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

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.

PANoptosis subtypes predict prognosis and immune efficacy in gastric cancer

PANoptosis is a form of inflammatory programmed cell death that is regulated by the PANoptosome. This PANoptosis possesses key characteristics of pyroptosis, apoptosis, and necroptosis, yet cannot be fully explained by any of these cell death modes. The unique nature of this cell death mechanism has garnered significant interest. However, the specific role of PANoptosis-associated features in gastric cancer (GC) is still uncertain. Patients were categorized into different PAN subtypes based on the expression of genes related to the PANoptosome. We conducted a systematic analysis to investigate the variations in prognosis and tumor microenvironment (TME) among these subtypes. Furthermore, we developed a risk score, called PANoptosis-related risk score (PANS), which is constructed from genes associated with the PANoptosis. We comprehensively analyzed the correlation between PANS and GC prognosis, TME, immunotherapy efficacy and chemotherapeutic drug sensitivity. Additionally, we performed in vitro experiments to validate the impact of Keratin 7 (KRT7) on GC. We identified two PAN subtypes (PANcluster A and B). PANoptosome genes were highly expressed in PANcluster A. PANcluster A has the characteristics of favorable prognosis, abundant infiltration of anti-tumor lymphocytes, and sensitivity to immunotherapy, thus it was categorized as an immune-inflammatory type. Meanwhile, our constructed PANS can effectively predict the prognosis and immune efficacy of GC. Patients with low PANS have a good prognosis, and have the characteristics of high tumor mutation load (TMB), high microsatellite instability (MSI), low tumor purity and sensitivity to immunotherapy. In addition, PANS can also identify suitable populations for different chemotherapy drugs. Finally, we confirmed that KRT7 is highly expressed in GC. Knocking down the expression of KRT7 significantly weakens the proliferation and migration abilities of GC cells. The models based on PANoptosis signature help to identify the TME features of GC and can effectively predict the prognosis and immune efficacy of GC. Furthermore, the experimental verification results of KRT7 provide theoretical support for anti-tumor treatment.

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.

Bioinformatics analysis of PANoptosis regulators in the diagnosis and subtyping of steroid-induced osteonecrosis of the femoral head

In this study, we aimed to investigate the involvement of PANoptosis, a form of regulated cell death, in the development of steroid-induced osteonecrosis of the femoral head (SONFH). The underlying pathogenesis of PANoptosis in SONFH remains unclear. To address this, we employed bioinformatics approaches to analyze the key genes associated with PANoptosis. Our analysis was based on the GSE123568 dataset, allowing us to investigate both the expression profiles of PANoptosis-related genes (PRGs) and the immune profiles in SONFHallowing us to investigate the expression profiles of PRGs as well as the immune profiles in SONFH. We conducted cluster classification based on PRGs and assessed immune cell infiltration. Additionally, we used the weighted gene co-expression network analysis (WGCNA) algorithm to identify cluster-specific hub genes. Furthermore, we developed an optimal machine learning model to identify the key predictive genes responsible for SONFH progression. We also constructed a nomogram model with high predictive accuracy for assessing risk factors in SONFH patients, and validated the model using external data (area under the curve; AUC = 1.000). Furthermore, we identified potential drug targets for SONFH through the Coremine medical database. Using the optimal machine learning model, we found that 2 PRGs, CASP1 and MLKL, were significantly correlated with the key predictive genes and exhibited higher expression levels in SONFH. Our analysis revealed the existence of 2 distinct PANoptosis molecular subtypes (C1 and C2) within SONFH. Importantly, we observed significant variations in the distribution of immune cells across these subtypes, with C2 displaying higher levels of immune cell infiltration. Gene set variation analysis indicated that C2 was closely associated with multiple immune responses. In conclusion, our study sheds light on the intricate relationship between PANoptosis and SONFH. We successfully developed a risk predictive model for SONFH patients and different SONFH subtypes. These findings enhance our understanding of the pathogenesis of SONFH and offer potential insights into therapeutic strategies.

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 of PANoptosis-related subtypes, construction of a prognosis signature, and tumor microenvironment landscape of hepatocellular carcinoma using bioinformatic analysis and experimental verification

Background

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. PANoptosis is a recently unveiled programmed cell death pathway, Nonetheless, the precise implications of PANoptosis within the context of HCC remain incompletely elucidated.

Methods

We conducted a comprehensive bioinformatics analysis to evaluate both the expression and mutation patterns of PANoptosis-related genes (PRGs). We categorized HCC into two clusters and identified differentially expressed PANoptosis-related genes (DEPRGs). Next, a PANoptosis risk model was constructed using LASSO and multivariate Cox regression analyses. The relationship between PRGs, risk genes, the risk model, and the immune microenvironment was studies. In addition, drug sensitivity between high- and low-risk groups was examined. The expression profiles of these four risk genes were elucidate by qRT-PCR or immunohistochemical (IHC). Furthermore, the effect of CTSC knock down on HCC cell behavior was verified using in vitro experiments.

Results

We constructed a prognostic signature of four DEPRGs (CTSC, CDCA8, G6PD, and CXCL9). Receiver operating characteristic curve analyses underscored the superior prognostic capacity of this signature in assessing the outcomes of HCC patients. Subsequently, patients were stratified based on their risk scores, which revealed that the low-risk group had better prognosis than those in the high-risk group. High-risk group displayed a lower Stromal Score, Immune Score, ESTIMATE score, and higher cancer stem cell content, tumor mutation burden (TMB) values. Furthermore, a correlation was noted between the risk model and the sensitivity to 56 chemotherapeutic agents, as well as immunotherapy efficacy, in patient with. These findings provide valuable guidance for personalized clinical treatment strategies. The qRT-PCR analysis revealed that upregulated expression of CTSC, CDCA8, and G6PD, whereas downregulated expression of CXCL9 in HCC compared with adjacent tumor tissue and normal liver cell lines. The knockdown of CTSC significantly reduced both HCC cell proliferation and migration.

Conclusion

Our study underscores the promise of PANoptosis-based molecular clustering and prognostic signatures in predicting patient survival and discerning the intricacies of the tumor microenvironment within the context of HCC. These insights hold the potential to advance our comprehension of the therapeutic contribution of PANoptosis plays in HCC and pave the way for generating more efficacious treatment strategies.

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.

Robust machine-learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer

BACKGROUND

A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC).

METHODS

101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross-validation framework, and patients with CRC were separated into high- and low-CERPI groups. Relationship between immune cell infiltration levels, immune-related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in-vitro verified.

RESULTS

The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low-CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single-cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration.

CONCLUSION

We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.

Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis

Background

The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANoptosis in sepsis has not been fully elucidated.

Methods

We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of differentially expressed genes and calculation of the PANoptosis score. A PANoptosis-based prognostic model was developed. In vitro experiments were performed to verify distinct PANoptosis-related genes. An external scRNA-seq dataset was used to verify cellular localization.

Results

Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, with different immune infiltration levels. Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. We developed PANscore based on these signature genes, which can distinguish different PANoptosis and clinical characteristics and may serve as a potential biomarker. Single-cell sequencing analysis identified six cell types, with high PANscore clustering relatively in B cells, and low PANscore in CD16+ and CD14+ monocytes and Megakaryocyte progenitors. ZBP1, XAF1, IFI44L, SOCS1, and PARP14 were relatively higher in cells with high PANscore.

Conclusion

We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis.

Diagnostic Model for Alzheimer's Disease Based on PANoptosis-Related Genes

BACKGROUND

The pathophysiology of Alzheimer's disease (AD) involves the interplay of three different processes: pyroptosis, apoptosis, and necroptosis.

OBJECTIVE

To explore role of PANoptosis, a novel pro-inflammatory programmed cell death pathway, in AD patients.

METHODS

We performed a consensus clustering analysis to identify distinct transcriptional profiles in the samples using the R package "ConsensusClusterPlus". The PANoptosis key genes were obtained by crossing the WGCNA brown module and differentially expressed PANoptosis genes. We accomplished regression analyses using the LASSO-Cox method, combined with pathological status and gene expression data. At the same time, we also constructed PANscore system. The expression of PANoptosis hub genes were validated by qRT-PCR in AD transgenic mice.

RESULTS

Our study utilized tissue expression profile data from AD patients to construct three distinct PANoptosis patterns, each with unique molecular and clinical characteristics. We have created a risk scoring system called PANscore, which can analyze patterns specific for each AD patient. Additionally, we observed significantly lower levels of follicular helper T (Tfh) cells in the high PANscore and AD patients. Further analysis revealed a significant negative correlation of Tfh with GSDMD and MLKL.

CONCLUSIONS

These findings provide a roadmap for personalized patient stratification, enabling clinicians to develop personalized treatment plans for AD patients and advance the field of precision medicine.

Current evidence and therapeutic implication of PANoptosis in cancer

Regulated cell death (RCD) is considered a critical pathway in cancer therapy, contributing to eliminating cancer cells and influencing treatment outcomes. The application of RCD in cancer treatment is marked by its potential in targeted therapy and immunotherapy. As a type of RCD, PANoptosis has emerged as a unique form of programmed cell death (PCD) characterized by features of pyroptosis, apoptosis, and necroptosis but cannot be fully explained by any of these pathways alone. It is regulated by a multi-protein complex called the PANoptosome. As a relatively new concept first described in 2019, PANoptosis has been shown to play a role in many diseases, including cancer, infection, and inflammation. This study reviews the application of PCD in cancer, particularly the emergence and implication of PANoptosis in developing therapeutic strategies for cancer. Studies have shown that the characterization of PANoptosis patterns in cancer can predict survival and response to immunotherapy and chemotherapy, highlighting the potential for PANoptosis to be used as a therapeutic target in cancer treatment. It also plays a role in limiting the spread of cancer cells. PANoptosis allows for the elimination of cancer cells by multiple cell death pathways and has the potential to address various challenges in cancer treatment, including drug resistance and immune evasion. Moreover, active investigation of the mechanisms and potential therapeutic agents that can induce PANoptosis in cancer cells is likely to yield effective cancer treatments and improve patient outcomes. Research on PANoptosis is still ongoing, but it is a rapidly evolving field with the potential to lead to new treatments for various diseases, including cancer.

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.

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.

Identification of PANoptosis-related biomarkers and immune infiltration characteristics in psoriasis

BACKGROUND

PANoptosis may play a vital role in psoriasis. We investigated the relationship between PANoptosis in psoriasis.

METHODS

Genes information was mainly obtained from GeneCards and the gene expression omnibus database. Genefunctions identification was based on gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Gene set enrichment analysis was used to identify enriched signaling pathways in psoriasis. We constructed PPI networks using the search tool for the retrieval of interacting genes database and Cytoscape and explored mRNA-miRNA, mRNA-TF, and mRNA-drug interaction networks. Receiver operating characteristic curves were performed to screen potential biomarkers among these hub genes. Immune cell infiltration was analyzed using the Pearson algorithm, and the correlation between immune-cell abundance and PANoptosis-related differentially expressed gene (PDGs) was investigated.

RESULTS

We identified 10 PDGs, which were mainly involved in pyroptosis, cytokine-mediated signaling pathways, Salmonella infection and NOD-like receptor signaling pathway. The activated pathways were mostly proinflammatory and immunoregulatory pathways between immune cells. BAK1, CASP4, IL18, and IRF1 were identified as hub genes in the mRNA-miRNA network, and BAK1, IRF1, and PYCARD were hub genes in the mRNA-TF network. CASP1 was found to be the most targeted gene by drugs or molecular compounds. We found PDGs were positively associated with proinflammatory immune cell infiltration and negatively associated with anti-inflammatory or regulatory immune cells.

CONCLUSION

We confirmed the role of PANoptosis in psoriasis for the first time and predicted hub genes and immune characteristics, which provides new ideas for further investigation of psoriasis on pathogenic mechanisms and therapeutic strategies.

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.

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.

PANoptosis-related molecular subtype and prognostic model associated with the immune microenvironment and individualized therapy in pancreatic cancer

Background

PANoptosis is an inflammatory type of programmed cell death regulated by PANopotosome. Mounting evidence has shown that PANoptosis could be involved in cancer pathogenesis and the tumor immune microenvironment. Nevertheless, there have been no studies on the mechanism of PANoptosis on pancreatic cancer (PC) pathogenesis.

Methods

We downloaded the data on transcriptomic and clinical features of PC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Additionally, the data on copy number variation (CNV), methylation and somatic mutations of genes in 33 types of cancers were obtained from TCGA. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic model using LASSO and Cox regression analyses. Moreover, RT-qPCR was performed to determine the expression of genes involved in the model.

Results

We obtained 66 PANoptosis-related genes (PANRGs) from published studies. Of these, 24 PC-specific prognosis-related genes were identified. Pan-cancer analysis revealed complex genetic changes, including CNV, methylation, and mutation in PANRGs were identified in various cancers. By consensus clustering analysis, PC patients were classified into two PANoptosis-related patterns: PANcluster A and B. In PANcluster A, the patient prognosis was significantly worse compared to PANcluster B. The CIBERSORT algorithm showed a significant increase in the infiltration of CD8⁺ T cells, monocytes, and naïve B cells, in patients in PANcluster B. Additionally, the infiltration of macrophages, activated mast cells, and dendritic cells were higher in patients in PANcluster A. Patients in PANcluster A were more sensitive to erlotinib, selumetinib and trametinib, whereas patients in PANcluster B were highly sensitive to irinotecan, oxaliplatin and sorafenib. Moreover, we constructed and validated the PANoptosis-related prognostic model to predict the patient's survival. Finally, the GEPIA and Human Protein Atlas databases were analyzed, and RT-qPCR was performed. Compared to normal tissues, a significant increase in CXCL10 and ITGB6 (associated with the model) expression was observed in PC tissues.

Conclusion

We first identified the PANoptosis-related molecular subtypes and established a PANoptosis-related prognostic model for predicting the survival of patients with PC. These results would aid in exploring the mechanisms of PANoptosis in PC pathogenesis.

The PANoptosis-related signature indicates the prognosis and tumor immune infiltration features of gliomas

Background

Gliomas are the most common primary tumors of the central nervous system, with high heterogeneity and highly variable survival rates. Accurate classification and prognostic assessment are key to the selection of treatment strategies. One hallmark of the tumor is resistance to cell death. PANoptosis, a novel mode of programmed cell death, has been frequently reported to be involved in the innate immunity associated with pathogen infection and played an important role in cancers. However, the intrinsic association of PANoptosis with glioma requires deeper investigation.

Methods

The genetics and expression of the 17 reported PANoptosome-related genes were analyzed in glioma. Based on these genes, patients were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between clusters, a prognostic model called PANopotic score was constructed after univariate Cox regression, LASSO regression, and multivariate Cox regression. The expression of the 5 genes included in the PANopotic score was also examined by qPCR in our cohort. The prognostic differences, clinical features, TME infiltration status, and immune characteristics between PANoptotic clusters and score groups were compared, some of which even extended to pan-cancer levels.

Results

Gene mutations, CNVs and altered gene expression of PANoptosome-related genes exist in gliomas. Two PANoptotic clusters were significantly different in prognosis, clinical features, immune characteristics, and mutation landscapes. The 5 genes included in the PANopotic score had significantly altered expression in glioma samples in our cohort. The high PANoptotic score group was inclined to show an unfavorable prognosis, lower tumor purity, worse molecular genetic signature, and distinct immune characteristics related to immunotherapy. The PANoptotic score was considered as an independent prognostic factor for glioma and showed superior prognostic assessment efficacy over several reported models. PANopotic score was included in the nomogram constructed for the potential clinical prognostic application. The associations of PANoptotic score with prognostic assessment and tumor immune characteristics were also reflected at the pan-cancer level.

Conclusion

Molecular subtypes of glioma based on PANoptosome-related genes were proposed and PANoptotic score was constructed with different clinical characteristics of anti-tumor immunity. The potential intrinsic association between PANoptosis and glioma subtypes, prognosis, and immunotherapy was revealed.

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

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.