Development and validation of a prognostic nomogram for patients with lung adenocarcinoma based on a novel 6-DNA repair-related gene signature.
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SUBMITTER: Chen M
PROVIDER: S-EPMC8129254 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundLung adenocarcinomas (LUAD) is the most common histological subtype of lung cancers. Tumor immune microenvironment (TIME) is involved in tumorigeneses, progressions, and metastases. This study is aimed to develop a robust immune-related signature of LUAD.MethodsA total of 1774 LUAD cases sourced from public databases were included in this study. Immune scores were calculated through ESTIMATE algorithm and weighted gene co-expression network analysis (WGCNA) was applied to identify immune-related genes. Stability selections and Lasso COX regressions were implemented to construct prognostic signatures. Validations and comparisons with other immune-related signatures were conducted in independent Gene Expression Omnibus (GEO) cohorts. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ImmuCellAI and gene set enrichment analysis (GSEA).ResultsIn Cancer Genome Atlas (TCGA) LUAD cohorts, immune scores of higher levels were significantly associated with better prognoses (P < .05). Yellow (n = 270) and Blue (n = 764) colored genes were selected as immune-related genes, and after univariate Cox regression analysis (P < .005), a total of 133 genes were screened out for subsequent model constructions. A four-gene signature (ARNTL2, ECT2, PPIA, and TUBA4A) named IPSLUAD was developed through stability selection and Lasso COX regression. It was suggested by multivariate and subgroup analyses that IPSLUAD was an independent prognostic factor. It was suggested by Kaplan-Meier survival analysis that eight out of nine patients in high-risk groups had significantly worse prognoses in validation data sets (P < .05). IPSLUAD outperformed other signatures in two independent cohorts.ConclusionsA robust immune-related prognostic signature with great performances in multiple LUAD cohorts was developed in this study.
| S-EPMC7433810 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Ferroptosis is a newly recognized process of cell death, which is different from other forms of cell death in morphology, biochemistry, and genetics, and has played a vital role in cancer biology. This study aimed to identify a ferroptosis-related gene signature associated with LUAD prognosis.MethodsDataset TCGA-LUAD which came from the TCGA portal was taken as the training cohort. GSE72094 and GSE68465 from the GEO database were treated as validation cohorts. Two hundred fifty-nine ferroptosis-related genes were retrieved from the FerrDb database. In the training cohort, Kaplan-Meier and univariate Cox analyses were conducted for preliminary screening of ferroptosis-related genes with potential prognostic capacity. These genes then entered into the LASSO Cox regression model, constructing a gene signature. The latter was then evaluated in the training and validation cohorts via Kaplan-Meier, Cox, and ROC analyses. In addition, the correlations between risk score and autophagy were examined by Pearson correlation coefficient. The analyses of GSEA and immune infiltrating were performed for better studying the function annotation of the gene signature and the character of each kind of immune cells played in the tumor microenvironment.ResultsA 15-gene signature was found from the training cohort and validated by Kaplan-Meier and Cox regression analyses, revealing its independent prognosis value in LUAD. Moreover, the ROC analysis was conducted, confirming a strong predictive ability that this signature owned for LUAD prognosis. One hundred fifty-one of 222 (68.01%) autophagy-related genes were discovered significantly correlated with risk scores. Analyses of GSEA and immune infiltration exhibited in detail the specific pathways that associate with the 15-gene signature and identified the crucial roles of resting mast cells and resting dendritic cells owned in the prognosis of the 15-gene signature.ConclusionIn this present study, a novel ferroptosis-related 15-gene signature (RELA, ACSL3, YWHAE, EIF2S1, CISD1, DDIT4, RRM2, PANX1, TLR4, ARNTL, LPIN1, HERPUD1, NCOA4, PEBP1, and GLS2) was built. It could accurately predict the prognosis of LUAD and was related to resting mast cells and resting dendritic cells, which provide potential for the personalized outcome prediction and the development of new therapies in LUAD population.
| S-EPMC8264775 | biostudies-literature
Project description:PurposeThe prognostic value of an N6-methyladenosine (m6A) methylation-related immune gene signature for lung adenocarcinoma (LUAD) was investigated.Patients and methodsGene expression and clinical phenotype data of LUAD patients were downloaded from The Cancer Genome Atlas database. A list of immune-related genes was retrieved from the InnateDB database. Correlation analysis, survival analysis, and univariate and multivariate Cox regression analyses were performed. After allocating patients into a high-risk or a low-risk group, the corresponding survival rates, immune microenvironment, expression of immune checkpoint genes, and modulation of Kyoto Encyclopedia of Genes and Genomes pathways were examined. Finally, the expression levels of prognostic biomarkers were assessed in the GSE126044 dataset.ResultsSeven m6A-related immune prognostic genes were identified. High expression of PSMD10P1, DIDO1, ABCA5, and CIITA was associated with high survival rates, while that of PRC1, ZWILCH, and ANLN was associated with low survival rates. The high- and low-risk groups showed significant differences in terms of the abundance of six tumor-infiltrating immune cell types and expression of 12 immune checkpoint genes. The risk group acted as an independent prognostic factor (hazard ratio = 0.398, 95% confidence interval = 0.217-0.729, P = 0.003). Finally, the developed nomogram could predict most efficiently the 1-, 2-, and 3-year survival probability of LUAD patients with a C-index of 0.833.ConclusionA seven-gene risk signature, associated with the immune microenvironment in LUAD, showed independent prognostic value.
| S-EPMC8643173 | biostudies-literature
Project description:BackgroundThe incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD.MethodsThe public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan-Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts.ResultsA prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29-8.11; P = 6.16E-06), GSE31210 (HR = 11.91, 95% CI 4.15-34.19; P = 4.10E-06), GSE50081 (HR = 3.63, 95% CI 1.90-6.95; P = 9.95E-05), the combined data set (HR = 3.15, 95% CI 1.98-5.02; P = 1.26E-06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49-3.13; P = 4.54E-05) and GSE72094 (HR = 2.95, 95% CI 1.86-4.70; P = 4.79E-06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design.ConclusionsOur study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.
| S-EPMC7542703 | biostudies-literature
Project description:This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan-Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.
| S-EPMC8301374 | biostudies-literature
Project description:PurposeTo establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).Results10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.ConclusionThe nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.
| S-EPMC9759395 | biostudies-literature
Project description:Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.
| S-EPMC3436895 | biostudies-literature
Project description:Disulfidptosis, a newly revealed form of cell death, regulated by numerous genes that has been recently identified. The exact role of disulfidptosis in lung adenocarcinoma (LUAD) still uncertain. Objective of this study was to explore potential prognostic markers among disulfidptosis genes in LUAD. By combining transcriptomic information from Gene Expression Omnibus databases and The Cancer Genome Atlas, we identified differentially expressed and prognostic disulfidptosis genes. By conducting least absolute shrinkage and selection operator with multivariate Cox regression, four disulfidptosis genes were selected to create the prognostic signature. The implementation of the signature separated the training and validation cohorts into groups with high- and low-risk. Subsequently, the model was verified by conducting an independent analysis of receiver operating characteristic (ROC) curves. Further comparisons were made between the two risk-divided groups with regards the tumor microenvironment, immune cell infiltration, immunotherapy response, and drug sensitivity. The signature was constructed using four disulfidptosis-related genes: SLC7A11, SLC3A2, NCKAP1, and GYS1. According to ROC curves, the signature was effective for predicting LUAD prognosis. In addition, the prognostic signature correlated with sensitivity to chemotherapeutic agents and the efficacy of immunotherapy in LUAD. Finally, through external validation, we showed that NCKAP1 are correlated with tumor migration, proliferation, and invasion of LUAD cells. GYS1 affects immune cell, especially M2 macrophage infiltration in the tumor microenvironment. The disulfidptosis four-gene model can reliably predict the prognosis of patients diagnosed with LUAD, thereby providing valuable information for clinical applications and immunotherapy.
| S-EPMC10784160 | biostudies-literature
Project description:BackgroundEpigenetic dysregulation has been increasingly proposed as a hallmark of cancer. Here, the aim of this study is to establish an epigenetic-related signature for predicting the prognosis of lung adenocarcinoma (LUAD) patients.ResultsFive epigenetic-related genes (ERGs) (ARRB1, PARP1, PKM, TFDP1, and YWHAZ) were identified as prognostic hub genes and used to establish a prognostic signature. According our risk score system, LUAD patients were stratified into high and low risk groups, and patients in the high risk group had a worse prognosis. ROC analysis indicated that the signature was precise in predicting the prognosis. A new nomogram was constructed based on the five hub genes, which can predict the OS of every LUAD patients. The calibration curves showed that the nomogram had better accuracy in prediction. Finally, candidate drugs that aimed at hub ERGs were identified, which included 47 compounds.ConclusionsOur epigenetic-related signature nomogram can effectively and reliably predict OS of LUAD patients, also we provide precise targeted chemotherapeutic drugs.MethodsThe genomic data and clinical data of LUAD cohort were downloaded from the TCGA database and ERGs were obtained from the EpiFactors database. GSE31210 and GSE50081 microarray datasets were included as independent external datasets. Univariate Cox, LASSO regression, and multivariate Cox analyses were applied to construct the epigenetic-related signature.
| S-EPMC8351720 | biostudies-literature
Project description:Background Studies have shown that the regulation of ferroptosis could be a new approach to cancer treatment and abnormal ferroptosis is closely associated with a dysregulated immune response. However, a combined signature with ferroptosis-related genes (FRGs) and immune-related genes (IRGs) is necessary to be constructed for predicting prognoses and guiding individualized precision therapy of lung adenocarcinoma (LUAD) patients. Methods In this study, based on the Cancer Genome Atlas (TCGA) cohort, prognosis-related FRGs and IRGs were first identified and incorporated into the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression model to generate a combined signature of ferroptosis- and immune-related genes (CSFI) values to predict the overall survivals (OSs) of LUAD patients. And patients with LUAD from the Gene Expression Omnibus (GEO) database were applied for the validation set. Nomogram was constructed based on multivariate Cox regression analysis. Subsequently, ferroptosis, immunity, and gene mutation status of patients between the CSFI-high and -low groups were compared. Additionally, the enrichment pathways in CSFI-high and -low groups were explored by Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) and Gene Set Enrichment Analysis (GSEA) analyses. Results As a result, the CSFI-low group showed a good prognosis instead of the CSFI-high group. CSFI was identified to be an independent prognosis factor for LUAD. In general, there were ferroptosis- and immune-suppressive states in CSFI-high patients. Notably, the mutation frequencies of TP53 were higher in CSFI-high patients. Conclusions In LUAD, CSFI which served as a novel classifier was offered for predicting the prognoses of patients and contributing to guiding personalized targeted therapy of patients. Therefore, based on these findings, it was believed that a synergistic treatment of ferroptosis and immunity would be more effective on LUAD patients with low CSFI values.
| S-EPMC9641098 | biostudies-literature