Necroptosis-related lncRNAs: establishment of a gene module and distinction between the cold and hot tumors in glioma

Cao, Kangxi; Su, Fengbo; Shan, Xuchun; Jiang, Xingyu; Ni, Zhaohui; Chen, Yan

doi:10.3389/fonc.2023.1087117

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…3 ). Our finding was consistent with the studies of Ouyang et al (2022) and Cao et al (2023) , suggesting that LYRM4-AS1 was a high-risk lncRNA in survival for glioma patients.…”

Section: Discussionsupporting

confidence: 92%

High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration

Wang,

Xie,

et al. 2023

PeerJ

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Background Many researches proved that non-coding RNAs are important in glioma development. We screened the differentially expressed genes through The Cancer Genome Atlas (TCGA) database and identified the molecule LYRM4-AS1 associated with prognosis. As a lncRNA, the expression level and role of LYRM4-AS1 in glioma are inconclusive. Therefore, we attempted to assess the clinical significance, expression and related mechanisms of LYRM4-AS1 in glioma by employing cell experiments and an integrative in silico methodology. Methods RNA-seq data were obtained from UCSC XENA and TCGA datasets. The Gene Expression Omnibus (GEO) database was used to download glioma-related expression profile data. The LYRM4-AS1 expression level was evaluated. Survival curves were constructed by the Kaplan–Meier method. Cox regression analysis was used to analyze independent variables. Patients were divided into high and low expression group base on the median LYRM4-AS1 expression value in glioma tissues. The DESeq2 R package was used to identify differentially expressed genes (DEGs) between two different expression LYRM4-AS1 groups. Gene set enrichment analysis (GSEA) was conducted. Next, the single-sample Gene Set Enrichment Analysis (ssGSEA) was done to quantify the immune infiltration of immune cells in glioma tissues. Gene expression profiles for glioma tumor tissues were used to quantify the relative enrichment score for each immune cell. Spearman correlation analysis was used to analyze the correlation between LYRM4-AS1 and biomarkers of immune cells as well as immune checkpoints in glioma. Finally, assays for cell apoptosis, cell viability and wound healing were conducted to evaluate the function on U87 MG and U251 cells after knocking down LYRM4-AS1. Results We found that LYRM4-AS1 was upregulated and related to the grade and malignancy of glioma. Survival analyses showed that high expression LYRM4-AS1 patients had poor clinical outcomes (P < 0.01). Cox regression analyses demonstrated that LYRM4-AS1 was an independent risk factor for overall survival (OS) in glioma (HR: 274 1.836; CI [1.278–2.639]; P = 0.001). Enrichment and immune infiltration analysis showed interferon signaling and cytokine-cytokine receptor interaction enriched in the LYRM4-AS1 high-expression phenotype, and LYRM4-AS1 showed significantly positively related to immune infiltration as well as immune checkpoints (P < 0.01). The knockdown of LYRM4-AS1 in U87 MG and U251 cells can inhibit migration and proliferation of cells (P < 0.05). Conclusions These findings indicated that the increased LYRM4-AS1 may be useful for the diagnosis and prognosis of glioma and might participate in the immune infiltration.

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“…3 ). Our finding was consistent with the studies of Ouyang et al (2022) and Cao et al (2023) , suggesting that LYRM4-AS1 was a high-risk lncRNA in survival for glioma patients.…”

Section: Discussionsupporting

confidence: 92%

High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration

Wang,

Xie,

et al. 2023

PeerJ

View full text Add to dashboard Cite

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“…[29,41] Therefore, patients were divided into 2 clusters according to the expressions of these lncRNAs. [30] In this study, ConsensusClusterPlus method was adopted to classify patients, which is different from previous studies on glioma [42] and ovarian cancer. [33] The main reason is that, ConsensusClusterPlus enables quick and accurate visualization of cluster boundaries, which are not labeled in ConsensusCluster and ConsensusClusterPlus extends CC algorithm.…”

Section: Discussionmentioning

confidence: 99%

Necrotic related-lncRNAs: Prediction of prognosis and differentiation between cold and hot tumors in head and neck squamous cell carcinoma

Shi,

Zhang,

Zuo

et al. 2023

Medicine

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Treatment of head and neck squamous cell carcinoma (HNSCC) is a substantial clinical challenge due to the high local recurrence rate and chemotherapeutic resistance. This project seeks to identify new potential biomarkers of prognosis prediction and precision medicine to improve this condition. A synthetic data matrix for RNA transcriptome datasets and relevant clinical information on HNSCC and normal tissues was downloaded from the Genotypic Tissue Expression Project and The Cancer Genome Atlas (TCGA). The necrosis-associated long-chain noncoding RNAs (lncRNAs) were identified by Pearson correlation analysis. Then 8-necrotic-lncRNA models in the training, testing and entire sets were established through univariate Cox (uni-Cox) regression and Lasso-Cox regression. Finally, the prognostic ability of the 8-necrotic-lncRNA model was evaluated via survival analysis, nomogram, Cox regression, clinicopathological correlation analysis, and receiver operating characteristic (ROC) curve. Gene enrichment analysis, principal component analysis, immune analysis and prediction of risk group semi-maximum inhibitory concentration (IC50) were also conducted. Correlations between characteristic risk score and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anti-cancer drug sensitivity were analyzed. Eight necrosis-associated lncRNAs (AC099850.3, AC243829.2, AL139095.4, SAP30L-AS1, C5orf66-AS1, LIN02084, LIN00996, MIR4435-2HG) were developed to improve the prognosis prediction of HNSCC patients. The risk score distribution, survival status, survival time, and relevant expression standards of these lncRNAs were compared between low- and high-risk groups in the training, testing and entire sets. Kaplan–Meier analysis showed the low-risk patients had significantly better prognosis. The ROC curves revealed the model had an acceptable predictive value in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 8 necrosis-associated lncRNAs were risk factors independent of various clinical parameters. We recombined the patients into 2 clusters through Consensus ClusterPlus R package according to the expressions of necrotic lncRNAs. Significant differences were found in immune cell infiltration, immune checkpoint molecules, and IC50 between clusters, suggesting these characteristics can be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. This risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.

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The fate and function of non-coding RNAs during necroptosis

Bozgeyik,

Elek,

Gocer

et al. 2024

Epigenomics

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Necroptosis-related lncRNAs: establishment of a gene module and distinction between the cold and hot tumors in glioma

Cited by 3 publications

References 57 publications

High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration

High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration

Necrotic related-lncRNAs: Prediction of prognosis and differentiation between cold and hot tumors in head and neck squamous cell carcinoma

The fate and function of non-coding RNAs during necroptosis

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