FerrDb: a manually curated resource for regulators and markers of ferroptosis and ferroptosis-disease associations

Construction of a Ferroptosis-Related Gene Signature for Head and Neck Squamous Cell Carcinoma Prognosis Prediction

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant cancers, and few studies have demonstrated the value of ferroptosis-related genes in prognosis.

Methods

The original counts of RNA sequencing data and clinicopathological data were obtained from TCGA and GSE65858 datasets. Common ferroptosis-related genes related to prognosis were identified from the training set and were included in LASSO to determine the best prognosis. To evaluate the efficacy, time-dependent ROC and Kaplan–Meier (KM) survival analyses were applied. Moreover, univariate and multivariate Cox regression analyses were used to screen independent parameters of prognosis and build a nomogram. Eventually, possible biological pathways were proposed based on GSEA.

Results

Among 242 ferroptosis-related genes, we identified that the FLT3, IL6, Keap1, NQO1, SOCS1 and TRIB3 genes were significantly connected with HNSCC patient prognosis as a six-gene signature. After, the patients were divided into high- and low-risk groups based on the six-gene signature. The KM survival curves demonstrated that the high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 dataset. Moreover, GSEA suggested that the epithelial mesenchymal transition pathway was abundant and that the mesenchymal status in the high-risk group was substantially higher than that in the low-risk group. Finally, the immune microenvironment and differences in the content of immune cell types were demonstrated.

Conclusion

We established a six-ferroptosis-related-gene model crossing clinical prognostic parameters that can predict HNSCC patient prognosis and provide a reliable prognostic evaluation tool to assist clinical treatment decisions.

Ferroptosis-Related Long Non-Coding RNA Signature Contributes to the Prediction of Prognosis Outcomes in Head and Neck Squamous Cell Carcinomas

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor, which makes the prognosis prediction challenging. Ferroptosis is an iron-dependent form of non-apoptotic regulated cell death, which could affect cancer development. However, the prognostic value of ferroptosis-related long non-coding RNA (lncRNA) in HNSCC is still limited.

Methods: In the current study, we employed the DESeq2 method to characterize the differentially expressed ferroptosis-related genes (FEGs) between cancer and normal samples. Next, the FEG-related lncRNAs (FElncRNAs) were identified using Spearman’s correlation analysis and multiple permutation hypotheses. Subsequently, LASSO and stepwise multivariate Cox regression analyses were undertaken to recognize the prognosis-related FElncRNA signature (PFLS) and risk scores.

Results: Herein, we first identified 60 dysregulated FEGs and their co-expressed FElncRNAs in HNSCC. Then, we recognized a set of six FElncRNAs PFLS (SLCO4A1-AS1, C1RL-AS1, PCED1B-AS1, HOXB-AS3, MIR9-3HG, and SFTA1P) for predicting patients’ prognostic risks and survival outcomes. We also assessed the efficiency of PFLS in the test set and an external validation cohort. Further parsing of the tumor immune microenvironment showed the PFLS was closely associated with immune cell infiltration abundances. Notably, the low-risk group of the PFLS showed a higher MHC score and cytolytic activity (CYT) score than the high-risk group, implying the low-risk group may have greater tumor surveillance and killing ability. In addition, we observed that the expression levels of two immune checkpoints (ICPs), i.e., programmed cell death protein 1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1), showed significant associations with patients’ risk score, prompting the role of the PFLS in ICP blockade therapy. Finally, we also constructed a drug–PFLS network to reinforce the clinical utilities of the PFLS.

Conclusion: In summary, our study indicated that FElncRNAs played an important role in HNSCC survival prediction. Identification of PFLS will contribute to the development of novel anticancer therapeutic strategies.

Identification of differentially expressed genes in mouse paraspinal muscle in response to microgravity

Lower back pain (LBP) is the primary reason leading to dyskinesia in patients, which can be experienced by people of all ages. Increasing evidence have revealed that paraspinal muscle (PSM) degeneration (PSMD) is a causative contributor to LBP. Current research revealed that fatty infiltration, tissue fibrosis, and muscle atrophy are the characteristic pathological alterations of PSMD, and muscle atrophy is associated with abnormally elevated oxidative stress, reactive oxygen species (ROS) and inflammation. Interestingly, microgravity can induce PSMD and LBP. However, studies on the molecular mechanism of microgravity in the induction of PSMD are strongly limited. This study identified 23 differentially expressed genes (DEGs) in the PSM (longissimus dorsi) of mice which were flown aboard the Bion M1 biosatellite in microgravity by bioinformatics analysis. Then, we performed protein-protein interaction, Gene Ontology function, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis for the DEGs. We found that Il6ra, Tnfaip2, Myo5a, Sesn1, Lcn2, Lrg1, and Pik3r1 were inflammatory genes; Fbox32, Cdkn1a, Sesn1, and Mafb were associated with muscle atrophy; Cdkn1a, Sesn1, Lcn2, and Net1 were associated with ROS; and Sesn1 and Net1 were linked to oxidative stress. Furthermore, Lcn2, Fbxo32, Cdkn1a, Pik3r1, Sesn1, Net1, Il6ra, Myo5a, Lrg1, and Pfkfb3 were remarkably upregulated, whereas Tnfaip2 and Mafb were remarkably downregulated in PSMD, suggesting that they might play a significant role in regulating the occurrence and development of PSMD. These findings provide theoretical basis and therapeutic targets for the treatment of PSMD.

Identification of potential ferroptosis-related biomarkers and a pharmacological compound in diabetic retinopathy based on machine learning and molecular docking

BACKGROUND

Diabetic retinopathy (DR), a neurovascular disease, is a leading cause of visual loss worldwide and severely affects quality of life. Several studies have shown that ferroptosis plays an important role in the pathogenesis of DR; however, its molecule mechanism remains incompletely elucidated. Hence, this study aimed to investigate the pathogenesis of ferroptosis and explore potential ferroptosis-related gene biomarkers and a pharmacological compound for treating DR.

METHODS

Ferroptosis-related differentially expressed genes (DEGs) were identified in the GSE102485 dataset. Functional enrichment analyses were then performed and a protein-protein interaction (PPI) network was constructed to screen candidates of ferroptosis-related hub genes (FRHGs). FRHGs were further screened based on least absolute shrinkage and selection operator (LASSO) regression and random forest algorithms, and were then validated with the GSE60436 dataset and previous studies. A receiver operating characteristic (ROC) curve monofactor analysis was conducted to evaluate the diagnostic performance of the FRHGs, and immune infiltration analysis was performed. Moreover, the pharmacological compound targeting the FRHGs were verified by molecular docking. Finally, the FRHGs were validated using quantitative real-time polymerase chain reaction (qRT-PCR) analysis.

RESULTS

The 40 ferroptosis-related DEGs were extracted, and functional enrichment analyses mainly implicated apoptotic signaling, response to oxidative stress, ferroptosis, and lipid and atherosclerosis pathways. By integrating the PPI, LASSO regression, and random forest analyses to screen the FRHGs, and through validation, we identified five FRHGs that performed well in the diagnosis (CAV1, CD44, NOX4, TLR4, and TP53). Immune infiltration analysis revealed that immune microenvironment changes in DR patients may be related to these five FRHGs. Molecular docking also showed that glutathione strongly bound the CAV1 and TLR4 proteins. Finally, the upregulated expression of FRHGs (CD44, NOX4, TLR4, and TP53) was validated by qRT-PCR analysis in human retinal capillary endothelial cells cultured under high-glucose environment.

CONCLUSIONS

CAV1, CD44, NOX4, TLR4, and TP53 are potential biomarkers for DR and may be involved in its occurrence and progression by regulating ferroptosis and the immune microenvironment. Further, glutathione exhibits potential therapeutic efficacy on DR by targeting ferroptosis. Our study provides new insights into the ferroptosis-related pathogenesis of DR, as well as its diagnosis and treatment.

An Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary.

Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS).

Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen–containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients.

Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.

A prognostic model for cervical cancer based on ferroptosis-related genes

BACKGROUND

Ferroptosis is widely involved in the occurrence and development of various cancers, but a specific mechanism involving ferroptosis in cervical cancer is still unclear.

METHODS

Based on the expressions of ferroptosis-related genes, a prognostic model was constructed using lasso regression, and the overall predictive performance of this model was verified. An in-depth analysis of the prognostic model was then conducted.

RESULTS

The prognostic model showed good predictive performance in both the validation and test sets. Mechanism analysis indicated that differences in the tumor microenvironment were the basis of the predictive ability of the model. Notably, CA9 mRNA was significantly overexpressed in cervical carcinoma, tissues but not in normal cervix tissues. A pair of ceRNAs (CA9/ULBP2) could be involved in the carcinogenesis and development of cervical cancer, and the potential target might be hsa-miR-34a. In addition, predicted miRNAs and drugs for these DEGs were identified.

CONCLUSIONS

We constructed a prognostic model with good predictive performance, based on the expression of ferroptosis-related genes. Further research found that the ceRNA pairs of ULBP2/CA9 could regulate cervical cancer through hsa-miR-34a. These results identified the mechanism of ferroptosis in cervical cancer, and might provide novel therapeutics for cervical cancer patients.

Revealing a novel contributing landscape of ferroptosis-related genes in Parkinson’s disease

Transcriptomics studies have yielded great insights into disease processes by detecting differentially expressed genes (DEGs). In this study, due to the high heritability of Parkinson’s disease (PD), we performed bioinformatics analyses on nine transcriptomic datasets regarding substantia nigra from Gene Expression Omnibus database, including seven microarray datasets and two next-generation sequencing datasets. As a result, between age-matched PD patients and normal control, we identified 630 DEGs, of which 22 hub DEGs involved in PD or ferroptosis were found to be associated with each other at the transcriptional level and protein-protein interaction network, suggesting their high correlations among these hub genes. Moreover, 16 DEGs were singled out due to their comparable AUC (>0.6) in random forest classifiers, including seven PD-related genes (MAP4K4, LRP10, UCHL1, PAM, RIT2, SNCA, GCH1) and nine ferroptosis-related genes (GCH1, DDIT4, RGS4, MAPK9, CAV1, RELA, DUSP1, ATP6V1G2, ATF4 and ISCU). Furthermore, to probe the potential of those hub genes in predicting the PD progression and survival, we constructed a Cox model featured by an eight-gene signature, including four PD-related genes (SNCA, UCHL1, LRP10, and GCH1) and four ferroptosis-related genes (DDIT4, RGS4, RELA, and CAV1), and validated it successful in an independent dataset, indicating that it would be an effective tool for clinical research to predict PD progression. In conclusion, ferroptosis-related DEGs identified in this study were closely correlated with the known PD-related genes, revealing the involvement of ferroptosis in the development of PD. This study presented the potential of several ferroptosis-related genes as novel clinical biomarkers for PD.

Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer

Background: Esophageal Squamous Cell Cancer (ESCC) is an aggressive disease associated with a poor prognosis. As a newly defined form of regulated cell death, ferroptosis plays a crucial role in cancer development and treatment and might be a promising therapeutic target. However, the expression patterns of ferroptosis-related genes (FRGs) in ESCC remain to be systematically analyzed. Methods: First, we retrieved the transcriptional profile of ESCC from TCGA and GEO datasets (GSE47404, GSE23400, and GSE53625) and performed unsupervised clustering to identify different ferroptosis patterns. Then, we used the ssGSEA algorithm to estimate the immune cell infiltration of these patterns and explored the differences in immune cell abundance. Common genes among patterns were finally identified as signature genes of ferroptosis patterns. Results: Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C. Cluster C was abundant in CD8⁺ T cells and other immune cell infiltration, while cluster A was immune-barren. By comparing the differently expressed genes between clusters of diverse datasets, we defined a gene signature for each cluster and successfully validated it in the TCGA-ESCC dataset. Conclusion: We provided a comprehensive insight into the expression pattern of ferroptosis genes and their interaction with immune cell infiltration. Additionally, we established a gene signature to define the ferroptosis patterns, which might be used to predict the response to immunotherapy.

Novel ferroptosis-related gene signature as a potential prognostic tool for gastric cancer

Objectives

Gastric cancer (GC) is a major global health concern and is difficult to diagnose in the early stage. Ferroptosis is an iron-dependent, novel form of non-apoptotic cell death. In recent years, inducing the upregulation of ferroptosis-related genes has become a promising therapeutic alternative for cancers, especially those resistant to traditional treatments. However, the relationship between ferroptosis-related genes and GC remains to be further elucidated.

Methods

In the present study, mRNA expression profiles and corresponding clinical data of patients with GC were retrieved from The Cancer Genome Atlas and used as test data. A multigene signature was constructed using the least absolute shrinkage and selection operator Cox regression model. Data of patients with GC from ‘GSE84426’ in the Gene Expression Omnibus database were used as Training data for validation.

Results

More than half ferroptosis-related genes were differentially expressed in GC tissues and adjacent normal tissue samples (58.43%) in the test data. Univariate Cox regression analysis showed that 16 differentially expressed genes were related to the prognosis of GC (all p < 0.05). Expression profiles of the 16 DGEs were analysed using LASSO Cox regression, and a prognostic model was established by selecting the 10 best genes for λ. These 10 genes were used to construct a 10-gene signature and stratify patients into two risk groups. Based on the median risk score in the test data, patients with GC were divided into high- and low-risk groups ( p < 0.001). Risk score was an independent predictor for overall survival in multivariate Cox regression analyses ( p < 0.001 and <0.01 in the test and training data, respectively; hazard ratio >1). Receiver operating characteristic curve analysis confirmed the predictive capacity of the 10-gene signature. Functional analysis revealed that tumour-infiltrating lymphocytes, antigen-presenting cell co-stimulation, and cytokine-cytokine receptors were enriched; however, the immune status differed between the two risk groups.

Conclusion

The novel ferroptosis-related gene signature can be used for GC prognosis. In addition, ferroptosis may provide a novel alternative for the diagnosis and treatment of GC.

Integrative analysis of the molecular mechanisms, immunological features and immunotherapy response of ferroptosis regulators across 33 cancer types

Ferroptosis is a recently described mode of cell death caused by the accumulation of intracellular iron and lipid reactive oxygen species (ROS), which play critical roles in tumorigenesis and cancer progression. However, the underlying molecular mechanisms and promising biomarkers of ferroptosis among cancers remain to be elucidated. In this study, 30 ferroptosis regulators in ferroptosis-related signaling pathways were identified and analyzed in 33 cancer types. We found transcriptomic aberrations and evaluated the prognostic value of ferroptosis regulators across 33 cancer types. Then, we predicted and validated potential transcription factors (including E2F7, KLF5 and FOXM1) and therapeutic drugs (such as cyclophosphamide, vinblastine, and gefitinib) that target ferroptosis regulators in cancer. Moreover, we explored the molecular mechanisms of ferroptosis and found that signaling pathways such as the IL-1 and IL-2 pathways are closely associated with ferroptosis. Additionally, we found that ferroptosis regulators have a close relationship with immunity-related parameters, including the immune score, immune cell infiltration level, and immune checkpoint protein level. Finally, we determined a ferroptosis score using GSVA method. We found that the ferroptosis score effectively predicted ferroptotic cell death in tumor samples. And ferroptosis score is served as an independent prognostic indicator for the incidence and recurrence of cancers. More importantly, patients with high ferroptosis scores received greater benefit from immunotherapy. We aslo created an online webserver based on the nomogram prognostic model to predict the survival in immunotherapy cohort. The reason for this outcome is partially the result of patients with a high ferroptosis rate also having high immune scores, HLA-related gene expression and immune checkpoint protein expression, such as PDL2 and TIM3. Moreover, patients with high ferroptosis scores exhibited CD8 T cell and TIL infiltration and immune-related signaling pathway enrichment. In summary, we systematically summarize the molecular characteristics, clinical relevance and immune features of ferroptosis across cancers and show that the ferroptosis score can be used as a prognostic factor and for the evaluation of immunotherapy effects.

Identification of ferroptosis-associated biomarkers for the potential diagnosis and treatment of postmenopausal osteoporosis

OBJECTIVE

Postmenopausal osteoporosis (PMOP) is one of the most commonly occurring conditions worldwide and is characterized by estrogen deficiency as well as persistent calcium loss with age. The aim of our study was to identify significant ferroptosis-associated biomarkers for PMOP.

METHODS AND MATERIALS

We obtained our training dataset from the Gene Expression Omnibus (GEO) database using GSE56815 expression profiling data. Meanwhile, we extracted ferroptosis-associated genes for further analysis. Differentially expressed ferroptosis-associated genes (DEFAGs) between OP patients and normal controls were selected using the "limma" package. We established a ferroptosis-associated gene signature using training models, specifically, random forest (RF) and support vector machine (SVM) models. It was further validated in another dataset (GSE56814) which also showed a high AUC: 0.98, indicating high diagnostic value. Using consensus clustering, the OP patient subtypes were identified. A ferroptosis associated gene (FAG)-Scoring scheme was developed by PCA. The important candidate genes associated with OP were also compared between different ferrclusters and geneclusters.

RESULTS

There were significant DEFAGs acquired, of which five (HMOX1, HAMP, LPIN1, MAP3K5, FLT3) were selected for establishing a ferroptosis-associated gene signature. Analyzed from the ROC curve, our established RF model had a higher AUC value than the SVM model (RF model AUC:1.00). Considering these results, the established RF model was chosen to be the most appropriate training model. Later, based on the expression levels of the five DEFAGs, a clinical application nomogram was established. The OP patients were divided into two subtypes (ferrcluster A, B and genecluster A, B, respectively) according to the consensus clustering method based on DEFAGs and differentially expressed genes (DEGs). Ferrcluster B and genecluster B had higher ferroptosis score than ferrcluster A and genecluster A, respectively. The expression of COL1A1 gene was significantly higher in ferrcluster B and gencluster B compared with ferrcluster A and gencluster A, respectively, while there is no statistical difference in term of VDR gene, COL1A2 genes, and PTH gene expressions between ferrcluster A and B, together with gencluster A and B.

CONCLUSIONS

On the basis of five explanatory variables (HMOX1, HAMP, LPIN1, MAP3K5 and FLT3), we developed a diagnostic ferroptosis-associated gene signature and identified two differently categorized OP subtypes that may potentially be applied for the early diagnosis and individualized treatment of PMOP. The ER gene, VDR gene, IL-6 gene, COL1A1 and COL1A2 genes, and PTH gene are important candidate gene of OP, however, more studies are still anticipated to further elucidate the relationship between these genes and ferroptosis in OP.

Ferroptosis-Related Long Noncoding RNA Signature Predicts Prognosis of Clear Cell Renal Carcinoma

Clear cell renal cell carcinoma (ccRCC) is very common and accounts for most kidney cancer deaths. While many studies are being conducted in finding the prognostic signatures of ccRCC, we believe that ferroptosis, which involves programmed cell death dependent on iron accumulation, has therapeutic potential in ccRCC. Recent research has shown that long noncoding RNAs (lncRNAs) are involved in ferroptosis-related tumour processes and are closely related to survival in patients with ccRCC. Hence, in this study we aim to further explore the role of ferroptosis-related lncRNAs (FRLs) in ccRCC, hoping to establish a signature to predict the survival outcome of ccRCC. We analysed transcriptome data from The Cancer Genome Atlas database (TCGA) and ferroptosis-related genes (FRGs) from FerrDb to identify FRLs using Pearson's correlation. Lasso Cox regression analysis and multivariate Cox proportional hazards models screened seventeen optimal FRLs for developing prognostic signatures. Kaplan-Meier survival curves and ROC curves were then plotted for validating the sensitivity, specificity, and accuracy of the identified signatures. Gene Set Enrichment Analysis and CIBERSORT algorithm were deployed to explore the role of these FRLs in the tumour microenvironment. It was concluded that these models demonstrate excellent performance in predicting prognosis among patients with ccRCC, also indicating association with the clinicopathologic parameters such as tumour grade, tumour stage and tumour immune infiltration. In conclusion, our findings provide novel insights into ferroptosis-related lncRNAs in ccRCC, which are important targets for investigating the tumorigenesis of ccRCC.

Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis

Primary osteoporosis has long been underdiagnosed and undertreated. Currently, ferroptosis may be a promising research direction in the prevention and treatment of primary osteoporosis. However, the specific mechanism of ferroptosis in primary osteoporosis remains a mystery. Differentially expressed genes (DEGs) were identified in bone mesenchymal stromal cells (BMSCs) of primary osteoporosis and heathy patients from the GEO databases with the help of bioinformatics analysis. Then, we intersected these DEGs with the ferroptosis dataset and obtained 80 Ferr-DEGs. Several bioinformatics algorithms (PCA, RLE, Limma, BC, MCC, etc.) were adopted to integrate the results. Additionally, we explored the potential functional roles of the Ferr-DEGs via GO and KEGG. Protein-protein interactions (PPI) were used to predict potential interactive networks. Finally, 80 Ferr-DEGs and 5 key Ferr-DEGs were calculated. The 5 key Ferr-DEGs were further verified in the OVX mouse model. In conclusion, through a variety of bioinformatics methods, our research successfully identified 5 key Ferr-DEGs associated with primary osteoporosis and ferroptosis, namely, sirtuin 1(SIRT1), heat shock protein family A (Hsp70) member 5 (HSPA5), mechanistic target of rapamycin kinase (MTOR), hypoxia inducible factor 1 subunit alpha (HIF1A) and beclin 1 (BECN1), which were verified in an animal model.

Ferroptosis-related mRNAs signature predicts prognosis of gastric cancer

BACKGROUND

Gastric cancer is a common gastrointestinal tumor with a poor prognosis. Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. Therefore, it is significant to construct a prognosis model of ferroptosis-related genes to predict the prognosis of gastric cancer and related therapeutic targets.

AIM

To explore the potential prognostic value of ferroptosis-related mRNAs in gastric cancer.

METHODS

Since ferroptosis is a type of cell death driven by lipid iron-dependent peroxidation, a predictive model was constructed based on differentially expressed ferroptosis-related mRNAs in gastric cancer.

RESULTS

We identified four differentially expressed mRNAs (DUSP1, MYB, CAV1, and NOX4) associated with gastric cancer prognosis. Kaplan-Meier analysis showed that the high-risk group was associated with a poor prognosis, and risk score was an independent prognostic indicator of survival. The developed prognostic model showed superiority over conventional clinical and pathological features in predicting the prognosis of gastric cancer. In addition, the low-risk and high-risk groups showed significant differences in immune cell infiltration and immune checkpoints.

CONCLUSION

A novel ferroptosis-related mRNA signature has been developed, which could precisely predict the prognosis of gastric cancer and serve as therapeutic targets for gastric cancer.

Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

Background: Ferroptosis is a newly identified regulated cell death characterized by iron-dependent lipid peroxidation and subsequent membrane oxidative damage, which has been implicated in multiple types of cancers. The multi-omics differences between cancer cell lines with high/low ferroptosis scores remain to be elucidated.

Methods and Materials: We used RNA-seq gene expression, gene mutation, miRNA expression, metabolites, copy number variation, and drug sensitivity data of cancer cell lines from DEPMAP to detect multi-omics differences associated with ferroptosis. Based on the gene expression data of cancer cell lines, we performed LASSO-Logistic regression analysis to build a ferroptosis-related model. Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), esophageal cancer (ESCA), bladder cancer (BLCA), cervical cancer (CESC), and head and neck cancer (HNSC) patients from the TCGA database were used as validation cohorts to test the efficacy of this model.

Results: After stratifying the cancer cell lines into high score (HS) and low score (LS) groups according to the median of ferroptosis scores generated by gene set variation analysis, we found that IC50 of 66 agents such as oxaliplatin (p < 0.001) were significantly different, among which 65 were higher in the HS group. 851 genes such as KEAP1 and NRAS were differentially muted between the two groups. Differentially expressed genes, miRNAs and metabolites were also detected—multiple items such as IL17F (logFC = 6.58, p < 0.001) differed between the two groups. Unlike the TCGA data generated by bulk RNA-seq, the gene expression data in DEPMAP are from pure cancer cells, so it could better reflect the traits of tumors in cancer patients. Thus, we built a 15-signature model (AUC = 0.878) based on the gene expression data of cancer cell lines. The validation cohorts demonstrated a higher mutational rate of NFE2L2 and higher expression levels of 12 ferroptosis-related genes in HS groups.

Conclusion: This article systemically analyzed multi-omics differences between cancer cell lines with high/low ferroptosis scores and a ferroptosis-related model was developed for multiple cancer types. Our findings could improve our understanding of the role of ferroptosis in cancer and provide new insight into treatment for malignant tumors.

A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Therapeutic Response of Gastric Cancer

Background: Accumulating literature demonstrates that long noncoding RNAs (lncRNAs) are involved in ferroptosis and gastric cancer progression. However, the predictive value of ferroptosis-related lncRNAs for prognosis and therapeutic response is yet to be elucidated in gastric cancer (GC). Method: The transcriptomic data and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. The association between ferroptosis-related lncRNAs and ferroptosis regulators was analyzed by Spearman correlation analysis. Then, we established a risk predictive model based on the ferroptosis-related lncRNAs using multivariate Cox regression analysis. Furthermore, we performed correlation analysis for the risk score and characteristics of biological processes, immune landscape, stromal activity, genomic integrity, drug response, and immunotherapy efficacy. Results: We constructed a 17-ferroptosis-related-lncRNA signature via multivariate Cox analysis to divide patients into two groups: low- and high-risk groups. The low-risk group was linked to prolonged overall survival and relapse-free survival. The risk score had good predictive ability to predict the prognosis of GC patients compared with other clinical biomarkers. We found that the high-risk group was associated with activation of carcinogenetic signaling pathways, including stromal activation, epithelial-mesenchymal-transition (EMT) activation, and immune escape through integrated bioinformatics analysis. In contrast, the low-risk group was associated with DNA replication, immune-flamed state, and genomic instability. Additionally, through Spearman correlation analysis, we found that patients in the high-risk group may respond well to drugs targeting cytoskeleton, WNT signaling, and PI3K/mTOR signaling, and drugs targeting chromatin histone acetylation, cell cycle, and apoptosis regulation could bring more benefits for the low-risk group. The high-risk group was associated with poor immunotherapy efficacy. Conclusion: Our study systematically evaluated the role of ferroptosis-related lncRNAs in t tumor microenvironment, therapeutic response, and prognosis of GC. Risk score-based stratification could reflect the characteristic of biological processes, immune landscape, stromal activity, genomic stability, and pharmaceutical profile in GC patients. The ferroptosis-related lncRNA signature could serve as a reliable biomarker to predict prognosis and therapeutic response of patients with GC.

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.

Identification of a ferroptosis-associated gene signature and the related therapeutic targets in head and neck squamous carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis due to its high rates of recurrence and metastasis. Herein, we designed and validated an individualized ferroptosis-associated gene signature (FGS) and further probed the potential survival mechanisms along with therapeutic targets for HNSCC.

METHODS

The FGS risk score was constructed using stepwise regression analysis and validated in the GSE41613 cohort. Characterization of the tumor microenvironment (TME) in patients with HNSCC, involving immune cells and immunomodulatory genes, was performed to investigate the survival mechanisms and therapeutic targets associated with FGS. To validate the role of FGS in TME, multiplex fluorescent immunohistochemistry (mfIHC) was performed on tissue sections from 55 patients with oral squamous carcinoma.

RESULTS

The risk score obtained from FGS showed good predictive power as an independent predictor of overall survival. From the tumor immune dysfunction and exclusion (TIDE) prediction, it was found that patients at low risk may benefit from immunotherapy. Furthermore, FGS was significantly associated with CD276, which was highly expressed in fibroblasts that enriched in angiogenesis and epithelial-mesenchymal transition pathways at a single-cell resolution, suggesting CD276 may play a critical mediator of the immunosuppressive microenvironment. Lastly, we identified ATG5 as a critical gene in FGS. And the immune-bioinformatics analysis combined with experimental validation showed a negative correlation between ATG5 expression and CD8 + T cells.

CONCLUSION

The FGS model provides a novel and effective method to predict the prognosis of patients with HNSCC and their survival can be prolonged through TME-related therapeutic targets.

Construction and validation of a risk scoring model for diffuse large B-cell lymphoma based on ferroptosis-related genes and its association with immune infiltration

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Categorization of DLBCL into four clusters based on survival- and ferroptosis-related factors.

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Establishment of an efficient risk scoring model is established for patients with DLBCL.

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Ferroptosis-based risk scoring model reveals immune infiltration correlation in DLBCL.

Backgrounds

The prognostic significance of ferroptosis-related genes is well known. However, survival- and ferroptosis-related genes are not currently considered in risk scoring models for diffuse large B-cell lymphoma (DLBCL).

Materials and methods

Ferroptosis regulators and markers were downloaded from the FerrDb database. The transcriptome profiling data were collected from the cancer genome atlas (TCGA). Transcriptome data and corresponding clinical information of DLBCL were downloaded from the gene expression omnibus (GEO). The validation data were downloaded using the UCSC Xena browser. ConsensusClusterPlus was used to categorize DLBCL samples according to gene expression profiles. The survival function was plotted with the Kaplan-Meier plots. The nomogram was built using multivariate logistic regression analysis and the Cox proportional hazards regression model.

Results

Based on the GSE11318 dataset of 203 samples and 267 ferroptosis-related gene expression profiles, we identified four clusters. A total of 19 survival-related genes were found associated with ferroptosis. The prognostic risk scoring model was constructed based on the regression coefficients. The obtained area under the receiver operating characteristic curve (AUC) values were 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival, respectively. DLBCL samples with cluster 2 or cancer stage IV have shorter survival. Correlations between the immune infiltration and risk scores of the 12 immune cells were demonstrated. The response of DLBCL to doxorubicin was effectively validated by the risk scoring model.

Conclusions

In this study, a ferroptosis-based risk scoring model for patients with DLBCL was constructed and validated in an independent dataset. This risk score model has a better efficacy in predicting survival compared to clinical characteristics.

Development and Validation of a Robust Ferroptosis-Related Gene Panel for Breast Cancer Disease-Specific Survival

Background: New biomarker combinations have been increasingly developed to improve the precision of current diagnostic and therapeutic modalities. Recently, researchers have found that tumor cells are more vulnerable to ferroptosis. Furthermore, ferroptosis-related genes (FRG) are promising therapeutic targets in breast cancer patients. Therefore, this study aimed to identify FRG that could predict disease-specific survival (DSS) in breast cancer patients. Methods: Gene expression matrix and clinical data were downloaded from public databases. We included 960, 1,900, and 234 patients from the TCGA, METABRIC, and GSE3494 cohorts, respectively. Data for FRG were downloaded from the FerrDb website. Differential expression of FRG was analyzed by comparing the tumors with adjacent normal tissues. Univariate Cox analysis of DSS was performed to identify prognostic FRG. The TCGA-BRCA cohort was used to generate a nine-gene panel with the LASSO cox regression. The METABRIC and GSE3494 cohorts were used to validate the panel. The panel's median cut-off value was used to divide the patients into high- or low-risk subgroups. Analyses of immune microenvironment, functional pathways, and clinical correlation were conducted via GO and KEGG analyses to determine the differences between the two subgroups. Results: The DSS of the low-risk subgroup was longer than that of the high-risk subgroup. The panel's predictive ability was confirmed by ROC curves (TCGA cohort AUC values were 0.806, 0.695, and 0.669 for 2, 3, and 5 years respectively, and the METABRIC cohort AUC values were 0.706, 0.734, and 0.7, respectively for the same periods). The panel was an independent DSS prognostic indicator in the Cox regression analyses. (TCGA cohort: HR = 3.51, 95% CI = 1.792-6.875, p < 0.001; METABRIC cohort: HR = 1.76, 95% CI = 1.283-2.413, p < 0.001). Immune-related pathways were enriched in the high-risk subgroup. The two subgroups that were stratified by the nine-gene panel were also associated with histology type, tumor grade, TNM stage, and Her2-positive and TNBC subtypes. The patients in the high-risk subgroup, whose CTLA4 and PD-1 statuses were both positive or negative, demonstrated a substantial clinical benefit from combination therapy with anti-CTLA4 and anti-PD-1. Conclusion: The new gene panel consisting of nine FRG may be used to assess the prognosis and immune status of patients with breast cancer. A precise therapeutic approach can also be possible with risk stratification.

Identification of immune subtypes of Ph-neg B-ALL with ferroptosis related genes and the potential implementation of Sorafenib

BACKGROUND

The clinical outcome of Philadelphia chromosome-negative B cell acute lymphoblastic leukemia (Ph-neg B-ALL) varies considerably from one person to another after clinical treatment due to lack of targeted therapies and leukemia's heterogeneity. Ferroptosis is a recently discovered programmed cell death strongly correlated with cancers. Nevertheless, few related studies have reported its significance in acute lymphoblastic leukemia.

METHODS

Herein, we collected clinical data of 80 Ph-neg B-ALL patients diagnosed in our center and performed RNA-seq with their initial bone marrow fluid samples. Throughout unsupervised machine learning K-means clustering with 24 ferroptosis related genes (FRGs), the clustered patients were parted into three variant risk groups and were performed with bioinformatics analysis.

RESULTS

As a result, we discovered significant heterogeneity of both immune microenvironment and genomic variance. Furthermore, the immune check point inhibitors response and potential implementation of Sorafenib in Ph-neg B-ALL was also analyzed in our cohort. Lastly, one prognostic model based on 8 FRGs was developed to evaluate the risk of Ph-neg B-ALL patients.

CONCLUSION

Jointly, our study proved the crucial role of ferroptosis in Ph-neg B-ALL and Sorafenib is likely to improve the survival of high-risk Ph-neg B-ALL patients.

Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC.

METHODS

A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues.

RESULTS

LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature's genes with the influence of ferroptosis induced by sorafenib.

CONCLUSIONS

We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients' prognoses and targeting ferroptosis may be an alternative for PTC's therapy.

Identification of ferroptosis-related genes as potential biomarkers of tongue squamous cell carcinoma using an integrated bioinformatics approach

Tongue squamous cell carcinoma (TSCC) is one of the deadliest cancers of the head and neck, but the role of the ferroptosis pathway in its development is still unknown. In this study we explored the pathogenetic mechanisms associated with ferroptosis in TSCC. We identified differentially expressed genes (DEGs) of TSCC patients and used gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) to annotate, visualize, and integrate these DEGs. Receiver operating characteristic curve (ROC) analysis was performed, and the STRING database was used to construct a protein–protein interaction network to evaluate the predictive value of ferroptosis‐related DEGs. A total of 219 DEGs were identified and GO, KEGG, and GSEA showed that extracellular matrix (ECM)‐receptor interaction and interleukin (IL)‐17 signaling pathways were substantially upregulated in TSCC. Univariate Cox analysis revealed that high expression of CA9, TNFAIP3, and NRAS were predictive of a worse outcome. We then constructed a prognostic model that predicted survival in the validation cohort at 1 year and 32 months. Finally, 60 cases of tongue carcinoma and normal tissues were collected, and immunohistochemistry was used to detect the expression of CA9. We found that CA9 was strongly expressed in tongue carcinoma tissues and absent in adjacent tissues. Overall, we found that ferroptosis‐related genes may affect TSCC prognosis through the ECM‐receptor interaction and IL‐17 signaling pathways. Additionally, immunohistochemistry confirmed that CA9 was highly expressed in tongue carcinoma tissues, and a model based on ferroptosis‐related genes showed a good ability to predict overall survival in TSCC.

Biological Functions and Prognostic Value of Ferroptosis-Related Genes in Bladder Cancer

Background: Every year, nearly 170,000 people die from bladder cancer worldwide. A major problem after transurethral resection of bladder tumor is that 40–80% of the tumors recur. Ferroptosis is a type of regulatory necrosis mediated by iron-catalyzed, excessive oxidation of polyunsaturated fatty acids. Increasing the sensitivity of tumor cells to ferroptosis is a potential treatment option for cancer. Establishing a diagnostic and prognostic model based on ferroptosis-related genes may provide guidance for the precise treatment of bladder cancer.

Methods: We downloaded mRNA data in Bladder Cancer from The Cancer Genome Atlas and analyzed differentially expressed genes based on and extract ferroptosis-related genes. We identified relevant pathways and annotate the functions of ferroptosis-related DEGs using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology functions. On the website of Search Tool for Retrieving Interacting Genes database (STRING), we downloaded the protein-protein interactions of DEGs, which were drawn by the Cytoscape software. Then the Cox regression analysis were performed so that the prognostic value of ferroptosis-related genes and survival time are combined to identify survival- and ferroptosis-related genes and establish a prognostic formula. Survival analysis and receiver operating characteristic curvevalidation were then performed. Risk curves and nomograms were generated for both groups to predict survival. Finally, RT-qPCR was applied to analyze gene expression.

Results: Eight ferroptosis-related genes with prognostic value (ISCU, NFE2L2, MAFG, ZEB1, VDAC2, TXNIP, SCD, and JDP2) were identified. With clinical data, we established a prognostic model to provide promising diagnostic and prognostic information of bladder cancer based on the eight ferroptosis-related genes. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues.

Conclusion: This study found that the ferroptosis-related genes is associated with bladder cancer, which may serve as new target for the treatment of bladder cancer.

A Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma

Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis.

Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups.

Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score.

Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

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.

Identification of the ferroptosis-related long non-coding RNAs signature to improve the prognosis prediction and immunotherapy response in patients with NSCLC

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with an unfavorable prognosis. Ferroptosis is involved in the development of multiple cancers. Whereas, the prognostic value of ferroptosis-related lncRNAs in NSCLC remains uncertain.

METHODS

Gene expression profiles and clinical information of NSCLC were retrieved from the TCGA database. Ferroptosis-related genes (FRGs) were explored in the FerrDb database and previous studies, ferroptosis-related lncRNAs (FRGs-lncRNAs) were identified by the correlation analysis and the LncTarD database. The differentially expressed FRGs-lncRNAs were screened and FRGs-lncRNAs associated with the prognosis were explored by univariate Cox regression analysis and Kaplan-Meier survival analysis. Then, an FRGs-lncRNAs signature was constructed and verified by the Lasso-penalized Cox analysis. Finally, the potential correlation between risk score, immune checkpoint genes, and chemotherapeutic sensitivity was further investigated.

RESULTS

129 lncRNAs with a potential regulatory relationship with 59 differentially expressed FRGs were found in NSCLC, of which 10 were related to the prognosis of NSCLC (P < 0.05). 9 prognostic-related FRGs-lncRNAs were used to construct the prognostic model and stratify NSCLC patients into high- and low-risk groups. A worse outcome was found in patients with high risk (P < 0.05). Moreover, a good predictive capacity of this signature in predicting NSCLC prognosis was confirmed. Additionally, 45 immune checkpoint genes and 4 chemotherapeutics drugs for NSCLC were identified to be correlated with the risk score.

CONCLUSION

A novel FRGs-lncRNAs signature was successfully constructed, which may contribute to improving the management strategies of NSCLC.

Role of ferroptosis-related genes in Stanford type a aortic dissection and identification of key genes: new insights from bioinformatic analysis

Stanford type A aortic dissection (TAAD) is one of the most dangerous vascular diseases worldwide, and the mechanisms of its development remain unclear. Further molecular pathology studies may contribute to a comprehensive understanding of TAAD and provide new insights into diagnostic markers and potential therapeutic targets. Recent studies have identified that ferroptosis, a form of cell death, may play a previously unrecognized role in influencing the development of TAAD. In this study, we explored the pathological role of ferroptosis in TAAD by performing bioinformatics analyses. Gene set enrichment analysis (GSEA) showed that the ferroptosis-related gene (FRG) set was significantly different between normal and TAAD aortic samples at an overall level (p < 0.001). Further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses explored the potential functions and pathways of FRG in TAAD. We further identified six key genes (CA9, HMOX1, IL6, CDKN1A, HIF1A, MYC) from differentially expressed FRGs in TAAD by constructing a protein-protein interaction (PPI) network, all key genes were upregulated in TAAD. Four of the key genes (CA9, IL6, CDKN1A, and HIF1A) were demonstrated to be correlated with cigarette smoke extract-induced ferroptosis in aortic vascular smooth muscle cells. These results suggest that ferroptosis is one of the essential pathological processes in the development of TAAD, and some FRGs affect TAAD development by mediating cellular ferroptosis, which provides deepening insights into the molecular mechanisms and potential therapeutic targets of TAAD.

DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).

Identification of a novel iron zinc finger protein 36 (ZFP36) for predicting the overall survival of osteosarcoma based on the Gene Expression Omnibus (GEO) database

Background

The purpose of this study is to explore the relationship between the ferroptosis-related gene zinc finger protein 36 (ZFP36) and the prognosis of osteosarcoma patients after surgery.

Methods

Differential expression genes (DEGs) between osteosarcoma and normal tissues were screened using osteosarcoma chip data in GEO database. Based on the median expression quantity, ferroptosis DEGs were divided into high and low expression groups. Combined with its corresponding clinical survival data, the survival analysis of ferroptosis DEGs was carried out using the Survival package, and ferroptosis-related genes related to prognosis were identified. Next, the clinical data of 60 osteosarcoma patients treated in Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Zhongda Hospital and Nanjing Drum Tower Hospital from January 2011 to January 2016 were retrospectively analyzed. Immunohistochemistry and reverse transcription quantitative polymerase chain reaction (qRT-PCR) were used to detect gene expression in osteosarcoma. The Kaplan-Meier method and log rank test were used for univariate survival analysis, the Cox regression method was used for multivariate analysis, and the nomogram was constructed for internal verification on this basis.

Results

Immunohistochemical and reverse transcription quantitative PCR results showed that the expression of ZFP36 was mainly higher in cancer-adjacent tissues than in tumor tissues. There were significant differences in age, tumor location, Enneking stage, and tumor specific growth factor (TSGF) between the high and low expression groups of ZFP36 (P<0.05). The final study included 60 patients, of whom 23 patients died (mortality rate: 38.33%), and 37 patients survived (survival rate: 61.67%), with a median progression-free survival (PFS) of 32.5 months and a median overall survival (OS) of 77 months. The Cox multivariate analysis showed that distant metastasis and ZFP36 were independent risk factors affecting tumor progression (P=0.021 and P=0.006, respectively). Elevated ZFP36 can significantly prolong the OS and PFS of osteosarcoma patients. In internal verification, the Concordance index (C-index) of the nomogram was 0.7211 [95% confidence interval (CI): 0.6308–0.8115], and the prediction model had certain accuracy.

Conclusions

Elevated ZFP36 can significantly prolong the OS and PFS in osteosarcoma patients. At the same time, ZFP36 could be used as a new predictive biomarker and novel therapeutic target for osteosarcoma patients.

Prognostic Value of a Ferroptosis-Related Gene Signature in Patients With Head and Neck Squamous Cell Carcinoma

Background: Ferroptosis is an iron-dependent programmed cell death (PCD) form that plays a crucial role in tumorigenesis and might affect the antitumor effect of radiotherapy and immunotherapy. This study aimed to investigate distinct ferroptosis-related genes, their prognostic value and their relationship with immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The differentially expressed ferroptosis-related genes in HNSCC were filtered based on multiple public databases. To avoid overfitting and improve clinical practicability, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were performed to construct a prognostic risk model. Moreover, a nomogram was constructed to forecast individual prognosis. The differences in tumor mutational burden (TMB), immune infiltration and immune checkpoint genes in HNSCC patients with different prognoses were investigated. The correlation between drug sensitivity and the model was firstly analyzed by the Pearson method. Results: Ten genes related to ferroptosis were screened to construct the prognostic risk model. Kaplan-Meier (K-M) analysis showed that the prognosis of HNSCC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001), and the area under the curve (AUC) of the 1-, 3- and 5-year receiver operating characteristic (ROC) curve increased year by year (0.665, 0.743, and 0.755). The internal and external validation further verified the accuracy of the model. Then, a nomogram was build based on the reliable model. The C-index of the nomogram was superior to a previous study (0.752 vs. 0.640), and the AUC (0.729 vs. 0.597 at 1 year, 0.828 vs. 0.706 at 3 years and 0.853 vs. 0.645 at 5 years), calibration plot and decision curve analysis (DCA) also shown the satisfactory predictive capacity. Furthermore, the TMB was revealed to be positively correlated with the risk score in HNSCC patients (R = 0.14; P < 0.01). The differences in immune infiltration and immune checkpoint genes were significant (P < 0.05). Pearson analysis showed that the relationship between the model and the sensitivity to antitumor drugs was significant (P < 0.05). Conclusion: Our findings identified potential novel therapeutic targets, providing further potential improvement in the individualized treatment of patients with HNSCC.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

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.

Comprehensive Analysis of Ferroptosis-Related Markers for the Clinical and Biological Value in Gastric Cancer

Gastric cancer is a highly malignant tumor with poor survival rate. Ferroptosis, a newly defined regulated cell death, is closely related to several tumors. Introduction of ferroptosis is promising for cancer treatments. However, the predictive role of ferroptosis in GC remains elusive. In this study, we screened the ferroptosis-related genes which were differentially expressed between normal and GC tissues. Then, based on these differentially expressed genes (DEGs), the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regressions were applied to construct the 10-gene prognostic signature (SP1, MYB, ALDH3A2, KEAP1, AIFM2, ITGB4, TGFBR1, MAP1LC3B, NOX4, and ZFP36) in TCGA training dataset. Based on the median risk score, all GC patients in TCGA training dataset and GSE84437 testing dataset were classified into a high- or low-risk group. GC patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P < 0.001). Combined with the clinical characteristics, the risk score was proven as an independent factor for predicting the OS of GC patients. Besides, the GC patients in the high- or low-risk group showed significantly different GO and KEGG functional enrichments, somatic mutation, fractions of immune cells, and immunotherapy response. Then, the expression levels of these genes in signature were further verified in the GC cell lines and our own GC samples (30-paired tumor/normal tissues). Furthermore, the effects of ferroptosis inducer Erastin on these 10 ferroptosis-related genes in GC cell lines were also explored in our study. In conclusion, our study constructed a prognostic signature of 10 ferroptosis-related genes, which could well predict the prognosis and immunotherapy for GC patients.

Identification of a Ferroptosis-Related Long Noncoding RNA Prognostic Signature and Its Predictive Ability to Immunotherapy in Hepatocellular Carcinoma

Background: Immune checkpoint blockers (ICBs) are increasingly being used to treat patients with advanced hepatocellular carcinoma (HCC), but only a third of these patients are sensitive to ICBs. Emerging evidence suggests that ferroptosis could be a novel target for antitumor treatment, and combined treatment with ferroptosis inducers might enhance sensitivity to immunotherapy. However, there is a lack of information on the crosstalk between ferroptosis-related lncRNAs and anti-tumor immunity. Therefore, we aim to explore prognostic value of ferroptosis-related lncRNAs and clarify potential role in ICBs of HCC.

Methods: We obtained mRNA and lncRNA expression data from two independent cohorts (TCGA and GEO database). Univariate Cox, the least absolute shrinkage and selection operator (Lasso) algorithm and multivariate Cox analysis were used to construct a lncRNA signature, which was evaluated using the area under the receiver operating characteristic curve (AUC) and Kaplan–Meier curves. Tumor-infiltrating cell (TIC) profiling and the tumor immune dysfunction and exclusion (TIDE) algorithm were used to validate the signature model and immunotherapy. Finally, we adopted RT-PCR assay to evaluate the differential expression of lncRNAs in HCC tissues in our hospital.

Results: The ferroptosis-related lncRNA signature included five lncRNAs, most of which were positively correlated with clinical stage and grade. The signature could stratify patients into two risk groups, with the high-risk group associated with a shorter overall survival (OS, p < 0.05) in TCGA-LIHC and GSE76427. Besides, the AUCs of the 1-, 3-, and 5-years OS were 0.772, 0.707, and 0.666, respectively. Gene set enrichment analysis (GESA) of lncRNAs revealed enrichment of oncogenic and immune-related pathways. The TIC profiling indicated a close correlation between the signature and immune cells. Furthermore, the high-risk group had a better response to immunotherapy than low-risk group. RT-PCR demonstrated these five lncRNAs were upregulated in cancerous tissue than normal tissues.

Conclusions: The ferroptosis-related lncRNA signature could accurately predict the OS of HCC patients and may serve as an independent clinical factor for patients’ outcomes. Ferroptosis-related lncRNAs may remodel the tumor microenvironment (TME) and affect the anti-cancer ability of ICBs, and therefore, could potentially act as an indicator for the response to immunotherapy in HCC.

A Ferroptosis-Related Gene Signature for Predicting the Prognosis and Drug Sensitivity of Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide and has a high mortality. Ferroptosis, an iron-dependent form of programmed cell death, plays a crucial role in tumor suppression and chemotherapy resistance in cancer. However, the prognostic and clinical values of ferroptosis-related genes (FRGs) in HNSCC remain to be further explored. In the current study, we constructed a ferroptosis-related prognostic model based on the Cancer Genome Atlas database and then explored its prognostic and clinical values in HNSCC via a series of bioinformatics analyses. As a result, we built a four-gene prognostic signature, including FTH1, BNIP3, TRIB3, and SLC2A3. Survival analysis showed that the high-risk group presented significantly poorer overall survival than the low-risk group. Moreover, the ferroptosis-related signature was found to be an independent prognostic predictor with high accuracy in survival prediction for HNSCC. According to immunity analyses, we found that the low-risk group had higher anti-tumor immune infiltration cells and higher expression of immune checkpoint molecules and meanwhile corelated more closely with some anti-tumor immune functions. Meanwhile, all the above results were validated in the independent HSNCC cohort GSE65858. Besides, the signature was found to be remarkably correlated with sensitivity of common chemotherapy drugs for HNSCC patients and the expression levels of signature genes were also significantly associated with drug sensitivity to cancer cells. Overall, we built an effective ferroptosis-related prognostic signature, which could predict the prognosis and help clinicians to perform individualized treatment strategy for HNSCC patients.

Ferroptosis Characterization in Lung Adenocarcinomas Reveals Prognostic Signature With Immunotherapeutic Implication

The iron-dependent cell death named ferroptosis has been implicated in the progression and therapeutic response of several tumors. However, potential role of ferroptosis in lung adenocarcinomas (LUAD) remained less well understood. In TCGA-LUAD cohort, unsupervised clustering was first conducted based on ferroptosis regulators extracted from FerrDb database. Comprehensive correlation analysis and comparisons were performed among ferroptosis subtypes. The ferroptosis-related prognostic (FRP) signature was identified based on filtered features and repeated LASSO and was validated in five independent cohorts. The clinical relevance between the risk score and therapeutic response was further explored by multiple algorithms. qPCR was implemented to verify gene expression. A total of 1,168 LUAD patients and 161 ferroptosis regulators were included in this study. Three ferroptosis subtypes were identified and patients in subtype B had the best prognosis among the three subtypes. Significant differences in immune microenvironment and biological function enrichment were illustrated in distinct subtypes. The Boruta algorithm was conducted on 308 common differentially expressed genes for dimensionality reduction. A total of 56 genes served as input for model construction and a six-gene signature with the highest frequencies of 881 was chosen as FRP. The prognostic significance of FRP was validated in five independent cohorts. High FRP risk score was also linked to increased tumor mutation burden, PD-L1 protein expression and number of neoantigens. Of the FRP genes, 83.3% was abnormally expressed in LUAD cell lines. In conclusion, ferroptosis plays a non-negligible role in LUAD. Exploration of the ferroptosis pattern will enhance the prognostic stratification of individual patients and move toward the purpose of personalized treatment.

Ferroptosis-Associated Classifier and Indicator for Prognostic Prediction in Cutaneous Melanoma

Ferroptosis plays a critical role in different types of cancers, but the prognostic impact of ferroptosis in cutaneous melanoma remains lacking. Therefore, ferroptosis-related genes (FRGs) were firstly obtained from the FerrDb database and the differentially expressed FRGs were identified by the “limma” algorithm. Next, the prognostic differentially expressed FRGs were screened out by univariate Cox regression, which were subsequently used to cluster melanomas into two subtypes (clusters A and B). Besides, the Boruta algorithm and principal component analysis (PCA) were performed to build a 15-FRGs indicator, which can robustly predict patients' overall survival (OS) and be considered as an independent prognostic factor in melanoma. The melanoma patients were further divided into high- and low-FRGs score groups. The high score group have a good prognosis, with higher T cell immune infiltrating and lower mutation frequencies in NRAS, KRAS, and NF1. Finally, we discovered that many immune processes and several chemotherapy drugs were closely associated with FRGs score. Thus, our study provides a novel ferroptosis-associated classifier and indicator to predict the prognosis of melanoma. Besides, we identified several potential chemotherapy drugs to induce ferroptosis and could supply additional effective treatments.

LncRNA RP11-89 facilitates tumorigenesis and ferroptosis resistance through PROM2-activated iron export by sponging miR-129-5p in bladder cancer

Long non-coding RNAs (lncRNAs) act as important regulators of tumorigenesis and development in bladder cancer. However, the underlying molecular mechanisms remain elusive. We previously identified a novel lncRNA signature related to immunity and progression in bladder cancer. Here we further explored the function of RP11-89, a lncRNA discovered in the previous signature. Loss- and gain-of function experiments were performed using CCK-8 assay, flow cytometry, Transwell assays, scratch tests and subcutaneous nude mouse models. High-throughput RNA sequencing was conducted to identify dysregulated genes in bladder cancer cells with RP11-89 knockdown or overexpression. Regulation of RP11-89 on miR-129-5p and PROM2 was explored through luciferase reporter assay, RIP assay and RNA pull-down assay. RP11-89 promoted cell proliferation, migration and tumorigenesis and inhibited cell cycle arrest via the miR-129-5p/PROM2 axis. We found that RP11-89 "sponges" miR-129-5p and upregulates PROM2. Elevated PROM2 in cells was associated with attenuated ferroptosis through iron export, formation of multivesicular bodies and less mitochondrial abnormalities. We demonstrated that RP11-89 is a novel tumorigenic regulator that inhibits ferroptosis via PROM2-activated iron export. RP11-89 may serve as a potential biomarker for targeted therapy in bladder cancer.

Ferroptosis regulators, especially SQLE, play an important role in prognosis, progression and immune environment of breast cancer

BACKGROUND

Ferroptosis, a new form of programmed cell death, has great potential for cancer treatment. However, the roles of ferroptosis-related (FR) genes in breast cancer (BC) remain elusive.

MATERIALS AND METHODS

Using TCGA database, a novel FR risk signature was constructed through the Lasso regression analysis. Meanwhile, its prognostic value was assessed by a series of survival analyses. Besides, a nomogram was constructed to predict the overall survival rate (OSR) of individual at 1,3,5 year. Four validation cohorts (n = 2248), including METABRIC, GSE58812, GSE20685 and ICGC-KR datasets, were employed to test the prognostic value of FR risk signature. The effects of FR risk signature on BC immune microenvironment were explored by CIBERSORT algorithm and ssGSEA method. The histological expressions of FR risk genes were presented by HPA database. The biofunctions of SQLE were determined by qPCR, MTT, wound-healing and Transwell assays.

RESULTS

We constructed a novel FR risk signature consisting of eight genes. High FR risk led a poor prognosis and was identified as an independent prognostic factor. Besides, A higher proportion of patients with luminal A type was observed in low-risk group (53%), while a higher proportion of patients with basal type in high-risk group (24%). FR risk score could discriminate the prognostic difference of most clinical subgroups, except for M1 stage, HER2 and basal types. Moreover, its prognostic value was successfully validated in other four cohorts. Through immune analyses, we found that the reduced infiltration levels of CD8+ and NK cells, whereas the enhanced activity of antigen presentation process appeared in high FR risk. Then, FR risk score was found to weakly correlate with the expressions of six immune checkpoints. Through the experiments in vitro, we confirmed that overexpression of SQLE could promote, whereas blocking SQLE could inhibit the proliferative, migrative and invasive abilities of BC cells.

CONCLUSIONS

FR risk signature was conducive to BC prognostic assessment. High FR risk level was closely associated with BC immunosuppression, but may not predict ICIs efficacy. Moreover, SQLE was identified as a crucial cancer-promoting gene in BC. Our findings provide new insights into prognostic assessment and molecular mechanism of BC.

Cigarettes, a skin killer! Cigarette smoke may cause ferroptosis in female skin

INTRODUCTION

Skin exposure to tobacco smoke is closely related to skin aging. Ferroptosis is an iron-dependent form of regulated cell death driven by the accumulation of lipid peroxides to levels sufficient to trigger cell death. However, few studies have examined ferroptosis in skin-related diseases.

METHODS

This study searched for differentially expressed genes by analyzing gene expression data downloaded from the Gene Expression Omnibus. Ferroptosis-related genes were obtained by analyzing the FerrDb database. We also investigated the regulatory mechanism of ferroptosis-related genes by enrichment analysis and constructing gene-miRNA and gene-transcription factor networks.

RESULTS

Based on these analyses, exposure to cigarette smoke can cause ferroptosis in female skin. Ferroptosis-associated genes might be regulated by 17 miRNAs and 20 TFs.

CONCLUSION

Finally, we found ferroptosis in the skin of women exposed to cigarette smoke, and the results provide an important theoretical basis for future research and anti-aging treatment of skin.

Ferroptosis-related gene AKR1C1 predicts the prognosis of non-small cell lung cancer

BACKGROUND

Ferroptosis is a newly discovered mode of cell death distinct from apoptosis and necrosis, and its activation contributes to anticancer therapy in a variety of cancers. However, the prognostic value of ferroptosis-related genes in non-small cell lung cancer (NSCLC) remains to be further investigated.

METHODS

NSCLC transcriptome mRNA-seq data set and corresponding clinical data set were downloaded from the Cancer Genome Atlas (TCGA). Then, bioinformatics approaches were subsequently employed to identify potential prognostic markers. Finally, the effects of candidate markers on NSCLC cell proliferation, migration, and ferroptosis were assessed by CCK8, colony formation, wound-healing assay, and functional assays related to ferroptosis.

RESULTS

A total of 37 common differentially expressed genes were screened based TCGA database. Six overall survival associated genes (ENPP2, ULK1, CP, LURAP1L, HIC1, AKR1C1) were selected to build survival model, of which hub gene AKR1C1 was with high expression and low ferroptosis level in NSCLC tumor. Further research showed that AKR1C1 was related with many pathways involved in the process of ferroptosis and associated with diverse cancer-infiltrating immune cells. Moreover, the results of in vitro experiments indicated that the expression of AKR1C1 was upregulated in NSCLC cell lines, and silencing AKR1C1 can inhibit the proliferation and migration of NSCLC cells and promote the occurrence of ferroptosis.

CONCLUSIONS

Our study revealed the potential role of ferroptosis-related gene AKR1C1 in NSCLC, which can be used for prognostic prediction in NSCLC.

Ferroptosis Patterns Correlate with Immune Microenvironment Characterization in Gastric Cancer

Objective

We aimed to build a ferroptosis-based classifier to characterize the molecular features of gastric cancers (GC) and investigate the relationship between different ferroptosis patterns and GC tumor microenvironment (TME).

Methods

Based on the genomic and clinical information from TCGA portal and GEO database, non-negative matrix factorization (NMF) was used to identify ferroptosis subtypes in GC patients. In order to estimate the ferroptosis levels, we established ferroptosis subtype score (FSS) to quantify ferroptosis patterns and ferroptosis potential index (FPI) by principal component analysis (PCA). The correlations of different ferroptosis patterns with TME cell-infiltrating characteristics (including immune cell infiltration, immune checkpoints expression levels, tumor mutational burden (TMB) and immunotherapy response) were systematically analyzed.

Results

Two ferroptosis subtypes, C1 (with lower FSS) and C2 (with higher FSS), were determined. C2 displayed a significantly lower FPI than C1. Besides, C2 was associated with diffuse subtype while C1 with intestinal subtype. As for TME characteristics, C2 was in accordance with the immune-excluded phenotype as it showed more active immune and stromal activities but lower TMB, less probability of immunotherapy response and poorer prognosis. C1 was linked to immune-inflamed phenotype as it had lower stromal activities but increased neoantigen load, enhanced response to immunotherapy and relatively better prognosis.

Conclusion

The systematic assessment of ferroptosis patterns and ferroptosis levels presented in our study implied that ferroptosis serves as an important factor in the formation of TME, which may expand the understanding of TME and provide a novel perspective for the development of targeted immunotherapeutic strategies for GC patients.

Ferroptosis-Related Genes in Bronchoalveolar Lavage Fluid Serves as Prognostic Biomarkers for Idiopathic Pulmonary Fibrosis

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease with unknown etiology and unfavorable prognosis. Ferroptosis is a form of regulated cell death with an iron-dependent way that is involved in the development of various diseases. Whereas the prognostic value of ferroptosis-related genes (FRGs) in IPF remains uncertain and needs to be further elucidated.

Methods: The FerrDb database and the previous studies were screened to explore the FRGs. The data of patients with IPF were obtained from the GSE70866 dataset. Wilcoxon's test and univariate Cox regression analysis were applied to identify the FRGs that are differentially expressed between normal and patients with IPF and associated with prognosis. Next, a multigene signature was constructed by the least absolute shrinkage and selection operator (LASSO)-penalized Cox model in the training cohort and evaluated by using calibration and receiver operating characteristic (ROC) curves. Then, 30% of the dataset samples were randomly selected for internal validation. Finally, the potential function and pathways that might be affected by the risk score-related differently expressed genes (DEGs) were further explored.

Results: A total of 183 FRGs were identified by the FerrDb database and the previous studies, and 19 of them were differentially expressed in bronchoalveolar lavage fluid (BALF) between IPF and healthy controls and associated with prognosis (p < 0.05). There were five FRGs (aconitase 1 [ACO1], neuroblastoma RAS viral (v-ras) oncogene homolog [NRAS], Ectonucleotide pyrophosphatase/phosphodiesterase 2 [ENPP2], Mucin 1 [MUC1], and ZFP36 ring finger protein [ZFP36]) identified as risk signatures and stratified patients with IPF into the two risk groups. The overall survival rate in patients with high risk was significantly lower than that in patients with low risk (p < 0.001). The calibration and ROC curve analysis confirmed the predictive capacity of this signature, and the results were further verified in the validation group. Risk score-related DEGs were found enriched in ECM-receptor interaction and focal adhesion pathways.

Conclusion: The five FRGs in BALF can be used for prognostic prediction in IPF, which may contribute to improving the management strategies of IPF.

Iron Metabolism and Idiopathic Pulmonary Arterial Hypertension: New Insights from Bioinformatic Analysis

Idiopathic pulmonary arterial hypertension (IPAH) is a rare vascular disease with a poor prognosis, and the mechanism of its development remains unclear. Further molecular pathology studies may contribute to a comprehensive understanding of IPAH and provide new insights into diagnostic markers and potential therapeutic targets. Iron deficiency has been reported in 43-63% of patients with IPAH and is associated with reduced exercise capacity and higher mortality, suggesting that dysregulated iron metabolism may play an unrecognized role in influencing the development of IPAH. In this study, we explored the regulatory mechanisms of iron metabolism in IPAH by bioinformatic analysis. The molecular function of iron metabolism-related genes (IMRGs) is mainly enriched in active transmembrane transporter activity, and they mainly affect the biological process of response to oxidative stress. Ferroptosis and fluid shear stress and atherosclerosis pathways may be the critical pathways regulating iron metabolism in IPAH. We further identified 7 key genes (BCL2, GCLM, MSMO1, SLC7A11, SRXN1, TSPAN5, and TXNRD1) and 5 of the key genes (BCL2, MSMO1, SLC7A11, TSPAN5, and TXNRD1) as target genes may be regulated by 6 dysregulated miRNAs (miR-483-5p, miR-27a-3p, miR-27b-3p, miR-26b-5p, miR-199a-5p, and miR-23b-3p) in IPAH. In addition, we predicted potential IPAH drugs-celastrol and cinnamaldehyde-that target iron metabolism based on our results. These results provide insights for further definition of the role of dysregulated iron metabolism in IPAH and contribute to a deeper understanding of the molecular mechanisms and potential therapeutic targets of IPAH.

Expression of Ferroptosis-Related Genes Shapes Tumor Microenvironment and Pharmacological Profile in Gastric Cancer

Background: Ferroptosis is a form of regulated cell death that occurs as a consequence of lethal lipid peroxidation. A wealth of studies has demonstrated that ferroptosis profoundly modulated numerous biological behaviors of tumor. However, its natural functions in gastric cancer (GC) remain to be explored.

Methods: Firstly, a total of over 1,000 GC patients from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database were included in our study. Secondly, 32 ferroptosis-related genes were extracted from the ferrDb website. Then, unsupervised clustering was performed to classify patients into three distinct ferroptosis-related clusters. Subsequently, we systematically and comprehensively explored the biological characteristics of each cluster. Finally, we constructed a scoring system, named ferroptosis score, to quantify each cluster and also investigated the predictive therapeutic value of the ferroptosis score for chemotherapy and immunotherapy.

Results: Based on the expressions of 32 ferroptosis-related genes, three distinct ferroptosis-related subtypes with various biological characteristics were determined. Integrated analysis showed that cluster 1 is a microsatellite instability (MSI)-like subtype, cluster 2 is an epithelial–mesenchymal transition (EMT)-like subtype, while cluster 3 tends to be a metabolic-like subtype. Prognostic analysis revealed that patients in cluster 2 had a worse overall survival and relapse-free survival. The distribution of the ferroptosis score was significantly different in clusters and gene clusters. The ferroptosis score could predict the biological characteristics of each cluster, the stromal activity, and progression of tumor. The low ferroptosis score group was characterized by the activation of antigen processing and presentation, DNA damage repair pathways, and metabolic pathways, while the high ferroptosis score group was characterized by stromal activation. In response to anticancer drugs, the ferroptosis score was highly negatively associated with drugs targeting MAPK signaling and PI3K/mTOR signaling, while it was positively correlated with drugs targeting the cell cycle, mitosis, and metabolism. Finally, we also proved that the ferroptosis score could serve as a reliable biomarker to predict response to immunotherapy.

Conclusion: This work revealed that tumor cells and their surrounding microenvironment could be shaped by varying the activation degrees of ferroptosis. Establishing ferroptosis-related subtypes would guide in predicting the biological features of individual tumors and selecting appropriate treatment protocols for patients.

A Novel Ferroptosis-Related Gene Signature Predicts Recurrence in Patients With Pancreatic Ductal Adenocarcinoma

Background: Recurrence after surgery is largely responsible for the extremely poor outcomes for patients with pancreatic ductal adenocarcinoma (PDAC). Ferroptosis is implicated in chemotherapy sensitivity and tumor recurrence, we aimed to find out survival-associated ferroptosis-related genes and use them to build a practical risk model with the purpose to predict PDAC recurrence. Methods: Univariate Cox regression analysis was conducted to obtain prognostic ferroptosis-related genes in The Cancer Genome Atlas (TCGA, N = 140) cohort. Multivariate Cox regression analysis was employed to construct a reliable and credible gene signature. The prognostic performance was verified in a MTAB-6134 (N = 286) validation cohort and a PACA-CA (N = 181) validation cohort. The stability of the signature was tested in TCGA and MTAB-6134 cohorts by ROC analyses. Pathway enrichment analysis was adopted to preliminary illuminate the biological relevance of the gene signature. Results: Univariate and multivariate Cox regression analyses identified a 5-gene signature that contained CAV1, DDIT4, SLC40A1, SRXN1 and TFAP2C. The signature could efficaciously stratify PDAC patients with different recurrence-free survival (RFS), both in the training and validation cohorts. Results of subgroup receiver operating characteristic curve (ROC) analyses confirmed the stability and the independence of this signature. Our signature outperformed clinical indicators and previous reported models in predicting RFS. Moreover, the signature was found to be closely associated with several cancer-related and drug response pathways. Conclusion: This study developed a precise and concise prognostic model with the clinical implication in predicting PDAC recurrence. These findings may facilitate individual management of postoperative recurrence in patients with PDAC.

Development and validation of a ferroptosis-related prognostic model for the prediction of progression-free survival and immune microenvironment in patients with papillary thyroid carcinoma

BACKGROUND

Ferroptosis is an iron-dependent and regulated cell death that has been widely reported in a variety of malignancies. The overall survival of papillary thyroid cancer (PTC) is excellent, but the identification of patients with poor prognosis still faces challenges. Nevertheless, whether ferroptosis-related genes (FRGs) can be used to screen high-risk patients is not clear.

METHODS

We obtained the clinical data of patients with PTC and FRGs from the UCSC Xena platform and the FerrDb respectively. Differentially expressed genes (DEGs) of FRGs were obtained from the entire The Cancer Genome Atlas (TCGA). Subsequently, the entire TCGA dataset was randomly split into two subsets: training and test datasets. Based on DEGs, we constructed a predictive model which was tested in the test dataset and the entire TCGA dataset to predict progression-free survival (PFS). Patients were categorized into high- or low-risk groups based on their median risk score. We analyzed differences in some aspects, including pathway enrichment analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), tumor microenvironment (TME), human leukocyte antigen (HLA) genes, and tumor mutation burden (TMB) analyses, between high-risk and low-risk groups.

RESULTS

A predictive model with three FRGs (HSPA5, AURKA, and TSC22D3) was constructed. Patients in the high-risk group had worse PFS compared with patients in the low-risk group. Functional analysis results revealed that ssGSEA, immune cell infiltration, TME, HLA, and TMB were closely associated with ferroptosis.

CONCLUSION

The prognostic model constructed in this study can effectively predict PFS for patients with PTC.

Establishment of a novel ferroptosis-related lncRNA pair prognostic model in colon adenocarcinoma

Long non-coding RNAs (lncRNAs) have been reported to be prognostic factors for cancer. Ferroptosis is an iron-dependent process of programmed cell death. Here, we established a ferroptosis-related lncRNA (frlncRNA) pair signature and revealed its prognostic value in colon adenocarcinoma (COAD) by analyzing the data from The Cancer Genome Atlas (TCGA). FrlncRNAs were identified based on co-expression analysis using the Pearson correlation. Differentially expressed frlncRNAs (DEfrlncRNAs) were recognized and paired, followed by prognostic assessment using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) penalized Cox analysis was used to determine and construct a risk score prognostic model, by which the receiver operating characteristic (ROC) curves for predicting the overall survival (OS) were conducted. Following the evaluation of whether it was an independent prognostic factor, correlations between the risk score model and clinicopathological characteristics, hypoxia- and immune-related factors, and somatic variants were investigated. In total, 148 DEfrlncRNA pairs were identified, 25 of which were involved in a risk score prognostic signature. The area under ROC curves (AUCs) representing the predictive effect for 1-, 3-, and 5-year survival rates were 0.860, 0.885, and 0.934, respectively. The risk score model was confirmed as an independent prognostic factor and was significantly superior to the clinicopathological characteristics. Correlation analyses showed disparities in clinicopathological characteristics, hypoxia- and immune-related factors, and somatic variants, as well as specific signaling pathways between high- and low-risk groups. The novel risk score prognostic model constructed by pairing DEfrlncRNAs showed promising clinical prediction value in COAD.

Identification of a ferroptosis-related lncRNA signature with prognosis for Wilms tumor

BACKGROUND

Wilms tumor (WT) is a widespread urologic tumor in children. Ferroptosis, on the other hand, is a novel form of cell death associated with tumor development. In this study, we aim to explore the predictability of ferroptosis-related biomarkers in estimating prognosis in WT patients.

METHODS

To determine a link between ferroptosis-related gene expression and WT prognosis, we first collected RNA sequencing data and clinical information, involving 124 WT and 6 healthy tissue samples, from the TARGET database. Next, we screened the collected information for ferroptosis-related long non-coding RNA using Cox regression analysis, and constructed a signature model, as well as a nomogram, related to prognosis. Finally, we explored a potential link between ferroptosis-related lncRNA and tumor immunity and screened for possible immune checkpoints.

RESULTS

We constructed a WT prognosis prediction signature containing 12 ferroptosis-related lncRNAs. The area under the curves values, from the ROC curves, predicting overall survival rates at the 1, 3-, and 5-year timepoints were 0.775, 0.867, and 0.891 respectively. Moreover, we generated a nomogram, using clinical features and risk scores, carrying a C-index value of 0.836, which suggested a high predictive value. We also demonstrated significant differences in tumor immunity between low- and high-risk WT patients, particularly in the presence of B cells, NK cells, Th1 cells, Treg cells, inflammation promoting, and type I and II IFN responses. In addition, we showed that immune checkpoints like SIRPA, ICOSLG, LAG3, PVRIG, NECTIN1, and SIRPB2 can serve as potential therapeutic targets for WT.

CONCLUSIONS

Based on our analyses, we generated a ferroptosis-related lncRNA signature that can both estimate prognosis of WT patients and may provide basis for future WT therapy.