUAD, potentially providing valuable insights for early diagnosis and clinical treatment, and broader applications in other tumours and complex diseases.

An individualized stemness-related signature to predict prognosis and immunotherapy responses for gastric cancer using single-cell and bulk tissue transcriptomes

BACKGROUND

Currently, many stemness-related signatures have been developed for gastric cancer (GC) to predict prognosis and immunotherapy outcomes. However, due to batch effects, these signatures cannot accurately analyze patients one by one, rendering them impractical in real clinical scenarios. Therefore, we aimed to develop an individualized and clinically applicable signature based on GC stemness.

METHODS

Malignant epithelial cells from single-cell RNA-Seq data of GC were used to identify stemness-related signature genes based on the CytoTRACE score. Using two bulk tissue datasets as training data, the enrichment scores of the signature genes were applied to classify samples into two subtypes. Then, using the identified subtypes as criteria, we developed an individualized stemness-related signature based on the within-sample relative expression orderings of genes.

RESULTS

We identified 175 stemness-related signature genes, which exhibited significantly higher AUCell scores in poorly differentiated GCs compared to differentiated GCs. In training datasets, GC samples were classified into two subtypes with significantly different survival times and genomic characteristics. Utilizing the two subtypes, an individualized signature was constructed containing 47 gene pairs. In four independent testing datasets, GC samples classified as high risk exhibited significantly shorter survival times, higher infiltration of M2 macrophages, and lower immune responses compared to low-risk samples. Moreover, the potential therapeutic targets and corresponding drugs were identified for the high-risk group, such as CD248 targeted by ontuxizumab.

CONCLUSIONS

We developed an individualized stemness-related signature, which can accurately predict the prognosis and efficacy of immunotherapy for each GC sample.

PANoptosis: Mechanisms, biology, and role in disease

Cell death can be executed through distinct subroutines. PANoptosis is a unique inflammatory cell death modality involving the interactions between pyroptosis, apoptosis, and necroptosis, which can be mediated by multifaceted PANoptosome complexes assembled via integrating components from other cell death modalities. There is growing interest in the process and function of PANoptosis. Accumulating evidence suggests that PANoptosis occurs under diverse stimuli, for example, viral or bacterial infection, cytokine storm, and cancer. Given the impact of PANoptosis across the disease spectrum, this review briefly describes the relationships between pyroptosis, apoptosis, and necroptosis, highlights the key molecules in PANoptosome formation and PANoptosis activation, and outlines the multifaceted roles of PANoptosis in diseases together with a potential for therapeutic targeting. We also discuss important concepts and pressing issues for future PANoptosis research. Improved understanding of PANoptosis and its mechanisms is crucial for identifying novel therapeutic targets and strategies.

Comprehensive Analysis of PANoptosis-Related Gene Signature of Ulcerative Colitis

Accumulating evidence shows that the abnormal increase in the mortality of intestinal epithelial cells (IECs) caused by apoptosis, pyroptosis, and necroptosis is closely related to the function of mucous membrane immunity and barrier function in patients with ulcerative colitis (UC). As a procedural death path that integrates the above-mentioned many deaths, the role of PANoptosis in UC has not been clarified. This study aims to explore the characterization of PANoptosis patterns and determine the potential biomarkers and therapeutic targets. We constructed a PANoptosis gene set and revealed significant activation of PANoptosis in UC patients based on multiple transcriptome profiles of intestinal mucosal biopsies from the GEO database. Comprehensive bioinformatics analysis revealed five key genes (ZBP1, AIM2, CASP1/8, IRF1) of PANoptosome with good diagnostic value and were highly correlated with an increase in pro-inflammatory immune cells and factors. In addition, we established a reliable ceRNA regulatory network of PANoptosis and predicted three potential small-molecule drugs sharing calcium channel blockers that were identified, among which flunarizine exhibited the highest correlation with a high binding affinity to the targets. Finally, we used the DSS-induced colitis model to validate our findings. This study identifies key genes of PANoptosis associated with UC development and hypothesizes that IRF1 as a TF promotes PANoptosome multicomponent expression, activates PANoptosis, and then induces IECs excessive death.

A Pilot Study About the Role of PANoptosis-Based Genes in Atherosclerosis Development

Background

As a chronic inflammatory disease, atherosclerosis (AS) and ischemia events are primarily affected by inflammation in AS. PANoptosis has been implicated in many human systemic disorders, including infection, cancer, neurodegeneration, and inflammation. On the other hand, little is understood about PANoptosis's function in AS.

Methods

We used consensus clustering to divide the GSE100927 dataset into two panoptosis-related subgroups. PANoptosis-associated genes were screened by differential analysis and weighted gene co-expression network analysis (WGCNA) and enriched by ClueGO software. Investigating LASSO regression and MCODE to identify AS Diagnostic Markers. Immunoinfiltration analysis and single-cell analysis were used to search for cell types associated with the diagnostic genes. Final validation was performed by polymerase chain reaction (PCR).

Results

We classified the GSE100927 dataset into two PANoptosis-related subtypes based on the expression of PANoptosis-related genes (PRGs) using consensus clustering. A total of 36 PANoptosis-associated genes were screened in the differentially expressed genes and WGCNA-related module. 4 hub genes were identified by MCODE and LASSO regression, and 3 AS diagnostic markers (ACP5, CCL3, HMOX1) were screened by external validation set. Immunoinfiltration analysis and single-cell analysis showed that the three diagnostic markers were associated with macrophages, and PCR results demonstrated that ACP5 and HMOX1 could be used as AS diagnostic markers.

Conclusion

Our study identified ACP5 and HMOX1 as diagnostic genes for AS that may be associated with PANoptosis. ACP5 and HMOX1 may be involved in the pathogenesis of AS by regulating macrophage PANoptosis.

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

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.

Tumor-associated neutrophils upregulate PANoptosis to foster an immunosuppressive microenvironment of non-small cell lung cancer

Tumor microenvironment (TME) cells orchestrate an immunosuppressive milieu that supports cancer cell proliferation. Tumor-associated neutrophils (TANs) have gained attention as inflammation biomarkers. However, the role of heterogeneous TAN populations in TME immune tolerance and their clinical potential remain unclear. Herein, we used public database to conduct single-cell transcriptomic analysis of 81 patients with non-small cell lung cancer (NSCLC) to elucidate TAN phenotypes linked to unfavorable clinical outcomes. We identified a pro-tumoral TAN cluster characterized by elevated HMGB1 expression, which could potentially engage with the TME through HMGB1-TIM-3 interaction. GATA2 was the transcription factor that drove HMGB1 expression in this pro-tumoral TAN subcluster. Further in vivo experiments confirmed the recruitment of HMGB1-positive TANs to the tumor lesion. Dual-luciferase reporter assays consolidated that the transcription factor GATA2 mediated HMGB1 expression by binding to its promoter region. Moreover, surgical NSCLC specimens verified the putative association between HMGB1-positive TAN and the pathological grades of primary tumors. Overall, this report revealed a pro-tumoral TAN cluster with HMGB1 overexpression that potentially dampen anti-tumoral immunity and contributed to immune evasion via the GATA2/HMGB1/TIM-3 axis. Moreover, this report suggests that this specific phenotype of TAN could serve as an indicator to clinical outcomes and immunotherapy effects for NSCLC.

PANoptosis in cancer, the triangle of cell death

BACKGROUND

PANoptosis is a novel form of programmed cell death (PCD) found in 2019 that is regulated by the PANoptosome. PANoptosis combines essential features of pyroptosis, apoptosis, and necroptosis, forming a "death triangle" of cells. While apoptosis, pyroptosis, and necroptosis have been extensively studied for their roles in human inflammatory diseases and many other clinical conditions, historically they were considered as independent processes. However, emerging evidence indicates that these PCDs exhibit cross talk and interactions, resulting in the development of the concept of PANoptosis.

METHODS

In this review, we offer a concise summary of the fundamental mechanisms of apoptosis, pyroptosis, and necroptosis. We subsequently introduce the notion of PANoptosis and detail the assembly mechanism of the PANoptosome complex which is responsible for inducing cell death. We also describe some regulatory networks of PANoptosis.

RESULTS

PANoptosis now has been associated with various human diseases including cancer. Although the exact function of PANoptosis in each tumor is not fully understood, it represents a prospective avenue for cancer therapy, offering promise for advancements in cancer therapy.

CONCLUSIONS

In the future, in-depth study of PANoptosis will continue to help us in understanding the fundamental processes underlying cell death and provide scientific support for cancer research.

The emerging role of PANoptosis in cancer treatment

Programmed cell death (PCD) is a key mechanism for the study of anticancer drugs and has a significant impact on the development and management of cancer. A growing amount of data indicates that different kinds of PCD, particularly pyroptosis, apoptosis, and necroptosis, interact closely. Recent research has revealed the existence of the distinct inflammatory PCD modality known as PANoptosis, which is controlled by complex PANoptosome complexes built by combining elements from different PCD pathways. No single PCD route is sufficient to explain all of the physiologic effects seen in PANoptosis. Numerous studies have demonstrated that PANoptosis can successfully stop cancer cells from growing, proliferating, and developing drug resistance. As a result, it has changed the focus of targeted anticancer therapy. In this review, we outlined the molecular processes of PANoptosis activation and modulation as well as the mechanisms of innate immune cell death. In order to provide a theoretical foundation for the development of drugs targeting PANoptosis as an anti-cancer target, we also highlight the PANoptosomes discovered to date and give an overview of the implications of PANoptosis in cancer treatment.

Different types of cell death in diabetic endothelial dysfunction

Diabetes mellitus is a metabolic disease caused by disorders of insulin secretion and utilization. Long-term hyperglycemia, insulin resistance, and disorders of glucose and lipid metabolism cause vascular endothelial cell damage. Endothelial dysfunction is a key feature of diabetic vascular complications such as diabetic nephropathy, retinopathy, neuropathy, and atherosclerosis. Importantly, cell death is thought to be a key factor contributing to vascular endothelial injury. Morphologically, cell death can be divided into three forms: type I apoptosis, type II autophagy, and type III necrosis. According to the difference in function, cell death can be divided into accidental cell death (ACD) and regulated cell death (RCD). RCD is a controlled process involving numerous proteins and precise signaling cascades. Multiple subroutines covered by RCD may be involved in diabetic endothelial dysfunction, including apoptosis, autophagy, necroptosis, pyroptosis, entosis, ferroptosis, ferroautophagy, parthanatos, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, cuproptosis, and PANoptosis. This article briefly reviews the mechanism and significance of cell death associated with diabetic endothelial dysfunction, which will help deepen the understanding of diabetic endothelial cell death and provide new therapeutic ideas.

Caspase-Linked Programmed Cell Death in Prostate Cancer: From Apoptosis, Necroptosis, and Pyroptosis to PANoptosis

Prostate cancer (PCa) is a complex disease and the cause of one of the highest cancer-related mortalities in men worldwide. Annually, more than 1.2 million new cases are diagnosed globally, accounting for 7% of newly diagnosed cancers in men. Programmed cell death (PCD) plays an essential role in removing infected, functionally dispensable, or potentially neoplastic cells. Apoptosis is the canonical form of PCD with no inflammatory responses elicited, and the close relationship between apoptosis and PCa has been well studied. Necroptosis and pyroptosis are two lytic forms of PCD that result in the release of intracellular contents, which induce inflammatory responses. An increasing number of studies have confirmed that necroptosis and pyroptosis are also closely related to the occurrence and progression of PCa. Recently, a novel form of PCD named PANoptosis, which is a combination of apoptosis, necroptosis, and pyroptosis, revealed the attached connection among them and may be a promising target for PCa. Apoptosis, necroptosis, pyroptosis, and PANoptosis are good examples to better understand the mechanism underlying PCD in PCa. This review aims to summarize the emerging roles and therapeutic potential of apoptosis, necroptosis, pyroptosis, and PANoptosis in PCa.

PANoptosis: Emerging mechanisms and disease implications

PANoptosis, a unique new form of programmed cell death (PCD), is characterized by pyroptosis, apoptosis, and necroptosis, but it cannot be explained by pyroptosis, apoptosis or necroptosis alone. Assembly of the PANoptosome complex is a key feature of PANoptosis. To date, four kinds of PANoptosomes with distinct sensors and regulators have been defined, namely Z-DNA binding protein 1 (ZBP1) PANoptosome, absent in melanoma 2 (AIM2) PANoptosome, receptor-interacting protein kinase 1 (RIPK1) PANoptosome, and nucleotide-binding leucine-rich repeat-containing receptor 12 (NLRP12). Each PANoptosome contains three components: sensors for pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs), adaptors as connected bridges, and catalytic effectors or executioners. Mechanistically, different PAMPs or DAMPs are recognized by the sensors in a context-dependent manner, which initiates PANoptosome assembly through adaptors, and ultimately engages synchronous activation of pyroptosis, apoptosis, and necroptosis via different catalytic effectors. Resultantly, PANoptosis is emerged as a prospective and promising therapeutic target for various diseases. This review covers the accumulating evidence about the roles and mechanisms of PANoptosis in innate immunity and discusses the attractive prospect of manipulating PANoptosis as a new treatment for diseases.

Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis

Cell death-related genes indicate prognosis in cancer patients. PANoptosis is a newly observed form of cell death that researchers have linked to cancer cell death and antitumor immunity. Even so, its significance in lung adenocarcinomas (LUADs) has yet to be elucidated. We extracted and analyzed data on mRNA gene expression and clinical information from public databases in a systematic manner. These data were utilized to construct a reliable risk prediction model for six regulators of PANoptosis. The Gene Expression Omnibus (GEO) database validated six genes with risk characteristics. The prognosis of LUAD patients could be accurately estimated by the six-gene-based model: NLR family CARD domain-containing protein 4 (NLRC4), FAS-associated death domain protein (FADD), Tumor necrosis factor receptor type 1-associated DEATH domain protein (TRADD), Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Proline-serine-threonine phosphatase-interacting protein 2 (PSTPIP2), and Mixed lineage kinase domain-like protein (MLKL). Group of higher risk and Cluster 2 indicated a poor prognosis as well as the reduced expression of immune infiltrate molecules and human leukocyte antigen. Distinct expression of PANoptosis-related genes (PRGs) in lung cancer cells was verified using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, we evaluated the relationship between PRGs and somatic mutations, tumor immune dysfunction exclusion, tumor stemness indices, and immune infiltration. Using the risk signature, we conducted analyses including nomogram construction, stratification, prediction of small-molecule drug response, somatic mutations, and chemotherapeutic response.

PANoptosis: Mechanism and Role in Pulmonary Diseases

PANoptosis is a newly defined programmed cell death (PCD) triggered by a series of stimuli, and it engages three well-learned PCD forms (pyroptosis, apoptosis, necroptosis) concomitantly. Normally, cell death is recognized as a strategy to eliminate unnecessary cells, inhibit the proliferation of invaded pathogens and maintain homeostasis; however, vigorous cell death can cause excessive inflammation and tissue damage. Acute lung injury (ALI) and chronic obstructive pulmonary syndrome (COPD) exacerbation is related to several pathogens (e.g., influenza A virus, SARS-CoV-2) known to cause PANoptosis. An understanding of the mechanism and specific regulators may help to address the pathological systems of these diseases. This review presents our understanding of the potential mechanism of PANoptosis and the role of PANoptosis in different pulmonary diseases.

Identification and immunological characterization of PLA2G2A and cell death-associated molecular clusters in idiopathic pulmonary fibrosis

AIMS

Idiopathic pulmonary fibrosis (IPF) is a severe pulmonary interstitial pneumonia. Our study focuses on the role of PLA2 enzyme in the IPF to explore a more effective diagnosis and treatment mechanism of IPF.

MAIN METHODS

Transcriptome data of IPF from GEO database and bleomycin-induced pulmonary fibrosis mice were analyzed to identify PLA2 enzyme and their metabolite, lysophosphatidylcholines 18:0, in IPF. Based on PLA2G2A and PLA2G2D / PLA2G2A-associated cell death genes (PCDs), the consensus clustering analysis was used to identify the subtypes of IPF and the correlation between PLA2G2A and prognosis was analyzed. The machine learning (ML) models and artificial neural network (ANN) model was used to validate the diagnostic accuracy of PLA2s and PCDs in diagnosing IPF. The gene and protein expression of NLRP3, GSDMD, and CASP-1 was estimated in recombinant PLA2G2A protein induced MLE-12 cells.

KEY FINDINGS

The expression of PLA2G2D, PLA2G2A, and LPC18 significantly changed in IPF. Furtherly, PLA2G2A has a significant correlation with poor patient prognosis, which could predict the 2 or 3-years mortality rates of IPF. Two subtypes of IPF patients, identified based on PCDs, showed significant different immunoinfiltration. Recombinant PLA2G2A protein could induce the pyrotosis in the MLE-12 cell. The generalized linear model and ANN model of PLA2s or PCDs accurate diagnosis IPF.

SIGNIFICANCE

PLA2G2A is the most robustly associated gene with IPF among the PLA2s, which demonstrates a potential in diagnosing and prognostic value in IPF, and provides a foundation for further understanding and breakthroughs in IPF diagnosis and treatment.

Multi-faceted attributes of salivary cell-free DNA as liquid biopsy biomarkers for gastric cancer detection

BACKGROUND

Recent advances in circulating cell-free DNA (cfDNA) analysis from biofluids have opened new avenues for liquid biopsy (LB). However, current cfDNA LB assays are limited by the availability of existing information on established genotypes associated with tumor tissues. Certain cancers present with a limited list of established mutated cfDNA biomarkers, and thus, nonmutated cfDNA characteristics along with alternative biofluids are needed to broaden the available cfDNA targets for cancer detection. Saliva is an intriguing and accessible biofluid that has yet to be fully explored for its clinical utility for cancer detection.

METHODS

In this report, we employed a low-coverage single stranded (ss) library NGS pipeline "Broad-Range cell-free DNA-Seq" (BRcfDNA-Seq) using saliva to comprehensively investigate the characteristics of salivary cfDNA (ScfDNA). The identification of cfDNA features has been made possible by applying novel cfDNA processing techniques that permit the incorporation of ultrashort, ss, and jagged DNA fragments. As a proof of concept using 10 gastric cancer (GC) and 10 noncancer samples, we examined whether ScfDNA characteristics, including fragmentomics, end motif profiles, microbial contribution, and human chromosomal mapping, could differentiate between these two groups.

RESULTS

Individual and integrative analysis of these ScfDNA features demonstrated significant differences between the two cohorts, suggesting that disease state may affect the ScfDNA population by altering nuclear cleavage or the profile of contributory organism cfDNA to total ScfDNA. We report that principal component analysis integration of several aspects of salivary cell-free DNA fragmentomic profiles, genomic element profiles, end-motif sequence patterns, and distinct oral microbiome populations can differentiate the two populations with a p value of < 0.0001 (PC1).

CONCLUSION

These novel features of ScfDNA characteristics could be clinically useful for improving saliva-based LB detection and the eventual monitoring of local or systemic diseases.

GPX8 regulates pan-apoptosis in gliomas to promote microglial migration and mediate immunotherapy responses

Introduction

Gliomas have emerged as the predominant brain tumor type in recent decades, yet the exploration of non-apoptotic cell death regulated by the pan-optosome complex, known as pan-apoptosis, remains largely unexplored in this context. This study aims to illuminate the molecular properties of pan-apoptosis-related genes in glioma patients, classifying them and developing a signature using machine learning techniques.

Methods

The prognostic significance, mutation features, immunological characteristics, and pharmaceutical prediction performance of this signature were comprehensively investigated. Furthermore, GPX8, a gene of interest, was extensively examined for its prognostic value, immunological characteristics, medication prediction performance, and immunotherapy prediction potential.

Results

Experimental techniques such as CCK-8, Transwell, and EdU investigations revealed that GPX8 acts as a tumor accelerator in gliomas. At the single-cell RNA sequencing level, GPX8 appeared to facilitate cell contact between tumor cells and macrophages, potentially enhancing microglial migration.

Conclusions

The incorporation of pan-apoptosis-related features shows promising potential for clinical applications in predicting tumor progression and advancing immunotherapeutic strategies. However, further in vitro and in vivo investigations are necessary to validate the tumorigenic and immunogenic processes associated with GPX8 in gliomas.

Robust analysis of a novel PANoptosis-related prognostic gene signature model for hepatocellular carcinoma immune infiltration and therapeutic response

PANoptosis, an interplay between pyroptosis, apoptosis, and necroptosis, is deeply involved in cancer development and immunity. However, the influence of PANoptosis in hepatocellular carcinoma (HCC) remains to be further investigated. The differentially expressed PANoptosis-related genes (PANRGs) was screened in The Cancer Genome Atlas (TCGA) database. Accordingly, mutation, bioinformatics, and consensus clustering analyses were performed. Then, a prognostic risk model was developed by least absolute shrinkage and selection operator (LASSO) Cox regression. Furthermore, the prognostic value, immunity correlation and therapeutic response prediction ability of risk model were explored. A total of 18 PANRGs were differently expressed in the TCGA-HCC cohort and were mainly involved in cancer- and cell death-related signal pathways. Using unsupervised clustering method, we identified two PANRGs-mediated clustering patterns. The remarkable differences between the two clusters on overall survival (OS) and clinical features were demonstrated respectively. Based on the five-gene prognostic risk model, the calculated PANRG-scores were used to categorize the subgroups as high- and low-risk. Notably, the high-risk subgroup had a dismal prognosis and exhibited much lower immune infiltration levels of mast cells, nature killer cells and pDCs, but higher levels of aDCs, iDCs and Treg cells than those in the low-risk subgroup. Furthermore, we constructed a reliable nomogram combining clinical traits and PANRG-score to predict the OS of HCC patients. The significantly negative correlation between PANoptosis and tumor mutation burden (TMB), ferroptosis were revealed. In drug sensitivity analysis, the high-risk subgroup had a considerably lower TIDE score, suggesting a preferable response to immunotherapy, and may be more sensitive to Tipifarnib, Imatinib, Doxorubicin, and Gemcitabine. The upregulated mRNA expressions of FADD were validated in 16 paired HCC tissues of Guangxi cohort. Based on PANoptosis-related genes, an integrated risk signature was constructed to provide a roadmap for patient stratification and predict HCC patient's prognosis. The patients with the higher PANRG-score may carry a dismal survival and relatively low immune infiltration, but a potential better immunotherapy response. Therefore, future HCC therapy perspectives should emphasize the setting of PANoptosis to achieve a personalized, practicable and effective therapeutic regimen.

Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer

Background

Accumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.

Methods

This research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.

Results

This study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.

Conclusion

Taken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.

Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma

Background

Previous studies have demonstrated that PANoptosis is strongly correlated with cancer immunity and progression. This study aimed to develop a PANoptosis-related signature (PANRS) to explore its potential value in predicting the prognosis and immunotherapy response of hepatocellular carcinoma (HCC).

Methods

Based on the expression of PANoptosis-related genes, three molecular subtypes were identified. To construct a signature, the differentially expressed genes between different molecular subtypes were subjected to multivariate least absolute shrinkage and selection operator Cox regression analyses. The risk scores of patients in the training set were calculated using the signature. The patients were classified into high-risk and low-risk groups based on the median risk scores. The predictive performance of the signature was evaluated using Kaplan-Meier plotter, receiving operating characteristic curves, nomogram, and calibration curve. The results were validated using external datasets. Additionally, the correlation of the signature with the immune landscape and drug sensitivity was examined. Furthermore, the effect of LPCAT1 knockdown on HCC cell behavior was verified using in vitro experiments.

Results

This study developed a PANRS. The risk score obtained by using the PANRS was an independent risk factor for the prognosis of patients with HCC and exhibited good prognostic predictive performance. The nomogram constructed based on the risk score and clinical information can accurately predicted the survival probability of patients with HCC. Patients with HCC in the high-risk groups have high immune scores and tend to generate an immunosuppressive microenvironment. They also exhibited a favorable response to immunotherapy, as evidenced by high tumor mutational burden, high immune checkpoint gene expression, high human leukocyte antigen gene expression, low tumor immune dysfunction and low exclusion scores. Additionally, the PANRS enabled the identification of 15 chemotherapeutic agents, including sorafenib, for patients with HCC with different risk levels, guiding clinical treatment. The signature gene LPCAT1 was upregulated in HCC cell lines. LPCAT1 knockdown markedly decreased HCC cell proliferation and migration.

Conclusion

PANRS can accurately predict the prognosis and immunotherapy response of patients with HCC and consequently guide individualized treatment.

Molecular characterization of PANoptosis-related genes with features of immune dysregulation in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. PANoptosis is a novel form of programmed cell death involved in various inflammatory diseases. This study aimed to identify the differentially-expressed PANoptosis-related genes (PRGs) involved in immune dysregulation in SLE. Five key PRGs, including ZBP1, MEFV, LCN2, IFI27, and HSP90AB1, were identified. The prediction model with these 5 key PRGs showed a good diagnostic performance in distinguishing SLE patients from controls. These key PRGs were associated with memory B cells, neutrophils and CD8 + T cells. Besides, these key PRGs were significantly enriched in pathways involving the type I interferon responses and IL-6-JAK-STAT3 signaling. The expression levels of the key PRGs were validated in peripheral blood mononuclear cells (PBMCs) of patients with SLE. Our findings suggest that PANoptosis may be implicated in the immune dysregulation in SLE by regulating the interferons and JAK-STAT signaling pathways in memory B cells, neutrophils and CD8 + T cells.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.