BackgroundFerroptosis is a recently recognized type of programmed cell death that is involved in the biological processes of various cancers. However, the mechanism of ferroptosis in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the role of ferroptosis-associated long non-coding RNAs (lncRNAs) in LUAD and to establish a prognostic model.MethodsWe downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. We randomly divided the data into training and validation sets. Ferroptosis-associated lncRNA signatures with the lowest Akaike information criteria were determined using COX regression analysis and the least absolute shrinkage and selection operator. The risk scores of ferroptosis-related lncRNAs were calculated, and patients with LUAD were assigned to high- and low-risk groups based on the median risk score. The prognostic value of the risk scores was evaluated using Kaplan–Meier curves, Cox regression analyses, and nomograms. We then explored relationships between ferroptosis-related lncRNAs and the immune response using gene set enrichment analysis (GSEA).ResultsTen ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan–Meier and Cox regression analyses confirmed that the risk scores were independent predictors of LUAD outcome in the training and validation sets (all P < 0.05). The area under the curve confirmed that the signatures could determine the prognosis of LUAD. The predictive accuracy of the established nomogram model was verified using the concordance index and calibration curve. The GSEA showed that the 10 ferroptosis-related lncRNAs might be associated with tumor immune response.ConclusionWe established a novel signature involving 10 ferroptosis-related lncRNAs (LINC01843, MIR193BHG, AC091185.1, AC027031.2, AL021707.2, AL031667.3, AL606834.1, AC026355.1, AC124045.1, and AC025048.4) that can accurately predict the outcome of LUAD and are associated with the immune response. This will provide new insights into the development of new therapies for LUAD.

Cysteine metabolism plays a critical role in cancer cell survival. Cysteine depletion was reported to inhibit tumor growth and induce pancreatic cancer cell ferroptosis. Nevertheless, the effect of cysteine depletion in chronic myeloid leukemia (CML) remains to be explored. In this work, we showed that cysteine depletion can induce K562/G01 but not K562 cell death in the form of ferroptosis. However, the glutathione (GSH)/glutathione peroxidase 4 (GPX4) pathways of the two CML cell lines were both blocked after cysteine depletion. This unexpected outcome guided us to perform RNA-Seq to screen the key genes that affect the sensitivity of CML cells to cysteine depletion. Excitingly, thioredoxin reductase 1 (TXNRD1), which related to cell redox metabolism, was significantly upregulated in K562/G01 cells after cysteine depletion. We further inferred that the upregulation is negatively feedback by the enzyme activity decrease of TXNRD1. Then, we triggered the ferroptosis by applying TXNRD1 shRNA and TXNRD1 inhibitor auranofin in K562 cells after cysteine depletion. In summary, we have reason to believe that TXNRD1 is a key regulator involved in the ferroptosis of CML cells induced by cysteine depletion in vitro. These findings highlight that cysteine depletion serves as a potential therapeutic strategy for overcoming chemotherapy resistance CML.

Background Ferroptosis is a newly discovered type of programmed cell death that participates in the biological processes of various cancers. However, the mechanism by which ferroptosis modulates acute myeloid leukemia (AML) remains unclear. This study aimed to investigate the role of ferroptosis-related long non-coding RNAs (lncRNAs) in AML and establish a corresponding prognostic model. Methods RNA-sequencing data and clinicopathological characteristics were obtained from The Cancer Genome Atlas database, and ferroptosis-related genes were obtained from the FerrDb database. The “limma” R package, Cox regression, and the least absolute shrinkage and selection operator were used to determine the ferroptosis-related lncRNA signature with the lowest Akaike information criteria (AIC). The risk score of ferroptosis-related lncRNAs was calculated and patients with AML were divided into high- and low-risk groups based on the median risk score. The Kaplan–Meier curve and Cox regression were used to evaluate the prognostic value of the risk score. Finally, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological functions of the ferroptosis-related lncRNAs. Results Seven ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan–Meier and Cox regression analyses confirmed that risk scores were independent prognostic predictors of AML in both the training and validation groups (All P < 0.05). In addition, the area under the curve (AUC) analysis confirmed that the signatures had a good predictive ability for the prognosis of AML. GSEA and ssGSEA showed that the seven ferroptosis-related lncRNAs were related to glutathione metabolism and tumor immunity. Conclusions In this study, seven novel ferroptosis-related lncRNA signatures (AP001266.2, AC133961.1, AF064858.3, AC007383.2, AC008906.1, AC026771.1, and KIF26B-AS1) were established. These signatures were shown to accurately predict the prognosis of AML, which would provide new insights into strategies for the development of new AML therapies.

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Background: We retrospectively analyzed the prognostic value of the albumin-tofibrinogen ratio (AFR)-neutrophil-to-lymphocyte ratio (NLR) score, comprising preoperative AFR and NLR, in esophageal squamous cell carcinoma (ESCC) patients after radical resection. Patients and Methods: Overall, 215 patients were included. The optimal cutoff value was determined using the receiver operating characteristic (ROC) curve. Based on a low AFR (<12.06) and high NLR (≥1.78), the AFR-NLR score was classified as 2 (both hematological abnormalities present), 1 (one abnormality present), or 0 (both abnormalities absent). Kaplan-Meier curves, Cox regression, and predicted nomogram were used to evaluate the prognostic value of the score. Results: The prognostic value of the AFR-NLR score was better than that of AFR or NLR alone (P <0.05). Multivariate analysis showed that a high AFR-NLR score was an independent predictor of poor prognosis for overall survival (P <0.001). Additionally, in the nomogram including the AFR-NLR score, the net reclassification improvement index increased by 35.5% (P <0.001), and the integrated discrimination improvement index increased by 9.0% (P <0.001). The predictive accuracy of the established nomogram model was proved using Harrell's concordance index (0.811, 95% confidence interval: 0.765-0.856) and calibration curve. Notably, the decision analysis curve showed that the nomogram had a higher net benefit within most of the threshold probability range, indicating better clinical applicability. Conclusion: The AFR-NLR score is a useful predictor of the prognosis of ESCC patients after radical resection, and the nomogram established on the basis of this score has a good prognostic value.