Immune-related gene prognostic index (IRGPI) for lung adenocarcinoma predicts patient prognosis and immunotherapy response.
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ABSTRACT:
SUBMITTER: Zhu Z
PROVIDER: S-EPMC10641371 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
Publications
Immune-related gene prognostic index (IRGPI) for lung adenocarcinoma predicts patient prognosis and immunotherapy response.
Zhu Zheng Z Feng Wei W Tan Xiao-Yan XY Gu Pin-Chao PC Song Wei W Ma Hai-Tao HT
International journal of clinical and experimental pathology 20231015 10
<h4>Objective</h4>We searched for a predictive biomarker that also predicts whether patients would benefit from immune checkpoint blockade (ICB) treatment from a few angles, because existing biomarkers no longer wholly replicate the interconnections of distinctive elements in the tumor microenvironment (TME).<h4>Methods</h4>We identified 55 pivotal IRGs by performing a WGCNA and univariate Cox regression analysis on a lung adenocarcinoma dataset from the TCGA database. The IRGPI model was then c ...[more]
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Project description:BackgroundThere is increasing evidence of the effectiveness of immune checkpoint blockade (ICB) therapy for the treatment of lung adenocarcinoma (LUAD). However, the benefits of ICB therapy vary among LUAD patients. Due to the research dimension, existing biomarkers, such as programmed death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB), could not reflect the complex tumor environment, and had low prediction accuracy of ICB. Therefore, we aimed to uncover a prognostic biomarker that could also predict whether a patient would benefit from ICB therapy and other common treatments from multiple dimensions, so as to improve the prediction accuracy of pre-treatment patients.MethodsBased on the LUAD dataset retrieved from The Cancer Genome Atlas (TCGA) database, 50 immune-related hub genes were identified using weighted gene co-expression network analysis and univariate Cox regression analyses. An immune-related gene prognostic index (IRGPI) was constructed using a Cox proportional-hazards model based on 15 genes and validated using GSE72094 dataset. We tested its prognostic accuracy by Kaplan-Meier (K-M) survival curves of the two datasets and assessed its predictive power by comparing area under curve (AUC) of IRGPI with existing biomarkers. Subsequently, we analyzed the molecular and immune characteristics, and evaluated the benefits of ICB by PD-L1 expression and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, predicted the inhibitory concentration 50 of common treatments drugs for two IRGPI score-related subgroups.ResultsPatients in the IRGPI-high subgroup had lower overall survival (OS) than patients in the IRGPI-low subgroup in K-M survival curve in two cohorts. And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively. A higher tumor mutation burden and PD-L1 expression and the tumor microenvironment (TME) landscape demonstrated that IRGPI-high subgroup patients may respond better to ICB therapy. Genomics of Drug Sensitivity in Cancer (GDSC) analysis indicated that the IRGPI-high subgroup showed greater sensitivity to chemotherapy.ConclusionsIRGPI is a prospective biomarker for evaluating whether a patient will benefit from ICB therapy and other treatments, and distinguishing patients with different molecular and immune characteristics.
| S-EPMC9264088 | biostudies-literature
Project description:Cuproptosis and associated immune-related genes (IRG) have been implicated in tumorigenesis and tumor progression. However, their effects on lung adenocarcinoma (LUAD) have not been elucidated. Therefore, we investigated the impact of cuproptosis-associated IRGs on the immunotherapy response and prognosis of LUAD using a bioinformatical approach and in vitro experiments analyzing clinical samples. Using the cuproptosis-associated IRG signature, we classified LUAD into two subtypes, cluster 1 and cluster 2, and identified three key cuproptosis-associated IRGs, NRAS, TRAV38-2DV8, and SORT1. These three genes were employed to establish a risk model and nomogram, and to classify the LUAD cohort into low- and high-risk subgroups. Biofunctional annotation revealed that cluster 2, remarkably downregulating epigenetic, stemness, and proliferation pathways activity, had a higher overall survival (OS) and immunoinfiltration abundance compared to cluster 1. Real-time quantitative PCR (RT-qPCR) validated the differential expression of these three genes in both subgroups. scRNA-seq demonstrated elevated expression of NRAS and SORT1 in macrophages. Immunity and oncogenic and stromal activation pathways were dramatically enriched in the low-risk subgroup, and patients in this subgroup responded better to immunotherapy. Our data suggest that the cuproptosis-associated IRG signature can be used to effectively predict the immunotherapy response and prognosis in LUAD. Our work provides enlightenment for immunotherapy response assessment, prognosis prediction, and the development of potential prognostic biomarkers for LUAD patients.
| S-EPMC10381686 | biostudies-literature
Project description:BackgroundThe tumor microenvironment plays an important role in the progression and malignancy of lung adenocarcinoma and affects the immunotherapy response. There is increasing evidence that long non-coding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) have significant functions in the development and treatment response of various kinds of cancer. This study aimed to explore the association between immune-related lncRNA-microRNA (miRNA)-messenger RNA (mRNA)-ceRNA networks, and the prognosis of and immunotherapy response in lung adenocarcinoma.MethodsRNA-sequencing (RNA-seq) and miRNA-seq data from The Cancer Genome Atlas (TCGA) were used to evaluate the infiltration of immune cells in lung adenocarcinoma samples by undertaking a single-sample gene set enrichment analysis (ssGSEA) to divide the cells into high and low immune cell infiltration groups. The differentially expressed mRNA (DEmRNA) was further analyzed by a weighted gene co-expression network analysis (WGCNA), search tool for recurring instances of neighboring genes (STRING), and Cytoscape to select hub genes. The ceRNA network was constructed using Cytoscape. Additionally, survival analyses were conducted to screen out prognostic candidate genes.ResultsSeven thousand five hundred and thirty-eight mRNAs, 540 lncRNAs, and 138 miRNAs were found to be differentially expressed between the high and low immune cell infiltration groups. The two DEmRNA modules most significantly associated with immune cell infiltration were further analyzed, and four clusters, including 179 DEmRNAs, were selected based on Molecular Complex Detection (MCODE) scores. The selected DEmRNAs in the four clusters were mainly enriched in pathways involved in regulating the immune response. Ultimately, a ceRNA network of SNHG6-hsa-miR-30e-5p-CYSLTR1 was identified as being associated with the prognosis of and immunotherapy response in lung adenocarcinoma.ConclusionsThe present study extends understandings of immune-related lncRNA-miRNA-mRNA-ceRNA networks and identifies novel targets and a regulatory pathway for anti-tumor immunotherapy.
| S-EPMC8506752 | biostudies-literature
Project description:Background: Constructed an immune-prognosis index (IPI) and divided lung adenocarcinoma (LUAD) patients into different subgroups according to IPI score, describe the molecular and immune characteristics of patients between different IPI subgroups, and explore their response to immune checkpoint blockade (ICB) treatment. Methods: Based on the transcriptome profile of LUAD patients in TCGA and immune gene sets from ImmPort and InnateDB, 15 hub immune genes were identified through correlation and Bayesian causal network analysis. Then, IPI was constructed with 5 immune genes by using COX regression analysis and verified with external datasets (GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222). Finally, the characteristics and the response to ICB treatment of LUAD patients between two different IPI subgroups were analyzed. Results: IPI was constructed based on the expression of 5 genes, including A2M, ADRB1, ADRB2, VIPR1 and PTH1R. IPI-high LUAD patients have a better overall survival than IPI-low LUAD patients, consistent with the results in the GEO cohorts. The comprehensive results showed that patients in the IPI-high subgroup were exhibited characters as metabolism-related signaling pathways activation, lower TP53 and TTN mutation rate, more infiltrations of CD8 T cells, dendritic cells and macrophages M1, especially earned more benefit from ICB treatment. In contrast, patients in the IPI-low subgroup were exhibited characters as p53 signaling pathways activation, higher TP53 and TTN mutation rate, more infiltrations of resting memory CD4 T cells, macrophages M2, immune-suppressive response and less benefit from ICB treatment. Conclusion: IPI is a potentially valuable prognostic evaluation method for LUAD, which works well in the benefit predicting of LUAD patients within ICB treatment.
| S-EPMC9124834 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.
| S-EPMC9013292 | biostudies-literature
Project description:BackgroundCuproptosis is a novel form of programmed cell death that disrupts the tricarboxylic acid (TCA) cycle and mitochondrial function. The mechanism of cuproptosis is quite different from that of common forms of cell death such as apoptosis, pyroptosis, necroptosis, and ferroptosis. However, the potential connection between cuproptosis and tumor immunity, especially in lung adenocarcinoma (LUAD), is poorly understood.MethodsWe used machine learning algorithms to develop a cuproptosis-related scoring system. The immunological features of the scoring system were investigated by exploring its association with clinical outcomes, immune checkpoint expression, and prospective immunotherapy response in LUAD patients. The system predicted the sensitivity to chemotherapeutic agents. Unsupervised consensus clustering was performed to precisely identify the different cuproptosis-based molecular subtypes and to explore the underlying tumor immunity.ResultsWe determined the aberrant expression and prognostic relevance of cuproptosis-related genes (CRGs) in LUAD. There were significant differences in survival, biological function, and immune infiltration among the cuproptosis subtypes. In addition, the constructed cuproptosis scoring system could predict clinical outcomes, tumor microenvironment, and efficacy of targeted drugs and immunotherapy in patients with LUAD. After validating with large-scale data, we propose that combining the cuproptosis score and immune checkpoint blockade (ICB) therapy can significantly enhance the efficacy of immunotherapy and guide targeted drug application in patients with LUAD.ConclusionThe Cuproptosis score is a promising biomarker with high accuracy and specificity for determining LUAD prognosis, molecular subtypes, immune cell infiltration, and treatment options for immunotherapy and targeted therapies for patients with LUAD. It provides novel insights to guide personalized treatment strategies for patients with LUAD.
| S-EPMC10050597 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high annual mortality. Tsvetkov et al. recently found a new form of regulated cell death, termed cuproptosis. The prognostic value of cuproptosis-related gene signature in LUAD remains uncertain.MethodsA training cohort is identified by the TCGA-LUAD dataset, whereas validation cohorts one and two are identified by GSE72094 and GSE68465, respectively. GeneCard and GSEA were used to extract genes related to cuproptosis. Cox regression, Kaplan-Meier regression, and LASSO regression were used to construct a gene signature. The model's applicability was evaluated by Kaplan-Meier estimators, Cox models, ROC, and tAUC across two independent validation cohorts. We examined the model's connections with other forms of regulated cell death. The immunotherapy ability of the signature was demonstrated by applying TMB, immune relevant signatures, and TIDE. The GSEA and immune infiltration analysis offer a better understanding of how the signature functions and the role of immune cells in its prognostic power.ResultsA ten-gene signature was built and demonstrated owning prognostic power by being applied to the validation cohorts. The GSEA uncovered that the unfolded protein response, glycolysis/gluconeogenesis, and MYC were highly related to the gene signature. The ten-gene signature is closely related to related genes of apoptosis, necroptosis, pyroptosis, and ferroptosis. Our signature may have utility in predicting immunotherapy efficacy in LUADs. Mast cells were identified as key players that support the predicting capacity of the ten-gene signature through the immune infiltrating analysis.ConclusionsThe novel ten-gene signature associated with apoptosis in cuproptosis that we obtained may contribute to improved LUAD management strategies and the ability to predict response to LUAD immunotherapy. It is suggested that mast cell infiltration might be related to the prognostic power of this signature.
| S-EPMC10331680 | biostudies-literature
Project description:BackgroundLung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care.MethodsWe inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets.ResultsWe found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy.ConclusionsOur findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.
| S-EPMC8753834 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD), a malignancy with high incidence and mortality rates worldwide, contains multiple genomic and epigenomic abnormalities. And the useful tumor markers associated with these abnormalities need further investigation. Whereas apoptosis is a form of programmed cell death, the expression of apoptosis-related genes in LUAD and its relationship with prognosis is unclear. In the present study, we identified 64 differentially expressed apoptosis-related genes (DEARGs) that were differentially expressed between LUAD tissue and normal lung tissue. Based on these DEARGs, all LUAD cases were classified into two subtypes using The Cancer Genome Atlas (TCGA) cohort to assess the prognostic value of apoptosis-related genes for survival. An 11-gene signature was established by applying the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression method to construct a multigene prediction model and classify all LUAD patients in the TCGA cohort into high or low AS-score groups. Patients in the low AS-score group had significantly higher survival and prognosis than those in the high AS-score group. Taking the median risk score of the AS-score, LUAD patients in the GSE68465 cohort were divided into two risk groups, low and high. The overall survival (OS) time was longer in the low AS-score group. Combined with clinical characteristics, the AS-score was an independent predictor of LUAD patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that the differential genes between the two groups were mainly enriched in cellular immunity. Further analysis revealed higher immune checkpoint protein expression and higher tumor mutational burden (TMB) in the high AS-score group, suggesting better efficacy of immunotherapy in the high AS-score group than the low AS-score group. And the high AS-score group was better in chemotherapy and targeted therapy efficiency. In conclusion, the AS-score constructed based on apoptosis-related genes can predict the prognosis of LUAD patients and provide some guidance for the antitumor treatment of LUAD patients.
| S-EPMC9094710 | biostudies-literature
Project description:Recent research revealed methionine metabolism as a key mediator of tumor initiation and immune evasion. However, the relationship between methionine metabolism and tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unknown. Here, we comprehensively analyzed the genomic alterations, expression patterns, and prognostic values of 68 methionine-related regulators (MRGs) in LUAD. We found that most MRGs were highly prognostic based on 30 datasets including 5024 LUAD patients. Three distinct MRG modification patterns were identified, which showed significant differences in clinical outcomes and TME characteristics: The C2 subtype was characterized by higher immune score, while the C3 subtype had more malignant cells and worse survival. We developed a MethScore to measure the level of methionine metabolism in LUAD. MethScore was positively correlated with T-cell dysfunction and tumor-associated macrophages (TAMs), indicating a dysfunctional TME phenotype in the high MethScore group. In addition, two immunotherapy cohorts confirmed that patients with a lower MethScore exhibited significant clinical benefits. Our study highlights the important role of methionine metabolism in modeling the TME. Evaluating methionine modification patterns will enhance our understanding of TME characteristics and can guide more effective immunotherapy strategies.
| S-EPMC10449287 | biostudies-literature