Dataset Information

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma.

ABSTRACT: Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.

SUBMITTER: Fu D

PROVIDER: S-EPMC7485220 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma.

Fu Denggang D Zhang Biyu B Yang Lei L Huang Shaoxin S Xin Wang W

Frontiers in genetics 20200828

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted usin ...[more]

PMID: 33005178

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Project description:Lung squamous cell carcinoma has so far lacked effective targets for diagnosis and treatment. In cancer research, long noncoding RNAs (LncRNAs) emerge as novel therapeutic targets and biomarkers. Cuprophosis is a new death type involving multiple biological processes in tumor cells. Here, we aimed to explore whether Cuprophosis-related lncRNAs could be used to predict prognosis, assess immune function, and test drug sensitivity in LUSC patients. The Cancer Genome Map (TCGA) was used to obtain genome and clinical data, and Cuprophosis-relevant genes were found in the literature. A cuproptosis-related lncRNA risk model was built using co-expression analysis, univariate/multivariate Cox regression, and LASSO analysis. The survival analysis was used to assess the model's prognostic value. The univariate and multivariate Cox regression analyses were performed to determine whether risk score, age, gender, or clinical stages could be used as independent prognostic factors. Gene Set Enrichment Analysis and mutation analysis were performed on differentially expressed mRNA between high-risk and low-risk groups. The (TIDE) algorithm was used to conduct immunological functional analysis and drug sensitivity testing. Five cuproptosis-related LncRNAs were identified, and the selected LncRNAs constructed a prognosis model. According to the Kaplan-Meier survival analysis, the overall survival time for patients in the high-risk group was shorter than for those in the low-risk group. For LUSC patients, the risk score serves as an independent prognostic indicator. The GO and KEGG enrichment analysis revealed that the differentially expressed mRNAs between the high- and low-risk groups were enriched in several immune-related processes. The enrichment score of differentially expressed mRNAs in the high-risk group is higher than that of the low-risk group in multiple immune function pathways, including the IFN-γ and MHC I pathways. The Tumor Immune Dysfunction and Exclusion (TIDE) test revealed that the high-risk group was more likely to experience immune escape. The drug sensitivity analysis showed that patients with low-risk ratings were likely to respond to GW441756 and Salubrinal. In contrast, patients with higher risk scores were more responsive to dasatinib and Z-LLNIe CHO. The 5-Cuprophosis-related lncRNA signature can be used to predict prognosis, assess immune function, and test drug sensitivity in LUSC patients.

Project description:BackgroundLung squamous cell carcinoma (LUSC) is a malignant tumour of the lung epithelium. A hypoxic environment can promote tumour cell proliferation and invasion. Therefore, this study aims to explore hypoxia-related genes and construct reliable models to predict the prognosis, cellular processes, immune microenvironment and target compounds of lung squamous carcinoma.MethodsThe transcriptome data and matched clinical information of LUSC were retrieved from The Cancer Genome Atlas (TCGA) database. The GSVA algorithm calculated each LUSC patient's hypoxia score, and all LUSC patients were divided into the high hypoxia score group and low hypoxia score group. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to screen out differentially expressed hypoxia-related genes (DE-HRGs) in LUSC microenvironment, and the underlying regulatory mechanism of DE-HRGs in LUSC was explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hereafter, we established a prognosis-related genetic signature for DE-HRGs using univariate and multivariate Cox regression analyses. The relationship between gene signature and immune cells was further evaluated. Finally, the Comparative Toxicogenomics Database (CTD) was utilized to predict the targeted drugs for the prognostic genes.ResultsWe obtained 376 DE-HRGs. Functional enrichment analysis indicated that the DE-HRGs were involved in the cell cycle-related regulatory processes. Next, we developed and validated 3 HRGs-based prognostic signature for LUSC, including HELLS, GPRIN1, and FAM83A. Risk score is an independent prognostic factor for LUSC. Functional enrichment analysis and immune landscape analysis suggested that the risk scoring system might be involved in altering the immune microenvironment of LUSC patients to influence patient outcomes. Ultimately, a total of 92 potential compounds were predicted for the three prognostic genes.ConclusionIn summary, we developed and validated a hypoxia-related model for LUSC, reflecting the cellular processes and immune microenvironment characteristics and predicting the prognostic outcomes and targeted compounds.

Project description:BackgroundImmune-related genes (IRGs) were linked to the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to identify the effects of an immune-related gene signature (IRGS) that can predict the of HNSCC prognosis.MethodsThe expression data of 770 HNSCC patients from the TCGA database and the GEO database were used. To explore a predictive model, the Cox proportional hazards model was applied. The Kaplan-Meier survival analysis, as well as univariate and multivariate analyses were performed to evaluate the independent predictive value of IRGS. To explore biological functions of IRGS, enrichment analyses and pathway annotation for differentially expressed genes (DEGs) in different immune groups were applied, as well as the immune infiltration.ResultsA prognostic signature comprising 27 IRGs was generated. IRGS significantly stratified HNSCC patients into high and low immune risk groups in regard to overall survival in the training cohort (HR = 3.69, 95% CI 2.73-4.98, P < 0.001). Likewise, IRGS could be linked to the prognosis of HNSCC in patients of the validation cohort (HR = 1.84, 95% CI 1.21-2.81, P < 0.01). Even after adjusting for TNM stage, IRGS was maintained as an independent predictor in the multivariate analysis (HR = 3.62, 95% CI 2.58-5.09, P < 0.001), and in the validation cohort (HR = 1.73, 95% CI 1.12-2.67, P = 0.014). The IFN-α response, the IFN-γ response, IL-2/STAT5 signaling, and IL-6/JAK/STAT3 signaling were all negatively correlated with the immune risk (P < 0.01). Immune infiltration of the high-risk group was significantly lower than that of the low-risk group (P < 0.01). Most notably, the infiltration of CD8 T cells, memory-activated CD4 T cells, and regulatory T cells was strongly upregulated in the low immune risk groups, while memory resting CD4 T cell infiltration was downregulated (P < 0.01).ConclusionOur analysis provides a comprehensive prognosis of the immune microenvironments and outcomes for different individuals. Further studies are needed to evaluate the clinical application of this signature.

Project description:Tobacco smoking is an established risk factor for squamous cell carcinoma (SCC). We obtained smoking-related SCC, including cervical SCC (CSCC), esophageal SCC (ESCC), head and neck SCC (HNSC), and lung SCC (LUSC), from The Cancer Genome Atlas (TCGA) database to investigate the association between smoking status (reformed and current smoking) and prognosis. We found that reformed smokers had a better prognosis than current smokers in CSCC (p = 0.003), HNSC (p = 0.019), and LUSC (p < 0.01) cohorts. Then, we selected LUSC cohorts as the training cohort and other SCC cohorts as the test cohorts. Function analysis revealed that homologous recombination (HR) was the most significant pathway involved in smoking-induced LUSC. Moreover, the effect of cross-talk between the smoking status and HR deficiency (HRD) on the prognosis was further evaluated, revealing that quitting smoking with high HRD scores could significantly improve patients' prognosis (p < 0.01). To improve prognosis prediction and more effectively screen suitable populations for platinum drugs and poly-ADP-ribose polymerase (PARP) inhibitors, we constructed a risk score model using smoking- and HRD-related genes in LUSC. The risk score model had high power for predicting 2-, 3-, and 5-year survival (p < 0.01, AUC = 0.67, 0.66, and 0.66). In addition, the risk scores were an independent risk factor for LUSC (HR = 2.34, 95%CI = 1.70-3.23). The practical nomogram was also built using the risk score, smoking status, and other clinical information with a good c-index (0.72, 95%CI = 0.70-0.74). Finally, we used other TCGA SCC cohorts to confirm the reliability and validity of the risk score model (p < 0.01 and AUC > 0.6 at 2, 3, and 5 years in CSCC and HNSC cohorts). In conclusion, the present study suggested that smoking cessation should be a part of smoking-related SCC treatment, and also provided a risk score model to predict prognosis and improve the effectiveness of screening the platinum/PARP population.

Project description:BackgroundInflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation-related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients.MethodsThe RNA-sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations-counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis.ResultsA two-IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low-risk group had more anti-tumor immune cells infiltrated while patient with high-risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in validation set. Time-dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone.ConclusionThe IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival.

Project description:BackgroundThe mechanistic aspects of the involvement of long noncoding RNAs (lncRNAs) in NETosis, the process of neutrophil extracellular trap (NET) formation in head and neck squamous cell carcinoma (HNSCC), lack comprehensive elucidation. The involvement of these molecules in the immune microenvironment and plausible HNSCC prognosis remain to see the light of the day. The plausible functioning of NETosis-related lncRNAs with their plausible prognostic impact in HNSCC was probed in this work.MethodsThe scrutiny of lncRNAs linked to NETosis entailed the probing of twenty-four genes associated with the process employing Pearson's correlation analysis on HNSCC patients' RNA sequencing data from The Cancer Genome Atlas (TCGA) database. The application of univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses yielded a NETosis-related lncRNA signature that was subjected to probing for its suitability in prognosis employing survival and nomogram analyses.ResultsThe NETosis-related lncRNA signature inclusive of five lncRNAs facilitated patients to be segregated as high-risk and low-risk groups with the former documenting a poor prognosis. Regression unearthed that the risk score was an independent factor for prognosis. The receiver operating characteristic (ROC) or receiver operating characteristic curve analysis documented a one-year area under time-dependent ROC curve (AUC) value of 0.711 that is corroborative of the accuracy of this signature. Additional probing documented an evident enriching of immune-linked pathways in the low-risk patients, while the high-risk patients documented an immunologically "cold" profile as per the infiltration of immune cells. We verified lncRNA expression from our NETosis-related lncRNA signature in vitro, which reflects the reliability of our model to a certain extent. Moreover, we also verified the function of the lncRNA. We found that LINC00426 contributes to the innate immune cGAS-STING signaling pathway, which explain to some extent the role of our prognostic model in predicting "hot" and "cold" tumors.ConclusionsThe plausible prognostic relevance of the NETosis-related lncRNA signature (with five lncRNAs) emerges that is suggestive of its promise in targeting HNSCC.

Dataset Information

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma.

Publications

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma.

Similar Datasets

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