Leveraging a disulfidptosis‐related lncRNAs signature for predicting the prognosis and immunotherapy of glioma
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SUBMITTER: Chen D
PROVIDER: S-EPMC10709922 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:PurposeLung adenocarcinoma (LUAD) is a prevalent malignant tumor worldwide, with high incidence and mortality rates. However, there is still a lack of specific and sensitive biomarkers for its early diagnosis and targeted treatment. Disulfidptosis is a newly identified mode of cell death that is characteristic of disulfide stress. Therefore, exploring the correlation between disulfidptosis-related long non-coding RNAs (DRGs-lncRNAs) and patient prognosis can provide new molecular targets for LUAD patients.MethodsThe study analysed the transcriptome data and clinical data of LUAD patients in The Cancer Genome Atlas (TCGA) database, gene co-expression, and univariate Cox regression methods were used to screen for DRGs-lncRNAs related to prognosis. The risk score model of lncRNA was established by univariate and multivariate Cox regression models. TIMER, CIBERSORT, CIBERSORT-ABS, and other methods were used to analyze immune infiltration and further evaluate immune function analysis, immune checkpoints, and drug sensitivity. Real-time polymerase chain reaction (RT-PCR) was performed to detect the expression of DRGs-lncRNAs in LUAD cell lines.ResultsA total of 108 lncRNAs significantly associated with disulfidptosis were identified. A prognostic model was constructed by screening 10 lncRNAs with independent prognostic significance through single-factor Cox regression analysis, LASSO regression analysis, and multiple-factor Cox regression analysis. Survival analysis of patients through the prognostic model showed that there were obvious survival differences between the high- and low-risk groups. The risk score of the prognostic model can be used as an independent prognostic factor independent of other clinical traits, and the risk score increases with stage. Further analysis showed that the prognostic model was also different from tumor immune cell infiltration, immune function, and immune checkpoint genes in the high- and low-risk groups. Chemotherapy drug susceptibility analysis showed that high-risk patients were more sensitive to Paclitaxel, 5-Fluorouracil, Gefitinib, Docetaxel, Cytarabine, and Cisplatin. Additionally, RT-PCR analysis demonstrated differential expression of DRGs-lncRNAs between LUAD cell lines and the human bronchial epithelial cell line.ConclusionsThe prognostic model of DRGs-lncRNAs constructed in this study has certain accuracy and reliability in predicting the survival prognosis of LUAD patients, and provides clues for the interaction between disulfidptosis and LUAD immunotherapy.
| S-EPMC11210254 | biostudies-literature
Project description:Recent studies have proved that pyroptosis-related long non-coding RNAs (PRlncRNAs) are closely linked to tumor progression, prognosis, and immunity. Here, we systematically evaluated the correlation of PRlncRNAs with glioma prognosis. This study included 3 glioma cohorts (The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gravendeel). Through Pearson correlation analysis, PRlncRNAs were screened from these 3 cohorts. Univariate Cox regression analysis was then carried out to determine the prognostic PRlncRNAs. A pyroptosis-related lncRNAs signature (PRLS) was then built by least absolute shrinkage and selection operator and multivariate Cox analyses. We systematically evaluated the correlation of the PRLS with the prognosis, immune features, and tumor mutation burden in glioma. A total of 14 prognostic PRlncRNAs overlapped in all cohorts and were selected as candidate lncRNAs. Based on The Cancer Genome Atlas cohort, a PRLS containing 7 PRlncRNAs was built. In all cohorts, the PRLS was proved to be a good predictor of glioma prognosis, with a higher risk score related to a poorer prognosis. We observed obvious differences in the immune microenvironment, immune cell infiltration level, and immune checkpoint expression in low- and high-risk subgroups. Compared with low-risk cases, high-risk cases had lower Tumor Immune Dysfunction and Exclusion scores and greater tumor mutation burden, indicating that high-risk cases can be more sensitive to immunotherapy. A nomogram combining PRLS and clinical parameters was constructed, which showed more robust and accurate predictive power. In conclusion, the PRLS is a potentially useful indicator for predicting prognosis and response to immunotherapy in glioma. Our findings may provide a useful insight into clinically individualized treatment strategies for patients.
| S-EPMC9907962 | biostudies-literature
Project description:BackgroundsDisulfidptosis, a newly discovered mechanism of programmed cell death, is believed to have a unique role in elucidating cancer progression and guiding cancer therapy strategies. However, no studies have yet explored this mechanism in glioma.MethodsWe downloaded data on glioma patients from online databases to address this gap. Subsequently, we identified disulfidptosis-related genes from published literature and verified the associated lncRNAs.ResultsThrough univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) regression algorithms analyses, we identified 10 lncRNAs. These were then utilized to construct prognostic prediction models, culminating in a risk-scoring signature. Reliability and validity tests demonstrated that the model effectively discerns glioma patients' prognosis outcomes. We also analyzed the relationship between the risk score and immune characteristics, and identified several drugs that may be effective for high-risk patients. In vitro experiments revealed that LINC02525 could enhances glioma cells' migration and invasion capacities. Additionally, knocking down LINC02525 was observed to promote glioma cell disulfidptosis.ConclusionThis study delves into disulfidptosis-related lncRNAs in glioma, offering novel insights into glioma therapeutic strategies.
| S-EPMC10655028 | biostudies-literature
Project description:BackgroundPancreatic adenocarcinoma (PAAD) is a common and deadly tumor. Currently, there is a severe lack of therapeutic options. As a novel mode of cell death, increasing evidence reveals the important role of the disulfidptosis in cancer. However, few studies have utilized disulfidptosis-related long-stranded non-coding RNAs (DRlncRNAs) to investigate the prognosis of PAAD.MethodsWe comprehensively analyzed the expression and prognostic value of 958 DRlncRNAs in PAAD using data from The Cancer Genome Atlas (TCGA). We established and validated a new DRlncRNAs-related prognostic index by least absolute shrinkage and selection operator (LASSO) and COX regression analysis. In addition, we built a nomogram consisting of risk score, age, gender, tumor grade and stage to validate the clinical feasibility of the index. We further evaluated the value of the index in terms of PAAD functional pathways, tumor microenvironment (TME) and tumor mutations.ResultsWe designed a risk model for five DRlncRNAs and demonstrated its accuracy using receiver operating characteristic (ROC) curves. COX regression suggested that the model may be an independent predictor of cancer prognosis. Tumor immune infiltration analysis revealed that low-risk subgroups had higher extent of immune infiltration, higher sensitivity to immunotherapy and a higher TME score. This is helpful for us to discover more precise immunotherapy for PAAD patients.ConclusionsIn conclusion, we established a DRlncRNA index comprising of five DRlncRNAs, which may provide new insights for clinical diagnosis and precision therapy.
| S-EPMC11009799 | biostudies-literature
Project description:BackgroundDisulfidptosis is a recently discovered programmed cell death pathway. However, the exact molecular mechanism of disulfidptosis in cutaneous melanoma remains unclear.MethodsIn this study, clustering analysis was performed using data from public databases to construct a prognostic model, which was subsequently externally validated. The biological functions of the model genes were then investigated through various experimental techniques, including qRT-PCR, Western blotting, CCK-8 assay, wound healing assay, and Transwell assay.ResultsWe constructed a signature using cutaneous melanoma (CM) data, which accurately predicts the overall survival (OS) of patients. The predictive value of this signature for prognosis and immune therapy response was validated using multiple external datasets. High-risk CM subgroups may exhibit decreased survival rates, alterations in the tumor microenvironment (TME), and increased tumor mutation burden. We initially verified the expression levels of five optimum disulfidptosis-related genes (ODRGs) in normal tissues and CM. The expression levels of these genes were further confirmed in HaCaT cells and three melanoma cell lines using qPCR and protein blotting analysis. HLA-DQA1 emerged as the gene with the highest regression coefficient in our risk model, highlighting its role in CM. Mechanistically, HLA-DQA1 demonstrated the ability to suppress CM cell growth, proliferation, and migration.ConclusionIn this study, a novel signature related to disulfidptosis was constructed, which accurately predicts the survival rate and treatment sensitivity of CM patients. Additionally, HLA-DQA1 is expected to be a feasible therapeutic target for effective clinical treatment of CM.
| S-EPMC10601311 | biostudies-literature
Project description:Glioma is the most common primary malignant tumor in the central nervous system. Disulfidptosis is a recently identified programmed cell death in tumor cells overexpressing SLC7A11 under glucose starvation. Clinical prognostic significance of disulfidptosis has been reported in several tumors, and in this study, we explored the correlation of disulfidptosis with clinical prognosis, immune cell infiltration, and immunotherapy response in glioma. A total of 1592 glioma patients were included in this study, including 691 glioma patients from The Cancer Genomic Atlas (TCGA), 300 patients with from the Chinese Glioma Genomic Atlas (CGGA) array, 325 patients from CGGA sequencing, and 276 patients from Gene Expression Omnibus (GEO) GSE16011. R software (V4.2.2) and several R packages were applied to develop the risk score model and correlation calculation and visualization. Three disulfidptosis-related genes, LRPPRC, RPN1, and GYS1, were screened out and applied to establish the risk score model. Low-risk patients exhibit favorable prognosis, and the disulfidptosis-related signature significantly correlated with clinicopathological properties, molecular subtypes, and immunosuppressive microenvironment of glioma patients. We developed a disulfidptosis-related risk model to predict the prognosis and immune features in glioma patients, and this risk model may be applied as an independent prognostic factor for glioma.
| S-EPMC10589205 | biostudies-literature
Project description:BackgroundDisulfidptosis is an emerging form of regulated cell death distinguished by abnormal disulfide stress and the collapse of the actin network. This study was to construct a prognostic model based on disulfidptosis-related lncRNAs (DRLs) to enhance survival prediction and assess their viability as biomarkers for immunotherapy response in neuroblastoma (NB).MethodsTranscriptomic and clinical data from NB patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. DRLs linked to overall survival (OS) were identified using Pearson correlation and univariate Cox regression analyses. Molecular subtypes of NB were determined through consensus clustering. Immune cell infiltration was assessed with multiple algorithms. A prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. Tumor mutational burden (TMB) analysis on somatic mutations from the TARGET database explored the TMB and risk score relationship. Patient responses to immunotherapy and anti-tumor drugs were predicted using tumor immune dysfunction and exclusion (TIDE), Tumor Inflammation Signature (TIS), Genomics of Drug Sensitivity in Cancer (GDSC) database, and CellMiner tools.ResultsWe identified 151 DRLs associated with OS and defined three distinct DRLs subtypes. Using eight of these, we created a prognostic model. This model was proven independently significant and divided NB patients into high and low-risk groups. The high-risk group showed poorer OS, reduced immune cell presence and infiltration, and weaker response to immunotherapy. Conversely, the low-risk group demonstrated potential immunotherapy effectiveness and increased sensitivity to anti-tumor drugs.ConclusionsWe established a prognostic model based on DRLs to predict the prognosis of NB patients, assess the immune cell infiltration, analyze TMB, evaluate the effectiveness of immunotherapy, and gauge sensitivity to anti-tumor drug treatments.
| S-EPMC11651765 | biostudies-literature
Project description:The accumulation of intracellular disulfides induces a novel and unique form of metabolic-related cell death known as disulfidptosis. A previous study revealed the prognostic value of a risk model of disulfidptosis-related genes in hepatocellular carcinoma (HCC). However, to date, no studies have investigated the relationship between disulfidptosis-related long non-coding RNAs (DRLs) and HCC. In this study, we collected and analyzed RNA sequencing data from 370 HCC samples to explore the DRLs in the tumorigenesis and development of HCC. By employing Lasso Cox regression and multivariate Cox regression analyses, we identified five prognostic DRLs, which were used to construct a prognostic signature. The signature was subsequently validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, Cox regression analyses, nomograms, and calibration curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed, revealing that the DRLs signature was associated with HCC and several cancer-related pathways. Furthermore, the DRLs signature showed correlations with the infiltration of M0 and M1 macrophages, immune-related functions, and multiple immune checkpoints, including PDCD1, LAG3, CTLA4, TIGIT, CD47, and others. Analysis using the tumor immune dysfunction and exclusion (TIDE) approach demonstrated that the DRLs signature could predict the response to immunotherapy. Finally, we screened potential chemotherapy drugs that could sensitize HCC. In conclusion, our novel DRLs signature provides valuable insights into predicting patient survival and immunotherapy responses.
| S-EPMC10817373 | biostudies-literature
Project description:BackgroundThe role of disulfidptosis-related lncRNAs remains unclear in lung adenocarcinoma.MethodsAnalysis in R software was conducted using different R packages, which are based on the public data from The Cancer Genome Atlas (TCGA) database. The transwell assay was used to evaluate the invasion and migration abilities of lung cancer cells.ResultsIn our study, we identified 1401 lncRNAs significantly correlated with disulfidptosis-related genes (|Cor| > 0.3 and P < 0.05). Then, we constructed a prognosis model consisting of 11 disulfidptosis-related lncRNAs, including AL133445.2, AL442125.1, AC091132.2, AC090948.1, AC020765.2, CASC8, AL606834.1, LINC00707, OGFRP1, U91328.1, and GASAL1. This prognosis model has satisfactory prediction performance. Also, the risk score and clinical information were combined to develop a nomogram. Analyses of biological enrichment and immune-related data were used to identify underlying differences between patients at high-risk and low-risk groups. Moreover, we noticed that the immunotherapy nonresponders have higher risk scores. Meanwhile, patients at a high risk responded more strongly to docetaxel, paclitaxel, and vinblastine. Furthermore, further analysis of the model lncRNA OGFRP1 was conducted, including clinical, immune infiltration, biological enrichment analysis, and a transwell assay. We discovered that by inhibiting OGFRP1, the invasion and migration abilities of lung cancer cells could be remarkably hindered.ConclusionThe results of our study can provide directions for future research in the relevant areas. Moreover, the prognosis signature we identified has the potential for clinical application.
| S-EPMC10421709 | biostudies-literature
Project description:Glioma is a highly invasive primary brain tumour, making it challenging to accurately predict prognosis for glioma patients. Cuproptosis is a recently discovered cell death attracting significant attention in the tumour field. Whether cuproptosis-related genes have prognostic predictive value has not been clarified. In this study, uni-/multi-variate Cox and Lasso regression analyses were applied to construct a risk model based on cuproptosis-related lncRNAs using TCGA and CGGA cohorts. A nomogram was constructed to quantify individual risk, including clinical and genic characteristics and risk. GO and KEGG analyses were used to define functional enrichment of DEGs. Tumour mutation burden (TMB) and immune checkpoint analyses were performed to evaluate potential responses to ICI therapy. Ten prognostic lncRNAs were obtained from Cox regression. Based on the median risk score, patients were divided into high- and low-risk groups. Either for grade 2-3 or for grade 4, glioma patients with high-risk exhibited significant poorer prognoses. The risk was an independent risk factor associated with overall survival. The high-risk group was functionally associated with immune responses and cancer-related pathways. The high-risk group was associated with higher TMB scores. The expression levels of many immune checkpoints in the high-risk group were significantly higher than those in the low-risk group. Differentiated immune pathways were primarily enriched in the IFN response, immune checkpoint and T-cell co-stimulation pathways. In conclusion, we established a risk model based on cuproptosis-related lncRNAs showing excellent prognostic prediction ability but also indicating the immuno-microenvironment status of glioma.
| S-EPMC9716210 | biostudies-literature