Dataset Information

A Novel Pyroptosis-Related Gene Signature for Early-Stage Lung Squamous Cell Carcinoma.

ABSTRACT:

Background

Diagnosis of early stage lung squamous cell carcinoma (LUSC) has improved; however, a comprehensive analysis of prognostic signatures is needed.

Purpose

To identify, establish, and validate a signature model based on pyroptosis-related genes for prognostic predictions of early stage LUSC.

Patients and methods

Two independent cohorts were included. RNA-seq transcriptome data from patients with early stage LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Thirty-three pyroptosis-related genes were analyzed between early stage LUSC and normal lung tissues. Cox regression analysis, random survival forest, and least absolute shrinkage and selection operator algorithms established a three-gene signature. Kaplan-Meier survival and receiver-operating characteristic curves assessed the prognostic efficacy of the model. Single-sample gene set enrichment analysis (ssGSEA) assessed the relationship between pyroptosis and immune cells. Patients with early stage LUSC from the GSE74777 dataset were used for validation. Pyroptosis-related genes were verified by RT-qPCR and Western blotting.

Results

Twenty-three differentially expressed pyroptosis-related genes were identified in the LUSC and adjacent normal tissues. Three differentially expressed pyroptosis-related genes were identified as hub genes in early stage LUSC. Patients with early stage LUSC in the TCGA cohort were classified into low- and high-risk subgroups according to the risk score. Overall survival (OS) was significantly short in the high-risk subgroup versus the low-risk subgroup. A similar result was found for the GSE74777 dataset. ssGSEA of immune cells and immune-related pathways between the low- and high-risk subgroups may explain the different OS for patients with early-stage LUSC. IL-6 expression was upregulated, which was inconsistent with the bioinformatic analysis. NOD1 and CASP4 were downregulated in LUSC (all P < 0.05) versus normal lung tissues.

Conclusion

Differentially expressed pyroptosis-related genes may be involved in early stage LUSC. Pyroptosis-related genes are important in tumor immunity and may be potential prognostic predictors for early stage LUSC.

SUBMITTER: Li X

PROVIDER: S-EPMC8502038 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

A Novel Pyroptosis-Related Gene Signature for Early-Stage Lung Squamous Cell Carcinoma.

Li Xiaoyan X He Jie J

International journal of general medicine 20211005

<h4>Background</h4>Diagnosis of early stage lung squamous cell carcinoma (LUSC) has improved; however, a comprehensive analysis of prognostic signatures is needed.<h4>Purpose</h4>To identify, establish, and validate a signature model based on pyroptosis-related genes for prognostic predictions of early stage LUSC.<h4>Patients and methods</h4>Two independent cohorts were included. RNA-seq transcriptome data from patients with early stage LUSC were obtained from The Cancer Genome Atlas (TCGA) data ...[more]

PMID: 34675612

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Project description:BackgroundEarly-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.MethodsGene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.ResultsA signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.ConclusionA signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

Project description:BackgroundDysregulation of fatty acid metabolism (FAM) represents a significant metabolic alteration in tumorigenesis. However, the role of FAM-related genes (FAMRGs) in early-stage lung squamous cell carcinoma (LUSC) remains incompletely understood.MethodsA series of bioinformatic analyses and machine learning strategies were performed to construct a FAMRGs-based signature to predict prognosis and guide personalized treatment for early-stage LUSC patients. FAMRGs were screened through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signature Database (MSigDB). Prognosis FAMRGs were identified using univariate Cox regression, and unsupervised clustering analysis facilitated the division of the cohort into different clusters. The least absolute shrinkage and selection operator (LASSO)-Cox regression and multivariate regression analysis were employed to develop a FAMRGs-based signature for predicting overall survival (OS). A nomogram was subsequently constructed to facilitate risk assessment for individual patients. Comprehensive analyses of metabolic pathways, immune infiltration, immunomodulators, and potentially applicable drugs were conducted across different FAMRGs-related risk groups.ResultsThe FAMRGs-based signature, comprising nine genes (ACOT11, APOH, BMX, CYP2R1, DPEP3, FABP6, FADS2, GLYATL2, and THRSP), demonstrated robust predictive capabilities for prognosis in The Cancer Genome Atlas (TCGA)-LUSC dataset and validated across six independent Gene Expression Omnibus (GEO)-LUSC datasets. Notably, the FAMRGs-base signature exhibited superior prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, the signature was closely associated with immune cell infiltration, human leukocyte antigen (HLA) genes, and immune checkpoint genes expression. Additionally, the signature demonstrated potential sensitivity to chemo-/target-therapy.ConclusionsThe FAMRGs-based signature demonstrated superior sensitivity in predicting the prognosis of early-stage LUSC. Detecting FAMRGs may provide predictive targets for the development of clinical treatment strategies.

Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is an extremely heterogeneous malignant cancer with poor prognosis. Pyroptosis is defined as a novel inflammation-dependent programmed cell death. However, the pyroptosis-associated gene expression in HNSCC and their relationship with prognosis are still indistinct.Material and methodsWe acquired the mRNA expression information of HNSCC patients from publicly available TCGA and GEO databases. We compared the tumor issues and adjacent normal tissues in terms of the gene expression for the purpose of identifying differentially expressed genes (DEGs). Based on these genes, we established a risk signature by the LASSO Cox regression in the TCGA cohort and validated the results in a GEO cohort. We also verified the levels of relevant mRNA expression in the model by RT-qPCR analysis. Eventually, functional enrichment approach was carried out to explore the potential mechanisms.ResultsOur team found a total of 18 differentially expressed genes (DEGs) between the HNSCC and healthy samples, and 4 DEGs displayed a remarkable association with the overall survival (OS) (P < 0.05). A 4-gene signature was constructed, presenting beneficial forecast power in both TCGA and GEO cohorts. Our team categorized patients into a group with high risk and another group with low risk as per the average risk value of the 4-gene feature. The individuals in the low risk group displayed a notably greater OS compared with the high risk one (P < 0.01). The Cox regression study demonstrated the independent forecast capability of the risk score. The receiver operating characteristic approach facilitated the verification of the forecast function of the gene signature. Posterior to verification, 4 genes were aberrantly expressed in the HNSCC and healthy samples. Functional study displayed that these groups presented diverse immunity conditions.ConclusionPyroptosis-associated genes are pivotal for the prognosis of HNSCC and can serve as potential therapeutic targets.

Project description:BackgroundA growing body of evidence suggests that pyroptosis-related lncRNAs (PRncRNAs) are associated with the prognoses of tumor patients and their tumor immune microenvironments. However, the function of PRlncRNAs in lung squamous cell carcinoma (LUSC) remains unclear.MethodsWe downloaded the transcriptome and clinical information of 551 LUSC samples from the The Cancer Genome Atlas (TCGA) database and randomly separated patients with complete information into two cohorts. Based on the training cohort, we developed a pyroptosis-related signature. We then examined the signature in the test cohort and all retained patients. We also clustered two risk groups in each cohort according to the signature and performed survival analysis, functional analysis, tumor immune microenvironment analysis and drug sensitivity analysis.ResultsA prognostic signature containing five PRlncRNAs (AP001189.1, PICART1, LINC02555, AC010422.4, and AL606469.1) was developed. A principal component analysis (PCA) indicated better differentiation between patients with different risk scores. Kaplan-Meier (K-M) analysis demonstrated poorer survival among patients with higher risk scores (P < 0.001). A receiver operating characteristic (ROC) curve analysis provided evidence confirming the accuracy of the signature, and univariate (p = 0.005) and multivariate (p = 0.008) Cox regression analyses confirmed the independent value of the risk score in prognoses. Clinical subgroup validation indicated that the signature was more suitable for patients with early-stage LUSC. We also created a nomogram to increase the accuracy of the prediction. Moreover, functional analysis revealed that pathways related to tumor development and pyroptosis were enriched in the high-risk group. Furthermore, the prognostic signature was proven to be a predictor of sensitivity to immunotherapy and chemotherapy.ConclusionsWe developed a novel pyroptosis-associated signature with independent value for the prognosis of LUSC patients. PRlncRNAs are closely associated with the tumor immune microenvironment in LUSC and might offer new directions for immunotherapy.

Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is a life-threatening disease with poor prognosis. Pyroptosis has been recently disclosed as a programmed cell death triggered by invasive infection, involved in cancer development. However, the prognosis role of pyroptosis-related genes in HNSCC has not been discussed.MethodsThe RNA sequence data of pyroptosis-related genes were obtained from The Cancer Genome Atlas (TCGA) database. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were performed to screen the HNSCC survival-related signature genes. We established a HNSCC risk model with the identified prognostic genes, then divided the HNSCC patients into low- and high-risk subgroups according to median risk score. Moreover, we utilized Gene Expression Omnibus (GEO) dataset to validate the risk model. Go and KEGG analyses were conducted to reveal the potential function of differential expression of genes that identified between low- and high-risk subgroups. ESTIMATE algorithm was performed to investigate the immune infiltration of tumors. Correlation between signature gene expression and drug-sensitivity was disclosed by Spearman's analysis.ResultsWe constructed a HNSCC risk model with identified seven pyroptosis-related genes (CASP1, GSDME, IL6, NLRP1, NLRP2, NLRP6, and NOD2) as prognostic signature genes. High-risk subgroup of HNSCC patients in TCGA cohort correlated with lower survival probability than patients from low-risk subgroup (p < .001), and the result is verified with GEO dataset. In addition, 161 genes were identified differentially expressed between the low- and high-risk subgroups in the TCGA cohort, mainly related to immune response. Higher PD-L1 expression level was found in the high-risk subgroup that indicated the possible employment of immune checkpoint inhibitors. IL6 was positively correlated with WZ3105 and MPS-1-IN-1 in the cancer therapeutics response portal database.ConclusionWe built and verified a risk model for HNSCC prognosis using seven pyroptosis-related signature genes, which could predict the overall survival of HNSCC patients and facilitate treatment.

Dataset Information

A Novel Pyroptosis-Related Gene Signature for Early-Stage Lung Squamous Cell Carcinoma.

Background

Purpose

Patients and methods

Results

Conclusion

Publications

A Novel Pyroptosis-Related Gene Signature for Early-Stage Lung Squamous Cell Carcinoma.

Similar Datasets

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