Dataset Information

A prognostic model of non small cell lung cancer based on TCGA and ImmPort databases.

ABSTRACT: Bioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC. The transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database ( http://www.immport.org/ ); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed. A total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P < 0.05). After including 19 differentially expressed immune genes with P < 0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan-Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic model curve confirmed that the prediction model had a certain accuracy (AUC = 0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment. The NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

SUBMITTER: Yang D

PROVIDER: S-EPMC8748945 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThere has been little investigation carried out into the activity of immune-related genes in the prognosis of non-small cell lung cancer (NSCLC). Our study set out to analyze the correlation between the differential expression of immune genes and NSCLC prognosis by screening the differential expression of immune genes. Based on the immune genes identified, we aimed to construct a prognostic risk model and explore some novel molecules which have predictive potential for therapeutic effect and prognosis in lung cancer.MethodsImmune gene transcriptome data and clinical data of NSCLC samples were extracted from TCGA database, and transcription factors in the ImmPort dataset were obtained. The data were divided into two groups: normal tissues and tumor tissues. The expression levels of immune genes were compared using the edgeR algorithm, and then differential expression analysis was performed. The survival analysis was carried out by combining differential immune genes with clinical survival time, so that the immune genes influencing the prognosis of NSCLC could be determined. A risk score was calculated based on the expression levels of the immune genes related to the prognosis of NSCLC and their corresponding coefficients to construct a prognostic risk model. This model was used to calculate patient risk scores and perform clinical correlation analysis. The selected molecules were further verified by clinical samples.ResultsBy comparing NSCLC tissues with normal tissues, a total of 6,778 differentially expressed genes were found (P<0.05), of which 490 were differential immune-related genes. Survival analysis determined 28 differential immune genes to be associated with prognosis (P<0.05). We calculated the patient risk value based on the immune gene prognosis model. The survival curve was drawn according to the patient risk score and showed that the survival prognosis was significantly different for the high-risk and the low-risk groups (P<0.05). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.723, which represented a relatively high true-positive rate. All of the results proved the reliability of our immune gene risk prognostic model. After drawing the risk curve, S100A16, IGKV4, S100P, ANGPTL4, SEMA4B, and LGR4 were found to be the high-risk immune genes in NSCLC. Clinical correlation analysis of survival-related differential immune genes revealed that in patients with lymph node metastasis, ANGPTL4 was positively correlated with T stage, S100a16 and SEMA4B were upregulated, and VIPR1 was downregulated. Further analysis revealed that VIPR1 was decreased in metastatic lung cancer compared to non-metastatic lung cancer. Furthermore, the real-time PCR detection of the clinical samples showed that S100A16 expression in lung cancer was increased, while VIPR1 expression in lung cancer was downregulated, which was consistent with the results of our bioinformatics analysis.ConclusionsBased on big data from the TCGA and ImmPort databases, our study analyzed the relationship between differential expression of immune-related genes and clinical data, and constructed a prognostic model based on the immune genes identified. Two novel molecules, S100A16 and VIPR1, were verified to possibly have significant biological function in NSCLC. Our research may provide us with new insight into the immune genes by which the malignant biological behavior of NSCLC is mediated.

Project description:Lung cancer is a leading cause of cancer-related death, and >80% of lung cancer diagnoses are non-small-cell lung cancer (NSCLC). However, when using current staging and prognostic indices, the prognosis can vary significantly. In the present study, we calculated a prognostic index for predicting overall survival (OS) in NSCLC patients. The data of 545 NSCLC patients were retrospectively reviewed. Univariate and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of clinicopathological factors. Age (hazard ratio [HR] = 1.25, 95% confidence interval [CI] = 1.02-1.54), TNM stage (III, HR = 1.64, 95% CI = 1.08-2.48; IV, HR = 2.33, 95% CI = 1.48-3.69), lung lobectomy (HR = 1.96, 95% CI = 1.45-2.66), chemotherapy (HR = 1.42, 95% CI = 1.15-1.74), and pretreatment hemoglobin level (HR = 1.61, 95% CI = 1.28-2.02) were independent prognosticators. A prognostic index for NSCLC (PInscl, 0-6 points) was calculated based on age (≥65 years, 1 point), tumor-node-metastasis (TNM) stage (III, 1 point; IV, 2 points), lung lobectomy (no, 1 point), chemotherapy (no, 1 point), and pretreatment hemoglobin level (low, 1 point). In comparison with the "PInscl = 0" subgroup (survival time = 2.71 ± 1.86 years), the "PInscl = 2" subgroup (survival time = 1.86 ± 1.24 years), "PInscl = 3" subgroup (survival time = 1.45 ± 1.07 years), "PInscl = 4" subgroup (survival time = 1.17 ± 1.06 years), "PInscl = 5" subgroup (survival time = 0.81 ± 0.78 years), and "PInscl = 6" subgroup (survival time = 0.65 ± 0.56 years) exhibited significantly shorter survival times. Kaplan-Meier survival analysis showed that patients with higher PInscl scores had poorer OS than those with lower scores (log-rank test: χ2 = 155.82, P < 0.0001). The area under the curve of PInscl for predicting the 1-year OS was 0.73 (95 % CI = 0.69-0.77, P < 0.001), and the PInscl had a better diagnostic performance than the Karnofsky performance status or TNM stage (P < 0.01). In conclusion, the PInscl, which is calculated from age, TNM stage, lung lobectomy, chemotherapy, and pretreatment hemoglobin level, significantly predicted OS in NSCLC patients.

Project description:BackgroundSquamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (≥ 60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database.MethodsThe gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors were identified using multivariate Cox proportional hazards regression analysis, which were utilized to conduct a nomogram for predicting survival. The novel nomogram was evaluated by Concordance index (C-index), calibration curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).Results32,474 elderly SCC patients were included in the analysis, who were randomly assigned to training cohort (n = 22,732) and validation cohort (n = 9742). The following factors were contained in the final prognostic model: age, sex, race, marital status, tumor site, AJCC stage, surgery, radiation and chemotherapy. Compared to AJCC stage, the novel nomogram exhibited better performance: C-index (training group: 0.789 vs. 0.730, validation group: 0.791 vs. 0.733), the areas under the receiver operating characteristic curve of the training set (1-year AUC: 0.846 vs. 0.791, 3-year AUC: 0.860 vs. 0.801, 5-year AUC: 0.859 vs. 0.794) and the validation set (1-year AUC: 0.846 vs. 0.793, 3-year AUC: 0.863 vs. 0.806, 5-year AUC: 0.866 vs. 0.801), and the 1-, 3- and 5-year calibration plots. Additionally, the NRI and IDI and 1-, 3- and 5-year DCA curves all confirmed that the nomogram was a great prognosis tool.ConclusionsWe constructed a novel nomogram that could be practical and helpful for precise evaluation of elderly SCC patient prognosis, thus helping clinicians in determining the appropriate therapy strategies for individual SCC patients.

Dataset Information

A prognostic model of non small cell lung cancer based on TCGA and ImmPort databases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets