Identification and Verification of Necroptosis-Related Gene Signature and Associated Regulatory Axis in Breast Cancer

The fate and function of non-coding RNAs during necroptosis

Necroptosis is a novel form of cell death which is activated when apoptotic cell death signals are disrupted. Accumulating body of observations suggests that noncoding RNAs, which are the lately discovered mystery of the human genome, are significantly associated with necroptotic signaling circuitry. The fate and function of miRNAs have been well documented in human disease, especially cancer. Recently, lncRNAs have gained much attention due to their diverse regulatory functions. Although available studies are currently based on bioinformatic analysis, predicted interactions desires further attention, as these hold significant promise and should not be overlooked. In the light of these, here we comprehensively review and discuss noncoding RNA molecules that play significant roles during execution of necroptotic cell death.

Polo-like kinase 1 as a biomarker predicts the prognosis and immunotherapy of breast invasive carcinoma patients

Background

Invasive breast carcinoma (BRCA) is associated with poor prognosis and high risk of mortality. Therefore, it is critical to identify novel biomarkers for the prognostic assessment of BRCA.

Methods

The expression data of polo-like kinase 1 (PLK1) in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases. PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Single sample gene set enrichment analysis (ssGSEA) was performed to evaluate immune infiltration in the BRCA microenvironment, and the random forest (RF) and support vector machine (SVM) algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore (IPS). The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration. Finally, a prognostic nomogram was constructed with the risk score and pathological stage, and its clinical potential was evaluated by plotting calibration charts and DCA curves. The application of the nomogram was further validated in an immunotherapy cohort.

Results

PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort. Furthermore, PLK1 expression level, age and stage were identified as independent prognostic factors of BRCA. While the IPS was unaffected by PLK1 expression, the TMB and MATH scores were higher in the PLK1-high group, and the TIDE scores were higher for the PLK1-low patients. We also identified 6 immune cell types with high infiltration, along with 11 immune cell types with low infiltration in the PLK1-high tumors. A risk score was devised using PLK1 expression and hub immune cells, which predicted the prognosis of BRCA patients. In addition, a nomogram was constructed based on the risk score and pathological staging, and showed good predictive performance.

Conclusions

PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.

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 of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Introduction

Breast cancer (BC) is now the most common type of cancer in women. Disulfidptosis is a new regulation of cell death (RCD). RCD dysregulation is causally linked to cancer. However, the comprehensive relationship between disulfidptosis and BC remains unknown. This study aimed to explore the predictive value of disulfidptosis-related genes (DRGs) in BC and their relationship with the TME.

Methods

This study obtained 11 disulfidptosis genes (DGs) from previous research by Gan et al. RNA sequencing data of BC were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) databases. First, we examined the effect of DG gene mutations and copy number changes on the overall survival of breast cancer samples. We then used the expression profile data of 11 DGs and survival data for consensus clustering, and BC patients were divided into two clusters. Survival analysis, gene set variation analysis (GSVA) and ss GSEA were used to compare the differences between them. Subsequently, DRGs were identified between the clusters used to perform Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a prognosis model. Finally, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. CCK-8 and a colony assay obtained by knocking down genes and gene sequencing were used to validate the model.

Result

Two DG clusters were identified based on the expression of 11DGs. Then, 225 DRGs were identified between them. RS, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-RS shows a better prognosis and higher immunotherapy response than high-RS. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. CCK-8 and colony assay obtained by knocking down genes have demonstrated that the knockdown of high-risk genes in the RS model significantly inhibited cell proliferation.

Discussion

This study elucidates the potential relationship between disulfidptosis-related genes and breast cancer and provides new guidance for treating breast cancer.

Identification and validation of a necroptosis-related gene prognostic signature for colon adenocarcinoma

Background

Necroptosis is a novel programmed cell death pathway proposed in 2005, which is mainly activated by the tumor necrosis factor (TNF) family and mediates cellular disassembly via receptor interacting serine/threonine kinase 1 (RIPK1), receptor interacting serine/threonine kinase 3 (RIPK3) and mixed lineage kinase domain like pseudokinase (MLKL). We tried to analyze the relationship of necroptosis-related genes (NRGs) expression with colon adenocarcinoma (COAD) and propose potential therapeutic targets through immunological analysis.

Methods

First, we evaluated the expression of NRGs in COAD patients and constructed a prognostic signature. The prognostic signature was validated using The Cancer Genome Atlas (TCGA)-COAD and GSE39582 datasets, respectively. And the Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis were used to evaluate the signature. Then we analyzed the enrichment of NRGs in the signature using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Finally, we analyzed the immunological characteristics of the COAD patients by single sample gene set enrichment analysis (ssGSEA) and predicted the possible immune checkpoints.

Results

We constructed a prognostic signature with 8 NRGs (RIPK3, MLKL, TRAF2, CXCL1, RBCK1, CDKN2A, JMJD7-PLA2G4B and CAMK2B). The Kaplan-Meier analysis, ROC curves, and principal component analysis demonstrated good predictivity of the signature. In addition, we constructed a nomogram with good individualized predictive ability (C-index =0.772). The immunological analysis revealed that the prognosis of COAD was associated with autoimmune function, and we proposed 10 potential therapeutic targets.

Conclusions

Overall, we constructed an NRGs prognostic signature and suggested potential therapeutic targets for the COAD treatment.

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.

Gene body hypomethylation of pyroptosis-related genes NLRP7, NLRP2, and NLRP3 facilitate non-invasive surveillance of hepatocellular carcinoma

Programmed cell death (PCD) resistance is a key driver of cancer occurrence and development. The prognostic relevance of PCD-related genes in hepatocellular carcinoma (HCC) has attracted considerable attention in recent years. However, there is still a lack of efforts to compare the methylation status of different types of PCD genes in HCC and their roles in its surveillance. The methylation status of genes related to pyroptosis, apoptosis, autophagy, necroptosis, ferroptosis, and cuproptosis was analyzed in tumor and non-tumor tissues from TCGA. Whole-genome bisulfite sequencing (WGBS) data of paired tumor tissue and buffy coat samples were used to filter the potential interference of blood leukocytes in cell-free DNA (cfDNA). The WGBS data of healthy individuals' and early-stage HCC patients' cfDNA were analyzed to evaluate the distinguishing ability. The average gene body methylation (gbDNAme) of pyroptosis-related genes (PRGs) was significantly altered in HCC tissues relative to normal tissues, and their distinguishing ability was higher compared to the other types of PCD-related genes. The gbDNAme of NLRP7, NLRP2, and NLRP3 was reflective of the hypomethylation in HCC tissues, and methylation levels of NLRP3 correlated positively with its expression level (r=0.51). The candidate hypomethylated PRGs could discriminate between early HCC patients and healthy controls in cfDNA analysis with high accuracy (area under the receiver operation curve, AUC=0.94). Furthermore, the hypomethylation of PRGs was associated with poor prognosis of HCC. Gene body hypomethylation of PRGs is a promising biomarker for early HCC detection, monitoring of tumor recurrence, and prognosis prediction.

Probing the Potential of Defense Response-Associated Genes for Predicting the Progression, Prognosis, and Immune Microenvironment of Osteosarcoma

BACKGROUND

The defense response is a type of self-protective response of the body that protects it from damage by pathogenic factors. Although these reactions make important contributions to the occurrence and development of tumors, the role they play in osteosarcoma (OS), particularly in the immune microenvironment, remains unpredictable.

METHODS

This study included the clinical information and transcriptomic data of 84 osteosarcoma samples and the microarray data of 12 mesenchymal stem cell samples and 84 osteosarcoma samples. We obtained 129 differentially expressed genes related to the defense response (DRGs) by taking the intersection of differentially expressed genes with genes involved in the defense response pathway, and prognostic genes were screened using univariate Cox regression. Least absolute shrinkage and selection operator (LASSO) penalized Cox regression and multivariate Cox regression were then used to establish a DRG prognostic signature (DGPS) via the stepwise method. DGPS performance was examined using independent prognostic analysis, survival curves, and receiver operating characteristic (ROC) curves. In addition, the molecular and immune mechanisms of adverse prognosis in high-risk populations identified by DGPS were elucidated. The results were well verified by experiments.

RESULT

BNIP3, PTGIS, and ZYX were identified as the most important DRGs for OS progression (hazard ratios of 2.044, 1.485, and 0.189, respectively). DGPS demonstrated outstanding performance in the prediction of OS prognosis (area under the curve (AUC) values of 0.842 and 0.787 in the training and test sets, respectively, adj-p < 0.05 in the survival curve). DGPS also performed better than a recent clinical prognostic approach with an AUC value of only 0.674 [metastasis], which was certified in the subsequent experimental results. These three genes regulate several key biological processes, including immune receptor activity and T cell activation, and they also reduce the infiltration of some immune cells, such as B cells, CD8+ T cells, and macrophages. Encouragingly, we found that DGPS was associated with sensitivity to chemotherapeutic drugs including JNK Inhibitor VIII, TGX221, MP470, and SB52334. Finally, we verified the effect of BNIP3 on apoptosis, proliferation, and migration of osteosarcoma cells through experiments.

CONCLUSIONS

This study elucidated the role and mechanism of BNIP3, PTGIS, and ZYX in OS progression and was well verified by the experimental results, enabling reliable prognostic means and treatment strategies to be proposed for OS patients.

Prediction and analysis of osteoarthritis hub genes with bioinformatics

Background

Osteoarthritis (OA) is the most common type of arthritis. OA can cause joint pain, stiffness, and loss of function. The pathogenesis of OA is not completely clear. Moreover, there is no effective treatment, and clinical management is limited to symptomatic relief or joint surgery. This study utilized bioinformatics to analyze normal and OA articular cartilage samples to find biomarkers and therapeutic targets for OA.

Methods

The GSE169077 gene chip dataset was downloaded from the public gene chip data platform of the National Biotechnology Information Center. The dataset included 6 samples of OA tissues and 5 samples of healthy cartilage tissues. Differentially expressed genes (DEGs) were screened using the R language "limma" function package under the threshold of log₂[fold change (FC)] ≥2 and a P value <0.05. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways of the target genes were enriched and analyzed using the database for annotation, visualization, and integrated discovery (DAVID), and a protein-protein interaction (PPI) network was further constructed using the search tool for the retrieval of interacting genes/proteins (STRING) database. The coexpression relationship of the genes in the module was visualized and screened with Cytoscape.

Results

A total of 27 DEGs were identified, including 9 downregulated genes and 18 upregulated genes. GO signal pathway enrichment analysis showed involvement in hypoxic response, fibrous collagen trimer, and extracellular matrix structural components. KEGG analysis demonstrated associations with protein digestion and absorption, extracellular matrix receptor interaction, and the peroxisome proliferator-activated receptor signal pathway, among several other pathways. A PPI network was obtained through STRING analysis, and the results were imported into Cytoscape software. The 27 DEGs were sequenced by the cytoHubba plug-in by various calculation methods, and 5 hub genes (COL1A1, COL1A2, POSTN, BMP1, and MMP13) were finally selected. These genes were analyzed by PPI again and annotated with GO and KEGG in different colors.

Conclusions

Bioinformatics technology effectively identified differential genes in the knee cartilage tissue of healthy controls and patients with OA, providing opportunities to further explore the mechanism and treatment of OA on a transcriptional level.

Prognostic implication and immunotherapy response prediction of a ubiquitination-related gene signature in breast cancer

Breast cancer (BC) is one of the most common tumor types and has poor outcomes. In this study, a ubiquitination-related prognostic signature was constructed, and its association with immunotherapy response in BC was explored. A list of ubiquitination-related genes was obtained from the molecular signatures database, and a ubiquitination-related gene signature was obtained by least absolute shrinkage and selection operator Cox regression. The genes, TCN1, DIRAS3, and IZUMO4, had significant influence on BC outcomes. Patients were categorized into two clusters-a high-risk group with poor survival and a low-risk group with greater chances of controlling BC progression. Univariate and multivariate Cox regression analyses revealed that the risk signature was an independent prognostic factor for BC. Gene set enrichment analysis suggested that the high-risk group was enriched in cell cycle and DNA replication pathways. The risk score was positively linked to the tumor microenvironment and negatively correlated with the immunotherapy response. The IC50 values for rapamycin were higher in the low-risk group, whereas those for axitinib, AZD6244, erlotinib, GDC0941, GSK650394, GSK269962A, lapatinib, and PD0325901 were higher in the high-risk group. Therefore, the ubiquitination-related signature is considered a promising tool for predicting a BC patient's immunotherapy response.

Construction of a Necroptosis-Related lncRNA Signature for Predicting Prognosis and Immune Response in Kidney Renal Clear Cell Carcinoma

Necroptosis is a new type of programmed cell death and involves the occurrence and development of various cancers. Moreover, the aberrantly expressed lncRNA can also affect tumorigenesis, migration, and invasion. However, there are few types of research on the necroptosis-related lncRNA (NRL), especially in kidney renal clear cell carcinoma (KIRC). In this study, we analyzed the sequencing data obtained from the TGCA-KIRC dataset, then applied the LASSO and COX analysis to identify 6 NRLs (AC124854.1, AL117336.1, DLGAP1-AS2, EPB41L4A-DT, HOXA-AS2, and LINC02100) to construct a risk model. Patients suffering from KIRC were divided into high- and low-risk groups according to the risk score, and the patients in the low-risk group had a longer OS. This signature can be used as an indicator to predict the prognosis of KIRC independent of other clinicopathological features. In addition, the gene set enrichment analysis showed that some tumor and immune-associated pathways were more enriched in a high-risk group. We also found significant differences between the high and low-risk groups in the infiltrating immune cells, immune functions, and expression of immune checkpoint molecules. Finally, we use the "pRRophetic" package to complete the drug sensitivity prediction, and the risk score could reflect patients' response to 8 small molecule compounds. In general, NRLs divided KIRC into two subtypes with different risk scores. Furthermore, this signature based on the 6 NRLs could provide a promising method to predict the prognosis and immune response of KIRC patients. To some extent, our findings helped give a reference for further research between NRLs and KIRC and find more effective therapeutic drugs for KIRC.

Identification of a novel necroptosis-associated miRNA signature for predicting the prognosis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignancies that have a poor prognosis. Necroptosis has been demonstrated in recent years to be a form of inflammatory cell death occurring in multicellular organism, which plays complex roles in cancer. However, the expression of necroptosis-related miRNAs and genes in HNSCC and their correlations with prognosis remain unclear. In this study, R software was used to screen differentially expressed miRNAs downloaded from The Cancer Genome Atlas. A prognostic model containing six necroptosis-related miRNAs (miR-141-3p, miR-148a-3p, miR-331-3p, miR-543, miR-425-5p, and miR-7-5p) was generated, whose risk score was validated as an independent prognostic factor for HNSCC. Target genes of the key miRNAs were obtained from TargetScan, miRDB, and miRTarBase, and 193 genes in the intersection of the three databases were defined as consensus genes. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses indicated that the composition of the tumor microenvironment as well as specific pathways may be closely related to necroptosis in HNSCC. Nine key genes were also obtained by the MCODE and cytoHubba plug-ins of Cytoscape: PIK3CD, NRAS, PTK2, IRS2, IRS1, PARP1, KLF4, SMAD2, and DNMT1. A prognostic model formed by the key gene was also established, which can efficiently predict the overall survival of HNSCC patients. In conclusion, necroptosis-related miRNAs and genes play important roles in tumor development and metastasis and can be used to predict the prognosis of HNSCC.

Database Mining Detected a Cuproptosis-Related Prognostic Signature and a Related Regulatory Axis in Breast Cancer

Background

Breast cancer is the frequent cause of disease burden related to cancer among women. It affects one in 20 women globally and up to one in eight women in high-income countries. Cuproptosis is a copper-induced modality of mitochondrial cell death that is involved in tumor proliferation and metastasis.

Methods

To construct a prognostic cuproptosis-related signature, LASSO Cox regression analysis was employed. Additionally, ceRNA was developed with an aim of exploring the possible lncRNA-miRNA-mRNA regulatory axis in breast cancer.

Results

The expression of FDX1, DLD, DLAT, LIAS, LIPT1, GLS MTF1, and PDHA1 was downregulated, while CDKN2A expression level was elevated in breast cancer in contrast with normal tissue. We furthermore reviewed the genetic mutation landscape of genes linked to cuproptosis in breast cancer. Prognosis analysis revealed poor OS and RFS rates in breast cancer patients with elevated levels of CDKN2A and PDHA1 and low levels of MTF1, DLD, LIPT1, and FDX1. We then constructed a cuproptosis-related signature with six genes (DKN2A, MTF1, PDHA1, DLD, LIPT1, and FDX1) for breast cancer, which predicted the OS rate with an accuracy that ranged from medium to high. Further analysis demonstrated a significant correlation between the cuproptosis-related prognostic signature and pTNM stage, MSI score, drug sensitivity, TMB score, and immune cell infiltration. Moreover, we identified the lncRNA XIST/miR-92b-3p/MTF1 regulatory axis for breast cancer.

Conclusion

Multiomics approaches were used to create a cuproptosis-related signature with six genes (DKN2A, MTF1, PDHA1, DLD, LIPT1, and FDX1) for breast cancer. We discovered the lncRNA XIST/miR-92b-3p/MTF1 regulatory axis for breast cancer, which has not yet been investigated previously.

The cuproptosis-related signature predicts prognosis and indicates immune microenvironment in breast cancer

Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.

Development and validation of a novel necroptosis-related score to improve the outcomes of clear cell renal cell carcinoma

Necroptosis has been indicated as a key regulator of tumor progression. However, the prognostic regulatory role of necroptosis in clear cell renal cell carcinoma (ccRCC) needs to be further investigated. In this study, necroptosis-related subtypes were identified by mining the public cohort (n = 530) obtained from The Cancer Genome Atlas. By applying Principal Component Analysis (PCA), the necroptosis-related scores (N-Score) were developed to assess the prognosis procession of ccRCC. The results were further validated by an external clinical cohort (n = 116) obtained from the First Affiliated Hospital of Wenzhou Medical University. It has been found that N-Score could precisely distinguish the prognostic outcomes of patients as an independent risk factor (Hazard ratio = 4.990, 95% confidence interval (CI) = 2.007–12.403, p < 0.001). In addition, changes in N-Score were associated with differences in tumor mutational burden as well as immune infiltration characterization. Moreover, higher N-Scores were also correlated significantly molecular drug sensitivity and stronger immune checkpoint activity. Notably, the prognosis of ccRCC could be effectively guided by combining the N-Scores and external clinical indicators. In conclusion, N-Scores could be served as a robust and effective biomarker to improve the prognosis outcomes and targeted therapy of ccRCC.

The Landscape of Cell Death Processes with Associated Immunogenic and Fibrogenic Effects in Arrhythmogenic Cardiomyopathy

Arrhythmogenic cardiomyopathy (ACM) is a heritable myocardial disease characterized by life-threatening ventricular arrhythmias and sudden cardiac death. Cardiomyocyte death is an essential pathogenic mechanism in ACM, but the cell death landscape has never been elucidated. Our study aimed to address this problem based on RNA-sequencing (RNA-seq) data. Myocardial RNA-seq data from arrhythmogenic right ventricular cardiomyopathy (ARVC) patients and normal controls were obtained from the Gene Expression Omnibus database (GSE107475, GSE107311, GSE107156, GSE107125). Signature gene sets of cell death processes, immune cells, and pathways were collected. Single-sample gene-set enrichment analysis calculated the enrichment scores for these signature gene sets. The RNA-seq data of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) derived from an ACM patient were used for validation (GSE115621). Weighted gene coexpression network analysis (WGCNA) was applied to identify coexpression modules. Immunogenic cell death, apoptosis, necroptosis, and pyroptosis were significantly up-regulated in ARVC. Positive correlations of these four up-regulated cell death processes with immune cells and pathways were found within the ARVC myocardium. In the ARVC sample cluster with higher cell death levels, central memory CD4 T cell, memory B cell, type 1 T helper cell, mast cell, natural killer T cell, and plasmacytoid dendritic cell were more substantially infiltrated. Similarly, immune pathways were more up-regulated in this cluster. Positive linear correlations were found between cell death, immune responses, and myocardial fibrosis within the ARVC samples. Eventually, WGCNA identified a shared coexpression module related to these mechanisms. This study first demonstrated the landscape of cell death processes in the ACM (ARVC) myocardium and their positive correlations with immune responses and myocardial fibrosis. These mechanisms have potential interactions and jointly contribute to the pathogenesis of ACM.

Construction and Validation of a Necroptosis-Related lncRNA Signature in Prognosis and Immune Microenvironment for Glioma

Background

Glioma is the most common primary brain tumor, representing approximately 80.8% of malignant tumors. Necroptosis triggers and enhances antitumor immunity and is expected to be a new target for tumor immunotherapy. The effectiveness of necroptosis-related lncRNAs as potential therapeutic targets for glioma has not been elucidated.

Methods

We acquired RNA-seq data sets from LGG and GBM samples, and the corresponding clinical characteristic information is from TCGA. Normal brain tissue data is from GTEX. Based on TCGA and GTEx, we used univariate Cox regression to sort out survival-related lncRNAs. Lasso regression models were then built. Then, we performed a separate Kaplan-Meier analysis of the lncRNAs used for modeling. We validated different risk groups via OS, DFS, enrichment analysis, comprehensive immune analysis, and drug sensitivity.

Results

We constructed a 12 prognostic lncRNAs model after bioinformatic analysis. Subsequently, the risk score of every glioma patient was calculated based on correlation coefficients and expression levels, and the patients were split into low- and high-risk groups according to the median value of the risk score. A nomogram was established for every glioma patient to predict prognosis. Besides, we found significant differences in OS, DFS, immune infiltration and checkpoints, and immune therapy between different risk subgroups.

Conclusion

Predictive models of 12 necroptosis-related lncRNAs can facilitate the assessment of the prognosis and molecular characteristics of glioma patients and improve treatment modalities.

Establishment of a Necroptosis-Related Prognostic Signature to Reveal Immune Infiltration and Predict Drug Sensitivity in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common type of primary liver cancer and has a poor prognosis. In recent times, necroptosis has been reported to be involved in the progression of multiple cancers. However, the role of necroptosis in HCC prognosis remains elusive.

Methods: The RNA-seq data and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes (DEGs) and prognosis-related genes were explored, and the nonnegative matrix factorization (NMF) clustering algorithm was applied to divide HCC patients into different subtypes. Based on the prognosis-related DEGs, univariate Cox and LASSO Cox regression analyses were used to construct a necroptosis-related prognostic model. The relationship between the prognostic model and immune cell infiltration, tumor mutational burden (TMB), and drug response were explored.

Results: In this study, 13 prognosis-related DEGs were confirmed from 18 DEGs and 24 prognostic-related genes. Based on the prognosis-related DEGs, patients in the TCGA cohort were clustered into three subtypes by the NMF algorithm, and patients in C3 had better survival. A necroptosis-related prognostic model was established according to LASSO analysis, and HCC patients in TCGA and ICGC were divided into high- and low-risk groups. Kaplan–Meier (K–M) survival analysis revealed that patients in the high-risk group had a shorter survival time compared to those in the low-risk group. Using univariate and multivariate Cox analyses, the prognostic model was identified as an independent prognostic factor and had better survival predictive ability in HCC patients compared with other clinical biomarkers. Furthermore, the results revealed that the high-risk patients had higher stromal, immune, and ESTIMATE scores; higher TP53 mutation rate; higher TMB; and lower tumor purities compared to those in the low-risk group. In addition, there were significant differences in predicting the drug response between the high- and low-risk groups. The protein and mRNA levels of these prognostic genes were upregulated in HCC tissues compared to normal liver tissues.

Conclusion: We established a necroptosis-related prognostic signature that may provide guidance for individualized drug therapy in HCC patients; however, further experimentation is needed to validate our results.

Identification of necroptosis‐related gene signature and characterization of tumour microenvironment infiltration in non‐small‐cell lung cancer

Necroptosis is a programmed necrosis in a caspase‐independent fashion. The role of necroptosis‐related genes (NRGs) in lung cancer remains unknow. Herein, we classified TCGA‐LUAD cohort into two necroptosis‐related subtypes (C1 and C2) by consensus clustering analysis. The result showed that subtype C1 had a favourable prognosis and higher infiltration levels of immune cells. Moreover, subtype C1 was more activated in immune‐associated pathways. Then, we established an NRG prognosis model (NRG score) composed of six NRGs (RIPK3, MLKL, TLR2, TLR4, TNFRSF1A, NDRG2) and divided the cohort into low‐ and high‐risk group. We found that the NRG score was associated with prognosis, tumour immune microenvironment and tumour mutation burden. We also constructed an accurate nomogram model to improve the clinical applicability of NRG score. The result indicated that NRG score may be an independent prognostic marker for lung cancer patients. Taken together, we established a prognosis model that may deepen the understanding of NRGs in lung cancer and provide a basis for developing more effective immunotherapy strategies.

Integrative Analysis of Pyroptosis-Related Prognostic Signature and Immunological Infiltration in Lung Squamous Cell Carcinoma

Background

Lung cancer is one of leading causes of human health threatening with approximately 2.09 million initially diagnosed cases and 1.76 million deaths worldwide annually. Pyroptosis is a programmed cell death mediated by Gasdermin family proteins. Pyroptosis could suppress the tumor oncogenesis and progression; nevertheless, pyroptosis could promote tumor growth by forming a suitable microenvironment.

Methods

LASSO Cox regression analysis was performed to construct prognostic pyroptosis-related gene (PRG) signature. A ceRNA was constructed to explore the potential lncRNA-miRNA-mRNA regulatory axis in LUSC.

Results

The expression of 26 PRGs were increased or decreased in LUSC. We also summarized simple nucleotide variation and copy number variation landscape of PRGs in LUSC. Prognosis analysis suggested a poor overall survival rate in LUSC patients with high expression of IL6, IL1B, ELANE, and CASP6. A pyroptosis-related prognostic signature was developed based on four prognostic PRGs. High-risk score LUSC patients had a poor overall survival rate versus low-risk score patients with an AUC of 0.565, 0.641, and 0.619 in 1-year, 3-year, and 5-year ROC curves, respectively. Moreover, the risk score was correlated with immune infiltration in LUSC. Further analysis revealed that pyroptosis-related prognostic signature was correlated with immune cell infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also constructed a ceRNA network and identified a lncRNA KCNQ1OT1/miR-328-3p/IL1B regulatory axis for LUSC.

Conclusion

A bioinformatics method was performed to develop a pyroptosis-related prognostic signature containing four genes (IL6, IL1B, ELANE, and CASP4) in LUSC. We also constructed a ceRNA network and identified a lncRNA KCNQ1OT1/miR-328-3p/IL1B regulatory axis for LUSC. Further in vivo and in vitro studies should be conducted to verify these results.

Establishment of a Cell Necroptosis Index to Predict Prognosis and Drug Sensitivity for Patients With Triple-Negative Breast Cancer

Background: Necroptosis has been an alternatively identified mechanism of programmed cancer cell death, which plays a significant role in cancer. However, research about necroptosis-related long noncoding RNAs (lncRNAs) in cancer are still few. Moreover, the potentially prognostic value of necroptosis-related lncRNAs and their correlation with the immune microenvironment remains unclear. The present study aimed to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship to immune microenvironment in triple-negative breast cancer (TNBC).

Methods: The RNA expression matrix of patients with TNBC was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Finally, 107 patients of GSE58812, 159 patients of TCGA, and 143 patients of GSE96058 were included. Necroptosis-related lncRNAs were screened by Cox regression and Pearson correlation analysis with necroptosis-related genes. By LASSO regression analysis, nine necroptosis-related lncRNAs were employed, and a cell necroptosis index (CNI) was established; then, we evaluated its prognostic value, clinical significance, pathways, immune infiltration, and chemotherapeutics efficacy.

Results: Based on the CNI value, the TNBC patients were divided into high- and low-CNI groups, and the patients with high CNI had worse prognosis, more lymph node metastasis, and larger tumor (p < 0.05). The receiver operating characteristic (ROC) analysis showed that the signature performed well. The result of the infiltration proportion of different immune cell infiltration further explained that TNBC patients with high CNI had low immunogenicity, leading to poor therapeutic outcomes. Moreover, we found significant differences of the IC50 values of various chemotherapeutic drugs in the two CNI groups, which might provide a reference to make a personalized chemotherapy for them.

Conclusion: The novel prognostic marker CNI could not only precisely predict the survival probability of patients with TNBC but also demonstrate a potential role in antitumor immunity and drug sensitivity.

Construction of a Necroptosis-Related miRNA Signature for Predicting the Prognosis of Patients With Hepatocellular Carcinoma

Background: Many miRNAs have been demonstrated to be associated with the prognosis of hepatocellular carcinoma (HCC). However, how to combine necroptosis-related miRNAs to achieve the best predictive effect in estimating HCC patient survival has not been explored.

Methods: The mRNA and miRNA expression profile were downloaded from a public database (TCGA-LIHC cohort). Necroptosis-related genes were obtained from previous references, and necroptosis-related miRNAs were identified using Pearson analysis. Subsequently, differential expression miRNAs (DEms) were identified in HCC and paracancer normal samples based on necroptosis-related miRNA expression. The whole set with HCC was randomized into a training set and testing set (1:1). LASSO-Cox regression analysis was used to construct an miRNA signature. Multiple statistical methods were used to validate the clinical benefit of signature in HCC patients, including receiver operator characteristic (ROC) curves, Kaplan–Meier survival analyses, and decision curve analysis (DCA). The downstream target genes of miRNAs were obtained from different online tools, and the potential pathways involved in miRNAs were explored. Finally, we conducted RT-qPCR in SK-HEP-1, THLE-3, and HUH-7 cell lines for miRNAs involved in the signature.

Results: The results showed that a total of eight specific necroptosis-related miRNAs were screened between HCC and adjacent tissues in the training set. Subsequently, based on the aforementioned miRNAs, 5-miRNA signature (miR-139-5p, hsa-miR-326, miR-10b-5p, miR-500a-3p, and miR-592) was generated by LASSO-Cox regression analysis. Multivariate Cox regression analysis showed that the risk scores were independent prognostic indicators in each set. The area under curves (AUCs) of 1 year, 3 years, 5 years, and 7 years were high in each set (AUC >0.7). DCA analysis also revealed that the risk score had a potential benefit than other clinical characteristics. Meanwhile, survival analysis showed that the high-risk group showed low survival probabilities. Moreover, the results of enrichment analysis showed that specific miRNAs were mainly enriched in the cAMP signaling pathway and TNF signaling pathway. Finally, the results of RT-qPCR were consistent with the prediction results in public databases.

Conclusion: Our study establishes a robust tool based on 5-necroptosis-related miRNAs for the prognostic management of HCC patients.