Clinical and prognostic implications of an immune-related risk model based on TP53 status in lung adenocarcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Song X
PROVIDER: S-EPMC8743672 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundLung adenocarcinoma (LUAD) results in a majority of cancer burden worldwide. TP53 is the most commonly mutated in LUAD. This study aimed to reveal the relation between TP53 and tumor microenvironment (TME) for improving LUAD treatment.MethodsDifferentially expressed genes (DEGs) related to immunity were analyzed between TP53-WT and TP53-MUT groups. Least absolute shrinkage and selection operator (LASSO) Cox regression was applied to screen prognostic DEGs. Two independent datasets were included to evaluate the robustness of the prognostic model.ResultsAn 8-gene prognostic model containing ANLN, CCNB1, DLGAP5, FAM83A, GJB2, NAPSA, SFTPB, and SLC2A1 was established based on DEGs. LUAD samples were classified into high- and low-risk groups with differential overall survival in the two datasets. M0 macrophages, M1 macrophages, and activated memory CD4 T cells were more enriched in high-risk group. Immune checkpoints of PDCD1, LAG3, and CD274 were also high-expressed in high-risk group.ConclusionThe study improved the understanding of the role of TP53 in the TME modulation. The 8-gene model had robust performance to predict LUAD prognosis in clinical practice. In addition, the eight prognostic genes may also serve as potential targets for designing therapeutic drugs for LUAD patients.
| S-EPMC9279974 | biostudies-literature
Project description:TP53 is the most frequently mutated gene in lung adenocarcinoma (LUAD). The tumor immune microenvironment (TIM) is considered a vital factor that influences tumor progression and survival rate. The influence of TP53 mutation on TIM in LUAD has not been fully studied. Here we systematically investigated the relationship and potential mechanisms between TP53 mutation status and immune response in LUAD. We constructed an immune prognostic model (IPM) using immune associated genes, which were expressed differentially between the TP53 mutant and wild type LUAD patients. We discovered that TP53 mutations were significantly associated with 5 immune related biological processes. Thirty-six immune genes were expressed differentially between TP53 mutant and wild type LUAD patients. An IPM was constructed using 3 immune genes to differentiate the prognostic survival in LUAD. The high-risk LUAD group displayed significantly higher proportions of dendritic cell resting, T cell CD4 memory resting and mast cell resting, and significantly low proportions of dendritic cell activated, T cell CD4 memory activated, and mast cell activated. Moreover, IPM was found to be an independent clinical feature and can be used to predict immunotherapy responses. In summary, we constructed and validated an IPM using 3 immune related genes, which provides a better understanding of the mechanism from an immunological perspectives.
| S-EPMC9275777 | biostudies-literature
Project description:PurposeTP53 mutation, one of the most frequent mutations in early-stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early-stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early-stage LUAD and formulate a TP53-associated immune prognostic model (IPM) that can estimate prognosis in early-stage LUAD patients.Materials and methodsImmune-associated differentially expressed genes (DEGs) between TP53 mutated (TP53MUT ) and TP53 wild-type (TP53WT ) early-stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune-associated DEGs. We constructed and validated an IPM based on the TCGA and a meta-GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application.ResultsTP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early-stage LUAD. Sixty-seven immune-associated DEGs were determined between TP53WT and TP53MUT cohort. An IPM consisting of two prognostic immune-associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta-GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43-3.06, p < 0.001). Increased expressions of PD-L1, CTLA-4, and TIGIT were revealed in the high-risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility.ConclusionThe IPM based on TP53 status is a reliable and robust immune signature to identify early-stage LUAD patients with high risk of unfavorable survival.
| S-EPMC7897963 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan-Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.
| S-EPMC8044985 | biostudies-literature
Project description:Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic model that may enhance the prognosis of CRC from an immunological perspective. We estimated the proportion of immune cells in the GSE39582 public dataset using the CIBERSORT (Cell type identification by estimating relative subset of known RNA transcripts) algorithm. Prognostic genes that were used to establish the immunoscore model were generated by the LASSO (Least absolute shrinkage and selection operator) Cox regression model. We established and validated the immunoscore model in GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) cohorts, respectively; significant differences of overall survival analysis were found between the low and high immunoscore groups or TP53 subgroups. In the multivariable Cox analysis, we observed that the immunoscore was an independent prognostic factor both in the GEO cohort (HR (Hazard ratio) 1.76, 95% CI (confidence intervals): 1.26-2.46) and the TCGA cohort (HR 1.95, 95% CI: 1.20-3.18). Furthermore, we established a nomogram for clinical application, and the results suggest that the nomogram is a better predictive model for prognosis than immunoscore or TNM staging.
| S-EPMC6895875 | biostudies-literature
Project description:Mitophagy, a conserved cellular mechanism, is crucial for cellular homeostasis through the selective clearance of impaired mitochondria. Its emerging role in cancer development has sparked interest, particularly in lung adenocarcinoma (LUAD). Our study aimed to construct a risk model based on mitophagy-related genes (MRGs) to predict survival outcomes, immune response, and chemotherapy sensitivity in LUAD patients. We mined the GeneCards database to identify MRGs and applied LASSO/Cox regression to formulate a prognostic model. Validation was performed using two independent Gene Expression Omnibus (GEO) cohorts. Patients were divided into high- and low-risk categories according to the median risk score. The high-risk group demonstrated significantly reduced survival. Multivariate Cox analysis confirmed the risk score as an independent predictor of prognosis, and a corresponding nomogram was developed to facilitate clinical assessments. Intriguingly, the risk score correlated with immune infiltration levels, oncogenic expression profiles, and sensitivity to anticancer agents. Enrichment analyses linked the risk score with key oncological pathways and biological processes. Within the model, MTERF3 emerged as a critical regulator of lung cancer progression. Functional studies indicated that the MTERF3 knockdown suppressed the lung cancer cell proliferation and migration, enhanced mitophagy, and increased the mitochondrial superoxide production. Our novel prognostic model, grounded in MRGs, promises to refine therapeutic strategies and prognostication in lung cancer management.
| S-EPMC10886790 | biostudies-literature
Project description:BackgroundLung cancer has been the focus of attention for many researchers in recent years due to its leading contribution to cancer-related death worldwide, with lung adenocarcinoma (LUAD) being the most common histological type. Ferroptosis, a novel iron-dependent form of regulated cell death, can be induced by sorafenib. Emerging evidence shows that triggering ferroptosis has potential as a cancer therapy. This work aimed to build a ferroptosis-related gene signature for predicting the outcome of LUAD.MethodsThe TCGA-LUAD dataset was set as the training cohort, and the GSE72094 and GSE68465 datasets were set as the validation cohorts. Sixty-two ferroptosis-related genes were retrieved from the literature. A univariate Cox regression model was constructed for the training cohort to preliminarily screen for potential prognostic ferroptosis-related genes. A gene signature was generated from a LASSO Cox regression model and assessed with the training and validation cohorts through Kaplan-Meier, Cox, and ROC analyses. In addition, the correlation between the risk score and autophagy-related genes was determined by the Pearson test. Finally, GSEA and immune infiltrating analyses were performed to better study the functional annotation of the signature and the role of each kind of immune cell.ResultsA ten-gene signature was constructed from the training cohort and validated in three cohorts by Kaplan-Meier and Cox regression analyses, revealing its independent prognostic value in LUAD. Moreover, a ROC analysis conducted with all cohort data confirmed the predictive ability of the ten-gene signature for LUAD prognosis. A total of 62.85% (308/490) of autophagy-related genes were found to be significantly correlated with risk scores. GSEA detailed the exact pathways related to the gene signature, and immune-infiltrating analyses identified crucial roles for resting mast cells and resting dendritic cells in the prognosis of LUAD.ConclusionsWe identified a novel ferroptosis-related ten-gene signature (PHKG2, PGD, PEBP1, NCOA4, GLS2, CISD1, ATP5G3, ALOX15, ALOX12B, and ACSL3) that can accurately predict LUAD prognosis and is closely linked to resting mast cells and resting dendritic cells.
| S-EPMC8339871 | biostudies-literature
Project description:BackgroundSmoking is one of the most hazardous risk factors for the development of lung adenocarcinoma (LUAD). Many survival and prognosis-related biomarkers were discovered using database mining. However, the precision of immune-related long noncoding RNAs (lncRNAs) predictions is insufficient. We identified a novel signature to improve the estimate of smoking-related LUAD prognosis.MethodsThe Cancer Genome Atlas database (TCGA) was used to obtain the LUAD lncRNA expression profiles. The smoking-related LUAD cohort was randomly split into discovery and validation cohorts. To determine the risk score, use the LASSO Cox regression technique on the prognostic immune-related lncRNA. The risk signature has been developed.ResultsA total of 643 immune-related lncRNAs were identified as potential candidates for a risk signature. Finally, six immune-related lncRNAs (AL359915.2, AP000695.1, HSPC324, TGFB2-AS1, AC026355.1, and AC002128.2) were identified and used to carry out risk signature, which showed a close association with overall survival in the discovery cohort. We classified patients as high risk or low risk based on a median risk score of 1.0783. In the discovery cohort, overall survival was marginally longer in the low-risk group than in the high-risk category (p = 2.28e08). The area under the curves (AUC) for 1-, 3-, and 5-year survival was 0.67, 0.7, and 0.82, respectively. Furthermore, we successfully validated and combined cohorts using this risk profile. We discovered a strong positive connection between HSPC324 and VIPR1 as a possible novel biomarker for smoking-related LUAD development in our study.ConclusionsOur research has established a six immune-lncRNA signature that may be used to predict the prognosis of smoking-related LUAD with great accuracy.
| S-EPMC9169227 | biostudies-literature
Project description:Lung adenocarcinoma accounts for the majority of lung cancer cases and impact survival rate of patients severely. Immunotherapy is an effective treatment for lung adenocarcinoma but is restricted by many factors including immune checkpoint expression and the inhibitory immune microenvironment. This study aimed to explore the immune microenvironment in lung adenocarcinoma via disulfidptosis. Public datasets of lung adenocarcinoma from the TCGA and GEO was adopted as the training and validation cohort. Based on the differences in the expression of disulfidptosis -related genes, a glucose metabolism and immune response prognostic model was constructed. The prognostic value and clinical relationship of the model were further explored. Immune-related analyses were performed according to CIBERSORT, ssGSEA, TIDE, IPS. We verified that the model could accurately predict the survival expectancy of lung adenocarcinoma patients. Patients with lung adenocarcinoma and a low-risk score had better survival outcomes according to the model. Moreover, the high-risk group tended to have an immunosuppressive effect, as reflected by the immune cell components, phenotypes and functions. We also found that the clinically relevant immune checkpoint CTLA-4 was significantly higher in low-risk group (P<0.05), indicating that the high-risk group may suffer worse tumor immunotherapy efficacy. Finally, we found that this model has accurate predictive value for the efficacy of immune checkpoint blockade in non-small cell lung cancer (P<0.05). The prognostic model demonstrated the feasibility of predicting survival and immunotherapy efficacy via disulfidptosis-related genes and will facilitate the development of personalized anticancer therapy.
| S-EPMC11291233 | biostudies-literature
Project description:Background: aldolase A (ALDOA) has been reported to be involved in kinds of cancers. However, the role of ALDOA in lung adenocarcinoma has not been fully elucidated. In this study, we explored the prognostic value and correlation with immune infiltration of ALDOA in lung adenocarcinoma. Methods: The expression of ALDOA was analyzed with the Oncomine database, the Cancer Genome Atlas (TCGA), and the Human Protein Atlas (HPA). Mann-Whitney U test was performed to examine the relationship between clinicopathological characteristics and ALDOA expression. The receiver operating characteristic (ROC) curve and Kaplan-Meier method were conducted to describe the diagnostic and prognostic importance of ALDOA. The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct PPI networks and identify hub genes. Functional annotations and immune infiltration were conducted. Results: The mRNA and protein expression of ALDOA were higher in lung adenocarcinoma than those in normal tissues. The overexpression of ALDOA was significantly correlated with the high T stage, N stage, M stage, and TNM stage. Kaplan-Meier showed that high expression of ALDOA was correlated with short overall survival (38.9 vs 72.5 months, p < 0.001). Multivariate analysis revealed that ALDOA (HR 1.435, 95%CI, 1.013-2.032, p = 0.042) was an independent poor prognostic factor for overall survival. Functional enrichment analysis showed that positively co-expressed genes of ALDOA were involved in the biological progress of mitochondrial translation, mitochondrial translational elongation, and negative regulation of cell cycle progression. KEGG pathway analysis showed enrichment function in carbon metabolism, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis. The "SCNA" module analysis indicated that the copy number alterations of ALDOA were correlated with three immune cell infiltration levels, including B cells, CD8+ T cells, and CD4+ T cells. The "Gene" module analysis indicated that ALDOA gene expression was negatively correlated with infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, and macrophages. Conclusion: Our study suggested that upregulated ALDOA was significantly correlated with tumor progression, poor survival, and immune infiltrations in lung adenocarcinoma. These results suggest that ALDOA is a potential prognostic biomarker and therapeutic target in lung adenocarcinoma.
| S-EPMC8678114 | biostudies-literature