Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients.
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ABSTRACT: Background
Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC).Methods
Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established.Results
An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients.Conclusions
The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.
SUBMITTER: Liu G
PROVIDER: S-EPMC8274920 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background5-methylcytosine (m5C) is a major site of RNA methylation modification, and its abnormal modification is associated with the development of gastric cancer (GC). This study aimed to explore the value of m5C-related genes on the prognosis of GC patients through bioinformatics.MethodsFirst, m5C-related genes were obtained by nonnegative matrix factorization (NMF) analysis and differentially expressed analysis. The m5C-related model was established and validated in distinct datasets. Moreover, a differential analysis of risk scores according to clinical characteristics was performed. The enrichment analysis was carried out to elucidate the underlying molecular mechanisms. Furthermore, we calculated the differences in immunotherapy and chemotherapy sensitivity between the high- and low-risk groups. Finally, we validated the expression levels of identified model genes by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of five m5C-related subtypes of GC patients in the TCGA database were identified. The m5C-related model was constructed based on APOD, ASCL2, MFAP2, and CREB3L3. Functional enrichment revealed that the m5C-related model might involve in the cell cycle and cell adhesion. Moreover, the high-risk group had a higher abundance of stromal and immune cells in malignant tumor tissues and a lower tumor purity than the low-risk group. The patients in the high-risk group were more sensitive to chemotherapy and had better sensitivity to CTLA4 inhibitors. Furthermore, qRT-PCR results from our specimens verified an over-expression of ASCL2, CREB3L3, and MFAP2 in the cancer cells compared with the normal cells.ConclusionA total of five GC subtypes were identified, and a risk model was constructed based on m5C modification.
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Project description:Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.
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Project description:BackgroundGastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.MethodsFerroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.ResultsWe constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410-2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.ConclusionsThis risk model was shown to be a novel method for predicting prognosis for GC patients.
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