Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics.

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, exhibiting high disability and mortality rates. Ferroptosis is vital for the progression of DKD, but the exact mechanism remains unclear. This study aimed to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with the immune and to identify new diagnostic biomarkers to help treat and diagnose DKD. GSE30122 and GSE47185 were obtained from the Gene Expression Omnibus database and were integrated into a merged dataset, followed by functional enrichment analysis. Then potential differentially expressed genes were screened. Ferroptosis-related differentially-expressed genes were identified, followed by gene ontology analysis. Protein-protein interaction networks were constructed and hub genes were screened. The immune cell-infiltrating state in the dataset was assessed using appropriate algorithms. Immune signature subtypes were constructed using the consensus clustering analysis. Hub gene expression was validated using qRT-PCR and immunohistochemistry. A total of Eleven screened ferroptosis-related differentially expressed genes were screened. Six potentially diagnostically favorable ferroptosis-related hub genes were identified. Significantly increased expression of γδT cells, resting mast cells, and macrophages infiltration was observed in the DKD group. Additionally, two distinct immune signature subgroups were identified. Ferroptosis-related hub genes were significantly correlated with differentially infiltrated immune cells. Six hub genes were significantly upregulated in HK-2 cells following high glucose treatment and in human kidney tissues of patients with DKD. Six ferroptosis-related hub genes were identified as potential biomarkers of diabetic kidney disease, but further validation is needed.

Enhancing Colorectal Cancer Immunotherapy: The Pivotal Role of Ferroptosis in Modulating the Tumor Microenvironment.

Colorectal cancer (CRC) represents a significant challenge in oncology, with increasing incidence and mortality rates worldwide, particularly among younger adults. Despite advancements in treatment modalities, the urgent need for more effective therapies persists. Immunotherapy has emerged as a beacon of hope, offering the potential for improved outcomes and quality of life. This review delves into the critical interplay between ferroptosis, an iron-dependent form of regulated cell death, and immunotherapy within the CRC context. Ferroptosis's influence extends beyond tumor cell fate, reshaping the tumor microenvironment (TME) to enhance immunotherapy's efficacy. Investigations into Ferroptosis-related Genes (OFRGs) reveal their pivotal role in modulating immune cell infiltration and TME composition, closely correlating with tumor responsiveness to immunotherapy. The integration of ferroptosis inducers with immunotherapeutic strategies, particularly through novel approaches like ferrotherapy and targeted co-delivery systems, showcases promising avenues for augmenting treatment efficacy. Furthermore, the expression patterns of OFRGs offer novel prognostic tools, potentially guiding personalized and precision therapy in CRC. This review underscores the emerging paradigm of leveraging ferroptosis to bolster immunotherapy's impact, highlighting the need for further research to translate these insights into clinical advancements. Through a deeper understanding of the ferroptosis-immunotherapy nexus, new therapeutic strategies can be developed, promising enhanced efficacy and broader applicability in CRC treatment, ultimately improving patient outcomes and quality of life in the face of this formidable disease.

Identification and validation of ferroptosis-related genes in patients infected with dengue virus: implication in the pathogenesis of DENV.

Ferroptosis, an iron-dependent form of regulated cell death, has been associated with many virus infections. However, the role of ferroptosis in dengue virus (DENV) infection remains to be clarified. In our study, a dengue fever microarray dataset (GSE51808) of whole blood samples was downloaded from the Gene Expression Omnibus (GEO), and a list of ferroptosis related genes (FRGs) was extracted from the FerrDb. We identified 37 ferroptosis-related differentially expressed genes (FR-DEGs) in DENV-infected patient blood samples compared to healthy individuals. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses as well as protein-protein interaction (PPI) network of FR-DEGs revealed that these 37 FR-DEGs were mainly related to the C-type lectin receptor and p53 signaling pathway. Nine out of the 37 FR-DEGs (HSPA5, CAV1, HRAS, PTGS2, JUN, IL6, ATF3, XBP1, and CDKN2A) were hub genes, of which 5 were validated by qRT-PCR in DENV-infected HepG2 cells. Finally, using miRNA-mRNA regulatory network, we identified has-miR-124-3p and has-miR-16-5p as the most critical miRNAs in regulating the expression of these hub genes. In conclusion, our findings demonstrated that 5 FR-DEGs, JUN, IL6, ATF3, XBP1, and CDKN2A, and two miRNAs, has-miR-124-3p and has-miR-16-5p may implicate an essential role of ferroptosis in DENV infection, and further studies are warranted to explore the underlying mechanisms.

Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer.

BACKGROUND: Rectal cancer (RC) is one of the most common malignant tumors. Ferroptosis is an iron-dependent form of cell death, which plays an important role in various cancers. However, the correlation between ferroptosis-related genes (FRGs) and prognosis in RC remains unclear. METHODS: Gene expression data from The Cancer Genome Atlas Rectum adenocarcinoma (TCGA-READ) and GSE87211 were downloaded. Clustering and functional enrichment were evaluated. A FRGs risk score was established based on the univariate Cox analysis and the Least absolute shrinkage and selection operator (LASSO) analysis. K-M analysis and ROC analysis were conducted to determine prognostic values. qRT-PCR was performed to validate levels of mRNA expression. Multivariate Cox analysis was used to build a prognostic prediction model based on the risk score. RESULTS: Based on FRGs, RC patients were grouped into two clusters. In the functional enrichment of differentially expressed genes between the two clusters, immune-related pathways dominated. A novel FRGs signature with 14 genes related to the overall survival (OS) of RC was established. qRT-PCR of the 14 genes identified TP63, ISCU, PLIN4, MAP3K5, OXSR, FANCD2 and ATM were overexpressed in RC tissue; HSPB1, MAPK1, ABCC1, PANX1, MAPK9 and ATG7 were underexpressed; TUBE1 had no difference. The high-risk group had a significantly lower OS than the low-risk group (P < 0.001), and ROC curve analysis confirmed the signature's predictive capacity. Multivariate analysis demonstrated that the risk score and age were independent prognostic factors. CONCLUSION: A novel FRGs model can be used to predict the prognosis in RC, as well as to guide individual treatment.

Classification of colorectal carcinoma subtypes based on ferroptosis-associated molecular markers.

BACKGROUND: Ferroptosis is associated with the development of many cancers; the molecular features of colorectal carcinoma (CRC) based on ferroptosis-related genes (FRGs) remain unknown. Herein, we aimed to identify ferroptosis-associated molecular subtypes of CRC based on the expression profiles of FRGs. METHODS: To explore ferroptosis-associated subtypes of CRC, the gene expression data and clinical information of 682 patients were extracted from The Cancer Genome Atlas and Gene Expression Omnibus databases. We performed consensus clustering to identify robust clusters of patients. Then the distribution of the subtypes in terms of prognosis significance, transcriptome features, immune microenvironment, drug sensitivity, gene mutations, and copy number alternations (CNAs) were evaluated respectively. In addition, we analyzed the correlation of these ferroptosis-associated molecular subtypes with the distribution of conventional clinical indicators in CRC. RESULTS: Four subtypes of CRC (C1, C2, C3, and C4) were identified in which the prognosis, immune cell infiltration, immune score, stromal score, and tumor purity were significantly different between the four subtypes. The C3 subtype had a higher infiltration of B cells, M2 macrophages, resting mast cells, monocytes, natural killer cells, plasma cells, and CD8 T cells. The C3 subtype had the highest immune and stromal scores and the lowest tumor purity. In contrast, the C4 subtype demonstrated the lowest immune and stromal scores and the highest tumor purity. Programmed cell death ligand 1 (PD-L1), an immune checkpoint protein, was differentially expressed in the four subtypes (P < 2e-16) and was significantly correlated with the expression of several FRGs in all subtypes. Significant differences in stem cell indices (P < 0.01) and drug sensitivity (P < 0.01) were observed in the four subtypes. Additionally, gene mutations analysis showed that FRGs such as TP53 had a high frequency of mutation in the four subtypes (49%, 62%, 61%, and 71%, respectively), and the CNAs showed significant difference among all subtypes (P < 0.001). CONCLUSION: In summary, the ferroptosis-associated subtypes could serve as an independent biomarker for estimating oncological outcomes in patients with CRC. Our results demonstrated that the high level of heterogeneity in the expression of FRGs might be useful for the stratification of patients with CRC and the implementation of individualized therapeutic strategies.

Identification of a ferroptosis-related gene signature predictive model in colon cancer.

BACKGROUND: The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. METHODS: The expression profiles of FRGs and relevant clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression model were performed to build a prognostic model in TCGA cohort. RESULTS: Ten FRGs, five of which had mutation rates ≥ 3%, were found to be related to the overall survival (OS) of patients with CC. Patients were divided into high- and low-risk groups based on the results of Cox regression and LASSO analysis. Patients in the low-risk group had a significantly longer survival time than patients in the high-risk group (P < 0.001). Enrichment analyses in different risk groups showed that the altered genes were associated with the extracellular matrix, fatty acid metabolism, and peroxisome. Age, risk score, T stage, N stage, and M stage were independent predictors of patient OS based on the results of Cox analysis. Finally, a nomogram was constructed to predict 1-, 3-, and 5-year OS of patients with CC based on the above five independent factors. CONCLUSION: A novel FRG model can be used for prognostic prediction in CC and may be helpful for individualized treatment.

Expression and Prognostic Characteristics of Ferroptosis-Related Genes in Colon Cancer.

Ferroptosis is a new type of programmed cell death, which occurs with iron dependence. Previous studies have showed that ferroptosis plays an important regulatory role in the occurrence and development of tumors. Colon cancer is one of the major morbidities and causes of mortality in the world. This study used RNA-seq and colon cancer clinical data to explore the relationship between ferroptosis-related genes and colon cancer. Based on the fifteen prognostic ferroptosis-related genes, two molecular subgroups of colon cancer were identified. Surprisingly, we also found cluster2 was characterized by lower mutation burden and expression of checkpoint genes, better survival, and higher expression of NOX1. Moreover, cluster2 has fewer BRAF mutations. We also found the expression of NOX1 is related to the status of BRAF. Finally, using 15 ferroptosis-related genes from The Cancer Genome Atlas cohort, we constructed a prognosis model, and this model may be used to predict the prognosis of patients in clinics.

Anti-Ferroptosis: A Promising Therapeutic Method for Thyroid Cancer.

At present, many problems remain to be solved in studying the pathogenesis of thyroid cancer. Ferroptosis is a programmed cell death mode discovered in recent years, and many studies have found that ferroptosis plays a significant role in the prognosis and progression of thyroid cancer. The researchers showed that ferroptosis-related genes are essential in diagnosing thyroid cancer. Therefore, this paper summarizes some pathological and clinical characteristics of thyroid cancer and makes a series of combs on the relationship between ferroptosis and the basis and function of thyroid cancer, thus providing specific ideas for the diagnosis and treatment of thyroid cancer.

Identification of a prognostic signature based on five ferroptosis-related genes for diffuse large B-cell lymphoma.

BACKGROUND: Therapies for diffuse large B-cell lymphoma (DLBCL) are limited due to the diverse gene expression profiles and complicated immune microenvironments, making it an aggressive lymphoma. Beyond this, researches have shown that ferroptosis contributes to tumorigenesis, progression, and metastasis. We thus are interested to dissect the connection between ferroptosis and disease status of DLBCL. We aim at generating a valuable prognosis gene signature for predicting the status of patients of DLBCL, with focus on ferroptosis-related genes (FRGs). OBJECTIVE: To examine the connection between ferroptosis-related genes (FRGs) and clinical outcomes in DLBCL patients based on public datasets. METHODS: An expression profile dataset for DLBCL was downloaded from GSE32918 (https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=gse32918), and a ferroptosis-related gene cluster was obtained from the FerrDb database (http://www. zhounan.org/ferrdb/). A prognostic signature was developed from this gene cluster by applying a least absolute shrinkage and selection operator (LASSO) Cox regression analysis to GSE32918, followed by external validation. Its effectiveness as a biomarker and the prognostic value was determined by a receiver operator characteristic curve mono factor analysis. Finally, functional enrichment was evaluated by the package Cluster Profiler of R. RESULTS: Five ferroptosis-related genes (FRGs) (GOP1, GPX2, SLC7A5, ATF4, and CXCL2) associated with DLBCL were obtained by a multivariate analysis. The prognostic power of these five FRGs was verified by TCGA (https://xenabrowser.net/datapages/?dataset=TCGA.DLBC.sampleMap%2FHiSeqV2_PANCAN&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A44) and GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse 32918) datasets, with ROC analyses. KEGG and GO analyses revealed that upregulated genes in the high-risk group based on the gene signature were enriched in receptor interactions and other cancer-related pathways, including pathways related to abnormal metabolism and cell differentiation. CONCLUSION: The newly developed signature involving GOP1, GPX2, SLC7A5, ATF4, and CXCL2 has the potential to serve as a prognostic biomarker. Furthermore, our results provide additional support for the contribution of ferroptosis to DLBCL.

Identification and validation of ferroptosis-related prognostic gene signature in patients with cervical cancer.

Background: Ferroptosis is an iron-dependent cell death, which is distinct from the other types of regulated cell death. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in cervical cancer (CC) remains unclear. This study aims to explore the ferroptosis-related prognostic genes (FRPGs) expression profiles and their prognostic values in CC. Methods: The ferroptosis-related genes (FRGs) were obtained from The Cancer Genome Atlas (TCGA) and FerrDb databases. Core FRGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) website. FRPGs were identified using univariate and multivariate Cox regressions, and the ferroptosis-related prognostic model was constructed. FRPGs were verified in clinical specimens. The relationship between FRPGs and tumor infiltrating immune cells were assessed through the CIBERSORT algorithm and the LM22 signature matrix. Bioinformatics functions of FRPGs were explored with the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Results: Thirty-three significantly up-regulated and 28 down-regulated FRGs were screened from databases [P<0.05; false discovery rate (FDR) <0.05; and |log2 fold change (FC)| ≥2]. Twenty-four genes were found closely interacting with each other and regarded as hub genes (degree ≥3). Solute carrier family 2 member 1 (SLC2A1), carbonic anhydrases IX (CA9), and dual oxidase 1 (DUOX1) were identified as independent prognostic signatures for overall survival (OS) in a Cox regression. Time-dependent receiver operating characteristic (ROC) curves showed the predictive ability of the ferroptosis-related prognostic model, especially for 1-year OS [area under the curve (AUC) =0.76]. Consistent with the public data, our experiments demonstrated that the mRNA levels of SLC2A1 and DUOX1, and the protein levels of SLC2A1, DUOX1, and CA9 were significantly higher in the tumor tissues. Further analysis showed that there was a significant difference in the proportion of tumor infiltrating immune cells between the low- and high-risk group based on our prognostic model. The function enrichment of FRPGs was explored by applying Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Conclusions: In this study, the features of FRPGs in CC were pictured. The results implicated that targeting ferroptosis may be a new reliable biomarker and an alternative therapy for CC.

Identification of new hub- ferroptosis-related genes in Lupus Nephritis.

Background: Lupus Nephritis (LN) is the primary causation of kidney injury in systemic lupus erythematosus (SLE). Ferroptosis is a programmed cell death. Therefore, understanding the crosstalk between LN and ferroptosis is still a significant challenge. Methods: We obtained the expression profile of LN kidney biopsy samples from the Gene Expression Omnibus database and utilised the R-project software to identify differentially expressed genes (DEGs). Then, we conducted a functional correlation analysis. Ferroptosis-related genes (FRGs) and differentially expressed genes (DEGs) crossover to select FRGs with LN. Afterwards, we used CIBERSORT to assess the infiltration of immune cells in both LN tissues and healthy control samples. Finally, we performed immunohistochemistry on LN human renal tissue. Results: 10619 DEGs screened from the LN biopsy tissue were identified. 22 hub-ferroptosis-related genes with LN (FRGs-LN) were screened out. The CIBERSORT findings revealed that there were significant statistical differences in immune cells between healthy control samples and LN tissues. Immunohistochemistry further demonstrated a significant difference in HRAS, TFRC, ATM, and SRC expression in renal tissue between normal and control groups. Conclusion: We developed a signature that allowed us to identify 22 new biomarkers associated with FRGs-LN. These findings suggest new insights into the pathology and therapeutic potential of LN ferroptosis inhibitors and iron chelators.

Characterization and validation of a prognostic model for the N6-methyladenosine-associated ferroptosis gene in colon adenocarcinoma.

Background: According to statistics, colon adenocarcinoma (COAD) ranks third in global incidence and second in mortality. The role of N6-methyladenosine (m6A) modification-dependent ferroptosis in tumor development and progression is gaining attention. Therefore, it is meaningful to explore the biological functions mediated by m6A ferroptosis related genes (m6A-Ferr-RGs) in the prognosis and treatment of COAD. This study aimed to explore the regulatory mechanisms and prognostic features of m6A-Ferr-RGs in COAD based on the COAD transcriptome dataset. Methods: The expression data of Ferr-RGs and the correlated analysis with prognosis related m6A regulators were conducted to obtain candidate m6A-Ferr-RGs. Then, the differentially expressed genes (DEGs) between COAD and normal samples were intersected with candidate m6A-Ferr-RGs to obtain differentially expressed m6A Ferr-RGs (DE-m6A-Ferr-RGs) in COAD. Cox regression analyses were performed to establish risk model and validated in the GSE17538 and GSE41258 datasets. The nomogram was constructed and verified by calibration curves. Moreover, tumor immune dysfunction and exclusion (TIDE) was used to assess immunotherapy response in two risk groups. Finally, the expression of m6A-Ferr-related prognostic genes was validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: In total, 6 model genes (HSD17B11, VEGFA, CXCL2, ASNS, FABP4, and GPX2) were obtained to construct the risk model. The nomogram was established based on the independent prognostic factors for predicting survival of COAD. TIDE assessed that the high-risk group suffered from greater immune resistance. Ultimately, the experimental results confirmed that the expression trends of all model genes were consistent among data from public database. Conclusions: In this study, m6A-Ferr-related prognostic model for COAD was constructed using transcriptome data and clinical data of COAD in public database, which may have potential immunotherapy and chemotherapy guidance implications.

Construction and validation of a predictive nomogram for ferroptosis-related genes in osteosarcoma.

BACKGROUND: Ferroptosis is a new type of cellular regulation of necrosis that has attracted great attention in recent years, which is different from the traditional mode of autophagy, apoptosis, and necrosis. Studies suggest that ferroptosis is key to the occurrence and development of tumors. METHODS: Here, we investigated the prognostic significance of ferroptosis-related genes (FRGs) in osteosarcoma (OS) using RNA transcriptome data from 88 OS samples collected from the UCSC Xena platform. We defined the OS sample from the UCSC platform as the training cohort and the GEO dataset (GSE21257 and GSE16091) as the validation cohorts. We assessed 73 up-regulated and 63 down-regulated FRGs. We divided patients from the UCSC database into groups at high risk and low risk and built a prognostic risk model to assess prognosis using five FRGs: MT1G, G6PD, ARNTL, BNIP3, and SQLE. RESULTS: High-risk OS patients presented a lower survival rate. These results were confirmed in the validation groups. In the training group, the areas under the ROC curves (AUC) were as follows: 0.880 for 1 year, 0.833 for 3 years, and 0.818 for 5 years. In the GSE21257 validation cohort, the AUC were as follows: 0.770 for 1 year, 0.641 for 3 years, and 0.632 for 5 years survival, and in the GSE16091 were 0.729 for 1 year, 0.663 for 3 years, and 0.735 for 5 years survival. CONCLUSIONS: These findings suggest that FRGs are associated with the prognosis of osteosarcoma. Moreover, our prognostic risk model can predict overall survival in osteosarcoma. This provides new ideas for the clinical diagnosis and personalized treatment of osteosarcoma.

The recent advancements of ferroptosis in the diagnosis, treatment and prognosis of ovarian cancer.

Ovarian cancer affects the female reproductive system and is the primary cause of cancer related mortality globally. The imprecise and non-specific nature of ovarian cancer symptoms often results in patients being diagnosed at an advanced stage, with metastatic lesions extending beyond the ovary. This presents a significant clinical challenge and imposes a substantial economic burden on both patients and society. Despite advancements in surgery, chemotherapy, and immunotherapy, the prognosis for most patients with ovarian cancer remains unsatisfactory. Therefore, the development of novel treatment strategies is imperative. Ferroptosis, a distinct form of regulated cell death, characterized by iron-dependent lipid peroxidation, differs from autophagy, apoptosis, and necrosis, and may hold promise as a novel cell death. Numerous studies have demonstrated the involvement of ferroptosis in various conventional signaling pathways and biological processes. Recent investigations have revealed the significant contribution of ferroptosis in the initiation, progression, and metastasis of diverse malignant tumors, including ovarian cancer. Moreover, ferroptosis exhibits a synergistic effect with chemotherapy, radiotherapy, and immunotherapy in restraining the proliferation of ovarian cancer cells. The aforementioned implies that ferroptosis holds considerable importance in the management of ovarian cancer and has the potential to serve as a novel therapeutic target. The present review provides a comprehensive overview of the salient features of ferroptosis, encompassing its underlying mechanisms and functional role in ovarian cancer, along with the associated signaling pathways and genes. Furthermore, the review highlights the prospective utility of ferroptosis in the treatment of ovarian cancer.

Identification of ferroptosis related markers by integrated bioinformatics analysis and In vitro model experiments in rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disease characterized by destructive and symmetrical joint diseases and synovitis. This research attempted to explore the mechanisms involving ferroptosis in RA, and find the biological markers by integrated analysis. METHODS: Gene expression data (GSE55235 and GSE55457) of synovial tissues from healthy and RA individuals were downloaded. By filtering the differentially expressed genes (DEGs) and intersecting them with the 484 ferroptosis-related genes (FRGs), the overlapping genes were identified. After the enrichment analysis, the machine learning-based approaches were introduced to screen the potential biomarkers, which were further validated in other two datasets (GSE77298 and GSE93272) and cell samples. Besides, we also analyze the infiltrating immune cells in RA and their correlation with the biomarkers. RESULTS: With the criteria, 635 DEGs in RA were included, and 29 of them overlapped in the reported 484 FRGs. The enrichments of the 29 differentially expressed ferroptosis-related genes indicated that they may involve in the FoxO signaling pathway and inherited metabolic disorder. RRM2, validating by the external datasets and western blot, were identified as the biomarker with the high diagnostic value, whose associated immune cells, such as Neutrophils and Macrophages M1, were also further evaluated. CONCLUSION: We preliminary explored the mechanisms between ferroptosis and RA. These results may help us better comprehend the pathophysiological changes of RA in basic research, and provide new evidences for the clinical transformation.

Identification and validation of the diagnostic signature associated with immune microenvironment of acute kidney injury based on ferroptosis-related genes through integrated bioinformatics analysis and machine learning.

Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning. Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets. Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury. Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature's versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment.

Identification of ferroptosis-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics.

Objective: This study investigated to probe ferroptosis-related diagnostic biomarkers and underlying molecular mechanisms in Diabetic nephropathy (DN). Methods: GSE30122 and GSE1009 from GEO database were used as training and verification sets, respectively, to screen differentially expressed ferroptosis-related genes (FRGs). These genes were further analyzed using GO, KEGG, and GSEA methods, and screened with PPI, LASSO, and SVM-RFE to identify ferroptosis-related diagnostic biomarkers for DN. A diagnostic model was established using the Glm function and verified with ROC curve. The relationship between these biomarkers and immune cell was analyzed, and qRT-PCR and Western blot were used to detect the expression of these biomarkers in kidney tissues and identify the effect of TP53 on DN development. Results: Fifty one differentially expressed FRGs were enriched in bioprocesses such as p53 signaling pathway, oxidative stress and chemical stress response, and mTOR signaling pathway. TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were identified as ferroptosis-related diagnostic biomarkers for DN. TP53 showed the most differential expression. ROC analysis showed that AUC values of TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were 0.751, 0.705, 0.725, 0.882, 0.691, and 0.675, respectively. The AUC value of DN diagnosis model was 0.939 in training set and 1.000 in verification set. qRT-PCR results confirmed significant differences in these six biomarkers between DN and normal kidney tissue (p < 0.05), and correlation analysis showed that five biomarkers were significantly correlated with infiltrating immune cells (p < 0.05). Furthermore, western blots showed that TP53 promotes apoptosis through PI3K-AKT signaling in DN. Conclusion: TP53, RB1, NF2, RRM2, PRDX1, and CDC25A have potential as diagnostic biomarkers for DN. The diagnostic model containing the above six biomarkers performs well in the diagnosis of DN. Five of the six biomarkers are strongly associated with several infiltrating immune cells. TP53 may play an essential role in the development of DN.

Diagnostic and prognostic value of ferroptosis-related genes in patients with Myelodysplastic neoplasms.

This study delves into the emerging role of ferroptosis in Myelodysplastic Neoplasms (MDS) and aims to identify a prognostic ferroptosis-related gene signature for MDS. Utilizing RNA-seq data and clinical information from the Gene Expression Omnibus database, the researchers extracted ferroptosis-related genes from the FerrDb website and conducted differential expression analysis using the 'limma' package in R. Hub ferroptosis-related genes in MDS were screened using the "RandomForest" and "carat" R packages. Kaplan -Meier and Cox regression analyses were employed to assess the prognostic role of three identified hub genes (BNIP3, MDM2, and RRM2). Receiver operator characteristic curve analysis confirmed the diagnostic efficacy of these genes. The study delved further into immune infiltration correlations, ncRNA-transcription factor coregulatory network analysis, and the identification of potential therapeutic drugs targeting hub ferroptosis-related genes in MDS. The researchers constructed a 3-gene signature-based risk score using datasets GSE58831 and GSE19429, demonstrating high accuracy (AUC > 0.75) in both datasets for survival prediction in MDS. A nomogram analysis reinforced the prognostic value of the risk-scoring model. Immunological analysis revealed an association between the risk score and immune infiltration. Quantitative reverse transcription polymerase chain reaction (qPCR) data indicated significant expression differences in MDM2, RRM2, and BNIP3 between MDS and healthy bone marrow samples. Notably, MDM2 and RRM2 showed decreased expression, while BNIP3 exhibited increased expression in MDS samples. This comprehensive study concludes that BNIP3, MDM2, and RRM2 hold diagnostic and prognostic significance in MDS and provide valuable insights into immune cell landscapes and potential therapeutic avenues for this condition.

Identification and Validation of a Novel Prognostic Signature Based on Ferroptosis-Related Genes in Ovarian Cancer.

BACKGROUND: Ovarian cancer is the most lethal gynecological tumor, with a poor prognosis due to the lack of early symptoms, resistance to chemotherapy, and recurrence. Ferroptosis belongs to the regulated cell death family, and is characterized by iron-dependent processes. Here, comprehensive bioinformatics analysis was applied to explore a valuable prognostic model based on ferroptosis-related genes, which was further validated in clinical OC samples. METHODS: mRNA data of normal and ovarian tumor samples were obtained separately from the GTEx and TCGA databases. The least absolute shrinkage and selection operator (LASSO) cox regression was applied to construct the prognostic model based on ferroptosis-associated genes. Expression of ALOX12 in OC cell lines, as well as cell functions, including proliferation and migration, were examined. Finally, the prognostic efficiency of the model was assessed in the clinical tissues of OC patients. RESULTS: A gene signature consisting of ALOX12, RB1, DNAJB6, STEAP3, and SELENOS was constructed. The signature divided TCGA, ICGC, and GEO cohorts into high-risk and low-risk groups separately. Receiver operating characteristic (ROC) curves and independent prognostic factor analysis were carried out, and the prognostic efficacy was validated. The expression levels of ALOX12 in cell lines were examined. Inhibition of ALOX12 attenuated cell proliferation and migration in HEY cells. Moreover, the prognostic value of ALOX12 expression was examined in clinical samples of OC patients. CONCLUSION: This work constructed a novel ferroptosis-associated gene model. Furthermore, the clinical predictive role of ALOX12 was identified in OC patients, suggesting that ALOX12 might act as a potential prognostic tool and therapeutic target for OC patients.

Construction of a two-gene prognostic model related to ferroptosis in renal cell carcinoma.

Background: Renal cell carcinoma (RCC) is a common and aggressive tumor. A newly discovered form of programmed cell death, ferroptosis, plays an important role in tumor development and progression. However, a clear prognostic correlation between Ferroptosis-related genes (FRGs) and RCC has not yet been established. In this study, prognostic markers associated with FRGs were investigated to improve the therapeutic, diagnostic, and preventive strategies available to patients with renal cancer. Methods: The present study analyzed the predictive value of 23 FRGs in RCC through bioinformatics techniques, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) tools, Kaplan-Meier survival analysis, Cox regression modeling, tumor mutational burden (TMB), CIBERSORT, and half maximal inhibitory concentration (IC50) difference analysis. Results: We screened FRGs by differentially expressed genes (DEGs) and overall survival (OS). Four candidate genes were obtained by hybridization. Then, we constructed a two-gene prognostic signature (NCOA4 and CDKN1A) via univariate Cox regression and multivariate stepwise Cox regression, which classified RCC patients into high- and low-risk groups, and patients in the high-risk group were found to have worse OS and progression-free survival (PFS). We also found that patients with higher TNM stage, T stage, and M stage had higher risk scores than those with lower TNM stage, T stage, and M stage (P<0.05). Males had higher risk scores than females. This signature was identified as an independent prognostic indicator for RCC. These results were validated in both the test cohort and the entire cohort. In addition, we also constructed a nomogram that predicted the OS in RCC patients, the consistency index (C-index) of the nomogram was 0.731 [95% confidence interval (CI): 0.672-0.790], the areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.728, 0.704, and 0.898 at 1-, 3-, and 5-year, respectively, which shows that nomogram has good prediction ability. and we also analyzed the immune status and drug sensitivity between the high- and low-risk groups. Conclusions: We constructed a prognostic model associated with ferroptosis, which may provide clinicians with a reliable predictive assessment tool and offer new perspectives for the future clinical management of RCC.