Constructing novel molecular subtypes and an 11-gene signature based on pyroptosis signaling for lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: Li L
PROVIDER: S-EPMC9360228 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
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Constructing novel molecular subtypes and an 11-gene signature based on pyroptosis signaling for lung adenocarcinoma.
Li Lu L He Qing Q Tao Zhenchao Z Zhang Rui R Cui Yayun Y Qian Liting L
American journal of cancer research 20220715 7
Pyroptosis plays important roles in various cancers. In this study, we focused on lung adenocarcinoma (LUAD) and aimed to develop new molecular subtypes based on pyroptosis signaling. Pyroptosis-related genes were used as a basis to classify molecular subtypes through unsupervised consensus clustering. Gene set enrichment analysis was performed to characterize tumor microenvironment (TME) and functional pathways. Univariate Cox regression and least absolute shrinkage and selection operator (LASS ...[more]
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Project description:BackgroundTreatment of cancer with pyroptosis is an emerging strategy. Molecular subtypes based on pyroptosis-related genes(PRGs) seem to be considered more conducive to individualized therapy. It is meaningful to construct a pyroptosis molecular subtypes-related prognostic signature (PMSRPS) to predict the overall survival (OS) of patients with pancreatic adenocarcinoma(PAAD) and guide treatment.MethodsBased on the transcriptome data of 23 PRGs, consensus clustering was applied to divide the TCGA and GSE102238 combined cohort into three PRGclusters. Prognosis-related differentially expressed genes(DEGs) among PRGclusters were subjected to LASSO Cox regression analysis to determine a PMSRPS. External cohort and in vitro experiments were conducted to verify this PMSRPS. The CIBERSORT algorithm, the ESTIMATE algorithm and the Immunophenoscore (IPS) were used to analyze the infiltrating abundance of immune cells, the tumor microenvironment (TME), and the response to immunotherapy, respectively. Wilcoxon analysis was used to compare tumor mutational burden (TMB) and RNA stemness scores (RNAss) between groups. RT-qPCR and in vitro functional experiments were used for evaluating the expression and function of SFTA2.ResultsBased on three PRGclusters, 828 DEGs were obtained and a PMSRPS was subsequently constructed. In internal and external validation, patients in the high-risk group had significantly lower OS than those in the low-risk group and PMSRPS was confirmed to be an independent prognostic risk factor for patients with PAAD with good predictive performance. Immune cell infiltration abundance and TME scores indicate patients in the high-risk group have typical immunosuppressive microenvironment characteristics. Analysis of IPS suggests patients in the high-risk group responded better to novel immune checkpoint inhibitors (ICIs) than PD1/CTLA4. The high-risk group had higher TMB and RNAss. In addition, 10 potential small-molecule compounds were screened out. Finally, we found that the mRNA expression of SFTA2 gene with the highest risk coefficient in PMSRPS was significantly higher in PAAD than in paracancerous tissues, and knockdown of it significantly delayed the progression of PAAD.ConclusionsPMSRPS can well predict the prognosis, TME and immunotherapy response of patients with PAAD, identify potential drugs, and provide treatment guidance based on individual needs.
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Project description:BackgroundPyroptosis is a newly found form of programmed cell death, accompanied by inflammatory response as well as immune response. Here, the specific function and prognosis predictive of pyroptosis-related genes (PRGs) were systematically explored in lung adenocarcinoma (LUAD).MethodsThe gene expression data and corresponding clinical information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA), and the expression level of PRGs was identified between normal and tumor tissues. Furthermore, univariate Cox proportional hazards regression was conducted to filter the PRGs related to overall survival, and least absolute shrinkage and selection operator (LASSO) regression was subsequent employed to establish the PRGs risk model. Besides, the correlation of risk score with patients' clinical features, tumor mutational burden (TMB) as well as tumor microenvironment (TME) was also investigated.ResultsA total of 5 PRGs (NLRC4, NLRP1, NLRP3, NOD1, PLCG1, and BAK1) was used to establish the risk prognostic model. According the median value of risk score, all the patients were classified into low- and high-risk score group. Kaplan-Meier analysis indicted that the LUAD patients in low-risk group exhibited a better survival outcome compared the patients in high-risk group (P<0.001). After adjusting for age, gender, and clinical stage, the risk score was also considered as and independent risk factor affecting the overall survival of LUAD patients (HR =2.949, 95% CI: 1.762-4.937). Moreover, low-risk score group exhibited a higher Immune score and lower Tumor purity compared with high-risk score group. ssGSEA results proved that the enrichment scores of most immune cells and immune related signal pathway in low-risk score group was significant higher than that in high-risk score group. In addition, the PRGs risk score was also positive correlated with TMB in LUAD tissues.ConclusionsIn this study, a novel prognostic model based on PRGs was constructed and used to predict the survival outcome of LUAD patients. In addition, the PRGs risk signature was also associated with TMB and anti-tumor immune environment. The induction of pyroptosis inside tumors might be considered a potential strategy in cancer treatments.
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Project description:Mast cells are a major component of the immune microenvironment in tumour tissues and modulate tumour progression by releasing pro-tumorigenic and antitumorigenic molecules. Regarding the impact of mast cells on the outcomes of patients with lung adenocarcinoma (LUAD) patient, several published studies have shown contradictory results. Here, we aimed at elucidating the role of mast cells in early-stage LUAD. We found that high mast cell abundance was correlated with prolonged survival in early-stage LUAD patients. The mast cell-related gene signature and gene mutation data sets were used to stratify early-stage LUAD patients into two molecular subtypes (subtype 1 and subtype 2). The neural network-based framework constructed with the mast cell-related signature showed high accuracy in predicting response to immunotherapy. Importantly, the prognostic mast cell-related signature predicted the survival probability and the potential relationship between TP53 mutation, c-MYC activation and mast cell activities. The meta-analysis confirmed the prognostic value of the mast cell-related gene signature. In summary, this study might improve our understanding of the role of mast cells in early-stage LUAD and aid in the development of immunotherapy and personalized treatments for early-stage LUAD patients.
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