Development and Validation of a Ferroptosis-Related Gene Signature for Overall Survival Prediction in Lung Adenocarcinoma

ChaC1 upregulation reflects poor prognosis in a variety of cancers: analysis of the major missense SNPs of ChaC1 as an aid to refining prognosis

The ChaC1 enzyme that catalyzes cytosolic glutathione degradation is highly upregulated in several cancers. In a systematic review of gene signature panels for cancer prognosis based on oxidative stress and ferroptosis genes, we observed that ChaC1 was found in panels in a wide variety of different cancers, with the upregulation correlating with poor prognosis. Since SNPs can have an impact on functionality and prognosis, ChaC1 SNPs from various databases were also investigated. Six frequently observed missense SNPs were chosen for reconstruction, and their functionality was evaluated. Three out of six SNPs resulted in either a partial or complete loss of ChaC1 function, and these SNPs had the changes R72Q, A156V, and G173S in their proteins. This study highlights the importance of ChaC1 in cancer prognosis across a wide variety of cancers. Additionally, the information on the SNPs of ChaC1 with altered enzymatic activities would improve the prognostic ability of these panels and facilitate treatment regimens.

A novel signature based on twelve programmed cell death patterns to predict the prognosis of lung adenocarcinoma

Programmed cell death (PCD) plays a pivotal role in tumor initiation and progression. However, the prognostic value and clinical characteristics of PCD-related genes (PRGs) remain unclear. We collected and analyzed genes associated with twelve PCD patterns, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis to construct a gene signature. Our analysis identified 215 differentially expressed PRGs out of 1254 in lung adenocarcinoma (LUAD) and normal lung tissues. Subsequently, we performed univariate Cox regression analysis and identified 58 prognostic PRGs. Based on LASSO Cox regression analysis, we constructed a risk score using the expression levels of seven genes: DAPK2, DDIT4, E2F2, GAPDH, MET, PIM2, and FOXF1. Patients with lower risk scores showed earlier stages of cancer, longer survival times, and better immune infiltrations and functions. Notably, we found that knockdown of DDIT4 significantly increased apoptosis and impaired the proliferation of human LUAD cell lines. Our study proposes a PRG-based prognostic signature that sheds light on the potential role of PCD-related genes in LUAD and provides valuable insights into future therapeutic strategies.

SLC7A11 inhibits ferroptosis and downregulates PD-L1 levels in lung adenocarcinoma

Introduction

Lung adenocarcinoma (LUAD) is a prevalent form of lung cancer originating from lung glandular cells with low survival rates despite recent therapeutic advances due to its diverse and complex nature. Recent evidence suggests a link between ferroptosis and the effectiveness of anti-PD-L1 therapy, with potential synergistic effects.

Methods

Our study comprehensively analyzed the expression patterns of ferroptosis regulators in LUAD and their association with prognosis and PD-L1 expression. Furthermore, we identified two distinct subtypes of LUAD through consensus clustering of ferroptosis regulators, revealing significant tumor heterogeneity, divergent PD-L1 expression, and varying prognoses between the subtypes.

Results

Among the selected ferroptosis regulators, SLC7A11 emerged as an independent prognostic marker for LUAD patients and exhibited a negative correlation with PD-L1 expression. Subsequent investigations revealed high expression of SLC7A11 in the LUAD population. In vitro experiments demonstrated that overexpression of SLC7A11 led to reduced PD-L1 expression and inhibited ferroptosis in A549 cells, underscoring the significant role of SLC7A11 in LUAD. Additionally, pan-cancer analyses indicated an association between SLC7A11 and the expression of immune checkpoint genes across multiple cancer types with poor prognoses.

Discussion

From a clinical standpoint, these findings offer a foundation for identifying and optimizing potential combination strategies to enhance the therapeutic effectiveness of immune checkpoint inhibitors and improve the prognosis of patients with LUAD.

Ferroptosis-Related lncRNA to Predict the Clinical Outcomes and Molecular Characteristics of Kidney Renal Papillary Cell Carcinoma

Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous type of kidney cancer, resulting in limited effective prognostic targets for KIRP patients. Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in the regulation of ferroptosis and iron metabolism, making them potential targets for the treatment and prognosis of KIRP. In this study, we constructed a ferroptosis-related lncRNA risk score model (FRM) based on the TCGA-KIRP dataset, which represents a novel subtype of KIRP not previously reported. The model demonstrated promising diagnostic accuracy and holds potential for clinical translation. We observed significant differences in metabolic activities, immune microenvironment, mutation landscape, ferroptosis sensitivity, and drug sensitivity between different risk groups. The high-risk groups exhibit significantly higher fractions of cancer-associated fibroblasts (CAFs), hematopoietic stem cells (HSC), and pericytes. Drugs (IC50) analysis provided a range of medication options based on different FRM typing. Additionally, we employed single-cell transcriptomics to further analyze the impact of immune invasion on the occurrence and development of KIRP. Overall, we have developed an accurate prognostic model based on the expression patterns of ferroptosis-related lncRNAs for KIRP. This model has the potential to contribute to the evaluation of patient prognosis, molecular characteristics, and treatment modalities, and can be further translated into clinical applications.

Implications of different cell death patterns for prognosis and immunity in lung adenocarcinoma

In recent years, lung adenocarcinoma (LUAD) has become a focus of attention due to its low response to treatment, poor prognosis, and lack of reliable indicators to predict the progression or therapeutic effect of LUAD. Different cell death patterns play a crucial role in tumor development and are promising for predicting LUAD prognosis. From the TCGA and GEO databases, we obtained bulk transcriptomes, single-cell transcriptomes, and clinical information. Genes in 15 types of cell death were analyzed for cell death index (CDI) signature establishment. The CDI signature using necroptosis + immunologic cell death-related genes was established in the TCGA cohort with the 1-, 2-, 3-, 4- and 5-year AUC values were 0.772, 0.736, 0.723, 0.795, and 0.743, respectively. The prognosis was significantly better in the low CDI group than in the high CDI group. We also investigated the relationship between the CDI signature and clinical variables, published prognosis biomarkers, immune cell infiltration, functional enrichment pathways, and immunity biomarkers. In vitro assay showed that HNRNPF and FGF2 promoted lung cancer cell proliferation and migration and were also involved in cell death. Therefore, as a robust prognosis biomarker, CDI signatures can screen for patients who might benefit from immunotherapy and improve diagnostic accuracy and LUAD patient outcomes.

Characterization of tumor microenvironment infiltration and therapeutic responses of cell cycle-related genes' signature in breast cancer

BACKGROUND

It is unknown how the cell cycle plays a role in breast cancer (BC). This study aimed to establish a clinically applicable predictive model to predict the therapeutic responses and overall survival in BC patients.

MATERIALS AND METHODS

Cell cycle-related genes (CCGs) were identified within the Cancer Genome Atlas cohort (n is equal to 1001) and the Gene Expression Omnibus cohort (n is equal to 3265). An analysis of univariate and multivariate Cox was then conducted to develop a nomogram based on CCGs. After validating the nomogram, risk cohort stratification was established and the predictive value was examined. Finally, immune cell infiltration and therapeutic responses were analysed.

RESULTS

Based on 15 CCGs, 4 prognostic predictors were identified and entered into the nomogram. According to the curves of calibration, the estimated and observed value of the nomogram is in optimal agreement. Subsequently, stratification into two risk cohorts showed that the predictive value, immune cell infiltration and overall survival were better among patients with low risk. Immune checkpoint expression in patients with BC at higher risk was downregulated. Furthermore, the results of the study revealed that doxorubicin, paclitaxel, docetaxel, cisplatin and vinorelbine all had higher IC50 values in patients with high-risk BC.

CONCLUSION

The nomogram based on CCG could assess tumour immune micro-environment regulation and therapeutic responses of immunotherapy in BC. Moreover, it may provide novel information on the control of immune micro-environment infiltration in BC and aid in the development of targeted immunotherapy.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

Introduction

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.

Methods

This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.

Results

Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.

Conclusion

To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

Multi-Omics Analysis Elucidates The Immune And Intratumor Microbes Characteristics Of Ubiquitination Subtypes In Lung Adenocarcinoma

Ubiquitination modification is closely related to cancer and participates in the regulation of tumor microenvironment. However, the role of ubiquitination modification in the immune response and prognosis of lung adenocarcinoma has not been elucidated. This study aims to establish a disease classification associated with ubiquitination and reveal the landscape of intratumor microbes in patients with lung adenocarcinoma for the first time. A total of 1314 patients with lung adenocarcinoma in the GEO and TCGA databases were included in our study. We constructed a ubiquitination scoring model using WGCNA and constructed ubiquitination subtypes using unsupervised clustering, analyzed the clinical characteristics, immune characteristics, and intratumor microbes characteristics, and screened out the relevant gene signatures, which were verified by RT-qPCR in human cancer cells. The results showed that the high ubiquitination subtype had poor prognosis, low degree of immune infiltration, high index of tumor stemness, and poor effect of immunotherapy. The subtypes with lower ubiquitination scores have better prognosis, higher tumor microenvironment score and better immunotherapy effect. The C2 subtype has high level of immune infiltration, lower intratumor microbes diversity and abundance, and good prognosis. The C3 subtype has low level of immune infiltration, higher intratumor microbes diversity and abundance, and poor prognosis. The C1 subtype has characteristics between C2 and C3. In summary, this paper constructs a scoring system and several subtypes based on ubiquitination genes, and analyzed the characteristics, which can help provide new methods for clinical treatment.

Construction and validation of a novel ferroptosis-related prognostic signature for lung adenocarcinoma

Background

Lung cancer has the highest prevalence and mortality of all cancers, and lung adenocarcinoma (LUAD) occupies the largest proportion of lung cancers. Herein, this study is aimed at constructing a ferroptosis-related prognostic signature for LUAD and conducting functional analysis based on the signature, highlighting the importance of ferroptosis in LUAD.

Methods

We employed RNA-sequencing (RNA-seq) and clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were conducted to build the ferroptosis-related genes (FRGs) prognostic signature. The efficacy of this FRG signature was further analyzed with Kaplan-Meier (KM) plot, multivariate Cox regression, and the receiver operating characteristic (ROC) curves. Enrichment analysis was used to evaluate key pathways. The expression of immunomodulators, immune infiltration status, and drug sensitivity correlation were explored to predict the response to various therapies. The expression of FRGs was validated in LUAD samples with western blot (WB) and immunohistochemistry (IHC) staining. Cell viability assay and lipid peroxidation detection were measured after small interfering RNA (siRNA) knockdown of two FRGs in lung cancer cell lines.

Results

A seven-gene signature was constructed and used to divide LUAD patients into high- and low-risk groups. High-risk patients were notably related to shorter overall survival (OS), and multivariate Cox regression demonstrated that our signature was an independent predictor of OS. ROC curve analysis presented a maximum area under the curve (AUC) value of 0.740 for the experimental cohort and 0.705 for the validation cohort. The low-risk group showed higher levels of plasma cell infiltration and higher expression of programmed cell death protein 1 (PDCD1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4). Ferroptosis inducers such as talazoparib or cisplatin had lower IC50 values in the high-risk group, while navitoclax (BCL-2 gene family inhibition and apoptosis inducer) had higher IC50 values in the high-risk group. Additionally, peroxiredoxin-6 (PRDX6) and acyl-CoA synthetase long chain family member 3 (ACSL3) were upregulated in LUAD tissues. Lipid peroxide assay showed that silencing PRDX6 or ACSL3 promoted lipid peroxidation and ferroptosis in lung cancer cells.

Conclusions

Our novel ferroptosis-related signature shows potential clinical and functional importance in LUAD patients, and further research on ferroptosis as a therapeutic target in LUAD is warranted.

Integrative analysis of multi-omics data for discovery of ferroptosis-related gene signature predicting immune activity in neuroblastoma

Immunotherapy for neuroblastoma remains unsatisfactory due to heterogeneity and weak immunogenicity. Exploring powerful signatures for the evaluation of immunotherapy outcomes remain the primary purpose. We constructed a ferroptosis-related gene (FRG) signature by least absolute shrinkage and selection operator and Cox regression, identified 10 independent prognostic FRGs in a training cohort (GSE62564), and then verified them in an external validation cohort (TCGA). Associated with clinical factors, the signature accurately predicts overall survival of 3, 5, and 10 years. An independent prognostic nomogram, which included FRG risk, age, stage of the International Neuroblastoma Staging System, and an MYCN status, was constructed. The area under the curves showed satisfactory prognostic predicting performance. Through bulk RNA-seq and proteomics data, we revealed the relationship between hub genes and the key onco-promoter MYCN gene and then validated the results in MYCN-amplified and MYCN-non-amplified cell lines with qRT-PCR. The FRG signature significantly divided patients into high- and low-risk groups, and the differentially expressed genes between the two groups were enriched in immune actions, autophagy, and carcinogenesis behaviors. The low-risk group embodied higher positive immune component infiltration and a higher expression of immune checkpoints with a more favorable immune cytolytic activity (CYT). We verified the predictive power of this signature with data from melanoma patients undergoing immunotherapy, and the predictive power was satisfactory. Gene mutations were closely related to the signature and prognosis. AURKA and PRKAA2 were revealed to be nodal hub FRGs in the signature, and both were shown to have significantly different expressions between the INSS stage IV and other stages after immunohistochemical validation. With single-cell RNA-seq analysis, we found that genes related to T cells were enriched in TNFA signaling and interferon-γ hallmark. In conclusion, we constructed a ferroptosis-related gene signature that can predict the outcomes and work in evaluating the effects of immunotherapy.

A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component-collagen, which would enable clinicians to treat patients with LUAD more individually.

Identification and integration analysis of a novel prognostic signature associated with cuproptosis-related ferroptosis genes and relevant lncRNA regulatory axis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a highly prevalent malignancy worldwide, and its clinical prognosis assessment and treatment is a major research direction. Both ferroptosis and cuproptosis are novel forms of cell death and are considered to be important factors involved in cancer progression. To further understand the correlation between the cuproptosis-related ferroptosis genes (CRFGs) and the prognosis of LUAD, we explore the molecular mechanisms related to the development of the disease. We constructed a prognostic signature containing 13 CRFGs, which, after grouping based on risk score, revealed that the LUAD high-risk group exhibited poor prognosis. Nomogram confirmed that it could be an independent risk factor for LUAD, and ROC curves and DCA validated the validity of the model. Further analysis showed that the three prognostic biomarkers (LIFR, CAV1, TFAP2A) were significantly correlated with immunization. Meanwhile, we found that a LINC00324/miR-200c-3p/TFAP2A regulatory axis could be involved in the progression of LUAD. In conclusion, our report reveals that CRFGs are well correlated with LUAD and provide new ideas for the construction of clinical prognostic tools, immunotherapy, and targeted therapy for LUAD.

Identification and validation of ferroptosis-related genes for chronic rhinosinusitis with nasal polyps

PURPOSE

Even though the great progress in the field of chronic rhinosinusitis with nasal polyps (CRSwNP) has been achieved, ferroptosis and its molecular mechanism in CRSwNP remain blank. We are the first to study the relationship between CRSwNP and ferroptosis, aiming to identify ferroptosis-related genes in the process of CRSwNP.

METHODS

Using the GEO database and the FerrDb database, significantly differentially expressed ferroptosis-related genes (DEFGs) were selected between CRSwNP-NP and CRSwNP-IT specimens. Then, the protein-protein interaction (PPI) network of ferroptosis-related genes was constructed. Functional enrichment analyses (GSVA, GO, KEGG, and GeneCodis analyses) were introduced in our study. Besides, based on the GSE136825 data set, DEFGs between CRSwNP-NP and CS-IT specimens were also analyzed. Finally, qRT-PCR was performed to validate the selected ferroptosis-related genes with clinical samples.

RESULTS

31 significantly DEFGs were identified between CRSwNP-NP and CRSwNP-IT specimens. Functional enrichment analyses and the analysis of GeneCodis 4 pointed out that DEFGs may potentially be involved in some related KEGG pathways. 8 DEFGs were selected between CRSwNP-NP and CS-IT specimens. The experimental verification indicated that 4 genes (GPX2, CDO1, CAV1, and TP53) were the important DEFGs of CRSwNP. The Venn diagrams proved that CDO1 and GPX2 were considered as the most important DEFGs genes of CRSwNP, especially GPX2.

CONCLUSIONS

Though a comprehensive bioinformatics analysis and the experimental verification, CDO1 and GPX2 were considered as the important ferroptosis-related genes of CRSwNP, especially GPX2. However, further molecular biological experiments would be still required to uncover the underlying mechanism between ferroptosis and CRSwNP.

A novel basement membrane-related gene signature for prognosis of lung adenocarcinomas

BACKGROUND

Lung adenocarcinoma (LUAD) remains a global health concern with its poor prognosis and high mortality. Whether tumor cells invade through the basement membrane (BM) is the key factor to determine the prognosis of LUAD. This study aimed to identify the BM-related gene signatures to improve the overall prognosis of LUAD.

MATERIALS & METHODS

A series of bioinformatics analyses were conducted based on TCGA and GEO datasets. Unsupervised consistent cluster analysis was performed, and 500 LUAD patients were assigned to two different groups according to expressions of 222 BM-related genes. The differentially expressed genes (DEGs) between the two clusters were identified, and Lasso regression, ROC curve, univariate and multivariate Cox regression analyses and enrichment analysis were conducted. Besides, ssGSEA, CIBERSORT and ESTIMATE algorithmwere were employed to understand the relationship between the tumor microenvironment (TME) and risk scores. Moreover, single cell clustering and trajectory analyses were performed to further understand the significance of BM-related genes. Finally, qRT-PCR was used to verify the prognosis model.

RESULTS

A total of 31 prognostic BM-related genes were determined for LUAD, and a novel 17-mRNA prognostic model named BMsocre was successfully established to predict the overall survival of LUAD patients. The high BMscore group indicated worse prognosis. Seventeen DEGs were enriched mainly in metabolism, ECM-receptor interaction and immune response. In addition, the high-risk group showed higher TMB and lower immune score. The low-risk group had a better immunotherapeutic response where immune escape was less likely. The BMscore model was verified in our patient cohort. Furthermore, NELL2 was mainly expressed in clusters of T cells, and was identified to play a critical role in T-cell differentiation.

CONCLUSIONS

A novel BMscore model was successfully established and might be effective for providing guidance to LUAD therapy.

A ferroptosis-related prognostic model with excellent clinical performance based on the exploration of the mechanism of oral squamous cell carcinoma progression

As a hot topic today, ferroptosis is closely involved in the progression and treatment of cancer. Accordingly, we built a prognostic model around ferroptosis to predict the overall survival of OSCC patients. We used up to 6 datasets from 3 different databases to ensure the credibility of the model. Then, through differentially expressed, Univariate Cox, and Lasso regression analyses, a model composed of nine prognostic-related differently expressed ferroptosis-related genes (CISD2, DDIT4, CA9, ALOX15, ATG5, BECN1, BNIP3, PRDX5 and MAP1LC3A) were constructed. Moreover, Kaplan-Meier curves, Receiver Operating Characteristic curves and principal component analysis used to verify the model's predictive ability showed the model's superiority. To deeply understand the mechanism of ferroptosis affecting the occurrence, development and prognosis of OSCC, we performed enrichment analysis in different risk groups identified by the model. The results showed that numerous TP53-related, immune-related and ferroptosis-related functions and pathways were enriched. Further immune microenvironment analysis and mutation analysis have once again revealed the correlation between risk score and immunity and TP53 mutation. Finally, the correlation between risk score and OSCC clinical treatment, as well as Nomogram show the brilliant clinical application prospects of the prognostic model.

GAPDH Is a Novel Ferroptosis-Related Marker and Correlates with Immune Microenvironment in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a prevalent form of lung cancer with high morbidity and fatality rates. Ferroptosis is a type of programmed cell death that is iron-dependent. Recent findings have suggested that ferroptosis inducers have promising prospects for the therapy of LUAD. However, ferroptosis-related gene expression in LUAD and its relationship with the tumor prognosis and tumor immune microenvironment remain unknown. We identified a total of 638 ferroptosis-related genes, built a LUAD ferroptosis-related risk model (FRRM) with the help of Least Absolute Shrinkage Selection Operator (LASSO) regression analysis based on The Cancer Genome Atlas (TCGA) database, split LUAD patients into high- and low-risk clusters, and verified the model utilizing the Gene Expression Omnibus (GEO) database. The results of the FRRM's principal component analysis (PCA) demonstrated its strong predictive power. Further, univariate and multivariate Cox and AUC curve analyses demonstrated that the model was independent of other clinical traits and served as an independent prognostic factor. The nomogram demonstrated strong predictive power for overall survival, according to calibration plots. We also explored variations in clinical characteristics, immune cell infiltration, immune-related function, and functional pathways between the high- and low-risk groups. Additionally, we used a protein-protein interaction (PPI) network of various genes in the two groups to search for potential target genes. GAPDH was then chosen for a follow-up investigation. An analysis was performed on the relationship between GAPDH and variations in survival prognosis, clinical traits, immune cell infiltration, immune checkpoints, and immunotherapy. In vitro tests further supported the probable functions of GAPDH as a ferroptosis marker in LUAD. In conclusion, a novel ferroptosis-related prognostic gene, GAPDH, was discovered, whose expression was connected to the tumor immune microenvironment. The combination of immunotherapy and the targeting of GAPDH to induce ferroptosis in LUAD may provide a novel therapeutical option.

Anoikis-related genes combined with single cell sequencing: Insights into model specification of lung adenocarcinoma and applicability for prognosis and therapy

Background: Anoikis has therapeutic potential against different malignancies including lung adenocarcinoma. This study used anoikis and bioinformatics to construct a prognostic model for lung adenocarcinoma and explore new therapeutic strategies. Methods: Several bioinformatic algorithms (co-expression analysis, univariate Cox analysis, multivariate Cox analysis, and cross-validation) were used to screen anoikis-related genes (ARGs) to construct a risk model. Lung adenocarcinoma patients were divided into training and testing groups at a ratio of 1:1. The prognostic model was validated by risk score comparison between high- and low-risk groups using receiver operating characteristic curve (ROC), nomograms, independent prognostic analysis and principal component analysis. In addition, two anoikis-related genes patterns were classified utilizing consensus clustering method and were compared with each other in survival time, immune microenvironment, and regulation in pathway. Single cell sequencing was applied to analyze anoikis-related genes constructed the model. Results: This study demonstrated the feasibility of the model based on seven anoikis-related genes, as well as identifying axitinib, nibtinib and sorafenib as potential therapeutic strategies for LUAD. Risk score based on this model had could be used as an independent prognostic factor for lung adenocarcinoma (HR > 1; p < 0.001) and had the highest accuracy to predict survival compared with the clinical characteristics. Single cell sequencing analysis discovered Keratin 14 (KRT14, one of the seven anoikis-related genes) was mainly expressed in malignant cells in various cancers. Conclusion: We identified seven anoikis-related genes and constructed an accurate risk model based on bioinformatics analysis that can be used for prognostic prediction and for the design of therapeutic strategies in clinical practice.

Bioinformatics analysis identifies ferroptosis‑related genes in the regulatory mechanism of myocardial infarction

Since ferroptosis is considered to be a notable cause of cardiomyocyte death, inhibiting ferroptosis has become a novel strategy in reducing cardiac cell death and improving cardiopathic conditions. Therefore, the aim of the present study was to search for ferroptosis-related hub genes and determine their diagnostic value in myocardial infarction (MI) to aid in the diagnosis and treatment of the disease. A total of 10,286 DEGs were identified, including 6,822 upregulated and 3.464 downregulated genes in patients with MI compared with healthy controls. After overlapping with ferroptosis-related genes, 128 ferroptosis-related DEGs were obtained. WGCNA successfully identified a further eight functional modules, from which the blue module had the strongest correlation with MI. Blue module genes and ferroptosis-related differentially expressed genes were overlapped to obtain 20 ferroptosis-related genes associated with MI. Go and KEGG analysis showed that these genes were mainly enriched in cellular response to chemical stress, trans complex, transferring, phosphorus-containing groups, protein serine/threonine kinase activity, FoxO signaling pathway. Hub genes were obtained from 20 ferroptosis-related genes through the PPI network. The expression of hub genes was found to be down-regulated in the MI group. Finally, the miRNAs-hub genes and TFs-hub genes networks were constructed. The GSE141512 dataset and the use of RT-qPCR assays on patient blood samples were used to confirm these results. The results showed that ATM, PIK3CA, MAPK8, KRAS and SIRT1 may play key roles in the development of MI, and could therefore be novel markers or targets for the diagnosis or treatment of MI.

GPX2 predicts recurrence-free survival and triggers the Wnt/β-catenin/EMT pathway in prostate cancer

Objective

This study aimed to establish a prognostic model related to prostate cancer (PCa) recurrence-free survival (RFS) and identify biomarkers.

Methods

The RFS prognostic model and key genes associated with PCa were established using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression from the Cancer Genome Atlas (TCGA)-PRAD and the Gene Expression Omnibus (GEO) GSE46602 datasets. The weighted gene co-expression network (WGCNA) was used to analyze the obtained key modules and genes, and gene set enrichment analysis (GSEA) was performed. The phenotype and mechanism were verified in vitro.

Results

A total of 18 genes were obtained by LASSO regression, and an RFS model was established and verified (TCGA, AUC: 0.774; GSE70768, AUC: 0.759). Three key genes were obtained using multivariate Cox regression. WGCNA analysis obtained the blue module closely related to the Gleason score (cor = -0.22, P = 3.3e - 05) and the unique gene glutathione peroxidase 2 (GPX2). Immunohistochemical analysis showed that the expression of GPX2 was significantly higher in patients with PCa than in patients with benign prostatic hyperplasia (P < 0.05), but there was no significant correlation with the Gleason score (GSE46602 and GSE6919 verified), which was also verified in the GSE46602 and GSE6919 datasets. The GSEA results showed that GPX2 expression was mainly related to the epithelial-mesenchymal transition (EMT) and Wnt pathways. Additionally, GPX2 expression significantly correlated with eight kinds of immune cells. In human PCa cell lines LNCaP and 22RV1, si-GPX2 inhibited proliferation and invasion, and induced apoptosis when compared with si-NC. The protein expression of Wnt3a, glycogen synthase kinase 3β (GSK3β), phosphorylated (p)-GSK3β, β-catenin, p-β-catenin, c-myc, cyclin D1, and vimentin decreased; the expression of E-cadherin increased; and the results for over-GPX2 were opposite to those for over-NC. The protein expression of GPX2 decreased, and β-catenin was unchanged in the si-GPX2+ SKL2001 group compared with the si-NC group.

Conclusion

We successfully constructed the PCa RFS prognostic model, obtained RFS-related biomarker GPX2, and found that GPX2 regulated PCa progression and triggered Wnt/β-catenin/EMT pathway molecular changes.

Overexpression of LINC00551 promotes autophagy-dependent ferroptosis of lung adenocarcinoma via upregulating DDIT4 by sponging miR-4328

According to mounting evidence, long noncoding RNAs (lncRNAs) play a vital role in regulated cell death (RCD). A potential strategy for cancer therapy involves triggering ferroptosis, a novel form of RCD. Although it is thought to be an autophagy-dependent process, it is still unclear how the two processes interact. This study characterized a long intergenic noncoding RNA, LINC00551, expressed at a low level in lung adenocarcinoma (LUAD) and some other cancers. Overexpression of LINC00551 suppresses cell viability while promoting autophagy and RSL-3-induced ferroptosis in LUAD cells. LINC00551 acts as a competing endogenous RNA (ceRNA) and binds with miR-4328 which up-regulates the target DNA damage-inducible transcript 4 (DDIT4). DDIT4 inhibits the activity of mTOR, promotes LUAD autophagy, and then promotes the ferroptosis of LUAD cells in an autophagy-dependent manner. This study provided an insight into the molecular mechanism regulating ferroptosis and highlighted LINC00551 as a potential therapeutic target for LUAD.

Prognostic value of lncRNAs related to fatty acid metabolism in lung adenocarcinoma and their correlation with tumor microenvironment based on bioinformatics analysis

Background

As a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques.

Methods

We obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs.

Results

We identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues.

Conclusion

In summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.

Ferroptosis-related gene signature predicts prognosis in kidney renal papillary cell carcinoma

Ferroptosis, an iron-dependent form of selective cell death, is involved in the development of many cancers. However, the role of ferroptosis-related genes (FRGs) in kidney renal papillary cell carcinoma (KIRP) is unclear. In this study, we examined the mRNA expression profiles and clinical data of patients with KIRP from the TCGA cohort. Consequently, 41 differentially-expressed FRGs were screened using the limma package, and 17 prognostic-related FRGs were identified by survival analysis and univariate Cox regression analyses. Thereafter, a ferroptosis-related gene prognostic index (FRGPI) was constructed based on five FRGs (AKR1C3, SAT1, FANCD2, HSBP1 and SQLE), using lasso Cox and multivariate Cox regression analyses. KIRP patients with high FRGPI scores displayed worse outcomes. Furthermore, the FRGPI was shown to be a reliable independent prognostic factor in both the training and testing cohorts. Comprehensive analysis also showed that the FRGPI can distinguish gene mutation, functional enrichment of immune cells and molecular function-related pathways. Interestingly, low FRGPI score could be more benefit from immune checkpoint inhibitors (ICIs) therapy. Then, the two hub prognostic genes (AKR1C3 and FANCD2) as a risk gene for KIRP were identified based on the FRGPI module, and the expression profiles of these two genes were validated using human KIRP cells, besides, we furthermore discovered that Fancd2 is significantly up-regulated in most cancers and is associated with prognosis. In conclusion, these findings showed that FRGPI can accurately predict the prognosis of patients with KIRP, suggesting that this risk model is a promising prognostic biomarker for these patients. Moreover, targeting ferroptosis (FANCD2) could be a potential therapeutic alternative for various cancers.

Identification of Ferroptosis-related molecular model and immune subtypes of hepatocellular carcinoma for individual therapy

BACKGROUND

Excessive iron accumulation and lipid peroxidation are primary characteristics of ferroptosis in hepatocellular carcinoma (HCC). Ferroptosis inducer combined with immunotherapy has become a new hope for HCC patients. Therefore, the construction and validation of subtype-specific sensitivity to ferroptosis inducer will be helpful for hierarchical management and precise individual therapy.

METHODS

RNA-seq transcriptome and clinical data of HCC patients were extracted from International Cancer Genome Consortium (ICGC) dataset and The Cancer Genome Atlas (TCGA) dataset. Consistency matrix and data clustering of the both cohorts were constructed by 'ConsensusClusterPlus' package. Single-sample gene set enrichment analysis (ssGSEA) analysis was performed to evaluate immune infiltration. Cox analysis was utilized to construct a ferroptosis phenotype-related prognostic model (FRPM) in HCC. The predictive efficiency of the constructed FRPM was evaluated through Kaplan Meier (K-M) survival analyses and Receiver Operating Characteristic (ROC) curves. The expression levels of candidate genes were detected and validated by Real-Time PCR between liver cancer tissues and adjacent non-tumor liver tissues.

RESULTS

45 differentially expressed ferroptosis-related genes (FRGs) were identified between HCC tissues and non-tumor liver tissues. Furthermore, four ferroptosis-associated clusters (FACs) of HCC were established via consensus clustering. Subsequently, we established a FRPM, consisting of four prognostic genes (SLC7A11, SLC1A5, GCLM and SAT1), to evaluate the survival of HCC patients, based on which, patients were divided into high-risk group and low-risk group. The high-risk group exhibited worse survival compared to low-risk group (p < 0.0001 both in TCGA and ICGC cohorts). Patients belong to different FACs or different risk scores showed distinct clinical characteristics. Moreover, in the validation experiment, the transcriptional expression levels of the four prognostic genes were consistent with the results drew from datasets.

CONCLUSION

We revealed a novel FRGs signature, which may provide the molecular characteristic data for effectively prognostic evaluation and potential personalized therapy for HCC patients.

Comprehensive Analysis of a Ferroptosis-Related lncRNA Signature for Predicting Prognosis and Immune Landscape in Osteosarcoma

Research on the implications of ferroptosis in tumors has increased rapidly in the last decades. There are evidences that ferroptosis is involved in several aspects of cancer biology, including tumor progression, metastasis, immunomodulation, and therapeutic response. Nonetheless, the interaction between ferroptosis-related lncRNAs (FRLs) and the osteosarcoma immune microenvironment is poorly understood. In this study, a risk model composed of FRLs was developed using univariate and LASSO Cox regression analyses. On the basis of this model, FRL scores were calculated to systematically explore the role of the model in predicting the prognosis and immune characteristics of osteosarcoma patients. Survival analysis showed that osteosarcoma samples with lower FRL-score had better overall survival. After predicting the abundance of immune cells in osteosarcoma microenvironment by single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE analysis, we found that the FRL-score could distinguish immune function, immune score, stromal score, tumor purity, and tumor infiltration of immune cells in different osteosarcoma patients. In addition, FRL-score was also associated with immune checkpoint gene expression and half-maximal inhibitory concentration of chemotherapeutic agents. Finally, we confirmed that knockdown of RPARP-AS1 suppressed the malignant activity of osteosarcoma cells in vitro experiments. In general, the FRL-based prognostic signature could promote our understanding of the immune microenvironment characteristics of osteosarcoma and guide more effective treatment regimens.

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.

In silico identification and verification of ferroptosis-related genes in type 2 diabetic islets

Type 2 diabetes (T2D) is a major global public health burden, with β-cell dysfunction a key component in its pathogenesis. However, the exact pathogenesis of β-cell dysfunction in T2D is yet to be fully elucidated. Ferroptosis, a recently discovered regulated form of non-apoptotic cell death, plays a vital role in the development of diabetes and its complications. The current study aimed to identify the key molecules involved in β-cell ferroptosis3 in patients with T2D using the mRNA expression profile data of GSE25724 by bioinformatic approaches. The differentially expressed mRNAs (DE-mRNAs) in human islets of patients with T2D were screened using the islet mRNA expression profiling data from the Gene Expression Omnibus and their intersection with ferroptosis genes was then obtained. Ferroptosis-related DE-mRNA functional and pathway enrichment analysis in T2D islet were performed. Using a protein-protein interaction (PPI) network constructed from the STRING database, Cytoscape software identified ferroptosis-related hub genes in the T2D islet with a Degree algorithm. We constructed a miRNA-hub gene network using the miRWalk database. We generated a rat model of T2D to assess the expression of hub genes. A total of 1,316 DE-mRNAs were identified in the islet of patients between T2D and non-T2D (NT2D), including 221 and 1,095 up- and down-regulated genes. Gene set enrichment analysis revealed that the ferroptosis-related gene set was significantly different in islets between T2D and NT2D at an overall level. A total of 33 ferroptosis-related DE-mRNAs were identified, most of which were significantly enriched in pathways including ferroptosis. The established PPI network with ferroptosis-related DE-mRNAs identified five hub genes (JUN, NFE2L2, ATG5, KRAS, and HSPA5), and the area under the ROC curve of these five hub genes was 0.929 in the Logistic regression model. We constructed a regulatory network of hub genes and miRNAs, and the results showed that suggesting that hsa-miR-6855-5p, hsa-miR-9985, and hsa-miR-584-5p could regulate most hub genes. In rat model of T2D, the protein expression levels of JUN and NFE2L2 in pancreatic tissues were upregulated and downregulated, respectively. These results contribute to further elucidation of ferroptosis-related molecular mechanisms in the pathogenesis of β-cell dysfunction of T2D.

Ferroptosis in Lung Cancer: From Molecular Mechanisms to Prognostic and Therapeutic Opportunities

Lung cancer is the second commonly diagnosed malignancy worldwide and has the highest mortality rate among all cancers. Tremendous efforts have been made to develop novel strategies against lung cancer; however, the overall survival of patients still is low. Uncovering underlying molecular mechanisms of this disease can open up new horizons for its treatment. Ferroptosis is a newly discovered type of programmed cell death that, in an iron-dependent manner, peroxidizes unsaturated phospholipids and results in the accumulation of radical oxygen species. Subsequent oxidative damage caused by ferroptosis contributes to cell death in tumor cells. Therefore, understanding its molecular mechanisms in lung cancer appears as a promising strategy to induce ferroptosis selectively. According to evidence published up to now, significant numbers of research have been done to identify ferroptosis regulators in lung cancer. Therefore, this review aims to provide a comprehensive standpoint of molecular mechanisms of ferroptosis in lung cancer and address these molecules’ prognostic and therapeutic values, hoping that the road for future studies in this field will be paved more efficiently.

Pre-Clinical Insights into the Iron and Breast Cancer Hypothesis

Population studies, systematic reviews, and meta-analyses have revealed no relationship between iron status and breast cancer, a weak positive association, or a small protective effect of low iron status. However, in those studies, the authors concluded that further investigation was merited. The set of experiments reported here used preclinical models to assess the likely value of further investigation. The effects of iron status on the initiation and promotion stage of mammary carcinogenesis are reported. Using the classical model of cancer initiation in the mammary gland, 7,12 dimethyl-benz[α]anthracene-induced carcinogenesis was unaffected by iron status. Similarly, excess iron intake showed no effect on the promotion stage of 1-methyl-1-nitrosurea-induced mammary carcinogenesis, though iron deficiency exerted a specific inhibitory effect on the carcinogenic process. Though iron-mediated cellular oxidation is frequently cited as a potential mechanism for effects on breast cancer, no evidence of increased oxidative damage to DNA attributable to excess iron intake was found. The reported preclinical data fail to provide convincing evidence that the further evaluation of the iron–breast cancer risk hypotheses is warranted and underscore the value of redefining the referent group in population-based studies of iron–cancer hypotheses in other tissues.