Dataset Information

A TP53 Related Immune Prognostic Model for the Prediction of Clinical Outcomes and Therapeutic Responses in Lung Adenocarcinoma.

ABSTRACT: 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.

SUBMITTER: Zhang X

PROVIDER: S-EPMC9275777 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A TP53 Related Immune Prognostic Model for the Prediction of Clinical Outcomes and Therapeutic Responses in Lung Adenocarcinoma.

Zhang Xiaonan X Min Simin S Yang Yifan Y Ding Dushan D Li Qicai Q Liu Saisai S Tao Tao T Zhang Ming M Li Baiqing B Zhao Shidi S Ge Rongjing R Yang Fan F Li Yan Y He Xiaoyu X Ma Xiaoxiao X Wang Lian L Wu Tianyu T Wang Tao T Wang Guowen G

Frontiers in immunology 20220628

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 expres ...[more]

PMID: 35837383

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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.

Project description:Background and objectiveIncreasing evidence has elucidated the clinicopathological significance of individual TME component in predicting outcomes and immunotherapeutic efficacy in lung adenocarcinoma (LUAD). Therefore, we aimed to investigate whether comprehensive TME-based signatures could predict patient survival and therapeutic responses in LUAD, and to assess the associations among TME signatures, single nucleotide variations and clinicopathological characteristics.MethodsIn this study, we comprehensively estimated the TME infiltration patterns of 493 LUAD patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of LUADs using two proposed computational algorithms. A TMEscore was then developed based on the TME signature genes, and its prognostic value was validated in different datasets. Bioinformatics analysis was used to evaluate the efficacy of the TMEscore in predicting responses to immunotherapy and chemotherapy.ResultsThree TME subtypes were identified with no prognostic significance exhibited. Among them, naïve B cells accounted for the majority in TMEcluster1, while M2 TAMs and M0 TAMs took the largest proportion in TMEcluster2 and TMEcluster3, respectively. A total of 3395 DEGs among the three TME clusters were determined, among which 217 TME signature genes were identified. Interestingly, these signature genes were mainly involved in T cell activation, lymphocyte proliferation and mononuclear cell proliferation. With somatic variations and tumor mutation burden (TMB) of the LUAD samples characterized, a genomic landscape of the LUADs was thereby established to visualize the relationships among the TMEscore, mutation spectra and clinicopathological profiles. In addition, the TMEscore was identified as not only a prognosticator for long-term survival in different datasets, but also a predictive biomarker for the responses to immune checkpoint blockade (ICB) and chemotherapeutic agents. Furthermore, the TMEscore exhibited greater accuracy than other conventional biomarkers including TMB and microsatellite instability in predicting immunotherapeutic response (p < 0.001).ConclusionIn conclusion, our present study depicted a comprehensive landscape of the TME signatures in LUADs. Meanwhile, the TMEscore was proved to be a promising predictor of patient survival and therapeutic responses in LUADs, which might be helpful to the future administration of personalized adjuvant therapy.

Project description:Lung adenocarcinoma (LUAD) has been the major cause of tumor-associated mortality in recent years and exhibits a poor outcome. New data revealed that peroxisomes have a function in the regulation of the development and progression of several tumors. However, the prognostic values of peroxisome-related genes (PRGs) were rarely reported. Genomic sequence, mutation, and clinical data of 535 LUAD tissues were obtained from TCGA data sets. Within the TCGA cohort, a multigene signature was constructed with the assistance of the LASSO Cox regression model. Three GEO data sets, including GSE3141, GSE31210, and GSE72094, were obtained as validation cohorts. ROC assays, Kaplan-Meier methods, and multivariate assays were applied to examine the prognostic capacities of the novel signature. Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. In this study, we identified 47 differentially expressed peroxisome-related genes (PRGs), including 25 increased and 22 decreased PRGs. A prognostic model of six PRGs was established. The univariate and multivariate Cox analyses both showed that the p value of risk score was less than 0.05. In LUAD patients, the strong connection between the risk score and overall survival was further verified in three other GEO data sets. TMB and cancer stem cell infiltration were shown to be significantly higher in the high-risk group in comparison to the low-risk group. The TIDE score of the group with the low risk was considerably greater than that of the group with the high risk. Several drugs, targeting PRG-related genes, were available for the treatments of LUAD. Overall, we developed a novel peroxisome-related prognostic signature for LUAD patients. This signature could successfully indicate LUAD patients' chances of survival as well as their immune system's responsiveness to treatments. In addition, it has the potential to serve as immunotherapeutic targets for LUAD patients.

Project description:BackgroundLung cancer (LC) is the most common malignant tumor in the world, and lung adenocarcinoma (LUAD) is the most common type of LC. Immune microenvironment plays a critical role in cancer from onset to relapse. We aimed to identify an effective immune-related prediction model for assessing prognosis and predicting the relevant tumor therapeutic drugs.MethodsAccording to the RNA sequencing data of LUAD transcriptome in The Cancer Genome Atlas (TCGA) database and the immune-related genes obtained from IMMPORT (The Immunology Database and Analysis Portal) database, immune prognosis-related genes were screened. Weighted gene co-expression network analysis (WGCNA) identified hub genes in differentially expressed immune-related genes (DEIRGs). Least absolute shrinkage and selection operator (LASSO) Cox and ten rounds of cross-validation were used to screen core genes to establish a prognostic model, and in situ hybridization was used to verify the expression of core genes in LUAD. Then the patients from the TCGA database were divided into high-risk group and low-risk group. The survival, tumor microenvironment (TME) and immune cell infiltration of different groups were further analyzed, and the differential genes between the two groups were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) enrichment analyses. Finally, the small molecular drugs that can inhibit the prognosis of LUAD were screened by Connectivity Map (CMAP), and the therapeutic mechanism of small molecular drug oxibendazole was verified by Cell Counting Kit-8 (CCK-8) experiment.ResultsA four-immunoprognosis-related gene model was established to forecast the overall survival (OS) of LUAD through LASSO Cox regression and ten rounds of cross-validation analysis. This prognostic model stratified LUAD patients into low-risk and high-risk groups. According to the findings of the survival analysis, the low-risk group had a greater OS than the high-risk group and the content of immune cells in LUAD was corrected with the survival prognosis of patients. Univariate and multivariate Cox regression also revealed that the prognostic model was an independent prognosis factor in LUAD. Five kinds of small molecular drugs which can inhibit the prognosis of LUAD were screened by CMAP. As shown by CCK-8 test, the small molecular drug "oxibendazole" can effectively inhibit the proliferation of LUAD cells.ConclusionsBased on immune-related prognostic genes, a new prognostic model for LUAD was constructed. Oxibendazole can inhibit the proliferation of LUAD cells, which provides a new idea for the treatment of LUAD.

Dataset Information

A TP53 Related Immune Prognostic Model for the Prediction of Clinical Outcomes and Therapeutic Responses in Lung Adenocarcinoma.

Publications

A TP53 Related Immune Prognostic Model for the Prediction of Clinical Outcomes and Therapeutic Responses in Lung Adenocarcinoma.

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