Prognostic Model Construction and Immune Microenvironment Analysis of Breast Cancer Based on Ferroptosis-Related lncRNAs

Cong, Jing; Yang, Fusheng; Ruining, Li

doi:10.2147/ijgm.s342783

Cited by 8 publications

(12 citation statements)

References 49 publications

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“…Type_II_IFN_Reponse, HLA and T_cell_co-stimulation are a group of low-risk pathways. Such findings were also found in the breast cancer model with iron droop-associated lncRNAs (69). In the tumor mutation burden analysis, we found no statistically significant TMB between the high-risk and low-risk subgroups.…”

Section: Discussionsupporting

confidence: 79%

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

Gao

et al. 2022

Front. Oncol.

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Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.

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Section: Discussionsupporting

confidence: 79%

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

Gao

et al. 2022

Front. Oncol.

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“…We also listed the other signatures that focus more on short-term (3- and 5-year survival) survival. We found our signature usually have better short-term survival prognostic value compared with them, such as the necroptosis-related lncRNA signature ( Zhang et al, 2022 ), hypoxia-related lncRNA signature ( Zhao et al, 2021 ), autophagy-related lncRNA signature ( Li et al, 2021 ), ferroptosis-related lncRNA signature ( Jia et al, 2021 ), N6-methyladenosine-related lncRNA signature ( Lv et al, 2021 ), acid metabolism-related lncRNA signature ( Dai et al, 2022 ), and CD4 + conventional T cells-related lncRNA signature ( Ning et al, 2022 ) and pyroptosis-related lncRNA signature ( Yang et al, 2021 ). In addition, our model only involves 7 lncRNAs, while other models (6/11) tend to have more, which is more convenient to use to a certain extent.…”

Section: Resultsmentioning

confidence: 96%

A lactate-related LncRNA model for predicting prognosis, immune landscape and therapeutic response in breast cancer

Jia

Zhang²,

Li³

et al. 2022

Front. Genet.

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Breast cancer (BC) has the highest incidence rate of all cancers globally, with high heterogeneity. Increasing evidence shows that lactate and long non-coding RNA (lncRNA) play a critical role in tumor occurrence, maintenance, therapeutic response, and immune microenvironment. We aimed to construct a lactate-related lncRNAs prognostic signature (LRLPS) for BC patients to predict prognosis, tumor microenvironment, and treatment responses. The BC data download from the Cancer Genome Atlas (TCGA) database was the entire cohort, and it was randomly assigned to the training and test cohorts at a 1:1 ratio. Difference analysis and Pearson correlation analysis identified 196 differentially expressed lactate-related lncRNAs (LRLs). The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to construct the LRLPS, which consisted of 7 LRLs. Patients could be assigned into high-risk and low-risk groups based on the medium-risk sore in the training cohort. Then, we performed the Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. The results indicated that the prognosis prediction ability of the LRLPS was excellent, robust, and independent. Furthermore, a nomogram was constructed based on the LRLPS risk score and clinical factors to predict the 3-, 5-, and 10-year survival probability. The GO/KEGG and GSEA indicated that immune-related pathways differed between the two-risk group. CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS) showed that low-risk patients had higher levels of immune infiltration and better immunotherapeutic response. The pRRophetic and CellMiner databases indicated that many common chemotherapeutic drugs were more effective for low-risk patients. In conclusion, we developed a novel LRLPS for BC that could predict the prognosis, immune landscape, and treatment response.

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“…Latest research has con rmed that FRLs can affect the occurrence and development of various cancers [9][10][11][12][13]. Relevant regulatory patterns have also been found in lung cancer [31].…”

Section: Discussionmentioning

confidence: 99%

“…Ferroptosis-related genes (FRGs) are related genes that might promote, prevent or prompt the occurrence of ferroptosis. Relevant studies have noted the potential role of FRGs in the occurrence and development of various cancers, but its speci c mechanism is still unclear [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Prognostic Model Establishment and Immune Microenvironment Analysis of Lung Adenocarcinoma Based on Ferroptosis-Related Long Noncoding RNAs

Yuan

Jiang

Wang

et al. 2023

Preprint

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Background: Lung cancer is a common malignant tumor, which is divided into many subtypes. Lung adenocarcinoma (LUAD) is a most common subtype. More and more studies have confirmed that ferroptosis is involved in the occurrence and development of lung cancer. In this paper, we studied the prognostic ferroptosis-related long noncoding RNAs (FRLs) to build a LUAD-related prognosis model. Methods: We first downloaded the relevant data of 598 patients from the TCGA-LUAD dataset of The Cancer Genome Atlas, and then randomly divided them into training group and testing group in a 1:1 ratio. After that, we used Pearson correlation analysis and univariate Cox regression analysis to determine the FRLs related to prognosis. Then, according to the least absolute shrinkage and selection operator (LASSO) algorithm, the risk model was constructed using the optimized prognostic FRLs subset. We further used the receiver operating characteristic (ROC) curve and survival analysis to evaluate the performance of our model, meanwhile, Cox regression analysis was performed to analyze the risk score (RS). Finally, we also carried out gene set enrichment analysis (GSEA) , and differential analysis of immune-related genes and m6a-related genes. Results: In this study, we identified a total of 34 FRLs associated with the prognosis of lung adenocarcinoma, and established a prognostic model with 7 of them. Kaplan-Meier analysis showed that relevant characteristics of patients in high-risk group were correlated with poorer prognosis. The AUC value of our model was quite ideal, indicating that it could accurately predict the prognosis of LUAD patients. Further GSEA results showed that FRLs of individuals in high-risk groups were mainly enriched in cell cycle and related regulatory pathways, while those in low-risk groups were mainly enriched in immune-related pathways. We also employed immune function analysis and immune checkpoints expression analysis, and found that CCR, check-point, HLA, T cell co−inhibition, T cell co−stimulation and Type II IFN Reponse had significant differences between two groups, while most immune checkpoints had higher expression levels in low-risk groups. Conclusion: Our research has proved that FRls could indeed be used as a prognostic feature to build a prognostic model of lung adenocarcinoma. On the basis of this theory, it is of great significance and value to further study new treatment methods.

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Prognostic Model Construction and Immune Microenvironment Analysis of Breast Cancer Based on Ferroptosis-Related lncRNAs

Cited by 8 publications

References 49 publications

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

A lactate-related LncRNA model for predicting prognosis, immune landscape and therapeutic response in breast cancer

Prognostic Model Establishment and Immune Microenvironment Analysis of Lung Adenocarcinoma Based on Ferroptosis-Related Long Noncoding RNAs

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