A Novel Defined Risk Signature of the Ferroptosis-Related Genes for Predicting the Prognosis of Ovarian Cancer

Yang, Ying; Dai, Qinjin; Li, Shuhong; He, Jie; Qi, Hongbo

doi:10.3389/fmolb.2021.645845

Cited by 32 publications

(32 citation statements)

References 41 publications

(25 reference statements)

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“…Previous reports indicated that increased abundance of NK cells can bring better immunotherapy efficacy [ 52 ], further suggesting the low-risk GC patients have a better response to immunotherapy. In fact, the relationship between ferroptosis and cancer immunotherapy has been reported in 2019 [ 50 , 53 ], showing the sensitivity of tumor cells to ferroptosis is parallel to immune functions, which may be the reasonable explanation for the better response to immunotherapy in low-risk patients. But the exact mechanisms of how these 10 ferroptosis-related genes interact to affect tumorigenesis and immune process are still unclear and further studies are demanded.…”

Section: Discussionmentioning

confidence: 99%

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

Shao

Jia

et al. 2021

Oxidative Medicine and Cellular Longevity

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

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Section: Discussionmentioning

confidence: 99%

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

Shao

Jia

et al. 2021

Oxidative Medicine and Cellular Longevity

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“…Ferroptosis is a unique form of regulated cell death associated with iron metabolism, which is different from apoptosis, necrosis, and autophagy (3). Although the detailed mechanism underlying the role of ferroptosis in tumors is still unclear, several studies have reported that ferroptosis is involved in various cancers, including breast cancer (4)(5)(6), pancreatic cancer (7), ovarian cancer (8), and HCC (9)(10)(11)(12). Compared to normal nontumor cells, cancer cells have a higher level of iron, which indicates the potential of ferroptosis inducers in new antitumor strategies (13,14).…”

Section: Introductionmentioning

confidence: 99%

Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma

et al. 2021

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BackgroundFerroptosis is a novel form of regulated cell death involved in tumor progression. The role of ferroptosis-related lncRNAs in hepatocellular carcinoma (HCC) remains unclear.MethodsRNA-seq and clinical data for HCC patients were downloaded from The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal. Bioinformatics methods, including weighted gene coexpression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) analysis, were used to identify signature markers for diagnosis/prognosis. The tumor microenvironment, immune infiltration and functional enrichment were compared between the low-risk and high-risk groups. Subsequently, small molecule drugs targeting ferroptosis-related signature components were predicted via the L1000FWD and PubChem databases.ResultsThe prognostic model consisted of 2 ferroptosis-related mRNAs (SLC1A5 and SLC7A11) and 8 ferroptosis-related lncRNAs (AC245297.3, MYLK-AS1, NRAV, SREBF2-AS1, AL031985.3, ZFPM2-AS1, AC015908.3, MSC-AS1). The areas under the curves (AUCs) were 0.830 and 0.806 in the training and test groups, respectively. Decision curve analysis (DCA) revealed that the ferroptosis-related signature performed better than all pathological characteristics. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor. The survival probability of low- and high-risk patients could be clearly distinguished by the principal component analysis (PCA) plot. The risk score divided HCC patients into two distinct groups in terms of immune status, especially checkpoint gene expression, which was further supported by the Gene Ontology (GO) biological process, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, several small molecule drugs (SIB-1893, geldanamycin and PD-184352, etc) targeting ferroptosis-related signature components were identified for future reference.ConclusionWe constructed a new ferroptosis-related mRNA/lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

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“…Then we screened out hub genes in a co-expression network and further identified survival-associated genes among them by using GEPIA (Gene Expression Profiling Interactive Analysis) ( Tang et al, 2017 ). Considering that Ridge, Elastic Net, and Lasso (least absolute shrinkage and selection operator) methods were widely used data mining methods especially for regression model construction ( Ye et al, 2021 ), based on the survival-associated genes, all the three methods were included for constructing multiple-gene signatures by using the data from TCGA database. Two prognostic models were constructed, which could significantly predict the OS (overall survival) of patients with ACC.…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Yan

Guo

et al. 2021

Front. Cell Dev. Biol.

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Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

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A Novel Defined Risk Signature of the Ferroptosis-Related Genes for Predicting the Prognosis of Ovarian Cancer

Cited by 32 publications

References 41 publications

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

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

Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

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