Identification of a Pyroptosis-Related Gene Signature for Prediction of Overall Survival in Lung Adenocarcinoma.
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ABSTRACT:
SUBMITTER: Dong Z
PROVIDER: S-EPMC8497135 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Lung adenocarcinoma (LUAD) is a highly malignant cancer with a bleak prognosis. Pyroptosis is crucial in LUAD. The present study investigated the prognostic value of a pyroptosis-related signature in LUAD. Methods: LUAD's genomic data were downloaded from TCGA and GEO databases. K-means clustering was used to classify the data based on pyroptosis-related genes (PRGs). The features of tumor microenvironment were compared between the two subtypes. Differentially expressed genes (DEGs) were identified between the two subtypes, and functional enrichment and module analysis were carried out. LASSO Cox regression was used to build a prognostic model. Its prognostic value was assessed. Results: In LUAD, genetic and transcriptional changes in PRGs were found. A total of 30 PRGs were found to be differentially expressed in LUAD tissues. Based on PRGs, LUAD patients were divided into two subgroups. Subtype 1 has a higher overall survival rate than subtype 2. The tumor microenvironment characteristics of the two subtypes differed significantly. Compared to subtype 1, subtype 2 had strong immunological infiltration. Between the two groups, 719 DEGs were discovered. WGCNA used these DEGs to build a co-expression network. The network modules were analyzed. A prognostic model based on seven genes was developed, including FOSL1, KRT6A, GPR133, TMPRSS2, PRDM16, SFTPB, and SFTA3. The developed model was linked to overall survival and response to immunotherapy in patients with LUAD. Conclusion: In LUAD, a pyroptosis-related signature was developed to predict overall survival and treatment responses to immunotherapy.
| S-EPMC9252528 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor worldwide, with an increasing incidence and mortality. Due to general resistance to antitumor drugs, only limited therapies are currently available for advanced HCC patients, leading to a poor prognosis with a 5-year survival rate less than 20%. Pyroptosis is a type of inflammation-related programmed cell death and may become a new potential target for cancer therapy. However, the function and prognostic value of pyroptosis-related genes (PRGs) in HCC remain unknown. Here, we identified a total of 58 PRGs reported before and conducted a six-PRG signature via the LASSO regression method in the GEO training cohort, and model efficacy was further validated in an external dataset. The HCC patients can be classified into two subgroups based on the median risk score. High-risk patients have significantly shorter overall survival (OS) than low-risk patients in both training and validation cohorts. Multivariable analysis indicated that the risk score was an independent prognostic factor for OS of HCC patients. Functional enrichment analysis and immune infiltration evaluation suggested that immune status was more activated in the low-risk group. In summary, PRGs can be a prediction factor for prognosis of HCC patients and targeting pyroptosis is a potential therapeutic alternative in HCC.
| S-EPMC8606528 | biostudies-literature
Project description:We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA-miRNA-lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.
| S-EPMC10504370 | biostudies-literature
Project description:Background: Ferroptosis is an iron-dependent programmed cell death process. Recent studies have found that ferroptosis inducers hold promising potential in the treatment of lung adenocarcinoma (LUAD). However, the comprehensive analysis about the prognostic value of ferroptosis-related genes in LUAD remains to be elucidated. Methods: The RNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 259 ferroptosis-related genes were extracted from FerrDb website. The ferroptosis-related prognostic signature was developed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis in TCGA LUAD cohort, and then validated by 5 independent GEO cohorts. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to identify the difference in biological processes and functions between different risk groups. The expression levels of core prognostic genes were then verified in LUAD samples by immunohistochemistry (IHC) and erastin-treated LUAD cell lines by real-time polymerase chain reaction (PCR). The potential roles of GPX2 and DDIT4 as ferroptosis drivers in LUAD cell line were further confirmed by in vitro experiments. Results: A total of 20 intersecting genes between 70 ferroptosis-related DEGs and 45 potential prognostic genes were obtained for LASSO Cox regression analysis. The ferroptosis-related prognostic signature was developed by 7 core prognostic DEGs, and stratified LUAD patients into two risk groups. Kaplan-Meier analysis showed that the overall survival (OS) of LUAD patients in the high-risk group was significantly worse than that of the low-risk group. External validation of 5 independent GEO cohorts further confirmed that the ferroptosis-related prognostic signature was an ideal biomarker for predicting the survival of LUAD patients. Significant enrichment of fatty acid metabolism and cell cycle-related pathways were found in different risk groups. The expression patterns of 7 core prognostic genes in LUAD and adjacent normal lung tissues were validated by IHC, which was almost consistent with the results from public database. Furthermore, the changes related to cell cycle and ferroptosis after erastin treatment were also validated in LUAD cell lines. In addition, silencing GPX2 or DDIT4 could partially reverse the erastin-induced ferroptosis. Conclusion: In summary, the ferroptosis-related prognostic signature based on 7 core prognostic DEGs indicated superior predictive performance of LUAD patients. Targeting ferroptosis holds potential to be a therapeutic alternative for LUAD.
| S-EPMC8294813 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer which is the leading cause of death in cancer patients. Circadian clock disruption has been listed as a likely carcinogen. However, whether the expression of circadian genes affects overall survival (OS) in LUAD patients remains unknown. In this article, we identified a circadian gene signature to predict overall survival in LUAD.MethodsRNA sequencing (HTSeq-FPKM) data and clinical characteristics were obtained for a cohort of LUAD patients from The Cancer Genome Atlas (TCGA). A multigene signature based on differentially expressed circadian clock-related genes was generated for the prediction of OS using Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and externally validated using the GSE72094 dataset from the GEO database.ResultsFive differentially expressed genes (DEGs) were identified to be significantly associated with OS using univariate Cox proportional regression analysis (P < 0.05). Patients classified as high risk based on these five DEGs had significantly lower OS than those classified as low risk in both the TGCA cohort and GSE72094 dataset (P < 0.001). Multivariate Cox regression analysis revealed that the five-gene-signature based risk score was an independent predictor of OS (hazard ratio > 1, P < 0.001). Receiver operating characteristic (ROC) curves confirmed its prognostic value. Gene set enrichment analysis (GSEA) showed that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to cell proliferation, gene damage repair, proteasomes, and immune and autoimmune diseases were significantly enriched.ConclusionA novel circadian gene signature for OS in LUAD was found to be predictive in both the derivation and validation cohorts. Targeting circadian genes is a potential therapeutic option in LUAD.
| S-EPMC8272922 | biostudies-literature
Project description:BackgroundThe widespread use of low-dose chest CT screening has improved the detection of early lung adenocarcinoma. Radical surgery is the best treatment strategy for patients with early lung adenocarcinoma; however, some patients present with postoperative recurrence and poor prognosis. Through this study, we hope to establish a model that can identify patients that are prone to recurrence and have poor prognosis after surgery for early lung adenocarcinoma.Materials and methodsWe screened prognostic and relapse-related genes using The Cancer Genome Atlas (TCGA) database and the GSE50081 dataset from the Gene Expression Omnibus (GEO) database. The GSE30219 dataset was used to further screen target genes and construct a risk prognosis signature. Time-dependent ROC analysis, calibration degree analysis, and DCA were used to evaluate the reliability of the model. We validated the TCGA dataset, GSE50081, and GSE30219 internally. External validation was conducted in the GSE31210 dataset.ResultsA novel four-gene signature (INPP5B, FOSL2, CDCA3, RASAL2) was established to predict relapse-related survival outcomes in patients with early lung adenocarcinoma after surgery. The discovery of these genes may reveal the molecular mechanism of recurrence and poor prognosis of early lung adenocarcinoma. In addition, ROC analysis, calibration analysis and DCA were used to verify the genetic signature internally and externally. Our results showed that our gene signature had a good predictive ability for recurrence and prognosis.ConclusionsWe established a four-gene signature and predictive model to predict the recurrence and corresponding survival rates in patients with early lung adenocarcinoma after surgery. These may be helpful for reforumulating post-operative consolidation treatment strategies.
| S-EPMC8349519 | biostudies-literature
Project description:Background: Lung cancer is the leading cause of cancer-related death worldwide, of which lung adenocarcinoma (LUAD) is one of the main histological subtypes. Mitochondria are vital for maintaining the physiological function, and their dysfunction has been found to be correlated with tumorigenesis and disease progression. Although, some mitochondrial-related genes have been found to correlate with the clinical outcomes of multiple tumors solely. The integrated relationship between nuclear mitochondrial genes (NMGs) and the prognosis of LUAD remains unclear. Methods: The list of NMGs, gene expression data, and related clinical information of LUAD were downloaded from public databases. Bioinformatics methods were used and obtained 18 prognostic related NMGs to construct a risk signature. Results: There were 18 NMGs (NDUFS2, ATP8A2, SCO1, COX14, COA6, RRM2B, TFAM, DARS2, GARS, YARS2, EFG1, GFM1, MRPL3, MRPL44, ISCU, CABC1, HSPD1, and ETHE1) identified by LASSO regression analysis. The mRNA expression of these 18 genes was positively correlated with their relative linear copy number alteration (CNA). Meanwhile, the established risk signature could effectively distinguish high- and low-risk patients, and its predictive capacity was validated in three independent gene expression omnibus (GEO) cohorts. Notably, a significantly lower prevalence of actionable EGFR alterations was presented in patients with high-risk NMGs signature but accompanied with a more inflame immune tumor microenvironment. Additionally, multicomponent Cox regression analysis showed that the model was stable when risk score, tumor stage, and lymph node stage were considered, and the 1-, 3-, and 5-year AUC were 0.74, 0.75, and 0.70, respectively. Conclusion: Together, this study established a signature based on NMGs that is a prognostic biomarker for LUAD patients and has the potential to be widely applied in future clinical settings.
| S-EPMC8573348 | biostudies-literature
Project description:Pyroptosis, defined as programmed cell death, results in the release of inflammatory mediators. Recent studies have revealed that pyroptosis plays essential roles in antitumor immunity and immunotherapy efficacy. Long noncoding RNAs (lncRNAs) are involved in a variety of biological behaviors in tumor cells, although the roles and mechanisms of lncRNAs in pyroptosis are rarely studied. Our study aimed to establish a novel pyroptosis-related lncRNA signature as a forecasting tool for predicting prognosis and ascertaining immune value. Based on lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA), we performed Pearson's correlation analysis to identify pyroptosis-related lncRNAs. After differentially expressed gene analysis and univariate Cox regression analysis, we selected prognosis-related and differentially expressed lncRNAs. Finally, we performed multivariate Cox regression analysis to establish the three pyroptosis-related lncRNA signature. Kaplan-Meier (KM) survival analyses and receiver operating characteristic (ROC) curves indicated the excellent performance for predicting the prognosis of LUAD patients. At the same time, we applied multidimensional approaches to further explore the functional enrichment, tumor microenvironment (TME) landscape, and immunotherapy efficacy among the different risk groups. A nomogram was constructed by integrating risk scores and clinical characteristics, which was validated using calibrations and ROC curves. Three lncRNAs, namely, AC090559.1, AC034102.8, and AC026355.2, were involved in this signature and used to classify LUAD patients into low- and high-risk groups. Overall survival time (OS) was higher in the low-risk group than in the high-risk group, which was also validated in our LUAD cohort from Shandong Provincial Hospital. TME landscape analyses revealed that a higher abundance of infiltrating immune cells and a greater prevalence of immune-related events existed in the low-risk group. Meanwhile, higher expression of immune checkpoint (ICP) genes, higher immunophenoscore (IPSs), and greater T cell dysfunction in the low-risk group demonstrated a better response to immunotherapy than the high-risk group. Combined with predictions from the Tumor Immune Dysfunction and Exclusion (TIDE) website, we found that LUAD patients in the low-risk group significantly benefited from programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) immune checkpoint blockade (ICB) therapy compared with those in the high-risk group. Furthermore, drug susceptibility analysis identified potential sensitive chemotherapeutic drugs for each risk group. In this study, a novel three pyroptosis-related lncRNA signature was constructed, which could accurately predict the immunotherapy efficacy and prognosis in LUAD patients.
| S-EPMC9345371 | biostudies-literature
Project description:BackgroundLung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) is one of the main subtypes of lung cancer. Hundreds of metabolic genes are altered consistently in LUAD; however, their prognostic role remains to be explored. This study aimed to establish a molecular signature that can predict the prognosis in patients with LUAD based on metabolic gene expression.MethodsThe transcriptome expression profiles and corresponding clinical information of LUAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) between LUAD and paired non-tumor samples were identified by the Wilcoxon rank sum test. Univariate Cox regression analysis and the lasso Cox regression model were used to construct the best-prognosis molecular signature. A nomogram was established comprising the prognostic model for predicting overall survival. To validate the prognostic ability of the molecular signature and the nomogram, the Kaplan-Meier survival analysis, Cox proportional hazards model, and receiver operating characteristic analysis were used.ResultsThe six-gene molecular signature (PFKP, PKM, TPI1, LDHA, PTGES, and TYMS) from the DEGs was constructed to predict the prognosis. The molecular signature demonstrated a robust independent prognostic ability in the training and validation sets. The nomogram including the prognostic model had a greater predictive accuracy than previous systems. Furthermore, a gene set enrichment analysis revealed several significantly enriched metabolic pathways, which suggests a correlation of the molecular signature with metabolic systems and may help explain the underlying mechanisms.ConclusionsOur study identified a novel six-gene metabolic signature for LUAD prognosis prediction. The molecular signature could reflect the dysregulated metabolic microenvironment, provide potential biomarkers for predicting prognosis, and indicate potential novel metabolic molecular-targeted therapies.
| S-EPMC7718790 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. Pyroptosis could regulate tumour cell proliferation, invasion, and metastasis, thereby affecting the prognosis of cancer patients. However, the role of pyroptosis-related genes (PRGs) in LUAD remains unclear. In our study, comprehensive bioinformatics analysis was performed to construct a prognostic gene model and ceRNA network. The correlations between PRGs and tumour-immune infiltration, tumour mutation burden, and microsatellite instability were evaluated using Pearson's correlation analysis. A total of 23 PRGs were upregulated or downregulated in LUAD. The genetic mutation variation landscape of PRG in LUAD was also summarised. Functional enrichment analysis revealed that these 33 PRGs were mainly involved in pyroptosis, the NOD-like receptor signalling pathway, and the Toll-like receptor signalling pathway. Prognosis analysis indicated a poor survival rate in LUAD patients with low expression of NLRP7, NLRP1, NLRP2, and NOD1 and high CASP6 expression. A prognostic PRG model constructed using the above five prognostic genes could predict the overall survival of LUAD patients with medium-to-high accuracy. Significant correlation was observed between prognostic PRGs and immune-cell infiltration, tumour mutation burden, and microsatellite instability. A ceRNA network was constructed to identify a lncRNA KCNQ1OT1/miR-335-5p/NLRP1/NLRP7 regulatory axis in LUAD. In conclusion, we performed a comprehensive bioinformatics analysis and identified a prognostic PRG signature containing five genes (NLRP7, NLRP1, NLRP2, NOD1, and CASP6) for LUAD patients. Our results also identified a lncRNA KCNQ1OT1/miR-335-5p/NLRP1/NLRP7 regulatory axis, which may also play an important role in the progression of LUAD. Further study needs to be conducted to verify this result.
| S-EPMC8257680 | biostudies-literature