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.

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.

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.

A Novel Defined Necroptosis-Related Genes Prognostic Signature for Predicting Prognosis and Treatment of Osteosarcoma

Osteosarcoma (OS) is a frequent primary malignant bone tumor, with a poor prognosis. Necroptosis is strongly correlated with OS and may be an influential target for treating OS. This study's objective was to establish a necroptosis-related gene (NRG) prognostic signature that could predict OS prognosis and guide OS treatment. First, we identified 20 NRGs associated with OS survival based on the TARGET database. We then derived a 7 NRG prognostic signature. Our findings revealed that the 7 NRG prognostic signature performed well in predicting the survival of OS patients. We next analyzed differences in immunological status and immune cell infiltration. In addition, we examined the relationship between chemo/immunotherapeutic response and the 7-NRG prognostic signature. In addition, to probe the mechanisms underlying the NRG prognostic signature, we performed functional enrichment assays including GO and KEGG. Finally, CHMP4C was selected for functional experiments. Silencing CHMP4C prevented OS cells from proliferating, migrating, and invading. This 7-NRG prognostic signature seems to be an excellent predictor that can provide a fresh direction for OS treatment.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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].

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma

Background

Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC.

Methods

Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion).

Results

Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion.

Conclusion

The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.

Coexistence of apoptosis, pyroptosis, and necroptosis pathways in celiac disease

Usually, the massive elimination of cells under steady-state conditions occurs by apoptosis, which is also acknowledged to explain the loss of enterocytes in the small intestine of celiac disease (CD) patients. However, little is known about the role of proinflammatory cell death pathways in CD. Here, we have used confocal microscopy, western blot, and RT-qPCR analysis to assess the presence of regulated cell death pathways in the duodenum of CD patients. We found an increased number of dead (TUNEL+) cells in the lamina propria of small intestine of CD patients, most of them are plasma cells (CD138+). Many dying cells expressed FAS and were in close contact with CD3+ T cells. Caspase-8 and caspase-3 expression was increased in CD, confirming the activation of apoptosis. In parallel, caspase-1, IL-1β, and GSDMD were increased in CD samples indicating the presence of inflammasome-dependent pyroptosis. Necroptosis was also present, as shown by the increase of RIPK3 and phosphorylate MLKL. Analysis of published databases confirmed that CD has an increased expression of regulated cell death -related genes. Together, these results reveal that CD is characterized by cell death of different kinds. In particular, the presence of proinflammatory cell death pathways may contribute to mucosal damage.

Single-cell RNA-seq analyses inform necroptosis-associated myeloid lineages influence the immune landscape of pancreas cancer

Introduction

Tumor-infiltrating myeloid cells (TIMs) are key regulators in tumor progression, but the similarity and distinction of their fundamental properties in pancreatic ductal adenocarcinoma (PDAC) remain elusive.

Method

In this study, we conducted scRNA-seq data analysis of cells from 12 primary tumor (PT) tissues, 4 metastatic (Met) tumor tissues, 3 adjacent normal pancreas tissues (Para), and PBMC samples across 16 PDAC patients, and revealed a heterogeneous TIMs environment in PDAC.

Result

Systematic comparisons between tumor and non-tumor samples of myeloid lineages identified 10 necroptosis-associated genes upregulated in PDAC tumors compared to 5 upregulated in paratumor or healthy peripheral blood. A novel RTM (resident tissue macrophages), GLUL-SQSTM1- RTM, was found to act as a positive regulator of immunity. Additionally, HSP90AA1+HSP90AB1+ mast cells exhibited pro-immune characteristics, and JAK3+TLR4+ CD16 monocytes were found to be anti-immune. The findings were validated through clinical outcomes and cytokines analyses. Lastly, intercellular network reconstruction supported the associations between the identified novel clusters, cancer cells, and immune cell populations.

Conclusion

Our analysis comprehensively characterized major myeloid cell lineages and identified three subsets of myeloid-derived cells associated with necroptosis. These findings not only provide a valuable resource for understanding the multi-dimensional characterization of the tumor microenvironment in PDAC but also offer valuable mechanistic insights that can guide the design of effective immuno-oncology treatment strategies.

CRISPR-Cas9 screening identified lethal genes enriched in necroptosis pathway and of prognosis significance in osteosarcoma

BACKGROUND

The present study aimed to identify indispensable genes associated with tumor cell viability according to the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) datasets, which may support new therapeutic targets for patients with osteosarcoma.

METHODS

The transcriptome patterns between tumor and normal tissues, which were obtained from the Therapeutically Applicable Research to Generate Effective Treatments dataset, were overlapped with the genomics associated with cell viability screened by CRISPR-Cas9 technology. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were employed to determine enrichment pathways related to lethal genes. Least absolute shrinkage and selection operator (LASSO) regression was employed to construct a risk model related to lethal genes for predicting clinical outcomes of osteosarcoma. Univariate and multivariate Cox regressions were conducted to assess the prognostic value of this feature. Weighted gene co-expression network analysis was performed to identify modules associated with patients with high-risk score.

RESULTS

In total, 34 lethal genes were identified in this investigation. These genes were enriched in the necroptosis pathway. The risk model based on LASSO regression algorithm distinguishes patients with high-risk score from patients with low-risk score. Compared with low-risk patients, high-risk patients showed a shorter overall survival rate in both the training and validation sets. The time-dependent receiver operating characteristic curves of 1, 3 and 5 years displayed that the risk score has great prediction performance. The necroptosis pathway represents the main difference in biological behavior between the high-risk group and the low-risk group. Meanwhile, CDK6 and SMARCB1 may serve as important targets for detecting osteosarcoma progression.

CONCLUSIONS

The present study developed a predictive model that outperformed classical clinicopathological parameters for predicting the clinical outcomes of osteosarcoma patients and identified specific lethal genes, including CDK6 and SMARCB1, as well as the necroptosis pathway. These findings may serve as potential targets for future osteosarcoma treatments.

SARS-CoV-2 envelope protein induces necroptosis and mediates inflammatory response in lung and colon cells through receptor interacting protein kinase 1

SARS-CoV-2 Envelope protein (E) is one of the crucial components in virus assembly and pathogenesis. The current study investigated its role in the SARS-CoV-2-mediated cell death and inflammation in lung and gastrointestinal epithelium and its effect on the gastrointestinal-lung axis. We observed that transfection of E protein increases the lysosomal pH and induces inflammation in the cell. The study utilizing Ethidium bromide/Acridine orange and Hoechst/Propidium iodide staining demonstrated necrotic cell death in E protein transfected cells. Our study revealed the role of the necroptotic marker RIPK1 in cell death. Additionally, inhibition of RIPK1 by its specific inhibitor Nec-1s exhibits recovery from cell death and inflammation manifested by reduced phosphorylation of NFκB. The E-transfected cells' conditioned media induced inflammation with differential expression of inflammatory markers compared to direct transfection in the gastrointestinal-lung axis. In conclusion, SARS-CoV-2 E mediates inflammation and necroptosis through RIPK1, and the E-expressing cells' secretion can modulate the gastrointestinal-lung axis. Based on the data of the present study, we believe that during severe COVID-19, necroptosis is an alternate mechanism of cell death besides ferroptosis, especially when the disease is not associated with drastic increase in serum ferritin.

Identification and validation of a novel necroptosis-related molecular signature to evaluate prognosis and immune features in breast cancer

Necroptosis has been shown to play an important role in the development of tumors. However, the characteristics of the necroptosis-related subtypes and the associated immune cell infiltration in the tumor microenvironment (TME) of breast cancer (BRCA) remain unclear. In this study, we identified three clusters related to necroptosis using the expression patterns of necroptosis-relevant genes (NRGs), and found that these three clusters had different clinicopathological features, prognosis and immune cell infiltration in the TME. Cluster 2 was characterized by less infiltration of immune cells in the TME and was associated with a worse prognosis. Then, a necroptosis risk score (NRS) composed of 14 NRGs was constructed using the least absolute shrinkage and selection operator regression (LASSO) Cox regression method. Based on NRS, all BRCA patients in the TCGA datasets were classified into a low-risk group and a high-risk group. Patients in the low-risk group were characterized by longer overall survival (OS), lower mutation burden, and higher infiltration level of immune cells in the TME. Moreover, the NRS was significantly associated with chemotherapeutic drug sensitivity. Finally, the knockdown of VDAC1 reduced the proliferation and migration of BRCA cells, and promoted cell death induced by necroptosis inducer. This study identified a novel necroptosis-related subtype of BRCA, and a comprehensive analysis of NRGs in BRCA revealed its potential roles in prognosis, clinicopathological features, TME, chemotherapy, tumor proliferation, and tumor necroptosis. These results may improve our understanding of NRGs in BRCA and provide a reference for developing individualized therapeutic strategies.

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.

A novel necroptosis signature for predicting survival in lung adenocarcinoma

BACKGROUND

To explore the necroptosis-related genes (NRGs) signature and its predictive values in lung adenocarcinoma (LUAD).

METHODS

The training cohort consisted of tumor samples from The Cancer Genome Atlas, and the validation set comprised data from the Gene Expression Omnibus. Univariate and multivariate Cox regression analyses were applied to identify the prognostic NRG signature as an independent molecular indicator. Correlation analysis was used for the association assessment between the NRG signature and immune checkpoint molecules.

RESULTS

NRGs involved in necroptosis and immune NOD-like receptor signaling. The NRG signature based on eight NRGs can divide tumors into high-risk and low-risk groups, which was significantly associated with worse survival. Multivariate Cox regression analysis showed that this NRG signature remained an independent prognostic indicator. Stratification analyses demonstrated that this NRG signature was still effective for predicting survival in each stratum of age, gender, and tumor stage. The ROC curve showed a good predictive ability using the NRG signature in the validation cohort (AUC = 0.81). The NRG signature was related to immune checkpoint molecules PD - 1, PD-L1, and PD-L2.

CONCLUSIONS

The NRG signature could be a novel predictor of the prognosis and may become a potential therapeutic target in LUAD.

Revealing the potential of necroptosis-related genes in prognosis, immune characteristics, and treatment strategies for head and neck squamous cell carcinoma

Necroptosis is a recently discovered apoptotic mechanism that has been linked to tumor formation, prognosis, and treatment response. However, the relationship between the TME and NRGs remains unclear. In this study, we analyzed the expression patterns of NRGs in 769 HNSCC cases from two distinct data sets. Our findings revealed distinct genetic groups and a correlation between patient clinical features, prognosis, TME cell infiltration characteristics, and NRG alterations. We then developed an NRG model to predict OS and confirmed its accuracy in predicting OS in HNSCC patients. Moreover, we have devised a precise nomogram that enhances the clinical utility of the NRG model substantially. The low-risk group had a better OS, and they were associated with immune suppression, more mutated genes, and higher TIDE scores. The risk score also had a significant correlation with the CSC index and susceptibility to anti-tumor agents. Our study provides insights into how NRGs affect prognosis, clinically significant features, TME, and immunotherapy response in HNSCC. With a better knowledge of NRGs in HNSCC, we could assess the prognosis and develop immunotherapy regimens that are more successful at opening up new doors.

Identification and validation of necroptosis-related gene signatures to predict clinical outcomes and therapeutic responses in acute myeloid leukemia

BACKGROUND

Necroptosis is a tightly regulated form of necrotic cell death that promotes inflammation and contributes to disease development. However, the potential roles of necroptosis-related genes (NRGs) in acute myeloid leukemia (AML) have not been elucidated fully.

METHODS

We conducted a study to identify a robust biomarker signature for predicting the prognosis and immunotherapy efficacy based on NRGs in AML. We analyzed the genetic and transcriptional alterations of NRGs in 151 patients with AML. Then, we identified three necroptosis clusters. Moreover, a necroptosis score was constructed and assessed based on the differentially expressed genes (DEGs) between the three necroptosis clusters.

RESULTS

Three necroptosis clusters were correlated with clinical characteristics, prognosis, the tumor microenvironment, and infiltration of immune cells. A high necroptosis score was positively associated with a poor prognosis, immune-cell infiltration, expression of programmed cell death 1/programmed cell death ligand 1 (PD-1/PD-L1), immune score, stromal score, interferon-gamma (IFNG), merck18, T-cell dysfunction-score signatures, and cluster of differentiation-86, but negatively correlated with tumor immune dysfunction and exclusion score, myeloid-derived suppressor cells, and M2-type tumor-associated macrophages. Our observations indicated that a high necroptosis score might contribute to immune evasion. More interestingly, AML patients with a high necroptosis score may benefit from treatment based on immune checkpoint blockade.

CONCLUSIONS

Consequently, our findings may contribute to deeper understanding of NRGs in AML, and facilitate assessment of the prognosis and treatment strategies.

The necroptosis signature and molecular mechanism of lung squamous cell carcinoma

BACKGROUND

Given the poor prognosis of lung squamous cell carcinoma (LUSC), the aim of this study was to screen for new prognostic biomarkers.

METHODS

The TGCA_LUSC dataset was used as the training set, and GSE73403 was used as the validation set. The genes involved in necroptosis-related pathways were acquired from the KEGG database, and the differential genes between the LUSC and normal samples were identified using the GSEA. A necroptosis signature was constructed by survival analysis, and its correlation with patient prognosis and clinical features was evaluated. The molecular characteristics and drug response associated with the necroptosis signature were also identified. The drug candidates were then validated at the cellular level.

RESULTS

The TCGA_LUSC dataset included 51 normal samples and 502 LUSC samples. The GSE73403 dataset included 69 samples. 159 genes involved in necroptosis pathways were acquired from the KEGG database, of which most showed significant differences between two groups in terms of genomic, transcriptional and methylation alterations. In particular, CHMP4C, IL1B, JAK1, PYGB and TNFRSF10B were significantly associated with the survival (p < 0.05) and were used to construct the necroptosis signature, which showed significant correlation with patient prognosis and clinical features in univariate and multivariate analyses (p < 0.05). Furthermore, CHMP4C, IL1B, JAK1 and PYGB were identified as potential targets of trametinib, selumetinib, SCH772984, PD 325901 and dasatinib. Finally, knockdown of these genes in LUSC cells increased chemosensitivity to those drugs.

CONCLUSION

We identified a necroptosis signature in LUSC that can predict prognosis and identify patients who can benefit from targeted therapies.

A systematic framework for identifying prognostic necroptosis-related lncRNAs and verification of lncRNA CRNDE/miR-23b-3p/IDH1 regulatory axis in glioma

Glioma remains the most frequent malignancy of the central nervous system. Recently, necroptosis has been identified as a cell death process that mediates the proliferation and development of tumor cells. LncRNAs play a key role in the diagnosis and treatment of various diseases. However, the impact that necrosis-related lncRNAs (NRLs) have on glioma remains unclear. In our studies, we selected 9 NRLs to construct a prognostic model. Meanwhile, we assessed the survival curves of these 9 NRLs. Our findings found ADGRA1-AS1 and WAC-AS1 were protective lncRNAs, while MIR210HG, LINC01503, CRNDE, HOXC-AS1, ZIM2-AS1, MIR22HG and PLBD1-AS1 were risk lncRNAs. Specifically, 12 immune cells, 25 immune-correlated pathways, and TME score were differentially expressed in the both risk groups. Additionally, the study predicted and validated the necroptosis-related lncRNA CRNDE/miR-23b-3p/IDH1 axis. CRNDE was strongly expressed in glioma specimens and several cell lines. Inhibiting CRNDE resulted in a substantial reduction in the proliferation and migration of U-118MG and U251 cells. Furthermore, the study predicted that CRNDE may exhibit oncogenic features by adsorbing miR-23b-3p and positively regulating IDH1 expression. Overall, the study constructed a prognostic model in glioma, and predicted a lncRNA CRNDE/miR-23b-3p/IDH1 axis, which could potentially be useful for gene therapy of glioma.

Comprehensive analysis of necroptosis-related genes in renal ischemia-reperfusion injury

Background

Oxidative stress is the primary cause of ischemia-reperfusion injury (IRI) in kidney transplantation, leading to delayed graft function (DGF) and implications on patient health. Necroptosis is believed to play a role in renal IRI. This research presents a comprehensive analysis of necroptosis-related genes and their functional implications in the context of IRI in renal transplantation.

Methods

The necroptosis-related differentially expressed genes (NR-DEGs) were identified using gene expression data from pre- and post-reperfusion renal biopsies, and consensus clustering analysis was performed to distinguish necroptosis-related clusters. A predictive model for DGF was developed based on the NR-DEGs and patients were divided into high- and low-risk groups. We investigated the differences in functional enrichment and immune infiltration between different clusters and risk groups and further validated them in single-cell RNA-sequencing (scRNA-seq) data. Finally, we verified the expression changes of NR-DEGs in an IRI mouse model.

Results

Five NR-DEGs were identified and were involved in various biological processes. The renal samples were further stratified into two necroptosis-related clusters (C1 and C2) showing different occurrences of DGF. The predictive model had a reliable performance in identifying patients at higher risk of DGF with the area under the curve as 0.798. Additionally, immune infiltration analysis indicated more abundant proinflammatory cells in the high-risk group, which was also found in C2 cluster with more DGF patients. Validation of NR-DEG in scRNA-seq data further supported their involvement in immune cells. Lastly, the mouse model validated the up-regulation of NR-DEGs after IR and indicated the correlations with kidney function markers.

Conclusions

Our research provides valuable insights into the identification and functional characterization of NR-DEGs in the context of renal transplantation and sheds light on their involvement in immune responses and the progression of IRI and DGF.

Exploration and validation of a novel signature of seven necroptosis-related genes to improve the clinical outcome of hepatocellular carcinoma

Necroptosis has been reported to be involved in cancer progression and associated with cancer prognosis. However, the prognostic values of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC) remain largely unknown. This study aimed to build a signature on the basis of NRGs to evaluate the prognosis of HCC patients. In this study, using bioinformatic analyses of transcriptome sequencing data of HCC (n = 370) from The Cancer Genome Atlas (TCGA) database, 63 differentially expressed NRGs between HCC and adjacent normal tissues were determined. 24 differentially expressed NRGs were found to be related with overall survival (OS). Seven optimum NRGs, determined using Lasso regression and multivariate Cox regression analysis, were used to construct a new prognostic risk signature for predicting the prognosis of HCC patients. Then survival status scatter plots and survival curves demonstrated that the prognosis of patients with high-Riskscore was worse. The prognostic value of this 7-NRG signature was validated by the International Cancer Genome Consortium (ICGC) cohort and a local cohort (Wenzhou, China). Notably, Riskscore was defined as an independent risk factor for HCC prognosis using multivariate cox regression analysis. Immune infiltration analysis suggested that higher macrophage infiltration was found in patients in the high-risk group. Finally, enhanced 7 NRGs were found in HCC tissues by immunohistochemistry. In conclusion, a novel 7-NRG prognostic risk signature is generated, which contributes to the prediction in the prognosis of HCC patients for the clinicians.

A five necroptosis-related lncRNA signature predicts the prognosis of bladder cancer and identifies hot or cold tumors

Bladder cancer (BC) is a leading cause of male cancer-related deaths globally. Immunotherapy is showing promise as a treatment option for BC. Numerous studies suggested that necroptosis and long noncoding RNAs (lncRNAs) were critical players in the development of cancers and interacting with cancer immunity. However, the prognostic value of necroptosis-related lncRNAs and their impact on immunotherapeutic response in patients with BC have yet to be well examined. Thus, this study aims to find new biomarkers for predicting prognosis and determining immune subtypes of BC to select appropriate patients from a heterogeneous population. The clinicopathology and transcriptome information from The Cancer Genome Atlas (TCGA) was downloaded, and coexpression analysis was performed to identify necroptosis-related lncRNAs. Then LASSO regression was employed to construct a prediction signature. The signature performance was evaluated by Kaplan-Meier (K-M) method, Time-dependent receiver operating characteristics (ROC). The functional enrichment, immune infiltration, immune checkpoint activation, and the half-maximal inhibitory concentration (IC50) of common drugs in risk groups were compared. The consensus clustering analysis based on lncRNAs associated with necroptosis was made to get 2 clusters to identify hot and cold tumors further. Lastly, the immune response between cold and hot tumors was discussed. In this study, a model containing 5 necroptosis-related lncRNAs was constructed. The risk score distribution of these lncRNAs was compared between low- and high-risk groups in the training, testing, and entire sets. K-M analysis showed that the low-risk patients had significantly better prognosis. The area under the ROC curve (AUC) for the 1-, 3-, and 5-year ROC curves in the entire sets were 0.690, 0.709, and 0.722, respectively. High-risk patients were enriched in lncRNAs related to tumor immunity and had better immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 1 and cluster 2, respectively. We developed a necroptosis-related signature based on 5 prognostic lncRNAs, expected to become a new tool for evaluating the prognosis of patients with BC and classifying hot or cold tumors, thus facilitating the development of precision therapy for BC.

Signatures of necroptosis-related genes as diagnostic markers of endometriosis and their correlation with immune infiltration

BACKGROUND

Endometriosis (EMS) occurs when normal uterine tissue grows outside the uterus and causes chronic pelvic pain and infertility. Endometriosis-associated infertility is thought to be caused by unknown mechanisms. In this study, using necroptosis-related genes, we developed and validated multigene joint signatures to diagnose EMS and explored their biological roles.

METHODS

We downloaded two databases (GSE7305 and GSE1169) from the Gene Expression Omnibus (GEO) database and 630 necroptosis-related genes from the GeneCards and GSEA databases. The limma package in Rsoftware was used to identify differentially expressed genes (DEGs). We interleaved common differentially expressed genes (co-DEGs) and necroptosis-related genes (NRDEGs) in the endometriosis dataset. The DEGs functions were reflected by gene ontology analysis (GO), pathway enrichment analysis, and gene set enrichment analysis (GSEA). We used CIBERSORT to analyze the immune microenvironment differences between EMS patients and controls. Furthermore, a correlation was found between necroptosis-related differentially expressed genes and infiltrating immune cells to better understand the molecular immune mechanism.

RESULTS

Compared with the control group, this study revealed that 10 NRDEGs were identified in EMS. There were two types of immune cell infiltration abundance (activated NK cells and M2 macrophages) in these two datasets, and the correlation between different groups of samples was statistically significant (P < 0.05). MYO6 consistently correlated with activated NK cells in the two datasets. HOOK1 consistently demonstrated a high correlation with M2 Macrophages in two datasets. The immunohistochemical result indicated that the protein levels of MYO6 and HOOK1 were increased in patients with endometriosis, further suggesting that MYO6 and HOOK1 can be used as potential biomarkers for endometriosis.

CONCLUSIONS

We identified ten necroptosis-related genes in EMS and assessed their relationship with the immune microenvironment. MYO6 and HOOK1 may serve as novel biomarkers and treatment targets in the future.

PTEN-induced kinase 1 exerts protective effects in diabetic kidney disease by attenuating mitochondrial dysfunction and necroptosis

Mitochondrial dysfunction plays a pivotal role in diabetic kidney disease initiation and progression. PTEN-induced serine/threonine kinase 1 (PINK1) is a core organizer of mitochondrial quality control; however, its function in diabetic kidney disease remains controversial. Here, we aimed to investigate the pathophysiological roles of PINK1 in diabetic tubulopathy, focusing on its effects on mitochondrial homeostasis and tubular cell necroptosis, which is a specialized form of regulated cell death. PINK1-knockout mice showed more severe diabetes-induced tubular injury, interstitial fibrosis, and albuminuria. The expression of profibrotic cytokines significantly increased in the kidneys of diabetic Pink1-/- mice, which eventually culminated in aggravated interstitial fibrosis. Additionally, the knockdown of PINK1 in HKC-8 cells upregulated the fibrosis-associated proteins, and these effects were rescued by PINK1 overexpression. PINK1 deficiency was also associated with exaggerated hyperglycemia-induced mitochondrial dysfunction and defective mitophagic activity, whereas PINK1 overexpression ameliorated these negative effects and restored mitochondrial homeostasis. Mitochondrial reactive oxygen species triggered tubular cell necroptosis under hyperglycemic conditions, which was aggravated by PINK1 deficiency and improved by its overexpression. In conclusion, PINK1 plays a pivotal role in suppressing mitochondrial dysfunction and tubular cell necroptosis under high glucose conditions and exerts protective effects in diabetic kidney disease.

Necroptosis-related regulatory pattern and scoring system for predicting therapeutic efficacy and prognosis in ovarian cancer

BACKGROUND

Ovarian cancer is difficult to treat and is, therefore, associated with a high fatality rate. Although targeted therapy and immunotherapy have been successfully used clinically to improve the diagnosis and treatment of ovarian cancer, most tumors become drug resistant, and patients experience relapse, meaning that the overall survival rate remains low.

AIMS

There is currently a lack of effective biomarkers for predicting the prognosis and/or outcomes of patients with ovarian cancer. Therefore, we used published transcriptomic data derived from a large ovarian cancer sample set to establish a molecular subtyping model of the core genes involved in necroptosis in ovarian cancer.

METHODS AND RESULTS

Clustering analysis and differential gene expression analyses were performed to establish the genomic subtypes related to necroptosis and to explore the patterns of regulatory gene expression related to necroptosis in ovarian cancer. A necroptosis scoring system (NSS) was established using principal component analysis according to different regulatory patterns of necroptosis. In addition, this study revealed important biological processes with essential roles in the regulation of ovarian tumorigenesis, including external encapsulating structure organization, leukocyte migration, oxidative phosphorylation, and focal adhesion. Patients with high NSS scores had unique immunophenotypes, such as more abundant M2 macrophages, monocytes, CD4+ memory T cells, and regulatory T cells. Immune checkpoint CD274 had a greater expression in patients with high NSS values.

CONCLUSION

This NSS could be used as an independent predictor of prognosis to determine the sensitivity of ovarian cancer to various small-molecule inhibitors, immune checkpoint inhibitors, and platinum-based chemotherapy drugs.

Comprehensive analysis of mitochondrial dysfunction and necroptosis in intracranial aneurysms from the perspective of predictive, preventative, and personalized medicine

Mitochondrial dysfunction and necroptosis are closely associated, and play vital roles in the medical strategy of multiple cardiovascular diseases. However, their implications in intracranial aneurysms (IAs) remain unclear. In this study, we aimed to explore whether mitochondrial dysfunction and necroptosis could be identified as valuable starting points for predictive, preventive, and personalized medicine for IAs. The transcriptional profiles of 75 IAs and 37 control samples were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis, and least absolute shrinkage and selection operator (LASSO) regression were used to screen key genes. The ssGSEA algorithm was performed to establish phenotype scores. The correlation between mitochondrial dysfunction and necroptosis was evaluated using functional enrichment crossover, phenotype score correlation, immune infiltration, and interaction network construction. The IA diagnostic values of key genes were identified using machine learning. Finally, we performed the single-cell sequencing (scRNA-seq) analysis to explore mitochondrial dysfunction and necroptosis at the cellular level. In total, 42 IA-mitochondrial DEGs and 15 IA-necroptosis DEGs were identified. Screening revealed seven  key genes invovled in mitochondrial dysfunction (KMO, HADH, BAX, AADAT, SDSL, PYCR1, and MAOA) and five genes involved in necroptosis (IL1B, CAMK2G, STAT1, NLRP3, and BAX). Machine learning confirmed the high diagnostic value of these key genes for IA. The IA samples showed  higher expression of mitochondrial dysfunction and necroptosis. Mitochondrial dysfunction and necroptosis exhibited a close association. Furthermore, scRNA-seq indicated that mitochondrial dysfunction and necroptosis were preferentially up-regulated in monocytes/macrophages and vascular smooth muscle cells (VSMCs) within IA lesions. In conclusion, mitochondria-induced necroptosis was involved in IA formation, and was mainly up-regulated in monocytes/macrophages and VSMCs within IA lesions. Mitochondria-induced necroptosis may be a novel potential target for diagnosis, prevention, and treatment of IA.

p53 at the crossroad between mitochondrial reactive oxygen species and necroptosis

p53 is a redox-sensitive transcription factor that can regulate multiple cell death programs through different signaling pathways. In this review, we assess the role of p53 in the regulation of necroptosis, a programmed form of lytic cell death highly involved in the pathophysiology of multiple diseases. In particular, we focus on the role of mitochondrial reactive oxygen species (mtROS) as essential contributors to modulate necroptosis execution through p53. The enhanced generation of mtROS during necroptosis is critical for the correct interaction between receptor-interacting serine/threonine-protein kinase 1 (RIPK1) and 3 (RIPK3), two key components of the functional necrosome. p53 controls the occurrence of necroptosis by modulating the levels of mitochondrial H2O2 via peroxiredoxin 3 and sulfiredoxin. Furthermore, in response to increased levels of H2O2, p53 upregulates the long non-coding RNA necrosis-related factor, favoring the translation of RIPK1 and RIPK3. In parallel, a fraction of cytosolic p53 migrates into mitochondria, a process notably involved in necroptosis execution via its interaction with the mitochondrial permeability transition pore. In conclusion, p53 is located at the intersection between mtROS and the necroptosis machinery, making it a key protein to orchestrate redox signaling during necroptosis.

Revealing a potential necroptosis-related axis (RP11-138A9.1/hsa-miR-98-5p/ZBP1) in periodontitis by construction of the ceRNA network

BACKGROUND AND OBJECTIVES

Periodontitis, a prevalent chronic inflammatory condition, poses a significant risk of tooth loosening and subsequent tooth loss. Within the realm of programmed cell death, a recently recognized process known as necroptosis has garnered attention for its involvement in numerous inflammatory diseases. Nevertheless, its correlation with periodontitis is indistinct. Our study aimed to identify necroptosis-related lncRNAs and crucial lncRNA-miRNA-mRNA regulatory axes in periodontitis to further understand the pathogenesis of periodontitis.

MATERIALS AND METHODS

Gene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus (GEO) database. Selecting hub necroptosis-related lncRNA and extracting the key lncRNA-miRNA-mRNA axes based on the ceRNA network by adding novel machine-learning models based on conventional analysis and combining qRT-PCR validation. Then, an artificial neural network (ANN) model was constructed for lncRNA in regulatory axes, and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Weighted correlation network analysis (WGCNA) and single-sample gene set enrichment analysis (ssGSEA) was performed to explore how these lncRNAs work in periodontitis.

RESULTS

Seven hub necroptosis-related lncRNAs and three lncRNA-miRNA-mRNA regulatory axes (RP11-138A9.1/hsa-miR-98-5p/ZBP1 axis, RP11-96D1.11/hsa-miR-185-5p/EZH2 axis, and RP4-773 N10.4/hsa-miR-21-5p/TLR3 axis) were predicted. WGCNA revealed that RP11-138A9.1 was significantly correlated with the "purple module". Functional enrichment analysis and ssGSEA demonstrated that the RP11-138A9.1/hsa-miR-98-5p/ZBP1 axis is closely related to the inflammation and immune processes in periodontitis.

CONCLUSION

Our study predicted a crucial necroptosis-related regulatory axis (RP11-138A9.1/hsa-miR-98-5p/ZBP1) based on the ceRNA network, which may aid in elucidating the role and mechanism of necroptosis in periodontitis.

Novel, non-conventional pathways of necroptosis in the heart and other organs: Molecular mechanisms, regulation and inter-organelle interplay

Necroptosis, a cell death modality that is defined as a necrosis-like cell death depending on the receptor-interacting protein kinase 3 (RIPK3) and mixed lineage kinase domain-like pseudokinase (MLKL), has been found to underlie the injury of various organs. Nevertheless, the molecular background of this cell loss seems to also involve, at least under certain circumstances, some novel axes, such as RIPK3-PGAM5-Drp1 (mitochondrial protein phosphatase 5-dynamin-related protein 1), RIPK3-CaMKII (Ca2+/calmodulin-dependent protein kinase II) and RIPK3-JNK-BNIP3 (c-Jun N-terminal kinase-BCL2 Interacting Protein 3). In addition, endoplasmic reticulum stress and oxidative stress via the higher production of reactive oxygen species produced by the mitochondrial enzymes and the enzymes of the plasma membrane have been implicated in necroptosis, thereby depicting an inter-organelle interplay in the mechanisms of this cell death. However, the role and relationship between these novel non-conventional signalling and the well-accepted canonical pathway in terms of tissue- and/or disease-specific prioritisation is completely unknown. In this review, we provide current knowledge on some necroptotic pathways being not directly associated with RIPK3-MLKL execution and report studies showing the role of respective microRNAs in the regulation of necroptotic injury in the heart and in some other tissues having a high expression of the pro-necroptotic proteins.

Pathogenic mechanisms and potential therapeutic targets for Parkinson disease revealed by bioinformatic analysis of necroptosis and immune cell infiltration

Parkinson disease (PD) is an age-dependent neurodegenerative disease with very high prevalence by age 80 years. Necroptosis is a newly identified form of programmed cell death implicated in neurodegenerative diseases, but has not yet been conclusively associated with PD. This study examined the contributions of necroptosis to PD using bioinformatics analysis. Datasets GSE26927, GSE49036, and GSE54536 from the gene expression omnibus database were analyzed for differentially expressed genes (DEGs). These DEGs were then subjected to gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis to identify associated functions and signaling mechanisms. Necroptosis-related differentially expressed genes (NRDEGs) were then identified by the overlap of DEGs and the necroptosis gene set hsa04217. The STRING database and Cytoscape software were then used to build and visualize a protein-protein interaction network and identify hubs and key functional modules among NRDEGs. In addition, immune cell type abundance was analyzed based on DEGs using ImmuCellAI. The identified DEGs, KEGG pathway enrichment terms, and protein-protein interaction network structures of NRDEGs were validated using an independent dataset (GSE54536). The necroptosis pathway was significantly enriched and activated in PD samples. Thirteen NRDEGs were identified in the GSE26927 and GSE49036 datasets, including receptor interacting serine/threonine kinase 1, CASP8 and FADD like apoptosis regulator, TNFRSF1A associated via death domain, and interleukin 1 beta, of which 6 were validated in the GSE54536 dataset. According to gene ontology and KEGG analyses, these NRDEGs are involved in necroptosis-related processes, apoptosis, B cell receptor signaling pathways, and NOD-like receptor signaling pathways. Analysis of DEGs also revealed significant increases in CD8 + T cell and Tex cell infiltration and significant decreases in B cell and T gamma delta cell infiltration within the PD brain. Necroptosis pathways are active in PD and associated with immune cell infiltration. The factors controlling necroptotic signaling and immune infiltration identified in this study may be valuable diagnostic markers and therapeutic targets for PD.

MEG3 activates necroptosis in human neuron xenografts modeling Alzheimer's disease

Neuronal cell loss is a defining feature of Alzheimer's disease (AD), but the underlying mechanisms remain unclear. We xenografted human or mouse neurons into the brain of a mouse model of AD. Only human neurons displayed tangles, Gallyas silver staining, granulovacuolar neurodegeneration (GVD), phosphorylated tau blood biomarkers, and considerable neuronal cell loss. The long noncoding RNA MEG3 was strongly up-regulated in human neurons. This neuron-specific long noncoding RNA is also up-regulated in AD patients. MEG3 expression alone was sufficient to induce necroptosis in human neurons in vitro. Down-regulation of MEG3 and inhibition of necroptosis using pharmacological or genetic manipulation of receptor-interacting protein kinase 1 (RIPK1), RIPK3, or mixed lineage kinase domain-like protein (MLKL) rescued neuronal cell loss in xenografted human neurons. This model suggests potential therapeutic approaches for AD and reveals a human-specific vulnerability to AD.

A novel necroptosis related gene signature and regulatory network for overall survival prediction in lung adenocarcinoma

We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA-miRNA-lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Background

Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.

Methods

Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.

Results

Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.

Conclusion

In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.

Establishment of a novel signature to predict prognosis and immune characteristics of pancreatic cancer based on necroptosis-related long non-coding RNA

BACKGROUND

Necroptosis plays an important role in tumorigenesis and tumour progression. Long noncoding RNAs (lncRNAs) have been proven to be regulatory factors of necroptosis in various tumours. However, the real role of necroptosis-related lncRNAs (NRLs) and their potential to predict the prognosis of pancreatic cancer (PC) remain largely unclear. The goal of this study was to identify NRLs and create a predictive risk signature in PC, explore its prognostic predictive performance, and further assess immunotherapy and chemotherapy responses.

METHODS

RNA sequencing data, tumour mutation burden (TMB) data, and clinical profiles of 178 PC patients were downloaded from The Cancer Genome Atlas (TCGA) database. NRLs were identified using Pearson correlation analysis. Then, patients were divided into the training set and the validation set at a 1:1 ratio. Subsequently, Cox and LASSO regression analyses were conducted to establish a prognostic NRL signature in the training set and validation set. The predictive efficacy of the 5-NRL signature was assessed by survival analysis, nomogram, Cox regression, clinicopathological feature correlation analysis, and receiver operating characteristic (ROC) curve analysis. Furthermore, correlations between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analysed. Finally, we used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the 5-NRLs.

RESULTS

A 5-NRL signature was established to predict the prognosis of PC, including LINC00857, AL672291.1, PTPRN2-AS1, AC141930.2, and MEG9. The 5-NRL signature demonstrated a high degree of predictive power according to ROC and Kaplan‒Meier curves and was revealed to be an independent prognostic risk factor via stratified survival analysis. Nomogram and calibration curves indicated the clinical adaptability of the signature. Immune-related pathways were linked to the 5-NRL signature according to enrichment analysis. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations and the half-maximal inhibitory concentration (IC50) of chemotherapeutic agents were significantly different between the two risk subgroups. These results suggested that our model can be used to evaluate the effectiveness of immunotherapy and chemotherapy, providing a potential new strategy for treating PC.

CONCLUSIONS

The novel 5-NRL signature is helpful for assessing the prognosis of PC patients and improving therapy options, so it can be further applied clinically.

Identification of necroptosis-related long non-coding RNAs prognostic signature and the crucial lncRNA in bladder cancer

BACKGROUND

Research on the relationships between long non-coding RNAs (lncRNAs) and cancer is attractive and has progressed very rapidly. Necroptosis-related biomarkers can potentially be used for predicting the prognosis of cancer patients. This study aimed to establish a necroptosis-related lncRNA (NPlncRNA) signature to predict the prognosis of patients with bladder cancer (BCa).

METHODS

First, NPlncRNAs were identified using Pearson correlation analysis and machine learning algorithms, including SVM-RFE, least absolute shrinkage and selection operator (LASSO) regression, and random forest. The prognostic NPlncRNA signature was constructed using univariate and multivariate Cox regression analyses and the diagnostic efficacy and clinically predictive efficiency were evaluated and validated. The biological functions of the signature were analysed using gene set enrichment analysis (GSEA) and functional enrichment analysis. We further integrated the RNA-seq dataset (GSE133624) with our outcomes to reveal the crucial NPlncRNA that was functionally verified by assessing cell viability, proliferation, and apoptosis in BCa cells.

RESULTS

The prognostic NPlncRNAs signature was composed of PTOV1-AS2, AC083862.2, MAFG-DT, AC074117.1, AL049840.3, and AC078778.1, and a risk score based on this signature was proven to be an independent prognostic factor for the BCa patients, indicated by poor overall survival (OS) of patients in the high-risk group. Additionally, the NPlncRNAs signature had a higher diagnostic validity than that of other clinicopathological variables, with a greater area under the receptor operating characteristic and concordance index curves. A nomogram established by integrating clinical variables and risk score confirmed that the signature can accurately predict the OS of patients and has high clinical practicability. Functional enrichment analysis and GSEA revealed that some cancer-related and necroptosis-related pathways were enriched in high-risk groups. The crucial NPlncRNA MAFG-DT was associated with poor prognosis and was highly expressed in BCa cells. MAFG-DT silencing notably inhibited proliferation and enhanced apoptosis of BCa cells.

CONCLUSIONS

A novel prognostic NPlncRNAs signature was identified in BCa in this study, which provides potential therapeutic targets among which MAFG-DT plays critical roles in the tumorigenesis of BCa.

Genetic variants in key necroptosis regulators predict prognosis of non-small cell lung cancer after surgical resection

BACKGROUND

Necroptosis is a regulated inflammatory cell death which plays a significant role in cancer development and progression. In this study, we evaluated whether genetic variants in key regulators of necroptosis may affect survival outcome of non-small cell lung cancer (NSCLC) patients after surgical resection.

METHODS

A total of 674 patients who underwent curative surgery were included. Fifteen genetic variants in key regulators of necroptosis (RIPK1, RIPK3, and MLKL) were selected. The association of these variants with survival outcomes was evaluated.

RESULTS

Two variants, RIPK1 rs17548629C > T and MLKL rs877375G > C, were associated with better overall survival and disease-free survival in multivariate analyses. When the patients were divided according to histology, the associations were significant only in adenocarcinoma, but not in squamous cell carcinoma. RIPK1 rs17548629 C-to-T change was associated with significantly increased luciferase activity by modulating the binding of miR-642a. Promoter assays showed a significantly increased promoter activity in MLKL rs877375C allele compared to G allele. Consistently, the mRNA expression level of RIPK1 and MLKL showed significant positive correlation with RIPK1 rs17548629C-to-T and MLKL rs877375G-to-C changes.

CONCLUSION

Two genetic variants in key regulators in necroptosis, RIPK1 rs17548629C > T and MLKL rs877375G > C, may be used as biomarkers to predict survival outcomes in surgically resected NSCLC patients.

Necroptosis-related LncRNAs in skin cutaneous melanoma: evaluating prognosis, predicting immunity, and guiding therapy

BACKGROUND

An increasing amount of research has speculated that necroptosis could be a therapeutic strategy for treating cancer. However, understanding the prognostic value of the necroptosis-related long non-coding RNAs (NRLs) in skin cutaneous melanoma (SKCM, hereafter referred to as melanoma) remains poor and needs to be developed. Our research aims to construct a model based on NRLs for the prognosis of patients with melanoma.

METHODS

We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database and retrieved 86 necroptosis-related genes from the GeneCards database. The lncRNAs associated with necroptosis were identified via the Pearson correlation coefficient, and the prognostic model of melanoma was constructed using LASSO regression. Next, we employed multiple approaches to verify the accuracy of the model. Melanoma patients were categorized into two groups (high-risk and low-risk) according to the results of LASSO regression. The relationships between the risk score and survival status, clinicopathological correlation, functional enrichment, immune infiltration, somatic mutation, and drug sensitivity were further investigated. Finally, the functions of AL162457.2 on melanoma proliferation, invasion, and migration were validated by in vitro experiments.

RESULTS

The prognostic model consists of seven NRLs (EBLN3P, AC093010.2, LINC01871, IRF2-DT, AL162457.2, AC242842.1, HLA-DQB1-AS1) and shows high diagnostic efficiency. Overall survival in the high-risk group was significantly lower than in the low-risk group, and risk scores could be used to predict melanoma survival outcomes independently. Significant differences were evident between risk groups regarding the expression of immune checkpoint genes, immune infiltration, immunotherapeutic response and drug sensitivity analysis. A series of functional cell assays indicated that silencing AL162457.2 significantly inhibited cell proliferation, invasion, and migration in A375 cells.

CONCLUSION

Our prognostic model can independently predict the survival of melanoma patients while providing a basis for the subsequent investigation of necroptosis in melanoma and a new perspective on the clinical diagnosis and treatment of melanoma.

A novel necroptosis-related long noncoding RNA model for predicting clinical features, immune characteristics, and therapeutic response in clear cell renal cell carcinoma

Background

Necroptosis is an immune-related cell death pathway involved in the regulation of the tumor microenvironment (TME). Here, we aimed to explore the role of necroptosis in clear cell renal cell carcinoma (ccRCC) and construct a necroptosis-related lncRNA (NRL) model to assess its potential association with clinical characteristics and immune status.

Methods

Gene expression profiles and clinical data for ccRCC patients were obtained from the Cancer Genome Atlas (TCGA). Pearson's correlation, univariate Cox, and least absolute shrinkage and selection operator analyses were used to develop an NRL model. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were used to determine the prognostic value of the NRL model. The clinical information was used to assess the diagnostic value of the NRL model. The TME, immune function, immune cell infiltration, and immune checkpoints associated with the NRL model risk score were studied using the ESTIMATE, GSEA, ssGSEA, and CIBERSORT algorithms. The immunophenoscore (IPS) and half-maximal inhibitory concentration (IC50) were used to compare the efficacies of immunotherapy and chemotherapy based on the NRL model. Finally, in vitro assays were performed to confirm the biological roles of NRLs.

Results

A total of 18 necroptosis-related genes and 285 NRLs in ccRCC were identified. A four-NRL model was constructed and showed good performance in the diagnosis and prognosis of ccRCC patients. The ESTIMATE scores, tumor mutation burden, and tumor stemness indices were significantly correlated with NRL model risk score. Immune functions such as chemokine receptors and immune receptor activity showed differences between different risk groups. The infiltration of immunosuppressive cells such as Tregs was higher in high-risk patients than in low-risk patients. High-risk patients were more sensitive to immunotherapy and some chemotherapy drugs, such as sunitinib and temsirolimus. Finally, the expression of NRLs included in the model was verified, and knocking down these NRLs in tumor cells affected cell proliferation, migration, and invasion.

Conclusion

Necroptosis plays an important role in the progression of ccRCC. The NRL model we constructed can be used to predict the clinical characteristics and immune features of ccRCC patients.