The lncRNA Signatures of Genome Instability to Predict Survival in Patients with Renal Cancer

Huang, Liang; Xie, Yu; Jiang, Sanyuan; Wang, Han; Zeng, Fanchang; Li, Daoyuan

doi:10.1155/2021/1090698

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The reason for the high malignancy and drug resistance observed in glioma has been found to be that these tumors can effectively evade ferroptosis. Currently, many studies on glioma have focused on the relationship between lncRNAs and ferroptosis ( Deng, et al, 2020 ; Huang et al, 2021a ). The identification of FRLs is essential for finding potential therapeutic targets.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma

Huang

Zhang²,

Gong³

et al. 2022

Front. Genet.

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Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma

Huang

Zhang²,

Gong³

et al. 2022

Front. Genet.

Self Cite

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“…lncRNA can mediate gene regulation by interacting with DNA, RNA or protein, and its mechanism involves transcriptional regulation and post-transcriptional regulation that affect the activity of genes encoding proteins. It can directly combine with some proteins to form nucleic acid protein complexes, which plays a regulatory role by affecting the localization of proteins in cells, and it involves multiple pathways closely related to cancer development and progression, such as p53, NF-κB, PI3K/AKT and Notch ( Huang et al, 2021 ; Baumann, 2022 ; Reggiardo et al, 2022 ). Nevertheless, there are insufficient researches on the regulation of lipid metabolism by lncRNA in renal cancer.…”

Section: Introductionmentioning

confidence: 99%

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Liu

Lai

et al. 2023

Front. Genet.

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Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism.Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed.Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model.Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

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“…Recent studies have shown that long non-coding RNAs (lncRNAs) are aberrantly expressed in various cancers and are involved in regulating genomic instability in cancer cells (12)(13)(14). LncRNAs are transcripts greater than 200 nt in length that do not encode proteins (15) and were considered to have no biological function (16).…”

Section: Introductionmentioning

confidence: 99%

Long non‑coding RNAs interact with RNA‑binding proteins to regulate genomic instability in cancer cells (Review)

2022

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Genomic instability, a feature of most cancers, contributes to malignant cell transformation and cancer progression due to the accumulation of genetic alterations. Genomic instability is reflected at numerous levels, from single nucleotide to the chromosome levels. However, the exact molecular mechanisms and regulators of genomic instability in cancer remain unclear. Growing evidence indicates that the binding of long non-coding RNAs (lncRNAs) to protein chaperones confers a variety of regulatory functions, including managing of genomic instability. The aim of the present review was to examine the roles of mitosis, telomeres, DNA repair, and epigenetics in genomic instability, and the mechanisms by which lncRNAs regulate them by binding proteins in cancer cells. This review contributes to our understanding of the role of lncRNAs and genomic instability in cancer and can potentially provide entry points and molecular targets for cancer therapies. Contents1. Introduction 2. LncRNAs affect chromosome instability via RBPs 3. LncRNAs are involved in DNA repair through RBPs 4. LncRNAs regulate other epigenetic modalities through RBPs 5.

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The lncRNA Signatures of Genome Instability to Predict Survival in Patients with Renal Cancer

Cited by 5 publications

References 20 publications

Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma

Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Long non‑coding RNAs interact with RNA‑binding proteins to regulate genomic instability in cancer cells (Review)

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