Dataset Information

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients.

ABSTRACT: Background: Although a majority of early-stage lung adenocarcinoma (es-LUAD) patients have a favorable prognosis, there are still some cases with a risk of recurrence and metastasis. Cuproptosis is a new form of death that differs from other programmed cell death. However, no study has been reported for setting a prognostic model of es-LUAD using cuproptosis pattern-related genes. Methods: Using multiple R packages, the data from the GEO database was processed, and es-LUAD patients was classified into two patterns based on cuproptosis-related genes. Key differentially expressed genes (DEGs) in the two patterns were screened to construct a prognostic signature to assess differences in biological processes and immunotherapy responses in es-LUAD. Tumor microenvironment (TME) in es-LUAD was analyzed using algorithms such as TIMER and ssGSEA. Then, a more accurate nomogram was constructed by combining risk scores with clinical factors. Results: Functional enrichment analysis revealed that DEGs in two patterns were correlated with organelle fission, nuclear division, chromosome segregation, and cycle-related pathways. Univariate Cox regression and Lasso-Cox regression analyses identified six prognostic genes: ASPM, CCNB2, CDC45, CHEK1, NCAPG, and SPAG5. Based on the constructed model, we found that the high-risk group patients had higher expression of immune checkpoints (CTLA4, LAG3, PD-L1, TIGIT and TIM3), and a lower abundance of immune cells. Lastly, the nomogram was highly accurate in predicting the 1-, 3-, and 5-year survival status of patients with es-LUAD based on risk scores and clinical factors. Conclusion: The cuproptosis pattern-related signature can serve as a potential marker for clinical decision-making. It has huge potential in the future to guide the frequency of follow-up and adjuvant therapy for es-LUAD patients.

SUBMITTER: Tang Y

PROVIDER: S-EPMC9515444 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients.

Tang Yu Y Li Qifan Q Zhang Daoqi D Ma Zijian Z Yang Jian J Cui Yuan Y Zhang Aiping A

Frontiers in genetics 20220914

<b>Background:</b> Although a majority of early-stage lung adenocarcinoma (es-LUAD) patients have a favorable prognosis, there are still some cases with a risk of recurrence and metastasis. Cuproptosis is a new form of death that differs from other programmed cell death. However, no study has been reported for setting a prognostic model of es-LUAD using cuproptosis pattern-related genes. <b>Methods:</b> Using multiple R packages, the data from the GEO database was processed, and es-LUAD patients ...[more]

PMID: 36186452

Similar Datasets

Project description:BackgroundCuproptosis plays a crucial role in cancer, and different subtypes of cuproptosis have different immune profiles in prostate adenocarcinoma (PRAD). This study aimed to investigate immune genes associated with cuproptosis and develop a risk model to predict prognostic characteristics and chemotherapy/immunotherapy responses of patients with PRAD.MethodsThe CIBERSORT algorithm was used to evaluate the immune and stromal scores of patients with PRAD in The Cancer Genome Atlas (TCGA) cohort. Validation of differentially expressed genes DLAT and DLD in benign and malignant tissues by immunohistochemistry, and the immune-related genes of DLAT and DLD were further screened. Univariable Cox regression were performed to select key genes. Least absolute shrinkage and selection operator (LASSO)-Cox regression analyse was used to develop a risk model based on the selected genes. The model was validated in the TCGA, Memorial Sloan-Kettering Cancer Center (MSKCC) and Gene Expression Omnibus (GEO) datasets, as well as in this study unit cohort. The genes were examined via functional enrichment analysis, and the tumor immune features, tumor mutation features and copy number variations (CNVs) of patients with different risk scores were analysed. The response of patients to multiple chemotherapeutic/targeted drugs was assessed using the pRRophetic algorithm, and immunotherapy was inferred by the Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenoscore (IPS).ResultsCuproptosis-related immune risk scores (CRIRSs) were developed based on PRLR, DES and LECT2. High CRIRSs indicated poor overall survival (OS), disease-free survival (DFS) in the TCGA-PRAD, MSKCC and GEO datasets and higher T stage and Gleason scores in TCGA-PRAD. Similarly, in the sample collected by the study unit, patients with high CRIRS had higher T-stage and Gleason scores. Additionally, higher CRIRSs were negatively correlated with the abundance of activated B cells, activated CD8+ T cells and other stromal or immune cells. The expression of some immune checkpoints was negatively correlated with CRIRSs. Tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH) and copy number variation (CNV) scores were all higher in the high-CRIRS group. Multiple chemotherapeutic/targeted drugs and immunotherapy had better responsiveness in the low-CRIRS group.ConclusionOverall, lower CRIRS indicated better response to treatment strategies and better prognostic outcomes.

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.

Project description:BackgroundCuproptosis is a newly identified form of cell death that is dependent on copper (Cu) ions, termed Cu-dependent cytotoxicity. This process is distinct from other forms of cell death such as apoptosis, necrosis, and ferroptosis. The accumulation of copper is known to play a significant role in various biological processes, including angiogenesis (the formation of new blood vessels) and metastasis (the spread of cancer cells to different parts of the body). These processes are crucial for tumor growth and progression, indicating that copper and the cuproptosis-related genes (CPRGs) might be indispensable in the context of cancer development and progression. Given this background, we aimed to explore the relationship between CPRGs and both prognostic predictions and tumor microenvironment (TME) infiltration in bladder cancer (BLCA).MethodsFor this study, we utilized data from The Cancer Genome Atlas (TCGA) to identify CPRGs and subsequently divided BLCA patients into three distinct molecular clusters based on these genes. To assess the proportions of various immune cell types within the TME, we employed single-sample gene set enrichment analysis (ssGSEA) and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) method. These computational techniques allowed us to quantify the infiltration of different immune cells, providing insights into the immune landscape of the tumors. Furthermore, we developed a risk score model using CPRGs to predict the survival prospects of BLCA patients.ResultsOur analysis identified three molecular clusters of BLCA patients, each exhibiting unique clinical features and patterns of TME infiltration. Among these clusters, cluster 1 was associated with a poor prognosis. Interestingly, this cluster also showed significant infiltration of activated CD4+ (ssGSEA P<0.001) and CD8+ T (ssGSEA P<0.05) cells, which are crucial components of the immune response against tumors. This finding suggests a complex interaction between the immune system and the tumor, where a high presence of T cells does not necessarily correlate with better outcomes. Additionally, our risk score model revealed that the high-risk group, characterized by a specific expression pattern of CPRGs, also had enhanced infiltration of CD4+ and CD8+ T cells. This indicates that the cuproptosis-based risk model has a robust ability to predict patient prognosis and can guide immunotherapy decisions.ConclusionsOur study sheds light on the biological functions of CPRGs within the TME of BLCA and their correlations with clinical parameters and patient prognosis. The identification of distinct molecular clusters with varying prognoses and immune cell infiltrations highlights the heterogeneity of BLCA and underscores the potential of CPRGs as biomarkers for prognosis and therapeutic targets. These findings offer new perspectives for the development of immunotherapeutic strategies in the treatment of BLCA patients, potentially leading to more personalized and effective cancer therapies.

Project description:Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.

Dataset Information

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients.

Publications

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets