Dataset Information

Development and validation of a robust necroptosis related classifier for colon adenocarcinoma.

ABSTRACT: Background: Necroptosis, a novel form of apoptosis, plays a crucial function in the progression of colon adenocarcinoma (COAD) and is expected to be triggered in cancer therapy for enhancing anti-tumor immunity. However, the function of necroptosis in tumors and its relationship with the tumor microenvironment (TME) remains largely unclear. Methods: Necroptosis-related genes (NRGs) were collected from high-quality literature. Using The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov) and the Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/geo) meta-cohorts, a robust risk model was constructed to systematically examine the clinical value, functional status, the role of TME based on the risk model, as also the genomic variations. Results: A risk model containing nine NRGs, including TNF receptor-associated factor (TRAF2), TNF receptor 1 associated via death domain (TRADD), ubiquitin carboxyl-terminal hydrolase 21 (USP21), TNF receptor superfamily, member 6 (FAS), tumor necrosis factor receptor superfamily 10B (TNFRSF10B), mitogen-activated protein kinase 8 (MAPK8), mixed lineage kinase domain-like (MLKL), TNF receptor-associated factor 5 (TRAF5), and recombinant receptor-interacting serine-threonine kinase 3 (RIPK3), was constructed. The risk model's stability and accuracy were demonstrated in training, as also the validation cohorts; it was verified as an independent prognostic model for COAD. High-risk group patients developed "cold" tumors having active tumor proliferation and immunosuppression, while those in the low-risk group developed "hot" tumors with active immune and cell killing functions. Moreover, a higher number of copy number variations in the genome and fewer somatic mutations were found in high-risk group patients. Furthermore, higher sensitivity towards immunotherapy and chemotherapy was seen in patients of the low-risk group. Conclusion: A reliable risk model based on NRGs to assess patient prognosis and guide clinical decision-making was constructed and validated. Our findings may contribute to the understanding of necroptosis and are expected to aid clinical management and guide precision treatment for patients with COAD.

SUBMITTER: Yang J

PROVIDER: S-EPMC9389450 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a robust necroptosis related classifier for colon adenocarcinoma.

Yang Jie J Chen Hua H Wang Yongqiang Y Chen Jian J

Frontiers in genetics 20220805

<b>Background:</b> Necroptosis, a novel form of apoptosis, plays a crucial function in the progression of colon adenocarcinoma (COAD) and is expected to be triggered in cancer therapy for enhancing anti-tumor immunity. However, the function of necroptosis in tumors and its relationship with the tumor microenvironment (TME) remains largely unclear. <b>Methods:</b> Necroptosis-related genes (NRGs) were collected from high-quality literature. Using The Cancer Genome Atlas (TCGA) (https://cancergeno ...[more]

PMID: 35991546

Similar Datasets

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Ferroptosis is a newly recognized process of cell death, which is different from other forms of cell death in morphology, biochemistry, and genetics, and has played a vital role in cancer biology. This study aimed to identify a ferroptosis-related gene signature associated with LUAD prognosis.MethodsDataset TCGA-LUAD which came from the TCGA portal was taken as the training cohort. GSE72094 and GSE68465 from the GEO database were treated as validation cohorts. Two hundred fifty-nine ferroptosis-related genes were retrieved from the FerrDb database. In the training cohort, Kaplan-Meier and univariate Cox analyses were conducted for preliminary screening of ferroptosis-related genes with potential prognostic capacity. These genes then entered into the LASSO Cox regression model, constructing a gene signature. The latter was then evaluated in the training and validation cohorts via Kaplan-Meier, Cox, and ROC analyses. In addition, the correlations between risk score and autophagy were examined by Pearson correlation coefficient. The analyses of GSEA and immune infiltrating were performed for better studying the function annotation of the gene signature and the character of each kind of immune cells played in the tumor microenvironment.ResultsA 15-gene signature was found from the training cohort and validated by Kaplan-Meier and Cox regression analyses, revealing its independent prognosis value in LUAD. Moreover, the ROC analysis was conducted, confirming a strong predictive ability that this signature owned for LUAD prognosis. One hundred fifty-one of 222 (68.01%) autophagy-related genes were discovered significantly correlated with risk scores. Analyses of GSEA and immune infiltration exhibited in detail the specific pathways that associate with the 15-gene signature and identified the crucial roles of resting mast cells and resting dendritic cells owned in the prognosis of the 15-gene signature.ConclusionIn this present study, a novel ferroptosis-related 15-gene signature (RELA, ACSL3, YWHAE, EIF2S1, CISD1, DDIT4, RRM2, PANX1, TLR4, ARNTL, LPIN1, HERPUD1, NCOA4, PEBP1, and GLS2) was built. It could accurately predict the prognosis of LUAD and was related to resting mast cells and resting dendritic cells, which provide potential for the personalized outcome prediction and the development of new therapies in LUAD population.

Project description:BackgroundThe incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD.MethodsThe public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan-Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts.ResultsA prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29-8.11; P = 6.16E-06), GSE31210 (HR = 11.91, 95% CI 4.15-34.19; P = 4.10E-06), GSE50081 (HR = 3.63, 95% CI 1.90-6.95; P = 9.95E-05), the combined data set (HR = 3.15, 95% CI 1.98-5.02; P = 1.26E-06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49-3.13; P = 4.54E-05) and GSE72094 (HR = 2.95, 95% CI 1.86-4.70; P = 4.79E-06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design.ConclusionsOur study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

Project description:Background Cuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods. Methods The COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis. Results The high-risk group was associated with shorter overall survival (OS) time in COAD patients (p<0.001). Multivariate Cox regression indicated that a high-risk score was an independent risk factor for poor prognosis (p<0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 3 years: 0.879). Gene Ontology (GO) enrichment analysis revealed that the signature was highly correlated with the immune response in biological processes. The immune function, the score of the immune cells, and the expression of immune checkpoints were significantly different between the two risk groups. Three drugs, LAQ824, FH535, YM155, were found to be more sensitive in the high-risk group. Finally, the expression levels of the ten lncRNAs comprising the signature were tested by qRT-PCR. Conclusion A ten-cuproptosis-related lncRNA signature was constructed that provided promising insights into personalized prognosis and drug selection among COAD patients.

Project description:BackgroundColon adenocarcinoma (COAD) is a serious public health issue due to high incidence and mortality rate. This study aimed to identify possible tumor antigens and necroptosis subtypes of COAD for the development of mRNA vaccines and the selection of appropriate patients for precision therapy.MethodsGene expression profiles and clinical information for COAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. We comprehensively studied the alterations in necroptosis-related genes (NRGs) using cBioPortal, and screened the hub NRGs associated with the prognosis of patients with COAD using Gene Expression Profiling Interactive Analysis 2. Consensuses clustering analysis was performed to identify necroptosis subtypes. Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules of the NRGs. The necroptosis landscape of COAD was assessed using graph learning-based dimensionality reduction. Finally, a drug sensitivity analysis of the two necroptosis subtypes was performed.FindingsTwo tumor antigens, BLC-2-associated X protein (BAX) and interleukin 1 beta (IL1B) were identified based on their associations with prognosis of patients and antigen presenting cell infiltration. Two necroptosis subtypes (N1 and N2) were distinguished in patients with COAD, and they were characterized by their differential survival status and molecular expression levels of immune checkpoint proteins and immunogenetic cell death modulators. Furthermore, the necroptosis landscape of COAD indicated that individual patients had obvious heterogeneity. Co-expression modules were identified using WGCNA, and the hub NRGs were found to be involved in various immune processes. Drug sensitivity analysis indicated that there were significant differences in drug sensitivity between the N1 and N2 subtypes. Cell experiments suggested that both overexpression of BAX and IL1B promoted necroptosis of COAD cells and enhanced the cytotoxicity of CD8+ T cells.InterpretationBAX and IL1B are potential antigens for the development of anti-COAD mRNA vaccines, specifically for patients with the N2 subtype. Consequently, this study will guide the development of more effective immunotherapeutic approaches and the identification of appropriate patients.

Project description:BackgroundThe heterogeneity of tumor tissue is one of the reasons for the poor effect of tumor treatment, which is mainly affected by the tumor immune microenvironment and metabolic reprogramming. But more research is needed to find out how the tumor microenvironment (TME) and metabolic features of colon adenocarcinoma (COAD) are related.MethodsWe obtained the transcriptomic and clinical data information of COAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Consensus clustering analysis was used to identify different molecular subtypes, identify differentially expressed genes (DEGs) associated with immune-and metabolism-related genes (IMRGs) prognosis. Univariate and multivariable Cox regression analysis and Lasso regression analysis were applied to construct the prognostic models based on the IMRG risk score. The correlations between risk scores and TME, immune cell infiltration, and immune checkpoint genes were investigated. Lastly, potential appropriate drugs related to the risk score were screened by drug sensitivity analysis.ResultsBy consensus clustering analysis, we identified two distinct molecular subtypes. It was also found that the multilayered IMRG subtypes were associated with the patient's clinicopathological characteristics, prognosis, and TME cell infiltration characteristics. Meanwhile, a prognostic model based on the risk score of IMRGs was constructed and its predictive power was verified internally and externally. Clinicopathological analysis and nomogram give it better clinical guidance. The IMRG risk score plays a key role in immune microenvironment infiltration. Patients in the high-risk groups of microsatellite instability (MSI) and tumor mutational burden (TMB) were found to, although with poor prognosis, actively respond to immunotherapy. Furthermore, IMRG risk scores were significantly associated with immune checkpoint gene expression. The potential drug sensitivity study helps come up with and choose a chemotherapy treatment plan.ConclusionOur comprehensive analysis of IMRG signatures revealed a broad range of regulatory mechanisms affecting the tumor immune microenvironment (TIME), immune landscape, clinicopathological features, and prognosis. And to explore the potential drugs for immunotherapy. It will help to better understand the molecular mechanisms of COAD and provide new directions for disease treatment.

Project description:BackgroundIdentifying populations that benefit from immune checkpoint blockade (ICB) therapy remains a major challenge in the treatment of lung adenocarcinoma (LUAD). Existing programmed cell death (PCD) related prognostic models only consider a single mechanism, such as ferroptosis, necroptosis, and pyroptosis, and do not reflect the interaction of multiple mechanisms. This study aims to explore lncRNAs associated with multiple modes of PCD and reveal a risk signature to assess prognosis and treatment outcomes in LUAD patients.MethodsBased on expression data in The Cancer Genome Atlas (TCGA) database, ferroptosis, necroptosis, and pyroptosis-related lncRNAs (FNPRlncRNAs) were obtained by taking the intersection of ferroptosis-related lncRNAs (FRlncRNAs), necroptosis-related lncRNAs (NRlncRNAs), and pyroptosis-related lncRNAs (PRlncRNAs) differentially expressed in LUAD and normal tissues. Patients with complete survival information and expression data from TCGA database were randomly assigned to training and testing sets (1:1). Univariate, LASSO, and multivariate Cox regression analyses were performed on the training set, and a risk signature was established. Kaplan-Meier survival curves were used to verify the prognostic ability of risk signature, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy. We then analyzed molecular and immune profile differences between high and low-risk subgroups. T-cell dysfunction and Exclusion (TIDE) scores were used to assess the response to immunotherapy in each risk subgroup. Finally, three LUAD clusters (C1, C2, and C3) were identified according to the risk signature.ResultsPatients in the low-risk subgroup had higher overall survival (OS) than that in the high-risk subgroup in the K-M survival curve. The area under ROC curves (AUC) of 1-, 3-, and 5-year ROC were 0.742, 0.762, and 0.749 in the training set, and 0.672, 0.642, and 0.563 in the testing set, respectively. Compared with the high-risk subgroup, patients in the low-risk subgroup have beneficial tumor immune microenvironment and molecular characteristics, but are less likely to benefit from immunotherapy. Finally, the three LUAD clusters (C1, C2, C3) identified by risk signature had different responses to drug treatment.ConclusionsThe prognosis risk signature constructed using FNPRlncRNAs is helpful to predict the prognosis of LUAD and may contribute to its individualized treatment.

Dataset Information

Development and validation of a robust necroptosis related classifier for colon adenocarcinoma.

Publications

Development and validation of a robust necroptosis related classifier for colon adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets