Identification and validation of a ferroptosis-related gene signature predictive of prognosis in breast cancer

Q, Liu; Ma, Jianying; Wu, Zeyu

doi:10.18632/aging.203472

Cited by 14 publications

(8 citation statements)

References 32 publications

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“…Recent studies have used bioinformatics methods to identify genes related to iron death. These studies can help identify potential disease-related key genes [ 16 ], predict disease prognosis models [ 17 ], discover potential biomarkers and therapeutic targets [ 18 ], and provide new strategies for individualized treatment. Liang et al found that most genes related to iron death are differentially expressed between liver cancer and adjacent normal tissues, and iron death-related gene markers can be used to predict the prognosis of liver cancer [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis of genes related to iron death in diabetic nephropathy through network and pathway levels based approaches

Liu

et al. 2021

PLoS ONE

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Diabetic nephropathy is one of the common microvascular complications of diabetes. Iron death is a recently reported way of cell death. To explore the effects of iron death on diabetic nephropathy, iron death score of diabetic nephropathy was analyzed based on the network and pathway levels. Furthermore, markers related to iron death were screened. Using RNA-seq data of diabetic nephropathy, samples were clustered uniformly and the disease was classified. Differentially expressed gene analysis was conducted on the typed disease samples, and the WGCNA algorithm was used to obtain key modules. String database was used to perform protein interaction analysis on key module genes for the selection of Hub genes. Moreover, principal component analysis method was applied to get transcription factors and non-coding genes, which interact with the Hub gene. All samples can be divided into two categories and principal component analysis shows that the two categories are significantly different. Hub genes (FPR3, C3AR1, CD14, ITGB2, RAC2 and ITGAM) related to iron death in diabetic nephropathy were obtained through gene expression differential analysis between different subtypes. Non-coding genes that interact with Hub genes, including hsa-miR-572, hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-208a-3p, hsa-miR-153-3p and hsa-miR-29c-3p, may be related to diabetic nephropathy. Transcription factors HIF1α, KLF4, KLF5, RUNX1, SP1, VDR and WT1 may be related to diabetic nephropathy. The above factors and Hub genes are collectively involved in the occurrence and development of diabetic nephropathy, which can be further studied in the future. Moreover, these factors and genes may be potential target for therapeutic drugs.

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Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis of genes related to iron death in diabetic nephropathy through network and pathway levels based approaches

Liu

et al. 2021

PLoS ONE

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“…FRGs-based signatures had been identified with prognostic values in colon cancer, [ 51 ] lung adenocarcinoma, [ 52 , 53 ] gastric cancer, [ 54 ] breast cancer, [ 55 ] and bladder cancer. [ 21 , 56 ] And nomograms built by FRGs for predicting survival probabilities had already been used in lung adenocarcinoma and oral squamous cell carcinoma patients.…”

Section: Discussionmentioning

confidence: 99%

A five ferroptosis-related genes risk score for prognostic prediction of osteosarcoma

Song

2022

Medicine

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Background: Osteosarcoma (OS) is the most common bone cancer in adolescents, and has a high propensity to metastasize. Ferroptosis is a unique modality of cell death, driving the metastasis of cancer cells. Identifying ferroptosis-related genes (FRGs) as prognostic factors will be critical to predict the outcomes of OS. This study aimed to explore the prognostic value of FRGs in OS and build a prognostic model to indirectly improve OS patients’ outcomes. Methods: OS data were downloaded from the TARGET database and 2 Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess FRGs. A risk score model basing on 5 FRGs was constructed via LASSO-Cox regression. Multivariate Cox regression analysis was used to determine the independent prognostic factors. The Nomogram model was built using independent prognostic factors. The relationship between the risk score and the immune cell infiltration was estimated by CIBERSORT, and the correlation between the risk score and immune checkpoints was also analyzed. Results: Based on the prognosis-related FRGs, we built a regression model: Risk score = (−0.01382853 × ACSL4) − (0.05371778 × HMOX1) − (0.02434655 × GPX4) − (0.16432810 × PRNP) − (0.15567120 × ATG7). OS patients with high risk score tended to suffer from poor prognosis, validated in 2 Gene Expression Omnibus datasets. The Nomogram model showed the combination of the risk score and the tumour-node-metastasis stage improved predictive effectiveness. The risk score was also related to immune cell infiltration and immune checkpoint expression. Conclusion: The risk score model based on 5 FRGs was a reliable prognostic predictive indicator for OS patients.

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“…On the basis of the outcome of the independent prognosis analysis, we used the independent prognostic factors to plot the nomogram [ 28 ]. The Harrell' concordance index (C-index) and the calibration curve about the patients' long-term survival probability (3- and 5-year probability) examined the accuracy and divergence of the nomogram.…”

Section: Methodsmentioning

confidence: 99%

The Prognostic and Molecular Landscape of Autophagy-Related Long Noncoding RNA in Colorectal Cancer

Sun

Cao

Guo

et al. 2022

BioMed Research International

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Current evidence suggests that autophagy is closely correlated with the pathogenesis and development of malignant tumors. This study is aimed at assessing the potential prognostic significance of autophagy-related long noncoding RNA (ARlncRNA) in colorectal cancer (CRC). 3145 ARlncRNAs were obtained from autophagy-related genes (ARGs) by Pearson correlation analysis, and we established a competing endogenous RNA (ceRNA) network mediated by ARlncRNAs. A novel six-ARlncRNA prognostic signature was constructed based on TCGA samples used as the training group. Kaplan–Meier survival analysis and independent prognosis analysis were performed on the internal (training and test groups) and external validations (GEO datasets) to assess the accuracy and clinical practicability. Moreover, the nomogram combining the two independent prognostic factors (age and ARlncRNA-risk score (ARlncRNA-RS)) intuitively displayed overall survival. Gene set enrichment analysis (GSEA) conducted on the prognostic signature revealed that the gene set of the high-risk group was significantly enriched in the hallmark gene set “hypoxia” and the gene set of the low-risk group was enriched in KEGG pathways, including “peroxisome,” “the citrate cycle (TCA cycle),” and “other glycan degradation.” Assessment of antineoplastic therapy susceptibility and microsatellite instability (MSI) analysis were performed on CRC samples based on the prognostic signature. Moreover, Spearman correlation analysis was conducted on the expression of six ARlncRNAs of the prognostic signature and cancer stem cell (CSC) index as well as the tumor microenvironment (TME). In conclusion, this study established a six-ARlncRNA prognostic signature, which yielded favorable prognostic significance and demonstrated the correlation between ARlncRNAs and CRC progression.

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Identification and validation of a ferroptosis-related gene signature predictive of prognosis in breast cancer

Cited by 14 publications

References 32 publications

Bioinformatics analysis of genes related to iron death in diabetic nephropathy through network and pathway levels based approaches

Bioinformatics analysis of genes related to iron death in diabetic nephropathy through network and pathway levels based approaches

A five ferroptosis-related genes risk score for prognostic prediction of osteosarcoma

The Prognostic and Molecular Landscape of Autophagy-Related Long Noncoding RNA in Colorectal Cancer

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