Dataset Information

Construction and Validation of a Necroptosis-Related lncRNA Signature in Prognosis and Immune Microenvironment for Glioma.

ABSTRACT:

Background

Glioma is the most common primary brain tumor, representing approximately 80.8% of malignant tumors. Necroptosis triggers and enhances antitumor immunity and is expected to be a new target for tumor immunotherapy. The effectiveness of necroptosis-related lncRNAs as potential therapeutic targets for glioma has not been elucidated.

Methods

We acquired RNA-seq data sets from LGG and GBM samples, and the corresponding clinical characteristic information is from TCGA. Normal brain tissue data is from GTEX. Based on TCGA and GTEx, we used univariate Cox regression to sort out survival-related lncRNAs. Lasso regression models were then built. Then, we performed a separate Kaplan-Meier analysis of the lncRNAs used for modeling. We validated different risk groups via OS, DFS, enrichment analysis, comprehensive immune analysis, and drug sensitivity.

Results

We constructed a 12 prognostic lncRNAs model after bioinformatic analysis. Subsequently, the risk score of every glioma patient was calculated based on correlation coefficients and expression levels, and the patients were split into low- and high-risk groups according to the median value of the risk score. A nomogram was established for every glioma patient to predict prognosis. Besides, we found significant differences in OS, DFS, immune infiltration and checkpoints, and immune therapy between different risk subgroups.

Conclusion

Predictive models of 12 necroptosis-related lncRNAs can facilitate the assessment of the prognosis and molecular characteristics of glioma patients and improve treatment modalities.

SUBMITTER: Jiang F

PROVIDER: S-EPMC9440826 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Construction and Validation of a Necroptosis-Related lncRNA Signature in Prognosis and Immune Microenvironment for Glioma.

Jiang Fan F Zhan Zheng Z Yang Yanbo Y Liu Guangjie G Liu Song S Gu Jingyu J Chen Zhouqing Z Wang Zhong Z Chen Gang G

Journal of oncology 20220827

<h4>Background</h4>Glioma is the most common primary brain tumor, representing approximately 80.8% of malignant tumors. Necroptosis triggers and enhances antitumor immunity and is expected to be a new target for tumor immunotherapy. The effectiveness of necroptosis-related lncRNAs as potential therapeutic targets for glioma has not been elucidated.<h4>Methods</h4>We acquired RNA-seq data sets from LGG and GBM samples, and the corresponding clinical characteristic information is from TCGA. Normal ...[more]

PMID: 36065303

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Project description:Glioma is one of the most common intracranial malignancies that plagues people around the world. Despite current improvements in treatment, the prognosis of glioma is often unsatisfactory. Necroptosis is a form of programmed cell death. As research progresses, the role of necroptosis in tumors has gradually attracted the attention of researchers. And lncRNA is regarded as a critical role in the development of cancer. Therefore, this study is aimed at establishing a prognostic model based on necroptosis-associated lncRNAs to accurately assess the prognosis and immune response of patients with glioma. The RNA sequences of glioma patients and normal brain samples were downloaded from The Cancer Genome Atlas (TCGA) and GTEx databases, respectively. The coexpression analysis was performed to identify the necroptosis-related lncRNAs. Then, we utilized LASSO analysis following univariate Cox analysis to construct a prognostic model. Subsequently, we applied the Kaplan-Meier curve, time-dependent receiver operating characteristics (ROC), and univariate and multivariate Cox regression analyses to assess the effectiveness of this model. And the functional enrichment analyses and immune-related analyses were employed to investigate the potential biological functions. A validation set was obtained from the Chinese Glioma Genome Atlas (CGGA) database. And qRT-PCR was employed to further validate the expression levels of selected necroptosis-associated lncRNAs. Seven necroptosis-related lncRNAs (FAM13A-AS1, JMJD1C-AS1, LBX2-AS1, ZBTB20-AS4, HAR1A, SNHG14, and LINC00900) were determined to construct a prognostic model. The area under the ROC curve (AUC) was 0.871, 0.901, and 0.911 at 1, 2, and 3 years, respectively. The risk score was shown to be an important independent predictor in both univariate and multivariate Cox regression analyses. Through functional enrichment analyses, we found that the differentially expressed genes (DEGs) were mainly enriched in protein binding and signaling-related biological functions and immune-associated pathways. In conclusion, we established and validated a novel necroptosis-related lncRNA signature, which could accurately predict the overall survival of glioma patients and serve as potential therapeutic targets.

Project description:Hepatocellular carcinoma (HCC) is one of the malignant tumors with high mortality and a worse prognosis globally. Necroptosis is a programmed death mediated by receptor-interacting Protein 1 (RIP1), receptor-interacting Protein 1 (RIP3), and Mixed Lineage Kinase Domain-Like (MLKL). Our study aimed to create a new Necroptosis-related lncRNAs (NRlncRNAs) risk model that can predict survival and tumor immunity in HCC patients. The RNA expression and clinical data originated from the TCGA database. Pearson correlation analysis was applied to identify the NRlncRNAs. The LASSO-Cox regression analysis was employed to build the risk model. Next, the ROC curve and the area under the Kaplan-Meier curve were utilized to evaluate the accuracy of the risk model. In addition, based on the two groups of risk model, we performed the following analysis: clinical correlation, differential expression, PCA, TMB, GSEA analysis, immune cells infiltration, and clinical drug prediction analysis. Plus, qRT-PCR was applied to test the expression of genes in the risk model. Finally, a prognosis model covering six necroptosis-related lncRNAs was constructed to predict the survival of HCC patients. The ROC curve results showed that the risk model possesses better accuracy. The 1, 3, and 5-years AUC values were 0.746, 0.712, and 0.670, respectively. Of course, we also observed that significant differences exist in the following analysis, such as functional signaling pathways, immunological state, mutation profiles, and medication sensitivity between high-risk and low-risk groups of HCC patients. The result of qRT-PCR confirmed that three NRlncRNAs were more highly expressed in HCC cell lines than in the normal cell line. In conclusion, based on the bioinformatics analysis, we constructed an NRlncRNAs associated risk model, which predicts the prognosis of HCC patients. Although our study has some limitations, it may greatly contribute to the treatment of HCC and medical progression.

Project description:Background: The necroptosis and long noncoding RNA (lncRNA) are critical in the occurrence and development of malignancy, while the association between the necroptosis-related lncRNAs (NRlncRNAs) and soft tissue sarcoma (STS) remains controversial. Therefore, the present study aims to construct a novel signature based on NRlncRNAs to predict the prognosis of STS patients and investigate its possible role. Methods: The transcriptome data and clinical characteristics were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression database (GTEx). A novel NRlncRNA signature was established and verified by the COX regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, the K-M survival analysis, ROC, univariate, multivariate Cox regression analysis, and nomogram were used to evaluate the predictive value of the signature. Also, a variety of bioinformatic analysis algorithms explored the differences between the potential mechanism, tumor immune status, and drug sensitivity in the two-risk group. Finally, the RT-qPCR was performed to evaluate the expression of signature NRlncRNAs. Results: A novel signature consisting of seven NRlncRNAs was successfully established and verified with stable prediction performance and general applicability for STS. Next, the GSEA showed that the patients in the high-risk group were mainly enriched with tumor-related pathways, while the low-risk patients were significantly involved in immune-related pathways. In parallel, we found that the STS patients in the low-risk group had a better immune status than that in the high-risk group. Additionally, there were significant differences in the sensitivity to anti-tumor agents between the two groups. Finally, the RT-qPCR results indicated that these signature NRlncRNAs were abnormally expressed in STS. Conclusion: To the best of our knowledge, it is the first study to construct an NRlncRNA signature for STS. More importantly, the novel signature displays stable value and translational potential for predicting prognosis, tumor immunogenicity, and therapeutic response in STS.

Project description:Background: Hepatocellular carcinoma (HCC) is among malignancies with the highest fatality toll globally and minimal therapeutic options. Necroptosis is a programmed form of necrosis or inflammatory cell death, which can affect prognosis and microenvironmental status of HCC. Therefore, we aimed to explore the prognostic value of necroptosis-related lncRNAs (NRLs) in HCC and the role of the tumor microenvironment (TME) in immunotherapy. Methods: The RNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). NRLs were identified by Pearson correlation analysis. The signature was constructed using the LASSO-Cox regression analysis and evaluated using the receiver operating characteristic curve (ROC) and the area under the Kaplan-Meier curve. The nomogram was built based on clinical information and risk score. Gene set enrichment analysis (GSEA), immunoassay, half-maximum inhibitory concentration (IC50) analysis of the risk group, and the HCC subtype identification based on NRLs were also carried out. Finally, we detected the expression of lncRNAs in HCC tissues and cell lines in vitro. Results: A total of 508 NRLs were screened out, and seven NRLs were constructed as a risk stratification system to classify patients into distinct low- and high-risk groups. Patients in the high-risk group had a significantly lower overall survival (OS) than those in the low-risk group. Using multivariate Cox regression analysis, we found that the risk score was an independent predictor of OS. Functional analysis showed that the immune status of different patients was different. The IC50 analysis of chemotherapy demonstrated that patients in the high-risk group were more sensitive to commonly prescribed drugs. qRT-PCR showed that three high-risk lncRNAs were upregulated in drug-resistant cells, and the expression in HCC tissues was higher than that in adjacent tissues. Conclusion: The prediction signature developed in this study can be used to assess the prognosis and microenvironment of HCC patients, and serve as a new benchmark for HCC treatment selection.

Project description:Background Esophageal cancer (EC), one highly malignant gastrointestinal cancer, is the 6th leading cause of cancer-related deaths worldwide. Necroptosis and long non-coding RNA (lncRNA) play important roles in the occurrence and development of EC, but the research on the role of necroptosis-related lncRNA in EC is not conclusive. This study aims to use bioinformatics to investigate the prognostic value of necroptosis-related lncRNA in EC. Methods Transcriptome data containing EC and normal samples, and clinical information were obtained from the Cancer Genome Atlas database. 102 necroptosis-related genes were obtained from Kanehisa Laboratories. Necroptosis-related lncRNAs were screened out via univariate, multivariate Cox and the least absolute shrinkage and selection operator regression analyses to construct the risk predictive model. The reliability of the risk model was evaluated mainly through quantitative real-time PCR (qRT-PCR), the receiver operating characteristic (ROC) curves and the constructed nomogram. KEGG pathways were explored in the high- and low-risk groups of EC patients via gene set enrichment analyses (GSEA) software. Immune microenvironment and potential therapeutic agents in risk groups were also analyzed. Results A 6 necroptosis-related lncRNAs risk model composed of AC022211.2, Z94721.1, AC007991.2, SAMD12-AS1, AL035461.2 and AC051619.4 was established to predict the prognosis level of EC patients. qRT-PCR analysis showed upregulated Z94721.1 and AL035461.2 mRNA levels and downregulated AC051619.4 mRNA level in EC tissues compared with normal tissues. According to clinical characteristics, the patients in the high-risk group had a shorter overall survival than the low-risk group. The ROC curve and nomogram confirmed this model as one independent and predominant predictor. GSEA analysis showed metabolic and immune-related pathways enriched in the risk model. Most of the immune cells and immune checkpoints were positively correlated with the risk model, mainly active in the high-risk group. For the prediction of potential therapeutic drugs, 16 compounds in the high-risk group and 2 compounds in the low-risk group exhibited higher sensitivity. Conclusions Our results supported the necroptosis-related lncRNA signature could independently predict prognosis of EC patients, and provided theoretical basis for improving the clinical treatment of EC. Supplementary Information The online version contains supplementary material available at 10.1186/s12876-022-02421-8.

Project description:Background: Immunotherapy has achieved remarkable success in treating advanced liver cancer. Current evidence shows that most of the available immune checkpoint inhibitor (ICB) treatments are suboptimal, and specific markers are needed for patients regarded as good candidates for immunotherapy. Necroptosis, a type of programmed cell death, plays an important role in hepatocellular carcinoma (HCC) progression and outcome. However, studies on the necroptosis-related lncRNA in HCC are scarce. In this view, the present study investigates the link among necroptosis-related lncRNA, prognosis, immune microenvironment, and immunotherapy response. Methods: Gene transcriptome and clinical data were retrieved from The Cancer Genome Atlas database. Pearson correlation analysis of necroptosis-related genes was performed to identify necroptosis-related lncRNAs. The Wilcoxon method was used to detect differentially expressed genes, and prognostic relevant lncRNAs were obtained by univariate Cox regression analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were utilized to perform functional enrichment analysis. Lasso-Cox stepwise regression analysis was employed to calculate risk score, which was involved in analyzing immune cells infiltration, immune checkpoints expression, and predicting immunotherapeutic efficacy. Quantitative RT-PCR (qRT-PCR) was performed to detect the expression pattern of lncRNA in cell lines. Results: The 10 lncRNAs generated in this study were used to create a prognostic risk model for HCC and group patients into groups based on risk. High-risk patients with HCC have a significantly lower OS rate than low-risk patients. Multivariate Cox regression analysis showed that risk score is an independent risk factor for HCC with high accuracy. Patients in the high-risk group exhibited a weaker immune surveillance and higher expression level of immune checkpoint molecules. In terms of drug resistance, patients in the low-risk group were more sensitive to sorafenib. The OS-related nomogram was constructed to verify the accuracy of our model. Finally, quantitative RT-PCR experiments were used to verify the expression patterns of candidate genes. Conclusion: The lncRNA signature established herein, encompassing 10 necroptosis-related lncRNAs, is valuable for survival prediction and holds promise as prognostic markers for HCC.

Project description:AimsThe dysregulation of TGF-β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-β signaling in glioma is currently unclear.MethodsData on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. In vitro and in vivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA.ResultsIn glioma, a TGF-β signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in in vitro and in vivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-β signaling pathway.ConclusionsA prognostic lncRNA signature of TGF-β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.

Dataset Information

Construction and Validation of a Necroptosis-Related lncRNA Signature in Prognosis and Immune Microenvironment for Glioma.

Background

Methods

Results

Conclusion

Publications

Construction and Validation of a Necroptosis-Related lncRNA Signature in Prognosis and Immune Microenvironment for Glioma.

Similar Datasets

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