Clinical Significance and Immune Landscape of a Pyroptosis-Derived LncRNA Signature for Glioblastoma

Long Non-Coding RNAs in Malignant Human Brain Tumors: Driving Forces Behind Progression and Therapy

Long non-coding RNAs (lncRNAs) play a pivotal role in regulating gene expression and are critically involved in the progression of malignant brain tumors, including glioblastoma, medulloblastoma, and meningioma. These lncRNAs interact with microRNAs (miRNAs), proteins, and DNA, influencing key processes such as cell proliferation, migration, and invasion. This review highlights the multifaceted impact of lncRNA dysregulation on tumor progression and underscores their potential as therapeutic targets to enhance the efficacy of chemotherapy, radiotherapy, and immunotherapy. The insights provided offer new directions for advancing basic research and clinical applications in malignant brain tumors.

Shedding light on function of long non-coding RNAs (lncRNAs) in glioblastoma

The brain tumors and especially glioblastoma, are affecting life of many people worldwide and due to their high mortality and morbidity, their treatment is of importance and has gained attention in recent years. The abnormal expression of genes is commonly observed in GBM and long non-coding RNAs (lncRNAs) have demonstrated dysregulation in this tumor. LncRNAs have length more than 200 nucleotides and they have been located in cytoplasm and nucleus. The current review focuses on the role of lncRNAs in GBM. There two types of lncRNAs in GBM including tumor-promoting and tumor-suppressor lncRNAs and overexpression of oncogenic lncRNAs increases progression of GBM. LncRNAs can regulate proliferation, cell cycle arrest and metastasis of GBM cells. Wnt, STAT3 and EZH2 are among the molecular pathways affected by lncRNAs in GBM and for regulating metastasis of GBM cells, these RNA molecules mainly affect EMT mechanism. LncRNAs are involved in drug resistance and can induce resistance of GBM cells to temozolomide chemotherapy. Furthermore, lncRNAs stimulate radio-resistance in GBM cells. LncRNAs increase PD-1 expression to mediate immune evasion. LncRNAs can be considered as diagnostic and prognostic tools in GBM and researchers have developed signature from lncRNAs in GBM.

Cell type-specific gene expression dynamics during human brain maturation

The human brain undergoes protracted post-natal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell type-specific gene expression dynamics. Here, using single nucleus (sn)RNA-seq on temporal lobe tissue, including samples of African ancestry, we build a joint paediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between paediatric and adult cell types, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in paediatric tissue. The new resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.

Exploration of functional relations among differentially co-expressed genes identifies regulators in glioblastoma

The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus. A thorough contextual investigation of these nodes could lead to a helpful beginning point for identifying potential causal linkages and guiding subsequent scientific investigations to uncover mechanisms underlying observed associations. Our methodology includes functional protein-protein Interaction (PPI) data and co-expression information and filters functional linkages through a series of critical steps, culminating in the identification of a robust set of regulators. Our analysis identified eleven key regulators-AKT1, BRCA1, CAMK2G, CUL1, FGFR3, KIF3A, NUP210, PRKACB, RAB8A, RPS6KA2 and TGFB3-in glioblastoma. These regulators play a pivotal role in disease classification, cell growth control, and patient survivability and exhibit associations with immune infiltrations and disease hallmarks. This underscores the importance of assessing correlation towards causality in unraveling complex biological insights.

Pyroptosis-related gene signature for predicting gastric cancer prognosis

Gastric cancer (GC) is a prevalent form of malignancy characterized by significant heterogeneity. The development of a specific prediction model is of utmost importance to improve therapy alternatives. The presence of H. pylori can elicit pyroptosis, a notable carcinogenic process. Furthermore, the administration of chemotherapeutic drugs is often employed as a therapeutic approach to addressing this condition. In the present investigation, it was observed that there were variations in the production of 17 pyroptosis-regulating proteins between stomach tissue with tumor development and GC cells. The predictive relevance of each gene associated with pyroptosis was assessed using the cohort from the cancer genome atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) was utilized to enhance the outcomes of the regression approach. Patients with gastric cancer GC in the cohort from the TCGA were categorized into low-risk or high-risk groups based on their gene expression profiles. Patients with a low risk of gastric cancer had a higher likelihood of survival compared to persons classified as high risk (P<0.0001). A subset of patients diagnosed with GC from a Genes Expression Omnibus (GEO) cohort was stratified according to their overall survival (OS) duration. The statistical analysis revealed a higher significance level (P=0.0063) regarding OS time among low-risk individuals. The study revealed that the GC risk score emerged as a significant independent prognostic factor for OS in patients diagnosed with GC. The results of Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) research revealed that genes associated with a high-risk group had significantly elevated levels of immune system-related activity. Furthermore, it was found that the state of immunity was diminished within this particular group. The relationship between the immune response to cancer and pyroptosis genes is highly interconnected, suggesting that these genes have the potential to serve as prognostic indicators for GC.

Non-coding RNAs are involved in tumor cell death and affect tumorigenesis, progression, and treatment: a systematic review

Cell death is ubiquitous during development and throughout life and is a genetically determined active and ordered process that plays a crucial role in regulating homeostasis. Cell death includes regulated cell death and non-programmed cell death, and the common types of regulatory cell death are necrosis, apoptosis, necroptosis, autophagy, ferroptosis, and pyroptosis. Apoptosis, Necrosis and necroptosis are more common than autophagy, ferroptosis and pyroptosis among cell death. Non-coding RNAs are regulatory RNA molecules that do not encode proteins and include mainly microRNAs, long non-coding RNAs, and circular RNAs. Non-coding RNAs can act as oncogenes and tumor suppressor genes, with significant effects on tumor occurrence and development, and they can also regulate tumor cell autophagy, ferroptosis, and pyroptosis at the transcriptional or post-transcriptional level. This paper reviews the recent research progress on the effects of the non-coding RNAs involved in autophagy, ferroptosis, and pyroptosis on tumorigenesis, tumor development, and treatment, and looks forward to the future direction of this field, which will help to elucidate the molecular mechanisms of tumorigenesis and tumor development, as well as provide a new vision for the treatment of tumors.

Immune-related interaction perturbation networks unravel biological peculiars and clinical significance of glioblastoma

The immune system is an interacting network of plentiful molecules that could better characterize the relationship between immunity and cancer. This study aims to investigate the behavioral patterns of immune-related interaction perturbation networks in glioblastoma. An immune-related interaction-perturbation framework was introduced to characterize four heterogeneous subtypes using RNA-seq data of TCGA/CGGA glioblastoma tissues and GTEx normal brain tissues. The stability and robustness of the four subtypes were validated in public datasets and our in-house cohort. In the four subtypes, C1 was an inflammatory subtype with high immune infiltration, low tumor purity, and potential response to immunotherapy; C2, an invasive subtype, was featured with dismal prognosis, telomerase reverse transcriptase promoter mutations, moderate levels of immunity, and stromal constituents, as well as sensitivity to receptor tyrosine kinase signaling inhibitors; C3 was a proliferative subtype with high tumor purity, immune-desert microenvironment, sensitivity to phosphatidylinositol 3'-kinase signaling inhibitor and DNA replication inhibitors, and potential resistance to immunotherapy; C4, a synaptogenesis subtype with the best prognosis, exhibited high synaptogenesis-related gene expression, prevalent isocitrate dehydrogenase mutations, and potential sensitivity to radiotherapy and chemotherapy. Overall, this study provided an attractive platform from the perspective of immune-related interaction perturbation networks, which might advance the tailored management of glioblastoma.

LncRNAs and regulated cell death in tumor cells

Regulated Cell Death (RCD) is a mode of cell death that occurs through drug or genetic intervention. The regulation of RCDs is one of the significant reasons for the long survival time of tumor cells and poor prognosis of patients. Long non-coding RNAs (lncRNAs) which are involved in the regulation of tumor biological processes, including RCDs occurring on tumor cells, are closely related to tumor progression. In this review, we describe the mechanisms of eight different RCDs which contain apoptosis, necroptosis, pyroptosis, NETosis, entosis, ferroptosis, autosis and cuproptosis. Meanwhile, their respective roles in the tumor are aggregated. In addition, we outline the literature that is related to the regulatory relationships between lncRNAs and RCDs in tumor cells, which is expected to provide new ideas for tumor diagnosis and treatment.

Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets.

METHOD

We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software "limma" package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines.

RESULT

CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A.

CONCLUSION

In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.

Cuproptosis-associated lncRNAs discern prognosis and immune microenvironment in sarcoma victims

Cuproptosis is a fresh form of the copper-elesclomol-triggered, mitochondrial tricarboxylic acid (TCA) dependent cell death. Yet, the subsumed mechanism of cuproptosis-associated lncRNAs in carcinoma is not wholly clarified. Here, We appraised 580 cuproptosis-associated lncRNAs in sarcoma and thereafter construed a module composing of 6 cuproptosis lncRNAs, entitled CuLncScore, utilizing a machine learning methodology. It could outstandingly discern the prognosis of patients in parallel with discriminating tumor immune microenvironment traits. Moreover, we simulate the classification system of cuproptosis lncRNAs by unsupervised learning method to facilitate differentiation of clinical denouement and immunotherapy modality options. Notably, Our Taizhou cohort validated the stability of CuLncScore and the classification system. Taking a step further, we checked these 6 cuproptosis lncRNAs by Quantitative real-time polymerase chain reaction (qRT-PCR) to ascertain their authenticity. All told, our investigations highlight that cuproptosis lncRNAs are involved in various components of sarcoma and assist in the formation of the tumor immune microenvironment. These results provide partial insights to further comprehend the molecular mechanisms of cuproptosis lncRNAs in sarcoma and could be helpful for the development of personalized therapeutic strategies targeting cuproptosis or cuproptosis lncRNAs.

Construction and Verification of a Novel Pyroptosis-Related lncRNA Signature Associated with Immune Landscape in Gliomas

Gliomas are the most common tumor in the central nervous system with limited prognostic markers making it difficult to research progression. Induction of cellular immunogenic death is a promising treatment for glioma. Pyroptosis is one of the recently discovered programmed immuogenic cell death modes which remains unclear in glioma. We obtained glioma datasets from the CGGA and TCGA websites. Pearson correlation analysis was used to find pyroptosis-related lncRNAs. Subsequently, the univariate, LASSO, and multivariate Cox regression were applied to construct a prognostic signature based on pyroptosis-related lncRNAs. Kaplan-Meier plots, ROC curves, and PCA were utilized for testing the prognostic performance of the signature. We conducted the univariate and multivariate Cox regressions to ascertain if the signature worked as an independent factor for predicting overall survival (OS) for individuals with glioma from other characteristics. For evaluating the immune landscape differences between the subgroups, ESTIMATE, CIBERTSORT, and ssGSEA were adopted. Additionally, biological functions and pathways of DEGs were identified by KEGG and GO. We also screened potential drugs and measured sensitivities of chemotherapeutics between the subgroups by CellMiner and pRRophetic package. Finally, shRNA was conducted to knockdown of COX10-AS1 in U87 cells to determine its relationship with pyroptosis. We successfully created an effective pyroptosis-related lncRNA signature that divided individuals into groups of low- and high-risk, and individuals in the high-risk group were with poor prognosis in comparison to the individuals in the other group. A nomogram including clinical factors and risk scores to predict the OS was built. Furthermore, the two groups appeared to have different immune landscapes; the high-risk group showed greater levels of ESTIMATE scores, immune cell infiltration, and immune checkpoints. Additionally, immune-related pathways and functions were shown to be enriched according to KEGG and GO findings. Knockdown of COX10-AS1 inhibited U87 cell growth, upregulated CASP1 and NLRP3, and released more IL1-β and IL-18 than the negative control. In summary, our study developed an lncRNA signature related to pyroptosis for OS prediction of gliomas and demonstrated its relationship with immune infiltration and drug sensitivity.

A novel pyroptosis-regulated gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Background: Pyroptosis, a newly discovered type of programmed cell death, has both anti-tumor and tumor-promoting effects on carcinogenesis. In hepatocellular carcinoma (HCC), however, the associations between pyroptosis-regulated genes and prognosis, immune microenvironment, and immunotherapy response remain unclear. Samples and methods: Sequencing data were collected from The Cancer Genome Atlas database, The International Cancer Genome Consortium (ICGC), and The Integrative Molecular Database of Hepatocellular Carcinoma (HCCDB). First, we investigated the expression levels and copy number variations (CNVs) of 56 pyroptosis genes in HCC and pan-cancer. Next, we identified 614 genes related to 56 pyroptosis-associated genes at the expression, mutation, and CNVs levels. Pathway enrichment analysis of 614 genes in the Hallmark, KEGG, and Reactome databases yielded a total of 253 significant signaling pathways. The pyroptosis-regulated genes (PRGs) comprised 108 genes that were derived from the top 20 signaling pathways, of which 57 genes had prognostic value in HCC. The least absolute shrinkage and selection operator (LASSO) analysis was performed to screen for PRGs with prognostic values. Ultimately, we constructed a risk score model with seven PRGs to predict HCC prognosis and validated its predictive value in three independent HCC cohorts. Risk scores were used to illustrate receiver operating characteristic (ROC) curves predicting 1, 3, and 5-years overall survival (OS). Single-sample gene set enrichment analysis (ssGSEA), was performed to study 28 types of immune cells infiltrated in HCC. The relationship between the risk signature and six immune checkpoint genes and immunotherapy was analyzed. Results: A total of seven PRGs were obtained following multiple screening steps. The risk score model containing seven PRGs was found to correlate significantly with the HCC prognosis of the training group. In addition, we validated the risk score model in two additional HCC cohorts. The risk score significantly correlated with infiltrating immune cells (i. e. CD4+ T cells, etc.), ICB key molecules (i. e. HAVCR2, etc.), and ICB response. Conclusions: This study demonstrated a vital role of PRGs in predicting the prognosis and immunotherapy response of HCC patients. The risk model could pave the way for drugs targeting pyroptosis and immune checkpoints in HCC.

Systematic transcriptome profiling of pyroptosis related signature for predicting prognosis and immune landscape in lower grade glioma

Background

Pyroptosis is a programmed cell death mediated by the gasdermin superfamily, accompanied by inflammatory and immune responses. Exogenously activated pyroptosis is still not well characterized in the tumor microenvironment. Furthermore, whether pyroptosis-related genes (PRGs) in lower-grade glioma (LGG) may be used as a biomarker remains unknown.

Methods

The RNA-Sequencing and clinical data of LGG patients were downloaded from publicly available databases. Bioinformatics approaches were used to analyze the relationship between PRGs and LGG patients’ prognosis, clinicopathological features, and immune status. The NMF algorithm was used to differentiate phenotypes, the LASSO regression model was used to construct prognostic signature, and GSEA was used to analyze biological functions and pathways. The expression of the signature genes was verified using qRT-PCR. In addition, the L1000FWD and CMap tools were utilized to screen potential therapeutic drugs or small molecule compounds and validate their effects in glioma cell lines using CCK-8 and colony formation assays.

Results

Based on PRGs, we defined two phenotypes with different prognoses. Stepwise regression analysis was carried out to identify the 3 signature genes to construct a pyroptosis-related signature. After that, samples from the training and test cohorts were incorporated into the signature and divided by the median RiskScore value (namely, Risk-H and Risk-L). The signature shows excellent predictive LGG prognostic power in the training and validation cohorts. The prognostic signature accurately stratifies patients according to prognostic differences and has predictive value for immune cell infiltration and immune checkpoint expression. Finally, the inhibitory effect of the small molecule inhibitor fedratinib on the viability and proliferation of various glioma cells was verified using cell biology-related experiments.

Conclusion

This study developed and validated a novel pyroptosis-related signature, which may assist instruct clinicians to predict the prognosis and immunological status of LGG patients more precisely. Fedratinib was found to be a small molecule inhibitor that significantly inhibits glioma cell viability and proliferation, which provides a new therapeutic strategy for gliomas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09982-7.

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

Background

Although studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.

Methods

Transcriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.

Results

Nineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.

Conclusion

Pyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma.