Dataset Information

Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

ABSTRACT: Background: The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs. Methods: Gene expression data about LUSC were obtained from The Cancer Genome Atlas (TCGA). Differential expression analysis and univariate Cox regression analysis were performed to identify prognostic differentially expressed IRGs. A prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Then we validated the performance of the prognostic model in training and test cohorts. Furthermore, associations with clinical variables and immune infiltration were also analyzed. Results: 593 differentially expressed IRGs were identified, and 8 of them were related to prognosis. Then a transcription factor regulatory network was established. A prognostic model consisted of 4 immune-related genes was constructed by using Lasso and multivariate Cox regression analyses. The prognostic value of this model was successfully validated in training and test cohorts. Further analysis showed that the prognostic model could be used independently to predict the prognosis of LUSC patients. The relationships between the risk score and immune cell infiltration indicated that the model could reflect the status of the tumor immune microenvironment. Conclusions: We constructed a risk model using four PDIRGs that can accurately predict the prognosis of LUSC patients. The risk score generated by this model can be used as an independent prognostic indicator. Moreover, the model can predict the infiltration of immune cells in patients, which is conducive to the prediction of patient sensitivity to immunotherapy.

SUBMITTER: Liu Z

PROVIDER: S-EPMC7330657 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

Liu Zeyu Z Wan Yuxiang Y Qiu Yuqin Y Qi Xuewei X Yang Ming M Huang Jinchang J Zhang Qiaoli Q

International journal of medical sciences 20200601 10

<b>Background:</b> The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs. <b>Methods:</b> Gene expression data about LUSC were obtained from The Canc ...[more]

PMID: 32624696

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Project description:Lung Squamous Cell Carcinoma (LUSC) is an aggressive malignancy with limited therapeutic options. The response to immune therapy is a determining factor for the prognosis of LUSC patients. This study aimed to develop a reliable immune-related prognostic signature in LUSC. We extracted gene expression and clinical data of LUSC from The Cancer Genome Atlas (TCGA). A total of 502 patients enrolled and were divided into respond and non-responder groups by the TIDE algorithm. The CIBERSORT algorithm and the LM22 gene signature were used to analyze the distribution of immune cells in LUSC. Efficacy and response strength of immunotherapy are calculated by the tumor mutation burden (TMB) and ESTIMATE Score. Differentially expressed genes (DEGs) between the two groups were analyzed. The differential expression genes related to overall survival were pointed as hub DEGs, and a prognostic signature was constructed with lasso regression analysis. LUSC patients were divided into responder and non-responder groups based on the response to immunotherapy. The distribution of immune cells was significantly different between the two groups. Forty-four DGEs were considered as overall survival-related genes. A prognostic signature was constructed, consisting of 11 hub-DGEs, including MMP20, C18orf26, CASP14, FAM71E2, OPN4, CGB5, DIRC1, C9orf11, SPATA8, C9orf144B, and ZCCHC5. The signature can accurately distinguish LUSC patients into high and low-risk groups. Moreover, the high-risk group had a shorter survival time than the low-risk group. The area under the ROC curve was 0.67. The multivariate Cox regression showed that the risk score calculated by the constructed signature was an independent prognostic predictor for LUSC patients. In short, we established a novel immune-related prognostic signature in LUCS, which has significant sensitivity and accuracy in predicting the prognosis of patients. Our research can guide the evaluation of the prognosis of LUSC patients in clinical, and the discovered immune-related genes can provide a theoretical basis for the discovery of new therapeutic targets.

Project description:Background and objectivesEsophageal squamous cell carcinoma (ESCC) is the most common pathological type of esophageal malignancy in most regions of the world. The study aimed to identify risk factors and develop a predictive model for ESCC following surgical resection.Patients and methodsA total of 533 ESCC patients who underwent surgical resection from Suining Central Hospital were enrolled in the study. Cox proportional hazards regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify significant prognostic factors. A prognostic model was constructed, and the receiver operating characteristic (ROC) curve, concordance index (C-index), and decision cure analysis (DCA) were used to evaluate the discrimination and calibration of the prognostic model. Subsequently, we built a nomogram for overall survival (OS) incorporating the prognostic factors, and a calibration plot was employed to assess the consistency between the predicted survival and the observed survival. Based on the model risk score, we split the patients into two subgroups, low-risk and high-risk, and we analyzed the survival time of these two groups using Kaplan-Meier (K-M) survival plots.ResultsFive independent prognosis factors were identified as independent risk factors for OS in ESCC patients who underwent surgical resection. The C-index, ROC curve, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort and validation cohort than other clinical features. A nomogram consisting of prognosis factors showed some superior net benefit. K-M survival plots showed significant differences in OS between the low-risk and high-risk groups. Similar results were observed in the subgroup analysis based on age, grade, and stage. Univariate and multivariate Cox regression analyses revealed that both risk score and risk group are independent prognostic factors in the patient cohort.ConclusionsThis study put forward a novel prognostic model based on clinical features; biopsy data and blood biomarkers may represent a promising tool for estimating OS in ESCC patients.

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:BackgroundLung squamous cell carcinoma (LUSC) is a highly malignant tumor with an extremely poor prognosis. Immune checkpoint inhibitors (ICIs) improve survival in some patients with LUSC. Tumor mutation burden (TMB) is a useful biomarker to predict the efficacy of ICIs. However, predictive and prognostic factors related to TMB in LUSC remain elusive. This study aimed to find effective biomarkers based on TMB and immune response and establish a prognostic model of LUSC.MethodsWe downloaded Mutation Annotation Format (MAF) files from The Cancer Genome Atlas (TCGA) database and identified immune-related differentially expressed genes (DEGs) between high- and low-TMB groups. The prognostic model was established using cox regression. The primary outcome was overall survival (OS). Receiver operating characteristic (ROC) curves and calibration curves were used to verify the accuracy of the model. GSE37745 acted as external validation set. The expression and prognosis of hub genes as well as their correlation with immune cells and somatic copy number variation (sCNA) were analyzed.ResultsThe TMB of patients with LUSC was correlated with prognosis and stage. High TMB group had higher survival rate (P<0.001). Five TMB-related hub immune genes (TINAGL1, FGFR2, CTSE, SFTPA1, and IGHV7-81) were identified and the prognostic model was constructed. The survival time of high-risk group was significantly shorter than that of low-risk group (P<0.001). The validation results of the model were quite stable in different data sets, and the area under curve (AUC) of training set and validation set were 0.658 and 0.644, respectively. Calibration chart, risk curve, and nomogram revealed that the prognostic model was reliable in predicting the prognostic risk of LUSC, and the risk score of the model could be used as an independent prognostic factor for LUSC patients (P<0.001).ConclusionsOur results show that high TMB is associated with poor prognosis in patients with LUSC. The prognostic model related to TMB and immunity can effectively predict the prognosis of LUSC, and risk score is one of the independent prognostic factors of LUSC. However, this study still has some limitations, which need to be further verified in large-scale and prospective studies.

Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is one of the most lethal malignant tumors worldwide, and a larger number of ESCC patients have unsatisfactory overall survival (OS) rates. While pyroptosis participates in the development of a variety of malignancies, the function of pyroptosis-related genes (PRGs) in ESCC is still obscure. The aim of this study was to construct the pyroptosis-related prognostic model for ESCC, which will be developed to stratify the risk hazards of ESCC patients and to provide theoretical evidence for individualized treatment.MethodsRNA-seq data of ESCC were download from the NCBI Gene Expression Omnibus (GEO) database. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to explore the potential biological functions or pathways. OS was considered as the primary prognosis outcome in this study. The riskscore was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis. The pyroptosis-related prognostic model was constructed based on all independent prognostic factors and verified by C-index, Receiver operating characteristic (ROC) curves, and Calibration curves, and the role of the riskscore in ESCC immunotherapy was evaluated by the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.ResultsThe current study found 31 differentially expressed PRGs (P<0.001), and functional enrichment analysis showed these PRGs were enriched in positive regulation of cytokine production, interleukin-1 beta production. Univariate and multivariate Cox regression analysis were applied to validate that the riskscore based on four prognostic PRGs (HMGB1, IL-18, NLRP7, and PLCG1) was an independent prognostic factor for ESCC, and the C-index of prognostic model related to the riskscore (C-index =0.705) was higher than that of tumor node metastasis (TNM) stage (0.620). The low-risk group showed a better efficacy of immune checkpoint inhibitors.ConclusionsThe riskscore related to PRGs was one of the independent prognostic factors for ESCC. Moreover, the prognostic model related to the riskscore could be used to predict the OS of ESCC patients effectively. However, there still were several limitations in this study, such as no external validation sample. In summary, our data provides a novel perspective in exploring the potential prognostic biomarkers of ESCC.

Project description:BackgroundNo biomarkers have been identified for the prognosis of lung squamous cell carcinoma (LUSC). Risk models based on m6A-lncRNAs help to predict survival in some cancers. However, very few studies have reported m6A-lncRNA risk models in LUSC. We aimed to construct a prognostic model based on m6A-lncRNAs in LUSC.MethodsThe clinical and RNA-sequencing information of 504 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-lncRNAs were identified by a Pearson correlation analysis and univariate Cox regression analysis. The ConsensusClusterPlus algorithm was used to cluster the prognostic m6A-lncRNAs. The overall survival (OS) and clinicopathological characteristics of the 2 clusters were compared. A gene set enrichment analysis (GSEA) analysis was performed to analyze the genes enriched in the 2 clusters. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct the risk-score model. Two hundred and forty eight patients were randomly chosen from TCGA-LUSC cohort for the training set. The receiver operating characteristic (ROC) curve analysis was used to assess the predictive ability of the model. The clinical characteristics and OS in the high- and low-risk groups were compared. The independent prognostic value of the model was tested by Cox regression analyses.ResultsThirteen m6A-lncRNAs were identified as prognostic lncRNAs and classified into cluster A and cluster B. The OS of patients in cluster A was better than that of patients in cluster B (P<0.001). Patients in cluster B had higher expressions of immune checkpoints. Immune score, stromal score, and ESTIMATE score were higher in cluster B (P<0.001). Seven of the 13 lncRNAs were used to construct the risk-score model. Patients in the high-risk group had a worse OS. ROC curves showed a under the curve (AUC) of 0.639 in the training set and 0.624 in the validation set. A high risk was associated with cluster B, a high immune score, and stage III-IV disease. Patients in the high-risk group had increased expressions of immune checkpoints. The Cox regression analyses showed that the risk-score model had independent prognostic value for OS. The risk-score model retained its prognostic value in different subgroups.ConclusionsThe m6A-lncRNA risk-score model is an independent prognostic factor for OS in LUSC patients. However, the risk-score model need to be further tested clinically.

Dataset Information

Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

Publications

Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

Similar Datasets

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