Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients.
Ontology highlight
ABSTRACT: Background
Patients with early-stage lung adenocarcinoma (LUAD) exhibit significant heterogeneity in overall survival. The current tumour-node-metastasis staging system is insufficient to provide precise prediction for prognosis.Methods
We quantified the levels of various hallmarks of cancer and identified hypoxia as the primary risk factor for overall survival in early-stage LUAD. Different bioinformatic and statistical methods were combined to construct a robust hypoxia-related gene signature for prognosis. Furthermore, a decision tree and a nomogram were constructed based on the gene signature and clinicopathological features to improve risk stratification and quantify risk assessment for individual patients.Results
The hypoxia-related gene signature discriminated high-risk patients at an early stage in our investigated cohorts. Survival analyses demonstrated that our gene signature served as an independent risk factor for overall survival. The decision tree identified risk subgroups powerfully, and the nomogram exhibited high accuracy.Conclusions
Our study might contribute to the optimization of risk stratification for survival and personalized management of early-stage LUAD.
SUBMITTER: Sun J
PROVIDER: S-EPMC7333486 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients.
Sun Jing J Zhao Tianyu T Zhao Di D Qi Xin X Bao Xuanwen X Shi Run R Su Chuan C
Therapeutic advances in medical oncology 20200702
<h4>Background</h4>Patients with early-stage lung adenocarcinoma (LUAD) exhibit significant heterogeneity in overall survival. The current tumour-node-metastasis staging system is insufficient to provide precise prediction for prognosis.<h4>Methods</h4>We quantified the levels of various hallmarks of cancer and identified hypoxia as the primary risk factor for overall survival in early-stage LUAD. Different bioinformatic and statistical methods were combined to construct a robust hypoxia-related ...[more]
Similar Datasets
Project description:Objective: The interaction between immunity and hypoxia in tumor microenvironment (TME) has clinical significance, and this study aims to explore immune-hypoxia related biomarkers in LUAD to guide accurate prognosis of patients. Methods: The LUAD gene expression dataset was downloaded from GEO and TCGA databases. The immune-related genes and hypoxia-related genes were acquired from ImmPort and MSigDB databases, respectively. Genes related to immune and hypoxia in LUAD were obtained by intersection. The significantly prognostic genes in LUAD were obtained by LASSO and Cox regression analyses and a prognostic model was constructed. Kaplan-Meier and receiver operating characteristic curves were generated to evaluate and validate model reliability. Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) were employed to analyze immune cell infiltration and pathway differences between high- and low-risk groups. Nomogram and calibration curves for survival curve and clinical features were drawn to measure prognostic value of the model. Results: The prognosis model of LUAD was constructed based on seven immune-hypoxia related genes: S100P, S100A16, PGK1, TNFSF11, ARRB1, NCR3, and TSLP. Survival analysis revealed a poor prognosis in high-risk group. ssGSEA result suggested that activities of immune cells in high-risk group was remarkably lower than in low-risk group, and GSVA result showed that immune-related pathway was notably activated in low-risk group. Conclusion: Immune-hypoxia related genes were found to be prognostic biomarkers for LUAD patients, based on which a 7-immune-hypoxia related gene-signature was constructed. This model can assess immune status of LUAD patients, and provide clinical reference for individualized prognosis, treatment and follow-up of LUAD patients.
| S-EPMC9573950 | biostudies-literature
Project description:Background: Lung adenocarcinoma (LUAD) is the most common and lethal subtype of lung cancer. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a target in cancer therapy. However, the prognostic value of ferroptosis-related genes (FRGs)x in LUAD remains to be explored. Methods: In this study, we used RNA sequencing data and relevant clinical data from The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset to construct and validate a prognostic FRG signature for overall survival (OS) in LUAD patients and defined potential biomarkers for ferroptosis-related tumor therapy. Results: A total of 86 differentially expressed FRGs were identified from LUAD tumor tissues versus normal tissues, of which 15 FRGs were significantly associated with OS in the survival analysis. Through the LASSO Cox regression analysis, a prognostic signature including 11 FRGs was established to predict OS in the TCGA tumor cohort. Based on the median value of risk scores calculated according to the signature, patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis indicated that the high-risk group had a poorer OS than the low-risk group. The area under the curve of this signature was 0.74 in the TCGA tumor set, showing good discrimination. In the GEO validation set, the prognostic signature also had good predictive performance. Functional enrichment analysis showed that some immune-associated gene sets were significantly differently enriched in two risk groups. Conclusion: Our study unearthed a novel ferroptosis-related gene signature for predicting the prognosis of LUAD, and the signature may provide useful prognostic biomarkers and potential treatment targets.
| S-EPMC8795866 | biostudies-literature
Project description:BackgroundThe widespread use of low-dose chest CT screening has improved the detection of early lung adenocarcinoma. Radical surgery is the best treatment strategy for patients with early lung adenocarcinoma; however, some patients present with postoperative recurrence and poor prognosis. Through this study, we hope to establish a model that can identify patients that are prone to recurrence and have poor prognosis after surgery for early lung adenocarcinoma.Materials and methodsWe screened prognostic and relapse-related genes using The Cancer Genome Atlas (TCGA) database and the GSE50081 dataset from the Gene Expression Omnibus (GEO) database. The GSE30219 dataset was used to further screen target genes and construct a risk prognosis signature. Time-dependent ROC analysis, calibration degree analysis, and DCA were used to evaluate the reliability of the model. We validated the TCGA dataset, GSE50081, and GSE30219 internally. External validation was conducted in the GSE31210 dataset.ResultsA novel four-gene signature (INPP5B, FOSL2, CDCA3, RASAL2) was established to predict relapse-related survival outcomes in patients with early lung adenocarcinoma after surgery. The discovery of these genes may reveal the molecular mechanism of recurrence and poor prognosis of early lung adenocarcinoma. In addition, ROC analysis, calibration analysis and DCA were used to verify the genetic signature internally and externally. Our results showed that our gene signature had a good predictive ability for recurrence and prognosis.ConclusionsWe established a four-gene signature and predictive model to predict the recurrence and corresponding survival rates in patients with early lung adenocarcinoma after surgery. These may be helpful for reforumulating post-operative consolidation treatment strategies.
| S-EPMC8349519 | biostudies-literature
Project description:BackgroundPrognostication tools for early-stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT) are currently lacking. The purpose of this study was to develop and externally validate a nomogram to predict overall survival in individual patients with peripheral early-stage disease.MethodsA total of 587 NSCLC patients treated with biologically effective dose > 100 Gy10 were eligible. A Cox proportional hazards model was used to build a nomogram to predict 6-month, 1-year, 3-year and 5-year overall survival. Internal validation was performed using bootstrap sampling. External validation was performed in a separate cohort of 124 NSCLC patients with central tumors treated with SBRT. Discriminatory ability was measured by the concordance index (C-index) while predictive accuracy was assessed with calibration slope and plots.ResultsThe resulting nomogram was based on six prognostic factors: age, sex, Karnofsky Performance Status, operability, Charlson Comorbidity Index, and tumor diameter. The slope of the calibration curve for nomogram-predicted versus Kaplan-Meier-estimated overall survival was 0.77. The C-index of the nomogram (corrected for optimism) was moderate at 0.64. In the external validation cohort, the model yielded a C-index of 0.62.ConclusionsWe established and validated a nomogram which can provide individual survival predictions for patients with early stage lung cancer treated with SBRT. The nomogram may assist patients and clinicians with treatment decision-making.
| S-EPMC7178957 | biostudies-literature
Project description:Background: Ferroptosis is an iron-dependent programmed cell death process. Recent studies have found that ferroptosis inducers hold promising potential in the treatment of lung adenocarcinoma (LUAD). However, the comprehensive analysis about the prognostic value of ferroptosis-related genes in LUAD remains to be elucidated. Methods: The RNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 259 ferroptosis-related genes were extracted from FerrDb website. The ferroptosis-related prognostic signature was developed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis in TCGA LUAD cohort, and then validated by 5 independent GEO cohorts. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to identify the difference in biological processes and functions between different risk groups. The expression levels of core prognostic genes were then verified in LUAD samples by immunohistochemistry (IHC) and erastin-treated LUAD cell lines by real-time polymerase chain reaction (PCR). The potential roles of GPX2 and DDIT4 as ferroptosis drivers in LUAD cell line were further confirmed by in vitro experiments. Results: A total of 20 intersecting genes between 70 ferroptosis-related DEGs and 45 potential prognostic genes were obtained for LASSO Cox regression analysis. The ferroptosis-related prognostic signature was developed by 7 core prognostic DEGs, and stratified LUAD patients into two risk groups. Kaplan-Meier analysis showed that the overall survival (OS) of LUAD patients in the high-risk group was significantly worse than that of the low-risk group. External validation of 5 independent GEO cohorts further confirmed that the ferroptosis-related prognostic signature was an ideal biomarker for predicting the survival of LUAD patients. Significant enrichment of fatty acid metabolism and cell cycle-related pathways were found in different risk groups. The expression patterns of 7 core prognostic genes in LUAD and adjacent normal lung tissues were validated by IHC, which was almost consistent with the results from public database. Furthermore, the changes related to cell cycle and ferroptosis after erastin treatment were also validated in LUAD cell lines. In addition, silencing GPX2 or DDIT4 could partially reverse the erastin-induced ferroptosis. Conclusion: In summary, the ferroptosis-related prognostic signature based on 7 core prognostic DEGs indicated superior predictive performance of LUAD patients. Targeting ferroptosis holds potential to be a therapeutic alternative for LUAD.
| S-EPMC8294813 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.
| S-EPMC7811787 | biostudies-literature
Project description:PurposeNew prognostic markers to guide treatment decisions in early stage non-small cell lung cancer are necessary to improve patient outcomes. In this report, we assess the utility of a predefined mRNA expression signature of cell-cycle progression genes (CCP score) to define 5-year risk of lung cancer-related death in patients with early stage lung adenocarcinoma.Experimental designA CCP score was calculated from the mRNA expression levels of 31 proliferation genes in stage I and stage II tumor samples from two public microarray datasets [Director's Consortium (DC) and GSE31210]. The same gene set was tested by quantitative PCR in 381 formalin-fixed paraffin-embedded (FFPE) primary tumors. Association of the CCP score with outcome was assessed by Cox proportional hazards analysis.ResultsIn univariate analysis, the CCP score was a strong predictor of cancer-specific survival in both the Director's Consortium cohort (P = 0.00014; HR = 2.08; 95% CI, 1.43-3.02) and GSE31210 (P = 0.0010; HR = 2.25; 95% CI, 1.42-3.56). In multivariate analysis, the CCP score remained the dominant prognostic marker in the presence of clinical variables (P = 0.0022; HR = 2.02; 95% CI, 1.29-3.17 in Director's Consortium, P = 0.0026; HR = 2.16; 95% CI, 1.32-3.53 in GSE31210). On a quantitative PCR platform, the CCP score maintained highly significant prognostic value in FFPE-derived mRNA from clinical samples in both univariate (P = 0.00033; HR = 2.10; 95% CI, 1.39-3.17) and multivariate analyses (P = 0.0071; HR = 1.92; 95% CI, 1.18-3.10).ConclusionsThe CCP score is a significant predictor of lung cancer death in early stage lung adenocarcinoma treated with surgery and may be a valuable tool in selecting patients for adjuvant treatment.
| S-EPMC3834029 | 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:Simple Summary Lung cancer is the leading cause of cancer-related deaths worldwide in part due to high rates of recurrence even with early-stage disease. This suggests that new biomarkers are needed to improve predictions for patient outcomes and better understand the underlying biology that drives poor outcomes. We believe the immune cells within the tumor microenvironment (TME) are a key differentiator between patients who are cured and patients who experience lung cancer relapse. In this study, we investigated the presence of CD14+ cells within the lung adenocarcinoma TME through immunohistochemistry and found that higher levels of CD14+ cells were correlated with shorter patient survival. In vitro studies revealed a bi-directional cross-talk between CD14+ cells and lung cancer that improved lung cancer cell recovery after chemotherapy. Our observations suggest that TME CD14+ infiltration is a prognostic marker in lung cancer and that further studies are needed to understand CD14+-cell-mediated mechanisms that influence patient outcomes. Abstract Patients with early-stage lung adenocarcinoma have a high risk of recurrent or metastatic disease despite undergoing curative intent therapy. We hypothesized that increased CD14+ cells within the tumor microenvironment (TME) could stratify patient outcomes. Immunohistochemistry for CD14 was performed on 189 specimens from patients with lung adenocarcinoma who underwent curative intent surgery. Outcomes and associations with clinical and pathologic variables were determined. In vitro studies utilized a coculture system to model the lung cancer TME containing CD14+ cells. Patients with high levels of TME CD14+ cells experienced a median overall survival of 5.5 years compared with 8.3 and 10.7 years for those with moderate or low CD14 levels, respectively (p < 0.001). Increased CD14+ cell tumor infiltration was associated with a higher stage at diagnosis and more positive lymph nodes at the time of surgery. This prognostic capacity remained even for patients with early-stage disease. Using an in vitro model system, we found that CD14+ cells reduced chemotherapy-induced cancer cell death. These data suggest that CD14+ cells are a biomarker for poor prognosis in early-stage lung adenocarcinoma and may promote tumor survival. CD14+ cell integration into the lung cancer TME can occur early in the disease and may be a promising new therapeutic avenue.
| S-EPMC9496975 | biostudies-literature
Project description:Background: Lung adenocarcinoma (LUAD) is a malignant tumor with high heterogeneity and poor prognosis. Ferroptosis, a form of regulated cell-death-related iron, has been proven to trigger inflammation-associated immunosuppression in the tumor microenvironment, which promotes tumor growth. Therefore, the clinical prognostic value of ferroptosis-related genes in LUAD needs to be further explored. Method: In this study, we downloaded the mRNA expression profiles and corresponding clinical data of LUAD patients from the Cancer Genome Atlas database. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct ferroptosis-related gene signature. Based on these, we established the nomograms for prognosis prediction and validated the model in the GSE72094 dataset. The cell type was identified using the CIBERSORT algorithm for estimating relative subsets of RNA transcripts, which was then used to screen significant tumor immune-infiltrating cells associated with the LUAD prognosis prediction model. Subsequently, we applied co-expression analysis to reveal the relationship between ferroptosis-related genes and significant immune cells. Results: The univariate COX regression analysis showed that 20 genes were associated with the overall survival (OS) as prognostic differentially expressed genes (DEGs) (FDR <0.05). Patients were divided into two risk groups using a 13-gene signature, with the high-risk group having a significantly worse OS than their low-risk counterparts (p < 0.001). We used receiver operating characteristic (ROC) curve analysis to confirm the predictive capacity of the signature. Besides, we identified seven pairs of ferroptosis-related genes and tumor-infiltrating immune cells associated with the prognosis of LUAD patients. Conclusion: In this study, we construct a ferroptosis-related gene signature that can be used for prognostic prediction in LUAD. In addition, we reveal a potential connection between ferroptosis and tumor-infiltrating immune cells.
| S-EPMC8360867 | biostudies-literature