Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer.

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

HIST3H2A promotes the progression of prostate cancer through inhibiting cell necroptosis.

In recent years, there has been an increase in the incidence and mortality rates of prostate cancer (PCa). However, the specific molecular mechanisms underlying its occurrence and development remain unclear, necessitating the identification of new therapeutic targets. Through bioinformatics analysis, we discovered a previously unstudied differential gene called HIST3H2A in prostate cancer. Our study revealed that HIST3H2A is highly expressed in PCa tissues, as confirmed by analysis of both the GEO and UALCAN databases. Further analysis using the KEGG database demonstrated that HIST3H2A regulates the pathway of programmed necroptosis in cells. Additionally, we observed significant up-regulation of HIST3H2A in PCa tissues and cell lines. HIST3H2A was found to regulate cell proliferation, migration, invasion, and the epithelial-mesenchymal transition (EMT) process in tumors. Notably, HIST3H2A's role in regulating programmed necroptosis in prostate cancer cells differs from its role in apoptosis. In vitro and in vivo experiments collectively support the key role of HIST3H2A in promoting the development of prostate cancer, highlighting its potential as a therapeutic target for patients with PCa.

LncRNAs in necroptosis: Deciphering their role in cancer pathogenesis and therapy.

Necroptosis, a controlled type of cell death that is different from apoptosis, has become a key figure in the aetiology of cancer and offers a possible target for treatment. A growing number of biological activities, including necroptosis, have been linked to long noncoding RNAs (lncRNAs), a varied family of RNA molecules with limited capacity to code for proteins. The complex interactions between LncRNAs and important molecular effectors of necroptosis, including mixed lineage kinase domain-like pseudokinase (MLKL) and receptor-interacting protein kinase 3 (RIPK3), will be investigated. We will explore the many methods that LncRNAs use to affect necroptosis, including protein-protein interactions, transcriptional control, and post-transcriptional modification. Additionally, the deregulation of certain LncRNAs in different forms of cancer will be discussed, highlighting their dual function in influencing necroptotic processes as tumour suppressors and oncogenes. The goal of this study is to thoroughly examine the complex role that LncRNAs play in controlling necroptotic pathways and how that regulation affects the onset and spread of cancer. In the necroptosis for cancer treatment, this review will also provide insight into the possible therapeutic uses of targeting LncRNAs. Techniques utilising LncRNA-based medicines show promise in controlling necroptotic pathways to prevent cancer from spreading and improve the effectiveness of treatment.

The necroptosis-related signature and tumor microenvironment immune characteristics associated with clinical prognosis and drug sensitivity analysis in stomach adenocarcinoma.

PURPOSE: Necroptosis plays an important role in the tumorigenesis, development, metastasis, and drug resistance of malignant tumors. This study explored the new model for assessing stomach adenocarcinoma (STAD) prognosis and immunotherapy by combining long noncoding RNAs associated with necroptosis. METHODS: Patient clinical data and STAD gene expression profiles were curated from The Cancer Genome Atlas (TCGA). Immune-related genes were sourced from a specialized molecular database. Perl software and R software were used for data processing and analysis. Necroptosis-related lncRNAs in STAD were pinpointed via R's correlation algorithms. These lncRNAs, in conjunction with clinical data, informed the construction of a prognostic lncRNA-associated risk score model using univariate and multivariate Cox regression analyses. The model's prognostic capacity was evaluated by Kaplan-Meier survival curves and validated as an independent prognostic variable. Further, a nomogram incorporating this model with clinical parameters was developed, offering refined individual survival predictions. Subsequent analyses of immune infiltration and chemosensitivity within necroptosis-related lncRNA clusters utilized an arsenal of bioinformatic tools, culminating in RT-PCR validation of lncRNA expression. RESULTS: Through rigorous Cox regression, 21 lncRNAs were implicated in the risk score model. Stratification by median risk scores delineated patients into high- and low-risk cohorts, with the latter demonstrating superior prognostic outcomes. The risk model was corroborated as an independent prognostic indicator for STAD. The integrative nomogram displayed high concordance between predicted and observed survival rates, as evidenced by calibration curves. Differential immune infiltration in risk-defined groups was illuminated by the single sample GSEA (ssGSEA), indicating pronounced immune presence in higher-risk patients. Tumor microenvironment (TME) analysis showed that cluster-C3 had the highest score in the analysis of the three TMEs. Through the differential analysis of immune checkpoints, it was found that almost all immune checkpoint-related genes were expressed differently in various tumor clusters. Among them, CD44 expression was the highest. By comparing all drug sensitivities, we screened out 29 drugs with differences in drug sensitivity across different clusters. Risk score gene expression identification results showed that these lncRNAs were abnormally expressed in gastric cancer cell lines. CONCLUSIONS: This investigation provides a robust methodological advance in prognosticating and personalizing immunotherapy for STAD, leveraging quantitatively derived tumor cluster risk scores. It posits the use of necroptosis-related lncRNAs as pivotal molecular beacons for guiding therapeutic strategies and enhancing clinical outcomes in STAD.

Predictive and Carcinogenic Roles of Necroptosis-Related miR-425-5p and miR-16-5p in Esophageal Squamous Cell Carcinoma.

OBJECTIVE: To examine the expression and function of necroptosis-associated miRNAs in esophageal squamous cell carcinoma. METHODS: A total of three microarray datasets, i.e., GSE122497, GSE114110, and GSE43732, were selected from the GEO database for differential analysis of necroptosis-related miRNA expression. The differentially expressed miRNAs were screened for target miRNAs using Kaplan-Meier survival analysis in the OncomiR database. The expression of the target miRNAs in the HEEC, KYSE-450, TE-1, and KYSE-410 cell lines was measured via qPCR. The expression of the target miRNAs in esophageal cancer cells was regulated by transfection with Lipofectamine 2000, and cell proliferation, cell migration, cell apoptosis and the cell cycle were detected by CCK-8, Transwell, and flow cytometry. RESULTS: The tumor tissue and peripheral blood of esophageal squamous cell cancer patients showed differential expression of 7 miRNAs related to necroptosis. Survival analysis revealed that miR-425-5p and miR-16-5p were negatively correlated with patient survival. The esophageal squamous cell carcinoma cell lines exhibited increased expression of miR-425-5p and miR-16-5p, with KYSE-410 exhibiting the most significant increase. Inhibition of miR-425-5p and miR-16-5p expression in the KYSE-410 cell line resulted in increased apoptosis, decreased proliferation, and decreased migration of esophageal cancer cells as well as a significant increase in the S phase and a decrease in the G2/M phase according to the cell cycle assay. CONCLUSION: The pro-carcinogenic role of miR-425-5p and miR-16-5p has been observed in esophageal squamous cell carcinoma.

Integrative analysis and validation of necroptosis-related molecular signature for evaluating diagnosis and immune features in Rheumatoid arthritis.

OBJECTIVES: Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that is characterized by persistent morning stiffness, joint pain, and swelling. However, there is a lack of reliable diagnostic markers and therapeutic targets that are both effective and trustworthy. METHODS: In this study, gene expression profiles (GSE89408, GSE55235, GSE55457, and GSE77298) were obtained from the Gene Expression Omnibus database. Differentially expressed necroptosis-related genes were attained from intersection of necroptosis-related gene set, differentially expressed genes, and weighted gene co-expression network analysis. The LASSO, random forest, and SVM-RFE machine learning algorithms were utilized to further screen potential diagnostic genes for RA. Immune cell infiltration was analyzed using the CIBERSORT method. The expressions of diagnostic genes were validated through quantitative real-time PCR, western blotting, immunohistochemistry, and immunofluorescence staining in synovial tissues collected from three trauma controls and three RA patients. RESULTS: Five core necroptosis-related genes (FAS, CYBB, TNFSF10, EIF2AK2, and BIRC2) were identified as potential biomarkers for RA. Two different necroptosis patterns based on these five genes were confirmed to significantly correlated with immune cells (especially macrophages). In vitro experiments showed significantly higher mRNA and protein expression levels of CYBB and EIF2AK2 in RA patients compared to normal controls, consistent with the bioinformatics analysis results. CONCLUSION: Our study identified a novel necroptosis-related subtype and five diagnostic biomarkers of RA, revealed vital roles in the development and occurrence of RA, and offered potential targets for clinical diagnosis and immunotherapy.

Novel diagnostic biomarkers related to necroptosis and immune infiltration landscape in acute myocardial infarction.

Background: Acute myocardial infarction (AMI) can occur suddenly, which may induce deadly outcomes, and the population suffering from AMI presents a younger trend. Necroptosis, the new cell necrosis type, is associated with the pathogenic mechanisms of diverse cardiovascular diseases (CVDs). Its diagnostic value and molecular mechanisms in AMI are still unclear. Objective: This study focused on determining key necroptosis-related genes as well as immune infiltration in AMI. Methods: We first examined the GSE66360 dataset for identifying necroptosis-related differentially expressed genes (NRDEGs). Thereafter, GO and functional annotation were performed, then a PPI network was built. In addition, "CIBERSORT" in R was applied in comparing different immune infiltration degrees in AMI compared with control groups. The receiver operating characteristic (ROC) curve was plotted to evaluate whether hub NRDEGs could be used in AMI diagnosis. Associations of immune cells with candidate NRDEGs biomarkers were examined by Spearman analysis. Finally, hub NRDEGs were validated by cell qPCR assays and another two datasets. Results: A total of 15 NRDEGs were identified and multiple enrichment terms associated with necroptosis were discovered through GO and KEGG analysis. Upon module analysis, 10 hub NRDEGs were filtered out, and the top six hub NRDEGs were identified after ROC analysis. These top six NRDEGs might have a certain effect on modulating immune infiltrating cells, especially for mast cells activated, NK cells activated and neutrophils. Finally, two AMI datasets and qPCR assay came to identical findings. Conclusion: Our results offer the reliable molecular biomarkers and new perspectives for necroptosis in AMI, which lay a certain foundation for developing novel anti-AMI therapeutic targets.

Analysis and experimental validation of necroptosis-related molecular classification, immune signature and feature genes in Alzheimer's disease.

Necroptosis, a programmed cell death pathway, has been demonstrated to be activated in Alzheimer's disease (AD). However, the precise role of necroptosis and its correlation with immune cell infiltration in AD remains unclear. In this study, we conducted non-negative matrix factorization clustering analysis to identify three subtypes of AD based on necroptosis-relevant genes. Notably, these subtypes exhibited varying necroptosis scores, clinical characteristics and immune infiltration signatures. Cluster B, characterized by high necroptosis scores, showed higher immune cell infiltration and was associated with a more severe pathology, potentially representing a high-risk subgroup. To identify potential biomarkers for AD within cluster B, we employed two machine learning algorithms: the least absolute shrinkage and selection operator regression and Random Forest. Subsequently, we identified eight feature genes (CARTPT, KLHL35, NRN1, NT5DC3, PCYOX1L, RHOQ, SLC6A12, and SLC38A2) that were utilized to develop a diagnosis model with remarkable predictive capacity for AD. Moreover, we conducted validation using bulk RNA-seq, single-nucleus RNA-seq, and in vivo experiments to confirm the expression of these feature genes. In summary, our study identified a novel necroptosis-related subtype of AD and eight diagnostic biomarkers, explored the roles of necroptosis in AD progression and shed new light for the clinical diagnosis and treatment of this disease.

Necroptosis pathway emerged as potential diagnosis markers in spinal cord injury.

The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.

Prognostic Model Associated with Necroptosis in Colorectal Cancer based on Transcriptomic Analysis and Experimental Validation.

PURPOSE: Numerous studies have emphasised the importance of necroptosis in the malignant progression of colorectal cancer (CRC). However, whether necroptosis-related genes (NRGs) can be used to predict the prognosis of CRC remains to be revealed. METHODS: Patients with CRC were divided into two clusters based on the expression of NRGs, and prognosis was compared between the two clusters. A prognostic model was established based on NRGs, and its predictive efficiency was validated using Kaplan-Meier (K-M) curves, receiver operating characteristic (ROC) curves and a nomogram. Immune infiltration, single-cell and drug sensitivity analyses were used to examine the effects of NRGs on the prognosis of CRC. RESULTS: The prognostic model served as a valid and independent predictor of CRC prognosis. Immune infiltration and single-cell analyses revealed that the unique immune microenvironment of CRC was regulated by NRGs. Drug sensitivity analysis showed that patients in the high- and low-risk groups were sensitive to different drugs. In addition, H2BC18 was found to play an important role in regulating the malignant progression of CRC. CONCLUSION: This study provides novel insights into precision immunotherapy based on NRGs in CRC. The NRG-based prognostic model may help to identify targeted drugs and develop more effective and individualised treatment strategies for patients with CRC.

Construction of a necroptosis-related lncRNA signature for predicting prognosis and revealing the immune microenvironment in bladder cancer.

BACKGROUND: Bladder cancer (BCa) is a common malignancy in the urinary system. Necroptosis, a recently discovered form of programmed cell death, is closely associated with the development and progression of various types of tumors. Targeting necroptosis through anti-cancer strategies has shown potential as a therapy for cancer. We aimed to develop a necroptosis-related lncRNAs (NRlncRNAs) risk model that can predict the survival and tumor immunity of BCa patients. METHODS: We analyzed sequencing data obtained from the TCGA database, and applied least absolute shrinkage and selection operator (LASSO) and Cox regression analysis to identify crucial NRlncRNAs for building a risk model. Using the risk score, we categorized patients into high- and low-risk groups, and assessed the accuracy with the area under the receiver operating characteristic (AUROC) and Kaplan-Meier curves. We performed the RT-qPCR to detect the expression differences of the genes based on the risk model. RESULTS: We identified a total of 296 NRlncRNAs, and 6 of them were included in the prognostic model. The AUC values for 1-, 3-, and 5-year predictions were 0.675, 0.726 and 0.734, respectively. Our risk model demonstrated excellent predictive performance and served as an independent predictor with high predictive power. Additionally, we performed PCA, TMB, GSEA analyses, and evaluated immune cell infiltration, to reveal significant differences between the high- and low-risk groups in functional signaling pathways, immunological status, and mutation profiles. Finally, we assessed the chemotherapeutic response of several drugs. According to the RT-qPCR results, we found that four NRlncRNAs of the risk model were more highly expressed in BCa cell lines than human immortalized uroepithelial cell line and regulated the occurrence and progression of bladder cancer. CONCLUSION: We constructed a novel NRlncRNAs-associated risk model, which could predict the prognosis and immune response of BCa patients.

Identification of necroptosis-associated miRNA signature for predicting prognosis and immune landscape in stomach adenocarcinoma.

PURPOSE: This study aims to identify the potential necroptosis related genes (NRGs)-associated miRNAs signature and explore the impact on the prognosis of stomach adenocarcinoma (STAD). METHODS: Employing rigorous methodologies, we utilized univariate Cox, Lasso and multivariate Cox regression analyses to develop a prognostic signature. Kaplan-Meier (K-M) and ROC curves were applied to assess the prognostic value of signature in a training group and an independent test group. Furthermore, we conducted Gene Set Enrichment Analysis (GSEA) for enrichment of tumor-related pathways. The risk score was calculated for each patient based on the expression of miRNAs which were enrolled in the signature. Patients were stratified into high- and low-risk groups. The immune cell infiltration and immunotherapy were compared between the two groups. Finally, the diagnostic potential of the miRNA was explored by RT-qPCR. RESULTS: We constructed a prognostic model based on 6 NRGs-associated miRNAs. K-M plots underscored superior survival outcomes in the low-risk group. GSEA results revealed the enrichment of several tumor-related pathways in the high-risk group. Notably, CD8+ T cells, Tregs and activated memory CD4+ T cells exhibited negative correlations with the risk score. Additionally, a few immune checkpoint genes, such as CTLA4, PD1 and PD-L1, were significantly upregulated in the low-risk group. Furthermore, the serum expression levels of all these 6 miRNAs were significantly elevated in STAD patients. CONCLUSIONS: Our study identified a robust risk score derived from a signature of 6 NRGs-associated miRNAs, demonstrating high efficacy for prognosis of STAD. These results not only contributed to our understanding of STAD pathogenesis, but also held promise for potential clinical applications, particularly in the realm of personalized immunotherapy for STAD patients.

Development of a necroptosis-related prognostic model for uterine corpus endometrial carcinoma.

Necroptosis is a recently identified caspase-independent form of cell death which plays a significant role in the onset and progression of cancer. MicroRNAs (miRNAs) are vital for the development of uterine corpus endometrial carcinoma (UCEC) because they are an important regulatory component in necroptosis. This study developed a new necroptosis-related miRNAs profile to predict the prognosis of patients with UCEC. The TCGA-UCEC cohort's RNA sequencing data, consisting of 534 tumor samples and 33 normal samples, was downloaded. Ten differentially expressed miRNAs related to necroptosis were identified. A prediction model for necroptosis-related miRNAs was then created through COX regression and nomograms analysis. Clinical and pathological parameters were integrated to construct a nomogram and evaluate the model. Prognosis-related miRNAs were further used to predict target genes, and functional analysis was conducted to explore the potential mechanisms of these target genes. Subsequently, immune infiltration analysis was performed using transcriptome data to identify immune genes associated with prognosis, and the expression levels of target gene was validated using UCEC tissues. We identified 7 up-regulated miRNAs (hsa-miR-577, hsa-miR-7-5p, hsa-miR-210-3p, hsa-miR-210-5p, hsa-miR-200a-5p, hsa-miR-141-3p, hsa-miR-425-5p) and 3 down-regulated miRNAs (hsa-miR-7-2-3p, hsa-miR-383-5p, hsa-miR-29a-3p). The risk signature was based on univariate and multivariate COX analyses, constructed using 2 independent prognostic factors and miRNAs (hsa-miR-425-5p, hsa-miR-7-5p) associated with necroptosis. Nomograms demonstrated the prognostic value of risk level, age, FIGO stage, and histological type. Kaplan-Meier analysis revealed significant differences in overall survival (OS) outcomes associated with the expression of hsa-miR-425-5p (P < 0.001) and hsa-miR-7-5p (P = 0.015). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) investigations indicated that these miRNAs play crucial roles in tumor development, metastasis, and prognosis. Immune infiltration analysis showed decreased infiltration of CD8+ T cells, CD8+ T cells, NK cells, and M1 macrophages in normal tissues. Subsequently, a necroptosis-related immune gene significantly associated with prognosis (THRB) was identified, western blot and immunohistochemical staining confirmed the differential expression of THRB in normal endometrial tissues and tumor. Our findings demonstrate a close association between necroptosis and UCEC. The two necroptosis-related miRNAs used in this study may serve as valuable prognostic markers for UCEC patients, and are associated with immune cell infiltration. This suggests that necroptosis may be involved in the development of UCEC through its interaction with immune responses.

Identification of a necroptosis-related gene signature for making clinical predictions of the survival of patients with lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a major pathological subtype of malignant lung cancer with a poor prognosis. Necroptosis is a caspase-independent programmed cell death mode that plays a pivotal role in cancer oncogenesis and metastasis. Here, we explore the prognostic values of different necroptosis-related genes (NRGs) in LUAD. Methods: mRNA expression data and related clinical information for LUAD samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. NRGs were identified using the GeneCards database. Least absolute shrinkage and selection operator Cox regression and multivariate Cox analysis were used to construct a prognostic risk model. Time-dependent receiver-operating characteristic curves and a nomogram were constructed to validate the predictive values of the prognostic signatures. A necroptosis-related protein-protein interaction network was visualised using the STRING database and Cytoscape software. Functional analyses, including Gene Ontology, Kyoto Encyclopaedia of Genes and Genomes pathway enrichment, gene set enrichment, and gene set variation analyses, were conducted to explore the underlying molecular mechanisms. Finally, the mRNA expression of the prognostic signatures in LUAD cell lines was assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. Results: A prognostic model was established for eight NRGs (CALM1, DDX17, FPR1, OGT, PGLYRP1, PRDX1, TUFM, and CPSF3) based on TCGA-cohort data and validated with the GSE68465 cohort. Patients with low-risk scores had better survival outcomes than those with high-risk scores (p = 0.00013). The nomogram was used to predict the prognosis of patients with LUAD. The prediction curves for 1-, 3-, and 5-year OS showed good predictive performance and the accuracy of the nomograms increased over time. RT-qPCR results demonstrated that these eight genes, especially CALM1, PRDX1, and PGLYRP1, were differentially expressed in LUAD cells. Conclusion: We constructed a reliable eight-NRG signature that provides new insights for guiding clinical practice in the prognosis and treatment of LUAD.

Predicting prognosis and immune status in sarcomas by identifying necroptosis-related lncRNAs.

BACKGROUND: Sarcomas are a type of highly heterogeneous malignant tumors originating from mesenchymal tissues. Necroptosis is intricately connected to the oncogenesis and progression of tumors. The main goal of this research is to assess the prognostic value of necroptosis-related lncRNAs (NRlncRNAs) in sarcomas and to develop a risk model based on NRlncRNAs to evaluate prognostic and immune status of the sarcomas. METHODS: We screened NRlncRNAs using the gene co-expression network, developed a prognostic risk model of sarcomas, and then verified the model. Following that, various bioinformatics analysis algorithms were employed to analyze the distinct characteristics of patients of the risk model. Furthermore, the function and regulatory mechanism of NRlncRNA SNHG6 in sarcomas were investigated through osteosarcoma cell experiments, such as qRT-PCR, Western blot, CCK-8, clone formation, and transwell assay. RESULTS: We successfully developed a NRlncRNAs-related prognostic risk model and screened 5 prognosis-related NRlncRNAs, with SNGH6 being the most significant for prognosis of patients. According to results, the significant differences exist in prognosis, clinical characteristics, and tumor immune status among patients of the risk model. The experiments of osteosarcoma cells demonstrated that NRlncRNA SNHG6 knockdown significantly attenuated the cells' proliferation, migration, and invasion. qRT-PCR and WB results showed that SNHG6 regulated AXL and AKT signaling. CONCLUSIONS: We have developed an innovative investigation on NRlncRNAs, which can serve as a reference for diagnosis, therapy, and prognosis of sarcomas. Additionally, we demonstrated that NRlncRNA SNHG6 regulated AXL and AKT signaling in osteosarcoma cells and the proliferation, migration, and invasion of tumor cells.

PARP5A and RNF146 phase separation restrains RIPK1-dependent necroptosis.

Phase separation is a vital mechanism that mediates the formation of biomolecular condensates and their functions. Necroptosis is a lytic form of programmed cell death mediated by RIPK1, RIPK3, and MLKL downstream of TNFR1 and has been implicated in mediating many human diseases. However, whether necroptosis is regulated by phase separation is not yet known. Here, we show that upon the induction of necroptosis and recruitment by the adaptor protein TAX1BP1, PARP5A and its binding partner RNF146 form liquid-like condensates by multivalent interactions to perform poly ADP-ribosylation (PARylation) and PARylation-dependent ubiquitination (PARdU) of activated RIPK1 in mouse embryonic fibroblasts. We show that PARdU predominantly occurs on the K376 residue of mouse RIPK1, which promotes proteasomal degradation of kinase-activated RIPK1 to restrain necroptosis. Our data demonstrate that PARdU on K376 of mouse RIPK1 provides an alternative cell death checkpoint mediated by phase separation-dependent control of necroptosis by PARP5A and RNF146.

Necroptosis inhibitors: mechanisms of action and therapeutic potential.

Necroptosis is a type of programmed cell death that is morphologically similar to necrosis. This type of cell death is involved in various pathophysiological disorders, including inflammatory, neurodegenerative, infectious, and malignant diseases. Receptor-interacting protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like protein (MLKL) pseudokinase constitute the core components of the necroptosis signaling pathway and are considered the most promising targets for therapeutic intervention. The discovery and characterization of necroptosis inhibitors not only accelerate our understanding of the necroptosis signaling pathway but also provide important drug candidates for the treatment of necroptosis-related diseases. Here, we will review recent research progress on necroptosis inhibitors, mechanisms of action and their potential applications for disease treatment.

Conduction and validation of a novel prognostic signature in cervical cancer based on the necroptosis characteristic genes via integrating of multiomics data.

The significance of necroptosis in recurrent or metastatic cervical cancer remains unclear. In this study, we utilized various bioinformatics methods to analyze the cancer genome atlas (TCGA) data, gene chip and the single-cell RNA-sequencing (scRNA seq) data. And a necroptosis-related genes signature for prognostic assessment of patients with cervical cancer was constructed successfully. Survival analysis, receiver operating characteristic (ROC) curve, the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest analysis were performed to validate this signature. Patients in TCGA-CESC cohort were grouped into "high-necroptosis score (H-NCPS)" vs "low-necroptosis score (L-NCPS)" subgroups based on the median of necroptosis score of each patient. Analyses of the tumor microenvironment manifested "H-NCPS" patients associated with lower degree of immune infiltration. Through the utilization of survival analysis, cell communication, and Gene Set Enrichment Analysis (GSEA), PGK1 was determined to be the pivotal gene within the 9-gene signature associated with necroptosis. The high expression of PGK1 in cervical cancer cells was confirmed through the utilization of quantitative real-time polymerase chain reaction (RT-qPCR) and the human protein atlas (HPA). In the interim, PGK1 prompted the transition of M1 macrophages to M2 macrophages and influenced the occurrence and development of necroptosis. In conclusion, the 9-gene signature developed from necroptosis-related genes has shown significant predictive capabilities for the prognosis of cervical cancer, offered valuable guidance for individualized and targeted treatment approaches for patients.

Analysis of multiple programmed cell death-related prognostic genes and functional validations of necroptosis-associated genes in oesophageal squamous cell carcinoma.

BACKGROUND: Oesophageal squamous cell carcinoma (ESCC) is a lethal malignancy. Immune checkpoint inhibitors (ICIs) showed great clinical benefits for patients with ESCC. We aimed to construct a model predicting prognosis and response to ICIs by integrating diverse programmed cell death (PCD) forms. METHODS: Genes related to 14 PCDs were collected to generate multi-gene signatures, including apoptosis, necroptosis, pyroptosis, ferroptosis, and cuproptosis. Bulk and single-cell RNA transcriptome datasets were used to develop and validate the model. We assessed the functions of two necroptosis-related genes in ESCC cells by Western blot, co-immunoprecipitation (Co-IP), LDH release assay, CCK-8, and migration assay, followed by immunohistochemistry (IHC) staining on samples of patients with ESCC (n = 67). FINDINGS: We built and validated a 16-gene prognostic combined cell death index (CCDI) by combining immunogenic cell death (ICD) and necroptosis signatures. The CCDI could also predict response to ICIs in cancer, as shown by Tumour Immune Dysfunction and Exclusion (TIDE) analysis, confirmed in four independent ICI clinical trials. Trajectory analysis revealed that HOOK1 and CUL4A might affect ESCC cell fate. We found that HOOK1 induced necroptosis and inhibited the proliferation and migration of ESCC cells, while CUL4A exhibited the opposite effects. Co-IP assay confirmed that HOOK1 and CUL4A promoted and reduced necrosome formation in ESCC cells. Data from patients with ESCC further supported that HOOK1 and CUL4A might be a tumour suppressor and oncogene, respectively. INTERPRETATION: We constructed a CCDI model with potential in predicting prognosis and response to ICIs in cancer. HOOK1 and CUL4A in the CCDI model are crucial prognostic biomarkers in ESCC. FUNDING: The Natural Science Foundation of China [82172786], The National Cancer Center Climbing Fund of China [NCC201908B06], The Natural Science Foundation of Heilongjiang Province [LH2021H077].

Unveiling Circular RNA-Mediated Regulatory Mechanisms in Necroptosis in Premature Ovarian Failure.

BACKGROUND: Necroptosis is a programmed necrotic cell death, in which dying cells rupture and release intracellular components that trigger a proinflammatory response. The current study aimed at probing the circular RNA (circRNA)-mediated regulatory mechanisms in necroptosis in premature ovarian failure (POF). METHODS: CircRNA sequencing analysis was conducted in ovarian tissues of control and POF rats and transcriptome microarrays were acquired from the GSE33423 dataset. Differential expression analysis of circRNAs and mRNAs was executed between the POF and control data. Both a necroptosis-based circRNA-microRNA (miRNA)-mRNA network and a protein-protein interaction (PPI) network were established. Then, the functional annotation and immunological traits were analyzed. RESULTS: Totally, 1266 upregulated and 1283 downregulated circRNAs as well as 1101 upregulated and 1168 downregulated mRNAs were determined in the POF rats versus the controls. The differentially expressed mRNAs predominantly correlated with necroptosis. The circRNA-miRNA-mRNA networks of downregulated necroptosis genes (comprising rno_circRNA_004995-rno-miR-148b-5p-H2afy2, rno_circRNA_016998-rno-miR-29a-5p-Hmgb1, and rno_circRNA_017593-rno-miR-29a-5p-Hmgb1) and upregulated necroptosis genes (comprising rno_circRNA_015900-rno-miR-935-Stat1, rno_circRNA_007946-rno-miR-328a-3p-Stat5a, rno_circRNA_007947-rno-miR-328a-3p-Stat5a, rno_circRNA_005064-rno-miR-18a-5p-Stat1, rno_circRNA_005064-rno-miR-18a-5p-Stat5a, rno_circRNA_005115-rno-miR-22-3p-Stat1, rno_circRNA_009028-rno-miR-342-5p-Stat1, rno_circRNA_011240-rno-miR-1224-Stat5a, rno_circRNA_016078-rno-miR-711-Stat5a) were built. POF-specific necroptosis genes (STAT1, STAT5A, PLA2G4A, HMG1L1, HMGB1, AGER, EGFR, HDAC7, IFNA1, IL10RB, IL27RA, PYGL, SOCS1, TRADD, CXCL10, DDX5, EZH2, FADS2, FER, H2AFY2, HIST1H2AF, IFI44L, IL27, IRGM, MX1, NFKB2, PAFAH2, PEMT, PGM2L1, PGR, PHKA2, and PLB1) were selected since they displayed notable associations with most immune cells, immune checkpoints, chemokines, human leukocyte antigen (HLA) molecules, and immune receptors. CONCLUSIONS: Altogether, we proposed the presence of widespread regulatory mechanisms of circRNAs in necroptosis and demonstrated that altered circRNA biogenesis might contribute to POF by affecting necroptosis.