Dataset Information

Identification and validation of a necroptosis-related gene prognostic signature for colon adenocarcinoma.

ABSTRACT:

Background

Necroptosis is a novel programmed cell death pathway proposed in 2005, which is mainly activated by the tumor necrosis factor (TNF) family and mediates cellular disassembly via receptor interacting serine/threonine kinase 1 (RIPK1), receptor interacting serine/threonine kinase 3 (RIPK3) and mixed lineage kinase domain like pseudokinase (MLKL). We tried to analyze the relationship of necroptosis-related genes (NRGs) expression with colon adenocarcinoma (COAD) and propose potential therapeutic targets through immunological analysis.

Methods

First, we evaluated the expression of NRGs in COAD patients and constructed a prognostic signature. The prognostic signature was validated using The Cancer Genome Atlas (TCGA)-COAD and GSE39582 datasets, respectively. And the Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis were used to evaluate the signature. Then we analyzed the enrichment of NRGs in the signature using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Finally, we analyzed the immunological characteristics of the COAD patients by single sample gene set enrichment analysis (ssGSEA) and predicted the possible immune checkpoints.

Results

We constructed a prognostic signature with 8 NRGs (RIPK3, MLKL, TRAF2, CXCL1, RBCK1, CDKN2A, JMJD7-PLA2G4B and CAMK2B). The Kaplan-Meier analysis, ROC curves, and principal component analysis demonstrated good predictivity of the signature. In addition, we constructed a nomogram with good individualized predictive ability (C-index =0.772). The immunological analysis revealed that the prognosis of COAD was associated with autoimmune function, and we proposed 10 potential therapeutic targets.

Conclusions

Overall, we constructed an NRGs prognostic signature and suggested potential therapeutic targets for the COAD treatment.

SUBMITTER: Zhang J

PROVIDER: S-EPMC10583017 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Identification and validation of a necroptosis-related gene prognostic signature for colon adenocarcinoma.

Zhang Jingyao J Liu Ziyue Z Chen Wenhao W Liu Hengchen H

Translational cancer research 20230831 9

<h4>Background</h4>Necroptosis is a novel programmed cell death pathway proposed in 2005, which is mainly activated by the tumor necrosis factor (TNF) family and mediates cellular disassembly via receptor interacting serine/threonine kinase 1 (RIPK1), receptor interacting serine/threonine kinase 3 (<i>RIPK3</i>) and mixed lineage kinase domain like pseudokinase (<i>MLKL</i>). We tried to analyze the relationship of necroptosis-related genes (NRGs) expression with colon adenocarcinoma (COAD) and ...[more]

PMID: 37859737

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Project description:BackgroundThe purpose of this study was to investigate the prognostic signature of necroptosis-related lncRNAs (NRLs) and explore their association with immune-related functions and sensitivity of the therapeutic drug in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).MethodsUCSC Xena provided lncRNA sequencing and clinical data about CESC, and a necroptosis gene list was obtained from the KEGG database. NRLs were selected by structuring a co-expression network of lncRNAs and necroptosis-related genes. To further screen lncRNAs, we used the univariate Cox regression method, Lasso regression, and multivariate Cox regression. Afterward, an NRL signature was established. We used the xCell algorithm and single-sample gene set enrichment analysis (ssGSEA) to clarify the pertinence between immune infiltration and NRL expressions in CESC patients and explored the relationship between the target lncRNAs and immune-related genes. By leveraging the GDSC database, the therapy-sensitive response of the prognostic signature was forecasted and an experimental validation was performed. We performed GSEA with the aim of recognizing the potential pathway related to the individual prognostic signature.ResultsThe two prognostic NRLs (AC009095.1 and AC005332.4) showed significant diversity and constituted the NRL signature. On the grounds of our signature, risk score was an independent element which was bound up with patient outcome (HR = 4.97 CI: 1.87-13.2, P = 0.001). The CESC patients were classified by the median risk score. Immune infiltration analysis revealed significant increases in CD4 + Tcm, eosinophils, epithelial cells, fibroblasts, NKT, plasma cells, platelets, and smooth muscle in the high-risk group (P< 0.05). Target lncRNAs also showed some correlation with NRGs. The estimated IC50 values of bicalutamide, CHIR.99021, and imatinib were lower in the high-risk group. Through the subsequent experimental validation, both AC009095.1 and AC005332.4 were significantly more highly expressed in SiHa than in Hela. AC009095.1 was expressed more highly in SiHa than in HUCEC, but the expression of AC005332.4 was reversed.ConclusionsThis study elucidated that NRLs, as a novel signature, were indispensable factors which can significantly influence the prognosis of patients with CESC and could provide novel clinical evidence to serve as a potential molecular biomarker for future therapeutic targets.

Project description:BackgroundEsophageal carcinoma (ESCA) is a common malignancy with a poor prognosis. Previous research has suggested that necroptosis is involved in anti-tumor immunity and promotes oncogenesis and cancer metastasis, which in turn affects tumor prognosis. However, the role of necroptosis in ESCA is unclear. This study aimed to investigate the relationships between necroptosis-related genes (NRGs) and ESCA.Methods and resultsThe clinical data and gene expression profiles of ESCA patients were extracted from The Cancer Genome Atlas (TCGA), and 159 NRGs were screened from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We then identified 52 differentially expressed NRGs associated with ESCA and used them for further analysis. Gene ontology (GO) and KEGG functional enrichment analyses showed that these NRGs were mostly associated with the regulation of necroptosis, Influenza A, apoptosis, NOD-like receptor, and NF-Kappa B signaling pathway. Next, univariate and multivariate Cox regression and LASSO analysis were used to identify the correlation between NRGs and the prognosis of ESCA. We constructed a prognostic model to predict the prognosis of ESCA based on SLC25A5, PPIA, and TNFRSF10B; the model classified patients into high- and low-risk subgroups based on the patient's risk score. Furthermore, the receiver operating characteristic (ROC) curve was plotted, and the model was affirmed to perform moderately well for prognostic predictions. In addition, Gene Expression Omnibus (GEO) datasets were selected to validate the applicability and prognostic value of our predictive model. Based on different clinical variables, we compared the risk scores between the subgroups of different clinical features. We also analyzed the predictive value of this model for drug sensitivity. Moreover, Immunohistochemical (IHC) validation experiments explored that these three NRGs were expressed significantly higher in ESCA tissues than in adjacent non-tumor tissues. In addition, a significant correlation was observed between the three NRGs and immune-cell infiltration and immune checkpoints in ESCA.ConclusionsIn summary, we successfully constructed and validated a novel necroptosis-related signature containing three genes (SLC25A5, PPIA, and TNFRSF10B) for predicting prognosis in patients with ESCA; these three genes might also play a crucial role in the progression and immune microenvironment of ESCA.

Project description:Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival‑associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was used to derive a risk signature which is significantly negatively correlated with OS and divide samples into high-, medium- and low-risk group. Univariate and multivariate Cox analyses suggested that the level of risk group was an independent prognostic factor of the overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5-years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30219 of Gene Expression Omnibus (GEO) was used for verification. We further explored the differences among risk groups in Gene set enrichment analysis (GSEA), tumor mutation and tumor microenvironment. Furthermore, Single Sample Gene Set Enrichment Analysis (ssGSEA) and the results of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) suggested the status of immune cell infiltration was highly associated with risk groups. We demonstrated the prediction effect of CTLA-4 and PD-1/PD-L1 inhibitors in the low-risk group was better than that in the high-risk group using two methods of immune score include immunophenoscore from The Cancer Immunome Atlas (TCIA) and TIDE score from Tumor Immune Dysfunction and Exclusion (TIDE). In addition, partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. Our findings provide a foundation for future research targeting inflammation-related genes to predictive prognosis and some reference significance for the selection of immunotherapy and drug regimen for lung adenocarcinoma.

Project description:Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors with a poor prognosis. Recently, necroptosis has been reported to participate in the progression of multiple tumors. However, few studies have revealed the relationship between necroptosis and PDAC, and the role of necroptosis in PDAC has not yet been clarified. Methods: The mRNA expression data and corresponding clinical information of PDAC patients were downloaded from the TCGA and GEO databases. The necroptosis-related genes (NRGs) were obtained from the CUSABIO website. Consensus clustering was performed to divide PDAC patients into two clusters. Univariate and LASSO Cox regression analyses were applied to screen the NRGs related to prognosis to construct the prognostic model. The predictive value of the prognostic model was evaluated by Kaplan-Meier survival analysis and ROC curve. Univariate and multivariate Cox regression analyses were used to evaluate whether the risk score could be used as an independent predictor of PDAC prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA) were used for functional enrichment analysis. Finally, using qRT-PCR examined NRGs mRNA expression in vitro. Results: Based on the TCGA database, a total of 22 differential expressed NRGs were identified, among which eight NRGs (CAPN2, CHMP4C, PLA2G4F, PYGB, BCL2, JAK3, PLA2G4C and STAT4) that may be related to prognosis were screened by univariate Cox regression analysis. And CAPN2, CHMP4C, PLA2G4C and STAT4 were further selected to construct the prognostic model. Kaplan-Meier survival analysis and ROC curve showed that there was a significant correlation between the risk model and prognosis. Univariate and multivariate Cox regression analyses showed that the risk score of the prognostic model could be used as an independent predictor. The model efficacy was further demonstrated in the GEO cohort. Functional analysis revealed that there were significant differences in immune status between high and low-risk groups. Finally, the qRT-PCR results revealed a similar dysregulation of NRGs in PDAC cell lines. Conclusion: This study successfully constructed and verified a prognostic model based on NRGs, which has a good predictive value for the prognosis of PDAC patients.

Project description: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.

Dataset Information

Identification and validation of a necroptosis-related gene prognostic signature for colon adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Identification and validation of a necroptosis-related gene prognostic signature for colon adenocarcinoma.

Similar Datasets

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