Low expression of ferritinophagy-related NCOA4 gene in relation to unfavorable outcome and defective immune cells infiltration in clear cell renal carcinoma

NCOA4: An Immunomodulation-Related Prognostic Biomarker in Colon Adenocarcinoma and Pan-Cancer

Treatment of cancer in humans requires a thorough understanding of the multiple pathways by which it develops. Recent studies suggest that nuclear receptor coactivator 4 (NCOA4) may be a predictive biomarker for renal cancer. In the present work, TCGA, GEPIA, and several bioinformatics approaches were used to analyze the NCOA4 expression patterns, prognostic relevance, and association between NCOA4 and clinicopathological features and immune cell infiltration. We investigated NCOA4 expression in malignancies. Low NCOA4 expression was associated with poor overall survival in individuals with malignancies such as cholangiocarcinoma, colon adenocarcinoma, and clear cell renal carcinoma. We also analyzed NCOA4 DNA methylation in normal and primary tumor tissues and investigated possible functional pathways underlying NCOA4-mediated oncogenesis. In conclusion, downregulation of NCOA4 is associated with poor prognosis, and NCOA4 may be a predictive biomarker for COAD.

A Novel Mitochondrial-Related Gene Signature for the Tumor Immune Microenvironment Evaluation and Prognosis Prediction in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. More and more studies have reported that mitochondrial-related genes (MTRGs) were associated with the clinical outcomes of multiple tumors solely. In this study, we aimed to develop a novel prognostic model based on MTRGs. Differentially expressed MTRGs were identified from TCGA-LUAD and GSE31210 cohorts. Univariate Cox regression analysis was utilized to screen differentially expressed MTRGs that were related to prognosis of LUAD. Then, LASSO Cox regression analysis was used to develop a prognostic signature. ESTIMATE was used for estimating the fractions of immune cell types. In this study, we identified 44 overlapping differentially expressed MTRGs in TCGA-LUAD and GSE31210 cohorts. Among 44 overlapping differentially expressed MTRGs, nine genes were associated with prognosis of LUAD. When the penalty parameter lambda was the minimum, there were six genes meeting the conditions of constructing the signature, including SERPINB5, CCNB1, FGR MAOB, SH3BP5, and CYP24A1. The survival analysis suggested that prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. Cox regression analyses showed that the risk score was an independent predictor of LUAD prognosis. As with the results of ESTIMATE score, the degree of immune cell infiltration in the low-risk group was higher than that in the high-risk group, such as TIL, Treg, and B cells. In addition, TMB and cancer stem cell infiltration were higher in the low-risk group than the high-risk group. In conclusion, we developed a novel MTRG signature acting as a negative independent prognostic factor. In the future, individualized treatments and medical decision-making may benefit from using the predicted model.

Novel Insights in the Regulatory Mechanisms of Ferroptosis in Hepatocellular Carcinoma

Ferroptosis is a newly defined programmed cell death, which by its mechanism differs from other programmed cell death processes such as apoptosis, necrosis, and autophagy. It has a unique morphology and biological properties that antioxidants and iron-chelating agents can regulate. Ferroptosis has the characteristics of iron ion deposition and dependence on lipid peroxidation. It can affect the progression of many cancers, including liver cancer, by inducing an intracellular iron-dependent accumulation of reactive oxygen species, providing new possibilities for cancer treatment. At present, great progress has been made in exploring the molecular mechanism of ferroptosis. In this review, we summarize the characteristics, mechanisms, and regulatory factors of ferroptosis in detail, discuss the progress of ferroptosis research in liver cancer, and provide directions and new ideas for the treatment of hepatocellular carcinoma.

Prognostic biomarker replication factor C subunit 5 and its correlation with immune infiltrates in acute myeloid leukemia

OBJECTIVE

To determine the role of replication factor C subunit 5 (RFC5) in acute myeloid leukemia (AML) from four aspects: expression, prognosis, biological functions, and its effects on the immune system.

METHODS

The RFC5 gene expression and survival analyses, biological function analyses including functional enrichment analysis of genes co-expressed with RFC5, RFC5-interacted gene network construction, gene set enrichment analysis (GSEA), and immune infiltration analysis were performed using data based on GDC TCGA and GEO. The CIBERSORT algorithm was employed to quantify immune cell fractions. All the statistical analyses were performed in SPSS software, GraphPad Prism, and R software.

RESULTS

RFC5 expression was abnormally expressed in AML (P <0.05). Notably, differential RFC5 expression was observed among different FAB AML subtypes and hematopoietic lineages (all P <0.05). More importantly, high RFC5 expression served as an independent prognostic factor for the poor overall survival of AML patients (P <0.001). Enrichment analyses revealed that RFC5 was involved in cell cycle-related pathways in AML. CIBERSORT analysis showed high proportions of M2 macrophages in the high RFC5 expression group.

CONCLUSIONS

RFC5 might serve as an effective and robust biomarker for the diagnosis and prognosis of AML. RFC5 might be involved in the AML progression via cell cycle regulation. Moreover, the correlation between RFC5 and immune cells might provide potential assistance for AML treatment.

SLC1A5 Prefers to Play as an Accomplice Rather Than an Opponent in Pancreatic Adenocarcinoma

Background: SLC1A5, a ferroptosis regulator gene, plays a dual role in cancer regulation. However, the roles of SLC1A5 in pancreatic adenocarcinoma (PAAD) remain elusive.

Methods: SLC1A5’s expression and somatic mutation information were determined by TCGA, GEO, Oncomine, and cBioPortal databases. Its prognostic value was assessed in TCGA cohort and was validated in three independent cohorts. The effects of SLC1A5 on the tumor immune microenvironment were analyzed by the CIBERSORT algorithm, ssGSEA method, and TISIDB and TIMER databases. The “oncoPredict” R package, TIDE algorithm, ImmuCellAI online tool, and GSE35141 and GSE59357 datasets were used to ascertain its therapeutic correlations. GSEA and Western blot were applied to reveal the effects of SLC1A5 on the mTORC1 signaling pathway and ferroptosis process. The biofunctions of SLC1A5 were assessed by MTT, wound-healing, Transwell, and xenograft assays.

Results: SLC1A5 was significantly upregulated in the PAAD samples but was not commonly accompanied with somatic mutation (2.3%). Overexpression of SLC1A5 led to a poor prognosis and was identified as an independent prognostic factor. Moreover, high SLC1A5 expression suppressed the antitumor immune process by changing the infiltrating levels of immune cells. As for therapeutic correlations, SLC1A5 was related to the efficacy of dasatinib, sunitinib, sorafenib, and imatinib but may not predict that of radiotherapy, chemotherapeutic drugs, and immune checkpoints inhibitors (ICIs). Notably, the overexpression of SLC1A5 could activate the mTORC1 signaling pathway and may increase the cellular sensitivity to ferroptosis. Finally, the overexpression of SLC1A5 markedly promoted proliferation, migration, and invasion of pancreatic cancer cells. At the in vivo level, SLC1A5 deletion inhibited tumor growth in a mice xenograft model.

Conclusions: SLC1A5 prefers to play as an accomplice rather than an opponent in PAAD. Our findings provide novel insights into PAAD treatment.

PIWI-Interacting RNA Pathway Genes: Potential Biomarkers for Clear Cell Renal Cell Carcinoma

Background. Clear cell renal cell carcinoma (ccRCC) is one of the most lethal malignancies in the urinary system, yet effective diagnostic and prognostic markers are lacking. Recently, several of piRNA pathway genes have been reported to be associated with cancer diagnosis and prognosis, but their role in ccRCC is still unclear. Methods. We analysed the expression of 27 piRNA pathway genes in 539 kidney renal clear cell carcinoma (KIRC) and 72 nontumor tissue samples (data from TCGA), and 12 mRNAs were significantly different. The aim was to sift the piRNA pathway genes that are correlated with ccRCC patient survival and to construct a piRNA pathway gene risk prognostic model using Kaplan-Meier survival curve and ROC curve, respectively. Results. 5 piRNA pathway genes (TDRD7, GPAT2, PLD6, SUV39H1, and DOM3Z) were picked out and used to construct the piRNA pathway gene risk model. Kaplan-Meier survival curve analysis showed that compared with that of the low-risk group of ccRCC patients, the OS of the high-risk group of ccRCC patients was significantly reduced. The predictive performance of the prognostic risk model was measured using a ROC curve, which individually showed AUC values for 1 year of 0.707, for 3 years of 0.713, and for 5 years of 0.701. Moreover, the mRNA and protein expression levels of TDRD7 were overexpressed in the ccRCC datasets (data from our cohort, TCGA, GEO, and CPTAC) and ccRCC cell lines, and the expression levels correlated with the clinicopathological characteristics in ccRCC. The Tumor Immune Estimation Resource (TIMER) showed that the mRNA expression level of TDRD7 was positively related to tumor immune infiltrating cells (TICs) in ccRCC. Mechanistically, gene set enrichment analysis (GSEA) was performed to uncover the mechanism of TDRD7 in ccRCC. In summary, the piRNA pathway genes,especially TDRD7, may be potential cancer diagnostic and prognostic biomarkers of ccRCC.

A Ferroptosis-Related Genes Model Allows for Prognosis and Treatment Stratification of Clear Cell Renal Cell Carcinoma: A Bioinformatics Analysis and Experimental Verification

Introduction

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor characterized by poor prognosis and difficult treatment. Ferroptosis is a relatively new form of programmed cell death that involved in cancer development and therapy resistance. Studies have shown that targeted ferroptosis may be a novel option for the treatment of ccRCC, but key genes and their roles between ferroptosis and ccRCC are limited so far. This study aims to develop a ccRCC stratified model based on ferroptosis-related genes to provide a reference for the prognosis prediction and the individualized treatment of ccRCC.

Materials and Methods

The mRNAs expression data of ccRCC and FRGs were obtained from TCGA and FerrDb database, respectively. Through multiple analysis, a 4-FRG based prognostic stratified model was constructed and its predictive performance was validated through various methods. Then, a nomogram based on the model was constructed and ccRCC patients stratified by the model were analyzed for tumor microenvironment, immune infiltration, sensitivity for immune checkpoint inhibitors (ICIs)/traditional anti-tumor therapy and tumor mutation burden (TMB). Functional enrichment analysis was performed to explore potential biological pathways. Finally, we verified our model by RT-qPCR, siRNA transfection, scratch assay and CCK-8 assay.

Results

In this study, the stratified model and a model-based nomogram can accurately predict the prognosis of ccRCC patients in TCGA database. The patients stratified by the model showed different tumor microenvironments, immune infiltration, TMB, resistance to traditional and ICIs therapy, and sensitivity to ferroptosis. Functional enrichment analysis suggested several biological pathways related to the process and prognosis of ccRCC. RT-qPCR confirmed the differential expression of ferroptosis-related genes. Scratch assay and CCK-8 assay indicated the promotion effects of CD44 on the proliferation and migration of ccRCC.

Conclusion

In this study, we established a novel ccRCC stratified model based on FRGs, which can accurately predict the prognosis of ccRCC patients and provide a reference for clinical individualized treatment.

A Ferroptosis-Related lncRNA Model to Enhance the Predicted Value of Cervical Cancer

Background

Cervical cancer (CC) is a common gynecological malignant tumor. Ferroptosis is a new type of programmed cell death, which plays a crucial part in cancer. However, current knowledge regarding ferroptosis-related long noncoding RNAs (lncRNAs) in CC is still limited. Therefore, our aim is to identify ferroptosis-related lncRNAs, build a steady prediction model, and improve the prediction value of CC.

Methods

We obtained RNA expression and ferroptosis-related gene data of female CC patients from TCGA and FerrDb databases, respectively. Then, the ferroptosis-related lncRNAs were obtained by the limma R package and Cytoscape 3.7.1. We constructed the prediction model by Cox regression analysis. Finally, the prediction model was verified by the median risk score, Kaplan–Meier analysis, the time-dependent receiver operating characteristic (ROC) curve, clinical features, and immunoinfiltration analysis.

Results

We acquired 1393 ferroptosis-related lncRNAs. The ferroptosis-related lncRNA signature was obtained by multivariate Cox regression analysis, and the patients were divided into a high-risk group and a low-risk group. The prognosis of the high-risk group was worse than that of the low-risk group. We found that the risk score can be used as an independent prognostic index by multivariate Cox regression analysis. The area under the time-dependent ROC curve reached 0.847 at 1 year, 0.906 at 2 years, 0.807 at 3 years, and 0.724 at 5 years in the training cohort. Principal component analysis showed that the diffusion directions of the two groups were different. Gene set enrichment analysis indicated that lncRNAs of two groups may be involved in tumorigenesis. Further analysis showed that high-risk groups were related to immune-related pathways. Ferroptosis-related lncRNAs are related to the proportion of tumor-infiltrating immune cells in CC.

Conclusion

We have constructed a ferroptosis-related lncRNA prediction model. The prognostic model had important clinical significance, including improving the predictive value and guiding the individualized treatment of CC patients.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

Autophagy-Dependent Ferroptosis-Related Signature is Closely Associated with the Prognosis and Tumor Immune Escape of Patients with Glioma

Background

Ferroptosis is an autophagy-dependent form of cell death, sometimes called “ferritinophagy”. Its related pathway has been proven to regulate the programmed death of glioma stem cells. Mining autophagy-dependent ferroptosis-related gene (AD-FRG) signature could facilitate the discovery of mechanisms and therapeutic targets showing drug resistance to chemotherapeutic drugs.

Methods

We exhaustively searched HADB, MSigDB and FerrDb datasets and obtained 25 genes confirmed to exist in autophagy and ferroptosis death pathways. Glioma gene expression and clinicopathological data were collected from TCGA and CGGA datasets.

Results

Lasso regression and Cox regression analysis were carried out to construct a nine AD-FRGs signature (SIRT1, MTDH, HSPB1, CISD2, HMOX1, ATG7, MTOR, PRKAA2 and EIF2AK4). ROC curve showed that nine genes signature could effectively predict 1- (AUC = 0.869), 3- (AUC = 0.922) and 5-year (AUC = 0.870) survival rates. Immunohistochemical images confirmed the protein expression level of the gene model. The prognostic nomogram of risk score, age, WHO grade, isocitrate dehydrogenase (IDH) wild-type condition, 1p/19q co-deletion state was built. The calibration curve demonstrated that the prediction of the nomogram is highly consistent with the actual results. Moreover, tumor microenvironment analysis showed that the high-risk group was associated with high immune infiltration status and high tumor purity. Correlation analysis showed that the expression of SIRT1, CISD2 and HSPB1 might be related to macrophage infiltration and immunotolerance in glioma tissues.

Conclusion

Based on autophagy-dependent ferroptosis-related genes, we established gene signature and nomogram that maybe effectively predict the overall survival rate of glioma and correlate with the immunosuppressive tumor microenvironment (TME).

A novel ferroptosis-related gene signature associated with cell cycle for prognosis prediction in patients with clear cell renal cell carcinoma

BACKGROUND

It is of great urgency to explore useful prognostic markers for patients with clear cell renal cell carcinoma (ccRCC). Prognostic models based on ferroptosis-related gene (FRG) in ccRCC is poorly reported for now.

METHODS

Comprehensive analysis of 22 FRGs were performed in 629 ccRCC samples from two independent patient cohorts. We carried out least absolute shrinkage and selection operator analysis to screen out prognosis-related FRGs and constructed prognosis model for patients with ccRCC. Weighted gene co-expression network analysis was also carried out for potential functional enrichment analysis.

RESULTS

Based on the TCGA cohort, a total of 11 prognosis-associated FRGs were selected for the construction of the prognosis model. Significantly differential overall survival (hazard ratio = 3.61, 95% CI: 2.68-4.87, p < 0.0001) was observed between patients with high and low FRG score in the TCGA cohort, which was further verified in the CPTAC cohort with hazard ratio value of 5.13 (95% CI: 1.65-15.90, p = 0.019). Subgroup survival analysis revealed that our FRG score could significantly distinguish patients with high survival risk among different tumor stages and different tumor grades. Functional enrichment analysis illustrated that the process of cell cycle, including cell cycle-mitotic pathway, cytokinesis pathway and nuclear division pathway, might be involved in the regulation of ccRCC through ferroptosis.

CONCLUSIONS

We developed and verified a FRG signature for the prognosis prediction of patients with ccRCC, which could act as a risk factor and help to update the tumor staging system when integrated with clinicopathological characteristics. Cell cycle-related pathways might be involved in the regulation of ccRCC through ferroptosis.

CRYAB predicts clinical prognosis and is associated with immunocyte infiltration in colorectal cancer

Background

αB-Crystallin (CRYAB) is differentially expressed in various tumors. However, the correlation between CRYAB and immune cell infiltration in colorectal cancer (CRC) remains unclear.

Materials & Methods

Kaplan-Meier survival curves in The Cancer Genome Atlas (TCGA) were used to evaluate the relationship between CRYAB expression and both overall survival and progression-free survival. The relationships between CRYAB expression and infiltrating immune cells and their corresponding gene marker sets were examined using the TIMER database.

Results

The expression of CRYAB was lower in CRC tumor tissues than in normal tissues (P < 0.05). High CRYAB gene expression and high levels of CRYAB gene methylation were correlated with high-grade malignant tumors and more advanced tumor, nodes and metastasis (TNM) cancer stages. In addition, in colorectal cancer, there was a positive correlation between CRYAB expression and immune infiltrating cells including neutrophils, macrophages, CD8 + T cells, and CD4 + T cells, as well as immune-related genes including CD2, CD3D, and CD3E. Methylation sites such as cg13084335, cg15545878, cg13210534, and cg15318568 were positively correlated with low expression of CRYAB.

Conclusion

Because CRYAB likely plays an important role in immune cell infiltration, it may be a potential tumor-suppressor gene in CRC and a potential novel therapeutic target and predictive biomarker for colorectal cancer (CRC).

Roles of ferroptosis in urologic malignancies

Ferroptosis, an iron-dependent form of non-apoptotic cell death, is believed to strongly contribute to the pathogenesis of multiple cancers. Recently, the positive association between ferroptosis and urologic malignancies has drawn considerable attention, while a comprehensive review focused on this issue is absent. Based on this review, ferroptosis has been implicated in the development and therapeutic responses of prostate cancer, kidney cancer, and bladder cancer. Mechanistically, a large number of biomolecules and tumor-associated signaling pathways, including DECR1, PANX2, HSPB1, ACOT8, SUV39H1, NCOA4, PI3K-AKT-mTOR signaling, VHL/HIF-2α pathway, and Hippo/TAZ signaling pathway, have been reported to regulate ferroptosis in urologic cancers. Ferroptosis inducers, such as erastin, ART, CPNPs, and quinazolinyl-arylurea derivatives, exert potential therapeutic effects per se and/or enhance the anticancer response of other anticancer drugs in urologic oncology. A better understanding of ferroptosis may provide a promising way to treat therapy-resistant urologic cancers.

Regulated Cell Death in Urinary Malignancies

Urinary malignancies refer to a series of malignant tumors that occur in the urinary system and mainly include kidney, bladder, and prostate cancers. Although local or systemic radiotherapy and chemotherapy, immunotherapy, castration therapy and other methods have been applied to treat these diseases, their high recurrence and metastasis rate remain problems for patients. With in-depth research on the pathogenesis of urinary malignant tumors, this work suggests that regulatory cell death (RCD) plays an important role in their occurrence and development. These RCD pathways are stimulated by various internal and external environmental factors and can induce cell death or permit cell survival under the control of various signal molecules, thereby affecting tumor progression or therapeutic efficacy. Among the previously reported RCD methods, necroptosis, pyroptosis, ferroptosis, and neutrophil extracellular traps (NETs) have attracted research attention. These modes transmit death signals through signal molecules, such as cysteine-aspartic proteases (caspase) family and tumor necrosis factor-α (TNF-α) that have a wide and profound influence on tumor proliferation or death and even change the sensitivity of tumor cells to therapy. This review discussed the effects of necroptosis, pyroptosis, ferroptosis, and NETs on kidney, bladder and prostate cancer and summarized the latest research and achievements in these fields. Future directions and possibility of improving the denouement of urinary system tumors treatment by targeting RCD therapy were also explored.

The Role of Iron in Cancer Progression

Iron is an essential trace element for the human body, and its deficiency or excess can induce a variety of biological processes. Plenty of evidences have shown that iron metabolism is closely related to the occurrence and development of tumors. In addition, iron plays an important role in cell death, which is very important for the development of potential strategies for tumor treatment. Here, we reviewed the latest research about iron metabolism disorders in various types of tumors, the functions and properties of iron in ferroptosis and ferritinophagy, and new opportunities for iron-based on treatment methods for tumors, providing more information regarding the prevention and treatment of tumors.

A Novel Ferroptosis-Related Gene Signature to Predict Prognosis in Patients with Head and Neck Squamous Cell Carcinoma

The clinical TNM staging system is currently used to evaluate the prognosis of head and neck squamous cell carcinoma (HNSCC). The 5-year survival rate for patients with HNSCC is less than 50%, which is attributed to the lack of reliable prognostic biomarkers. Ferroptosis-related genes (FRGs) regulate cancer initiation and progression. Therefore, we analyzed the correlation between FRGs and the clinical outcomes of patients with HNSCC. A typical prognostic model of FRGs for HNSCC was constructed using bioinformatics tools and data from public databases, including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and GeneCards. The model was generated based on the following six FRGs: ATG5, PRDX6, OTUB1, FTH1, SOCS1, and MAP3K5. The accuracy of model prediction was analyzed systematically. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. The AUC for 1-year, 3-year, and 5-year survival were 0.645, 0.721, and 0.737, respectively, in the training set (TCGA cohort) and 0.726, 0.620, and 0.584, respectively, in the validation set (GSE65858). The multivariate Cox regression analysis revealed that the risk score was an independent prognostic factor for HNSCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that six FRGs were enriched in the ferroptosis pathway. A novel FRG prognostic signature model was established for HNSCC. The findings of this study reveal that FRGs are potential biomarkers for HNSCC.

The mechanisms and therapeutic targets of ferroptosis in cancer

INTRODUCTION

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

AREA COVERED

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

EXPERT OPINION

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

The roles of ferroptosis regulatory gene SLC7A11 in renal cell carcinoma: A multi-omics study

Background

The ferroptosis inhibitory gene Solute carrier family 7 member 11 (SLC7A11) provides a new strategy for anticancer treatment. However, its function in renal cell carcinoma (RCC) remains elusive.

Methods

The expression and somatic mutation information of SLC7A11 in RCC samples were determined using The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), Oncomine, and cBioPortal databases. The prognostic value of SLC7A11 was assessed through survival analysis, Receiver operating characteristic curve (ROC) analysis, independent prognostic analysis, clinical subgroup analysis, and nomogram. Its prognostic value was also validated in the ICGC and GSE29607 cohorts. Gene set enrichment analysis (GSEA) was employed to investigate the effects of SLC7A11 on multiple metabolic pathways. The CIBERSORT algorithm and single‐sample gene set enrichment analysis (ssGSEA) method were applied to evaluate the effects of SLC7A11 on the tumor immune microenvironment (TIM). SLC7A11’s therapeutic correlations were analyzed using the GSE87121, GSE67501, and GSDC datasets. Finally, the biofunctions of SLC7A11 in renal cancer cells and ferroptosis were ascertained by MTT, wound healing, transwell, and western blot assays.

Results

Through multiple datasets, SLC7A11 was found to be markedly upregulated in RCC. In terms of prognosis, SLC7A11 overexpression conferred a worse prognosis and was identified as an independent prognostic factor. Its prognostic value was validated in ICGC cohort. Moreover, high SL7CA11 expression could stimulate nucleotides, fatty acids, and amino acid metabolism to meet the proliferative consumption of tumor cells. As for the immune effect, SLC7A11 suppressed antitumor immunity by reducing the abundances of CD8+ T and NK cells. Regarding the therapeutic response, SLC7A11 expression was not correlated with the sensitivities of most chemotherapy and targeted drugs. Finally, SLC7A11 promoted the proliferation, migration, and invasion of renal cancer cells by enhancing GPX4 output, which in turn inhibits ferroptosis.

Conclusions

SLC7A11 not only deeply influences RCC prognosis and TIM, but also promotes RCC progression by inhibiting ferroptosis and inducing metabolic reprogramming. In addition, SLC7A11 weakly affects the therapeutic effect and sensitivities of multiple chemotherapy and targeted drugs.

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.

Analysis of Ferroptosis-Related Gene Expression and Prognostic Factors of Renal Clear Cell Carcinoma Based on TCGA Database

Introduction

Renal clear cell carcinoma (ccRCC) is a common tumor of the urinary system, most of which are primary malignant tumors with high metastatic rate and remaining incurable. Ferroptosis is a newly discovered form of iron-dependent programmed cell necrosis in recent years, which is inextricably linked to the occurrence and development of tumors progression. Due to the complexity of the interaction between genes in ccRCC, the research on the pathogenesis of ccRCC is still not remarkably accurate. Therefore, whether ferroptosis-related genes (FRGs) can play a role in predicting prognosis in ccRCC needs to be discussed.

Methods

We entered the Cancer Genome Mapping Project (TCGA) database and downloaded the relevant genes and clinical research data of ccRCC patients. Lasso Cox regression was used to construct a multi-gene prognostic model in the TCGA cohort. R language software was used for drawing pictures related to our study.

Results

Most of the genes involved in ferroptosis (86.2%) existing differences between the tumor and normal tissues in the TCGA public gene database. In terms of univariate Cox regression analysis, 20 differentially expressed genes (DEGs) were associated with prognosis and survival (P<0.05). A prognostic model of 12 FRGs was constructed, and patients were segmented into two different groups depending on how risky they are. Considering overall survival, the high-risk group is dramatically lower than the low-risk group (P<0.001). In multivariate Cox regression analysis, risk scores and stage turned out be an independent prognostic factor (P<0.001). GO and KEGG analysis and ssGSEA analysis of DEGs revealed that these genes were related to immune-related pathways (P<0.05).

Conclusion

This study established and identified the changes in FRGs expression and prognostic factors of ccRCC, which can be helpful for prognosis evaluation and clinical treatment of this disease.

A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma

Background

Cutaneous melanoma is a common but aggressive tumor. Ferroptosis is a recently discovered cell death with important roles in tumor biology. Nevertheless, the prognostic power of ferroptosis-linked genes remained unclear in cutaneous melanoma.

Methods

Cutaneous melanoma patients of TCGA (The Cancer Genome Atlas) were taken as the training cohort while GSE65904 and GSE22153 as the validation cohorts. Multifactor Cox regression model was used to build a prognostic model, and the performance of the model was assessed. Functional enrichment and immune infiltration analysis were used to clarify the mechanisms.

Results

A five ferroptosis-linked gene predictive model was developed. ALOX5 and GCH1 were illustrated as independent predictive factors. Functional assessment showed enriched immune-linked cascades. Immune infiltrating analysis exhibited the distinct immune microenvironment.

Conclusion

Herein, a novel ferroptosis-related gene prognostic model was built in cutaneous melanoma. This model could be used for prognostic prediction, and maybe helpful for the targeted and immunotherapies.

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

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

Ferroptosis-Related Gene-Based Prognostic Model and Immune Infiltration in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10^-11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.

Molecular and Metabolic Subtypes in Sporadic and Inherited Clear Cell Renal Cell Carcinoma

The promise of personalized medicine is a therapeutic advance where tumor signatures obtained from different omics platforms, such as genomics, transcriptomics, proteomics, and metabolomics, in addition to environmental factors including metals and metalloids, are used to guide the treatments. Clear cell renal carcinoma (ccRCC), the most common type of kidney cancer, can be sporadic (frequently) or genetic (rare), both characterized by loss of the von Hippel-Lindau (VHL) gene that controls hypoxia inducible factors. Recently, several genomic subtypes were identified with different prognoses. Transcriptomics, proteomics, metabolomics and metallomic data converge on altered metabolism as the principal feature of the disease. However, in view of multiple biochemical alterations and high level of tumor heterogeneity, identification of clearly defined subtypes is necessary for further improvement of treatments. In the future, single-cell combined multi-omics approaches will be the next generation of analyses gaining deeper insights into ccRCC progression and allowing for design of specific signatures, with better prognostic/predictive clinical applications.