Ferroptosis-related gene signature as a prognostic marker for lower-grade gliomas

Identification of a novel signature derived from ferroptosis-related genes to predict prognosis, immune landscape and chemotherapeutic sensitivity in head and neck squamous cell carcinoma

BACKGROUND

Ferroptosis resistance is increasingly appreciated as an indispensable factor for tumor initiation, progression, and therapeutic resistance in various human malignancies including head and neck squamous cell carcinoma (HNSCC). Herein, we sought to develop a novel signature utilizing ferroptosis-related genes (FRGs) for prognosis and therapeutic prediction in HNSCC.

METHODS

A prognostic signature specific to HNSCC was developed using univariate Cox regression and LASSO-penalized multivariate Cox regression analyses. A nomogram incorporating this signature and selected clinicopathological factors was created through multivariate Cox regression. The effectiveness of the FRG signature in predicting tumor mutation burden (TMB), immune status, and responses to chemotherapy was also evaluated.

RESULTS

The FRG signature based on eight genes (AURKA, LPIN1, MIOX, CDKN2A, PRKAA2, CISD2, TRIB3, and ASNS) successfully classified patients into subgroups with distinct outcomes across multiple cohorts. A FRG nomogram was constructed with good-prognostic performance. Additionally, higher FRG signature scores were positively associated with TMB and negatively correlated with tumor-infiltrating immune cells, which were linked to sensitivity to several chemotherapeutic drugs.

CONCLUSIONS

Our findings provide strong evidence that the FRG-derived signature/nomogram can effectively predict both prognosis and therapeutic response in HNSCC.

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

PURPOSE

This study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications.

METHODS

Microarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein-protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed.

RESULTS

A total of 686 DEGs were identified between the two groups-of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA-mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs.

CONCLUSION

FA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia via its action potential function.

Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis

Background

The bioinformatics analysis on glioma has been a hot point recently. The purpose of this study was to provide an overview of the research in this field using a bibliometric method.

Methods

The Web of Science Core Collection (WOSCC) database was used to search for literature related to the bioinformatics analysis of gliomas. Countries, institutions, authors, references, and keywords were analyzed using VOSviewer, CiteSpace, and Microsoft Excel software.

Result

China was the most productive country, while the USA was the most cited. Capital Medical University had the largest number of publications and citations. Institutions tend to collaborate more with other institutions in their countries rather than foreign ones. The most productive and most cited author was Jiang Tao. Two citation paths were identified, with literature in basic research journals often cited in clinical journals. Immune-related vocabularies appeared frequently in recent studies.

Conclusion

Glioma bioinformatics analyses spanned a wide range of fields. The international communication in this field urgently needs to be strengthened. Glioma bioinformatics approaches are developing from basic research to clinical applications. Recently, immune-related research has become a focus.

Knockdown of HNRNPM inhibits the progression of glioma through inducing ferroptosis

PURPOSE

Ferroptosis acts as an important regulator in diverse human tumors, including the glioma. This study aimed to screen potential ferroptosis-related genes involved in the progression of glioma.

MATERIALS AND METHODS

Differently expressed genes (DEGs) were screened based on GSE31262 and GSE12657 datasets, and ferroptosis-related genes were separated. Among the important hub genes in the protein-protein interaction networks, HNRNPM was selected as a research target. Following the knockdown of HNRNPM, the viability, migration, and invasion were detected by CCK8, wound healing, and transwell assays, respectively. The role of HNRNPM knockdown was also verified in a xenograft tumor model in mice. Immunohistochemistry detected the expression levels of HNRNPM and Ki67. Moreover, the ferroptosis was evaluated according to the levels of iron, glutathione peroxidase (GSH), and malondialdehyde (MDA), as well as the expression of PTGS2, GPX4, and FTH1.

RESULTS

Total 41 overlapping DEGs relating with ferroptosis and glioma were screened, among which 4 up-regulated hub genes (HNRNPM, HNRNPA3, RUVBL1, and SNRPPF) were determined. The up-regulation of HNRNPM presented a certain predictive value for glioma. In addition, knockdown of HNRNPM inhibited the viability, migration, and invasion of glioma cells in vitro, and also the tumor growth in mice. Notably, knockdown of HNRNPM enhanced the ferroptosis in glioma cells. Furthermore, HNRNPM was positively associated with SMARCA4 in glioma.

CONCLUSIONS

Knockdown of HNRNPM inhibits the progression of glioma via inducing ferroptosis. HNRNPM is a promising molecular target for the treatment of glioma via inducing ferroptosis. We provided new insights of glioma progression and potential therapeutic guidance.

Comprehensive pan-cancer analysis and the regulatory mechanism of AURKA, a gene associated with prognosis of ferroptosis of adrenal cortical carcinoma in the tumor micro-environment

Background: The only curative option for patients with locally or locally advanced adrenocortical carcinoma is primary tumor curative sexual resection (ACC). However, overall survival remains low, with most deaths occurring within the first 2 years following surgery. The 5-year survival rate after surgery is less than 30%. As a result, more accurate prognosis-related predictive biomarkers must be investigated urgently to detect patients' disease status after surgery. Methods: Data from FerrDb were obtained to identify ferroptosis-related genes, and ACC gene expression profiles were collected from the GEO database to find differentially expressed ACC ferroptosis-related genes using differential expression analysis. The DEFGs were subjected to Gene Ontology gene enrichment analysis and KEGG signaling pathway enrichment analysis. PPI network building and predictive analysis were used to filter core genes. The expression of critical genes in ACC pathological stage and pan-cancer was then investigated. In recent years, immune-related factors, DNA repair genes, and methyltransferase genes have been employed in diagnosing and prognosis of different malignancies. Cancer cells are mutated due to DNA repair genes, and highly expressed DNA repair genes promote cancer. Dysregulation of methyltransferase genes and Immune-related factors, which are shown to be significantly expressed in numerous malignancies, also plays a crucial role in cancer. As a result, we investigated the relationship of AURKA with immunological checkpoints, DNA repair genes, and methyltransferases in pan-cancer. Result: The DEGs found in the GEO database were crossed with ferroptosis-related genes, yielding 42 differentially expressed ferroptosis-related genes. Six of these 42 genes, particularly AURKA, are linked to the prognosis of ACC. AURKA expression was significantly correlated with poor prognosis in patients with multiple cancers, and there was a significant positive correlation with Th2 cells. Furthermore, AURKA expression was positively associated with tumor immune infiltration in Lung adenocarcinoma (LUAD), Liver hepatocellular carcinoma (LIHC), Sarcoma (SARC), Esophageal carcinoma (ESCA), and Stomach adenocarcinoma (STAD), but negatively correlated with the immune score, matrix score, and calculated score in these tumors. Further investigation into the relationship between AURKA expression and immune examination gene expression revealed that AURKA could control the tumor-resistant pattern in most tumors by regulating the expression level of specific immune examination genes. Conclusion: AURKA may be an independent prognostic marker for predicting ACC patient prognosis. AURKA may play an essential role in the tumor microenvironment and tumor immunity, according to a pan-cancer analysis, and it has the potential to be a predictive biomarker for multiple cancers.

Ferroptosis Biomarkers for Predicting Prognosis and Immunotherapy Efficacy in Adrenocortical Carcinoma

BACKGROUND

Numerous studies have suggested that ferroptosis plays an important regulatory role in cancer cell death. Nonetheless, the potential effects of ferroptosis regulators on the prognosis, the expression of immunomodulatory factors in the tumor microenvironment and on the efficacy of immunotherapy in adrenocortical carcinoma (ACC) remain largely unknown.

METHODS

Public ACC datasets were used to investigate the relationship between ferroptosis regulators and prognosis and clinical features. A ferroptosis scoring system was established for individual cases of ACC using principal component analysis algorithms. Hub ferroptosis-related genes involved in immunoregulation and immunotherapy efficacy in ACC were further identified.

RESULTS

Twenty ferroptosis regulators were differentially expressed in ACC and 17 ferroptosis regulators were closely related to prognosis in ACC. A ferroptosis scoring system was developed based on ACSL4, FANCD2, and SLC7A1 expression, and the ferroptosis regulators could serve as an independent prognostic factor for ACC. Further analyses indicated that the ferroptosis score integrated with the tumor mutation burden (TMB), and immune-checkpoint gene expression could predict prognosis in ACC. RNA isolation and reverse transcription‑quantitative polymerase chain reaction (RT-qPCR) demonstrated significant differences in the expression levels of ACSL4, FANCD2, and SLC7A1 between ACC and normal tissues. Furthermore, FANCD2 was significantly related to immunotherapy efficacy and prognosis in ACC.

CONCLUSION

Our study demonstrated that ferroptosis was significantly associated with prognosis, clinical characteristics, immune-checkpoint gene expression, and tumor microenvironment immune cell infiltration in ACC. The current study provides comprehensive evidence for further research on ferroptosis regulators in ACC and provides new insight into the epigenetic regulation of the antitumor immune response.

A ferroptosis associated gene signature for predicting prognosis and immune responses in patients with colorectal carcinoma

Background: Colorectal carcinoma (CRC) is one of the most prevalent malignancies globally. Ferroptosis, a novel type of cell death, is critical in the development and treatment of tumors.

Objective: This study was designed to establish a genetic signature for ferroptosis which has a predictive effect on the outcomes and immunotherapeutic response of CRC.

Methods: Data of CRC patients were retrieved from TCGA and GEO databases. The genes associated with ferroptosis were obtained from GeneCards. The genetic signature for ferroptosis was identified by performing Cox regression analysis. Kaplan–Meier and ROC analysis were performed to assess the prognosis role of the genetic signature. CIBERSORT tool was used to identify a potential association of the genetic signature with the immune cells. The potential immunotherapeutic signatures and drug sensitivity prediction targeting this signature were also discussed. Immunohistochemistry was used to detect expression of ferroptosis-associated genes in CRC tissues and adjacent tissues.

Results: A ferroptosis-associated gene signature comprised of three genes (CDKN2A, FDFT1, and ACSL6) was developed for prediction of prognosis and evaluation of immune responses in CRC. Patients in the high-risk group tended to have a poor prognosis. In CRC, the ferroptosis-associated gene signature may function as independent predictors. Additionally, the expressional levels of the immune checkpoint proteins PD-L1 and CTLA-4 were substantially increased in the high-risk group. Moreover, we can distinguish between patients based on their immunotherapeutic responses more effectively if we categorize them by this signature. Additionally, candidate compounds were identified for the differentiation of CRC subtypes.

Conclusion: The ferroptosis-associated gene signature identified in this study is effective in predicting the prognosis and evaluating immunotherapeutic response in CRC patients, and provides us with novel insights into the potential effect of ferroptosis targeted treatment on CRC.

A Metabolic Plasticity-Based Signature for Molecular Classification and Prognosis of Lower-Grade Glioma

Background: Glioma is one of the major health problems worldwide. Biomarkers for predicting the prognosis of Glioma are still needed. Methods: The transcriptome data and clinic information on Glioma were obtained from the CGGA, TCGA, GDC, and GEO databases. The immune infiltration status in the clusters was compared. The genes with differential expression were identified, and a prognostic model was developed. Several assays were used to detect RPH3A’s role in Glioma cells, including CCK-8, colony formation, wound healing, and transwell migration assay. Results: Lower Grade Glioma (LGG) was divided into two clusters. The immune infiltration difference was observed between the two clusters. We screened for genes that differed between the two groups. WGCNA was used to construct a co-expressed network using the DEGs, and four co-expressed modules were identified, which are blue, green, grey, and yellow modules. High-risk patients have a lower overall survival rate than low-risk patients. In addition, the risk score is associated with histological subtypes. Finally, the role of RPH3A was detected. The overexpression of RPH3A in LGG cells can significantly inhibit cell proliferation and migration and regulate EMT-regulated proteins. Conclusion: Our study developed a metabolic-related model for the prognosis of Glioma cells. RPH3A is a potential therapeutic target for Glioma.

Mechanism of Ferroptosis and Its Relationships with Other Types of Programmed Cell Death: Insights for Potential Therapeutic Benefits in Traumatic Brain Injury

Traumatic brain injury (TBI) is a serious health issue with a high incidence, high morbidity, and high mortality that poses a large burden on society. Further understanding of the pathophysiology and cell death models induced by TBI may support targeted therapies for TBI patients. Ferroptosis, a model of programmed cell death first defined in 2012, is characterized by iron dyshomeostasis, lipid peroxidation, and glutathione (GSH) depletion. Ferroptosis is distinct from apoptosis, autophagy, pyroptosis, and necroptosis and has been shown to play a role in secondary brain injury and worsen long-term outcomes after TBI. This review systematically describes (1) the regulatory pathways of ferroptosis after TBI, (2) the neurobiological links between ferroptosis and other cell death models, and (3) potential therapies targeting ferroptosis for TBI patients.

Remarkable immune and clinical value of novel ferroptosis-related genes in glioma

Ferroptosis is a neoteric model of regulated cell death that shows great potential for the understanding of tumor immunology and as a target for therapy. The present study aimed to identify ferroptosis-related differentially expressed genes (DEGs) in glioma and to explore their value through systematic analysis. Ferroptosis-related DEGs were identified through the Gene Expression Omnibus database in combination with the FerrDb database and analyzed in the Genotype-Tissue Expression database and The Cancer Genome Atlas database. Possible signaling pathways involved were explored by construction of enrichment analysis and protein–protein interaction of these DEGs. Potential regulation of the immune microenvironment, immune checkpoint and chemokine was postulated by immune analysis. A prognosis model for glioma was developed using survival analysis, exhibited by the nomogram and evaluated by the calibration curve. The prognostic value of the model was validated by using an independent cohort. A total of 15 ferroptosis-related DEGs were identified, including 7 down-regulated and 8 up-regulated, with ATP6V1G2, GABARAPL1 and GOT1 as hub genes. The expression of all 3 hub genes was positively correlated with T follicular helper cells and natural killer CD56bright cells. These hub genes were negatively correlated with the macrophage cell type as well as B7H3, PDCD1, LAG3 and CXCL16, CXCR4, CCR5. Low expression of all 3 hub genes was associated with poor prognosis in glioma cases. ATP6V1G2 might be an independent prognostic factor and, as such, a high-precision prognostic model of glioma was constructed. We identified novel ferroptosis-related genes with clinical value in glioma and revealed their possible tumor immune relevance. Furthermore, in glioma, we pinpointed underlying critical elements of the chemokine, immune microenvironment and immune checkpoint, and were able to develop a predictive model of prognosis.

Cationic Carbon Nanoparticles Induce Inflammasome-Dependent Pyroptosis in Macrophages via Lysosomal Dysfunction

Carbon nanomaterials, including carbon dots (CDs), form a growing family of engineered nanoparticles (NPs) with widespread applications. As the rapid expansion of nanotechnologies raises safety concerns, interaction of NPs with the immune system is receiving a lot of attention. Recent studies have reported that engineered NPs may induce macrophage death by pyroptosis. Therefore, this study investigated whether cationic CDs induce pyroptosis in human macrophages and assessed the role of inflammasome and lysosome in this process. Cationic CDs were synthetized by microwave-assisted pyrolysis of citric acid and high molecular weight branched polyethyleneimine. The NPs evoked a dose-dependent viability loss in THP-1-derived macrophages. A cell leakage, an increase in IL-1β secretion and an activation of caspase-1 were also observed in response to the NPs. Inhibition of caspase-1 decreased CD-induced cell leakage and IL-1β secretion, while restoring cell viability. Besides, CDs triggered swelling and loss of integrity of lysosome, and inhibition of the lysosomal enzyme cathepsin B decreased CD-induced IL-1β secretion. Thus, our data provide evidence that cationic CDs induce inflammasome-dependent pyroptosis in macrophages via lysosomal dysfunction.

Ferroptosis Modulation: Potential Therapeutic Target for Glioblastoma Treatment

Glioblastoma multiforme is a lethal disease and represents the most common and severe type of glioma. Drug resistance and the evasion of cell death are the main characteristics of its malignancy, leading to a high percentage of disease recurrence and the patients’ low survival rate. Exploiting the modulation of cell death mechanisms could be an important strategy to prevent tumor development and reverse the high mortality and morbidity rates in glioblastoma patients. Ferroptosis is a recently described type of cell death, which is characterized by iron accumulation, high levels of polyunsaturated fatty acid (PUFA)-containing phospholipids, and deficiency in lipid peroxidation repair. Several studies have demonstrated that ferroptosis has a potential role in cancer treatment and could be a promising approach for glioblastoma patients. Thus, here, we present an overview of the mechanisms of the iron-dependent cell death and summarize the current findings of ferroptosis modulation on glioblastoma including its non-canonical pathway. Moreover, we focused on new ferroptosis-inducing compounds for glioma treatment, and we highlight the key ferroptosis-related genes to glioma prognosis, which could be further explored. Thereby, understanding how to trigger ferroptosis in glioblastoma may provide promising pharmacological targets and indicate new therapeutic approaches to increase the survival of glioblastoma patients.

Quantification of ferroptosis pattern in bladder carcinoma and its significance on immunotherapy

The role of ferroptosis in tumor development and therapy has been previously proved. Nonetheless, its potential role in tumor microenvironment (TME) and immunotherapy for bladder carcinoma remains unclear. Based on 38 ferroptosis-related genes, the characteristic of ferroptosis patterns and interactions with immune cell-infiltrating features in 2043 bladder cancer samples were systematically investigated. We further proposed the FerrScore to quantify the ferroptosis patterns for each patient. As results, three diverse ferroptosis patterns with distinct tumor-infiltrating immune cell features were established. By determination of ferroptosis patterns of each patient, we found that high FerrScore was related to lower proportion of luminal-papillary molecular subtype, more frequent TP53 mutations, activation of immunity and stroma, and lower 5-year survival. High FerrScore also seemed to be associated with decreased neoantigen load, tumor mutational burden and poorer response to anti-PD-L1/1 therapy. External verification in two immunotherapy cohorts showed FerrScore was an independent and effective prognostic factor for therapeutic effect and survival outcome. Overall, the present study indicated the ferroptosis strongly is closely correlated with TME diversity. Evaluation of the ferroptosis patterns may strengthen the cognition of TME immune cell infiltrations and guide more individualized immunotherapeutic strategies in bladder carcinoma.

Prognostic Features of the Tumor Immune Microenvironment in Glioma and Their Clinical Applications: Analysis of Multiple Cohorts

Glioma is the most common malignant tumor of the central nervous system. Tumor purity is a source of important prognostic factor for glioma patients, showing the key roles of the microenvironment in glioma prognosis. In this study, we systematically screened functional characterization related to the tumor immune microenvironment and constructed a risk model named Glioma MicroEnvironment Functional Signature (GMEFS) based on eight cohorts. The prognostic value of the GMEFS model was also verified in another two glioma cohorts, glioblastoma (GBM) and low-grade glioma (LGG) cohorts, from The Cancer Genome Atlas (TCGA). Nomograms were established in the training and testing cohorts to validate the clinical use of this model. Furthermore, the relationships between the risk score, intrinsic molecular subtypes, tumor purity, and tumor-infiltrating immune cell abundance were also evaluated. Meanwhile, the performance of the GMEFS model in glioma formation and glioma recurrence was systematically analyzed based on 16 glioma cohorts from the Gene Expression Omnibus (GEO) database. Based on multiple-cohort integrated analysis, risk subpathway signatures were identified, and a drug–subpathway association network was further constructed to explore candidate therapy target regions. Three subpathways derived from Focal adhesion (path: 04510) were identified and contained known targets including platelet derived growth factor receptor alpha (PDGFRA), epidermal growth factor receptor (EGFR), and erb-b2 receptor tyrosine kinase 2 (ERBB2). In conclusion, the novel functional signatures identified in this study could serve as a robust prognostic biomarker, and this study provided a framework to identify candidate therapeutic target regions, which further guide glioma patients’ clinical decision.

Characterization of the Ferroptosis-Related Genes for Prognosis and Immune Infiltration in Low-Grade Glioma

Background: Although ferroptosis has been validated to play a crucial role in some types of tumors, the influence of ferroptosis-related genes (FRGs) on the immune microenvironment in low-grade glioma (LGG) remains unclear. In this research, we screen the FRGs to assess the prognosis value and immune microenvironment in LGG, to provide reliable diagnosis and treatment evidence for the clinic.

Methods: A total of 1,239 patients of LGG samples were selected for subsequent analyses from The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and the Repository of Molecular Brain Neoplasia Data datasets. Univariate Cox regression analysis was used to screen for prognostic FRGs. Consensus clustering was utilized to determine ferroptosis subtypes of LGG patients. Next, the prognostic model was constructed based on differentially expressed FRGs and validation in the validating datasets. The immune microenvironment, biological pathway, and hypoxia score were explored by single-sample gene set enrichment analysis. The potential response of chemotherapy and immune checkpoint blockade therapy was also estimated. In addition, the correlation between the risk score and autophagy-related genes was examined by the Pearson correlation coefficient.

Results: A total of three ferroptosis subtypes were identified by consensus clustering for prognostic FRGs which exhibited different outcomes, clinicopathological characteristics, and immune microenvironment. Afterward, a prognostic model that performed great predictive ability based on nine prognostic FRGs has been constructed and validated. Moreover, the prognostic model had the potential to screen the sensitivity to chemotherapy and immunotherapy in LGG patients. Finally, we also found that the prognostic model has a great connection to autophagy and hypoxia.

Conclusion: We developed a ferroptosis-related prognostic model which strongly linked to diagnosis, treatment, prognosis, and recurrence of LGG. This study also reveals the connection between ferroptosis and tumor immune microenvironment.

Construction of a Risk Model to Predict the Prognosis and Immunotherapy of Low-Grade Glioma Ground on 7 Ferroptosis-Related Genes

Purpose

Ferroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model.

Patients and Methods

The data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient's response to anti-PD-1/PD-L1 immunotherapy was also investigated.

Results

FRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different.

Conclusion

A prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.

APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

BACKGROUND

Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions.

METHODS

Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review.

FINDINGS

APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness.

INTERPRETATION

APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO).

FUNDING

National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

Ferroptosis Mediation Patterns Reveal Novel Tool to Implicate Immunotherapy and Multi-Omics Characteristics in Bladder Cancer

Background: The regulatory role of ferroptosis in malignant tumours has been recently demonstrated. However, the potential roles of ferroptosis mediation patterns in bladder cancer remain elusive. Materials and Methods: The ferroptosis mediation patterns of 889 bladder cancer samples were comprehensively evaluated based on ferroptosis-related genes. The underlying correlations between these mediation patterns and multi-omic characteristics of bladder cancer were systematically analysed. The ferroptosis mediation patterns of individual samples were quantified by ferropscore using the principal component analysis algorithm. The typical ferroptosis-related genes with prognostic roles were further randomly validated using immunohistochemical staining, real-time polymerase chain reaction and western blotting. Results: Three different ferroptosis mediation patterns were identified. The abundance of infiltration of 23 immune cells was different among the three mediation patterns. The quantification of ferroptosis mediation patterns in individual samples served as a promising tool for predicting patient survival outcomes; immune cell infiltration abundance; tumour mutation burden; oncogenic mutation status and tumour grade, stage and molecular subtypes. Low ferropscore combined with high tumour mutation burden was associated with the best survival prognosis. Expressions of PD-L1 (p < 0.001), PD-1 (p = 0.002) and CTLA-4 (p = 0.003) were all significantly upregulated in the high ferropscore group. Low ferropscores also predicted good immunotherapy response for anti-CTLA4 strategy. The mRNA and protein levels of FADS2, a typical ferroptosis-related gene used in the study, were higher in bladder cancer cell lines than in controlled SV-HUC-1 cells. In addition, immunohistochemical staining revealed significantly higher expression levels of FADS2 in human bladder cancer tumour tissues than in normal tissues. Conclusion: This study identified three distinct ferroptosis mediation patterns in bladder cancer. Quantification of ferroptosis mediation patterns in individual samples may help to improve the understanding of multiomic characteristics and guide future immunotherapy responses to bladder cancer.

Genetic Profiles of Ferroptosis in Malignant Brain Tumors and Off-Target Effects of Ferroptosis Induction

Glioblastoma represents the most devastating form of human brain cancer, associated with a very poor survival rate of patients. Unfortunately, treatment options are currently limited and the gold standard pharmacological treatment with the chemotherapeutic drug temozolomide only slightly increases the survival rate. Experimental studies have shown that the efficiency of temozolomide can be improved by inducing ferroptosis – a recently discovered form of cell death, which is different from apoptosis, necrosis, or necroptosis and, which is characterized by lipid peroxidation and reactive oxygen species accumulation. Ferroptosis can also be activated to improve treatment of malignant stages of neuroblastoma, meningioma, and glioma. Due to their role in cancer treatment, ferroptosis-gene signatures have recently been evaluated for their ability to predict survival of patients. Despite positive effects during chemotherapy, the drugs used to induce ferroptosis – such as erastin and sorafenib – as well as genetic manipulation of key players in ferroptosis – such as the cystine-glutamate exchanger xCT and the glutathione peroxidase GPx4 – also impact neuronal function and cognitive capabilities. In this review, we give an update on ferroptosis in different brain tumors and summarize the impact of ferroptosis on healthy tissues.

New Autophagy-Ferroptosis Gene Signature Predicts Survival in Glioma

Background: Ferroptosis plays an important role in glioma and significantly affects the prognosis, but the specific mechanism has not yet been elucidated. Recent studies suggest that autophagy regulates the process of ferroptosis. This study aimed to find potential autophagy-ferroptosis genes and explore the prognostic significance in glioma.

Methods: Ferroptosis and autophagy genes were obtained from two online databases (zhounan.org/ferrdb and autophagy.lu/). The RNAseq data and clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Univariate, multivariate, lasso and Cox regression analysis screened out prognosis-related genes, and a risk model was constructed. Receiver operating characteristic (ROC) curve analysis evaluated the predictive efficiency of the model. Finally, a nomogram was constructed to more accurately predict the prognosis of glioma.

Results: We developed a Venn diagram showing 23 autophagy-ferroptosis genes. A total of 660 cases (including RNA sequences and complete clinical information) from two different cohorts (training group n = 413, verification group n = 247) of the CGGA database was acquired. Cohorts were screened to include five prognosis-related genes (MTOR, BID, HSPA5, CDKN2A, GABARAPLA2). Kaplan-Meier curves showed that the risk model was a good prognostic indicator (p < 0.001). ROC analysis showed good efficacy of the risk model. Multivariate Cox analysis also revealed that the risk model was suitable for clinical factors related to prognosis, including type of disease (primary, recurrence), grade (III-IV), age, temozolomide treatment, and 1p19q state. Using the five prognosis-related genes and the risk score, we constructed a nomogram assessed by C-index (0.7205) and a calibration plot that could more accurately predict glioma prognosis.

Conclusion: Using a current database of autophagy and ferroptosis genes, we confirmed the prognostic significance of autophagy-ferroptosis genes in glioma, and we constructed a prognostic model to help guide treatment for high grade glioma in the future.

A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.

Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.

Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.

Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

Development and Validation of a Ferroptosis-Related Gene Signature and Nomogram for Predicting the Prognosis of Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor with high mortality and poor prognosis. Ferroptosis is a newly discovered form of cell death induced by iron-catalyzed excessive peroxidation of polyunsaturated fatty acids (PUFAs). However, the prognostic value of ferroptosis-related genes (FRGs) for ESCC remains unclear. Based on the ESCC dataset from the Gene Expression Omnibus (GEO) database, we identified 39 prognostic FRGs through univariate Cox regression analysis. After LASSO regression and multivariate Cox regression analyses, a multigene signature based on 10 prognostic FRGs was constructed and successfully divided ESCC patients into two risk groups. Patients in the low-risk group showed a significantly better prognosis than patients in the high-risk group. In addition, we combined the risk score with clinical predictors to construct a nomogram for ESCC. The predictive ability of the nomogram was further verified by ROC curves and calibration plots in both the training and validation sets. The predictive power of the nomogram was demonstrated to be better than that of either the risk score or clinical variable alone. Furthermore, functional analysis revealed that the 10-FRG signature was mainly associated with ferroptosis, differentiation and immune response. Connectivity map analysis identified potential compounds capable of targeting FRGs in ESCC. Finally, we demonstrated the prognostic value of SRC gene in ESCC using the clinical samples and found that SRC inhibition sensitized ESCC cells to ferroptosis inducers by in vitro experiments. In conclusion, we identified and verified a 10-FRG prognostic signature and a nomogram, which provide individualized prognosis prediction and provide insight into potential therapeutic targets for ESCC.

Investigation of Prognostic Markers of Lung Adenocarcinoma Based on Tumor Metabolism-Related Genes

Lung adenocarcinoma (LUAD) is a prevalent cancer killer. Investigation on potential prognostic markers of LUAD is crucial for a patient’s postoperative planning. LUAD-associated datasets were acquired from Gene Expression Omnibus (GEO) as well as The Cancer Genome Atlas (TCGA). LUAD metabolism-associated differentially expressed genes were obtained, combining tumor metabolism-associated genes. COX regression analyses were conducted to build a five-gene prognostic model. Samples were divided into high- and low-risk groups by the established model. Survival analysis displayed favorable prognosis in the low-risk group in the training set. Favorable predictive performance of the model was discovered as hinted by receiver’s operative curve (ROC). Survival analysis and ROC analysis in the validation set held an agreement. Gene Set Enrichment Analysis (GSEA), tumor mutation bearing (TMB), and immune infiltration differential analysis were performed. The two groups displayed differences in glycolysis gluconeogenesis, P53 signaling pathway, etc. The high-risk group showed higher TP53 mutation frequency as well as TMB. The low-risk group displayed higher immune activity along with immune score. Altogether, this study casts light on further development of novel prognostic markers for LUAD.

The mechanisms and therapeutic targets of ferroptosis in cancer

INTRODUCTION

Ferroptosis, a form of programmed cell death, is mediated primarily by lipid peroxidation via a unique iron-dependent process. The mechanisms of ferroptosis involve the metabolisms of amino acids, irons, and lipids, and the regulation of antioxidant systems. Evidence supports the roles of ferroptosis in cancer, while metabolic reprogramming (a hallmark of cancer) renders tumor cells highly vulnerable to ferroptosis and thus provides a rationale for ferroptosis-targeted therapy for cancer.

AREA COVERED

This article examines the current understanding of the mechanisms and related signaling pathways involving ferroptosis; it focuses on novel targets in cancer and its treatment and drug resistance. The development of ferroptosis-targeted therapy, especially in combination with conventional or non-conventional therapies, are considered with dilemmas and key questions in this research area.

EXPERT OPINION

An increasing number of potential targets and ferroptosis inducers (FINs) have been identified to treat cancer. However, no specific FIN has entered clinical trials thus far, likely due to poor efficacy and high toxicity in vivo. Thus, new FINs with high selectivity and bioavailability are required to target tumor cells more specifically and potently. Particularly, the combination of FINs with chemotherapy, radiotherapy, targeted therapy, and immunotherapy warrants clinical investigation in the future.

Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

Background

Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC).

Methods

Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established.

Results

An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients.

Conclusions

The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

A New Prognostic Risk Signature of Eight Ferroptosis-Related Genes in the Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma and has poor prognosis in the locally advanced stage. Ferroptosis, a relatively new type of cell death, has gained significant attention in recent years. This study aimed to explore the prognostic value of ferroptosis-related genes (FRGs) in ccRCC. In this study, 50 differentially expressed FRGs between ccRCC and adjacent normal kidney tissues were identified, 26 of them correlated with overall survival (OS) (P <0.05). Eight optimal FRGs were selected by Lasso regression and multivariate Cox regression analysis, and used to construct a new prognostic risk signature to predict the prognosis of ccRCC patients. In addition, the signature passed the validation of prognostic survival analyses by a significant margin, and the risk score was identified as an independent prognostic marker via Cox regression analyses. Further studies indicated that the signature was significantly correlated with immune cell infiltration. Moreover, the levels of eight FRGs were examined in ccRCC. Collectively, the 8-FRG prognostic risk signature helps the clinicians predict the prognosis and OS of the patients, and standardize prognostic assessments.

Ferroptosis-related gene signature as a prognostic marker for lower-grade gliomas

Ferroptosis is a newly discovered form of programmed cell death, which has unique biological effects on metabolism and redox biology. In this study, the prognostic value of ferroptosis‐related genes was investigated in lower‐grade gliomas (LGG). We downloaded the ferroptosis‐related genes from the FerrDb dataset. Univariate Cox and LASSO regression analyses were applied to identify genes correlated with overall survival (OS). Subsequently, 12 ferroptosis‐related genes were screened to establish the prognostic signature using stepwise multivariate Cox regression. According to the median value of risk scores, patients were divided into low‐ and high‐risk subgroups. The Kaplan‐Meier curves showed the high‐risk group had a lower OS. The predictive power of the risk model was validated using the CGGA. Functional analysis revealed that the terms associated with plasma membrane receptor complex, immune response and glutamate metabolic process were primarily related to the risk model. Moreover, we established a nomogram that had a strong forecasting ability for the 1‐, 3‐ and 5‐year OS. In addition, we compared the risk scores between different clinical features. We also detected infiltration of macrophages and monocytes in different subgroups. Overall, our study identified the prognostic signature of 12 ferroptosis‐related genes, which has the potential to predict the prognosis of LGG.

Identification of a Ferroptosis-Related Signature Associated with Prognosis and Immune Infiltration in Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is a rare malignant tumor with poor prognosis. Ferroptosis, a new form of cell death, differs from other forms of cell death and plays a vital role in tumor progress. Our study aimed to establish a ferroptosis-related signature with prognostic value in ACC. RNA-seq data and corresponding clinical characteristics for ACC were downloaded from TCGA and GEO databases. Genes included in ferroptosis risk signature were assessed by univariable and multivariable Cox regression analysis as well as lasso regression analysis. The prognostic value of the ferroptosis risk signature was assessed using K-M and ROC curves. Furthermore, we performed GSEA to discover the enriched gene sets in high-risk group. Additionally, TIMER website was applied to detect a possible connection between the signature and immune cells infiltration. ssGSEA was performed to evaluate scores of immune cells and immune-related pathways in two groups. A ferroptosis signature comprised of six genes (SLC7A11, TP53, HELLS, ACSL4, PCBP2, and HMGB1) was constructed to predict prognosis and reflect the immune infiltration in ACC. Patients in high-risk group were inclined to have worse prognosis. The ferroptosis model performed well in predicting prognosis and could be served as an independent indicator in ACC. GSEA revealed that gene sets correlated with biological processes including cell cycle, DNA replication, base excision repair, and P53 signaling pathway were highly enriched in high-risk group. In addition, we discovered that the expressional levels of hub genes were linked to six immune cells' infiltration in ACC tumor. ssGSEA revealed that contents of most immune cells significantly decreased in the high-risk group. In conclusion, the novel ferroptosis risk signature could be useful in predicting prognosis and reflecting immune infiltration in ACC. It also brings us new insights into the possible value of targeting ferroptosis during the therapy of ACC.