An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma.
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ABSTRACT:
SUBMITTER: Xu Q
PROVIDER: S-EPMC8283194 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Inflammation is an important hallmark of cancer and plays a role in both neogenesis and tumor development. Despite this, inflammatory-related genes (IRGs) remain to be poorly studied in lung adenocarcinoma (LUAD). We aim to explore the prognostic value of IRGs for LUAD and construct an IRG-based prognosis signature. The transcriptomic profiles and clinicopathological information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression were applied in the TCGA set to generate an IRG risk signature. LUAD cases with from the GSE31210 and GSE30219 datasets were used to validate the predictive ability of the signature. Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1. This signature was used to divide patients into two risk groups with different survival rates. Multivariate Cox regression analysis verified that the risk score from the five-IRG signature negatively correlated with patient outcome. A nomogram was developed using the IRG risk signature and stage, with C-index values of 0.687 (95% CI: 0.644-0.730) in the TCGA training cohort, 0.678 (95% CI: 0.586-0.771) in GSE30219 cohort, and 0.656 (95% CI: 0.571-0.740) in GSE30219 cohort. Calibration curves were consistent between the actual and the predicted overall survival. The immune infiltration analysis in the TCGA training cohort and two GEO validation cohorts showed a distinctly differentiated immune cell infiltration landscape between the two risk groups. The IRG risk signature for LUAD can be used to predict patient prognosis and guide individual treatment. This risk signature is also a potential biomarker of immunotherapy.
| S-EPMC8766344 | biostudies-literature
Project description:Background Aging, an inevitable process characterized by functional decline over time, is a significant risk factor for various tumors. However, little is known about aging-related genes (ARGs) in breast cancer (BC). We aimed to explore the potential prognostic role of ARGs and to develop an ARG-based prognosis signature for BC. Methods RNA-sequencing expression profiles and corresponding clinicopathological data of female patients with BC were obtained from public databases in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). An ARG-based risk signature was constructed in the TCGA cohort based on results of least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, and its prognostic value was further validated in the GSE20685 cohort. Results A six ARG-based signature, including CLU, DGAT1, MXI1, NFKBI, PIK3CA and PLAU, was developed in the TCGA cohort and significantly stratified patients into low- and high-risk groups. Patients in the former group showed significantly better prognosis than those in the latter. Multivariate Cox regression analysis demonstrated that the ARG risk score was an independent prognostic factor for BC. A predictive nomogram integrating the ARG risk score and three identified factors (age, N- and M-classification) was established in the TCGA cohort and validated in the GSE20685 cohort. Calibration plots showed good consistency between predicted survival probabilities and actual observations. Conclusion A novel ARG-based risk signature was developed for patients with BC, which can be used for individual prognosis prediction and promoting personalized treatment.
| S-EPMC8578840 | biostudies-literature
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.
| S-EPMC9972108 | biostudies-literature
Project description:A substantial proportion of prostatic adenocarcinoma (PRAD) patients experience biochemical failure (BCF) after radical prostatectomy (RP). The immune microenvironment plays a vital role in carcinogenesis and the development of PRAD. This study aimed to identify a novel immune-related gene (IRG)-based signature for risk stratification and prognosis of BCF in PRAD. Weighted gene coexpression network analysis was carried out to identify a BCF-related module in a discovery cohort of patients who underwent RP at the Massachusetts General Hospital. The median follow-up time was 70.32 months. Random forest and multivariate stepwise Cox regression analyses were used to identify an IRG-based signature from the specific module. Risk plot analyses, Kaplan-Meier curves, receiver operating characteristic curves, univariate and multivariate Cox regression analyses, stratified analysis, and Harrell's concordance index were used to assess the prognostic value and predictive accuracy of the IRG-based signature in the internal discovery cohort; The Cancer Genome Atlas database was used as a validation cohort. Tumor immune estimation resource database analysis and CIBERSORT algorithm were used to assess the immunophenotype of PRAD. A novel IRG-based signature was identified from the specific module. Five IRGs (BUB1B, NDN, NID1, COL4A6, and FLRT2) were verified as components of the risk signature. The IRG-based signature showed good prognostic value and predictive accuracy in both the discovery and validation cohorts. Infiltrations of various immune cells were significantly different between low-risk and high-risk groups in PRAD. We identified a novel IRG-based signature that could function as an index for assessing tumor immune status and risk stratification in PRAD.
| S-EPMC8486177 | biostudies-literature
Project description:Aberrant expression of microRNAs may affect tumorigenesis and progression by regulating their target genes. This study aimed to construct a risk model for predicting the prognosis of patients with lung adenocarcinoma (LUAD) based on differentially expressed microRNA-regulated target genes. The miRNA sequencing data, RNA sequencing data, and patients' LUAD clinical data were downloaded from the The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs and genes were screened out by combining differential analysis with LASSO regression analysis to further screen out miRNAs associated with patients' prognosis, and target gene prediction was performed for these miRNAs using a target gene database. Overlapping gene screening was performed for target genes and differentially expressed genes. LASSO regression analysis and survival analysis were then used to identify key genes. Risk score equations for prognostic models were established using multifactorial COX regression analysis to construct survival prognostic models, and the accuracy of the models was evaluated using subject working characteristic curves. The groups were divided into high- and low-risk groups according to the median risk score, and the correlation with the clinicopathological characteristics of the patients was observed. A total of 123 up-regulated miRNAs and 22 down-regulated miRNAs were obtained in this study. Five prognosis-related miRNAs were screened using LASSO regression analysis and Kaplan-Meier method validation, and their target genes were screened with the overlap of differentially expressed genes before multifactorial COX analysis finally resulted in an 11-gene risk model for predicting patient prognosis. The area under the ROC curve proved that the model has high accuracy. The 11-gene risk-prediction model constructed in this study may be an effective predictor of prognosis.
| S-EPMC8602086 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Therefore, the identification of a novel prediction signature for predicting the prognosis risk and survival outcomes is urgently demanded.MethodsWe integrated a machine-learning frame by combing the Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify the LUAD-related long non-coding RNA (lncRNA) survival biomarkers. Subsequently, the Spearman correlation test was employed to interrogate the relationships between lncRNA signature and tumor immunity and constructed the competing endogenous RNA (ceRNA) network.ResultsHerein, we identified an eight-lncRNA signature (PR-lncRNA signature, NPSR1-AS1, SATB2-AS1, LINC01090, FGF12-AS2, AC005256.1, MAFA-AS1, BFSP2-AS1, and CPC5-AS1), which contributes to predicting LUAD patient's prognosis risk and survival outcomes. The PR-lncRNA signature has also been confirmed as the robust signature in independent datasets. Further parsing of the LUAD tumor immune infiltration showed the PR-lncRNAs were closely associated with the abundance of multiple immune cells infiltration and the expression of MHC molecules. Furthermore, by constructing the PR-lncRNA-related ceRNA network, we interrogated more potential anti-cancer therapy targets.ConclusionlncRNAs, as emerging cancer biomarkers, play an important role in a variety of cancer processes. Identification of PR-lncRNA signatures allows us to better predict patient's survival outcomes and disease risk. Finally, the PR-lncRNA signatures could help us to develop novel LUAD anti-cancer therapeutic strategies.
| S-EPMC8605161 | biostudies-literature
Project description:BackgroundThe immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction.MethodsDifferently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed.ResultsFive DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets.ConclusionsThe prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.
| S-EPMC10585734 | biostudies-literature
Project description:Increasing evidence has highlighted the important roles of dysregulated long non-coding RNA (lncRNA) expression in tumorigenesis, tumor progression and metastasis. However, lncRNA expression patterns and their prognostic value for tumor relapse in lung adenocarcinoma (LUAD) patients have not been systematically elucidated. In this study, we evaluated lncRNA expression profiles by repurposing the publicly available microarray expression profiles from a large cohort of LUAD patients and identified specific lncRNA signature closely associated with tumor relapse in LUAD from significantly altered lncRNAs using the weighted voting algorithm and cross-validation strategy, which was able to discriminate between relapsed and non-relapsed LUAD patients with sensitivity of 90.9% and specificity of 81.8%. From the discovery dataset, we developed a risk score model represented by the nine relapse-related lncRNAs for prognosis prediction, which classified patients into high-risk and low-risk subgroups with significantly different recurrence-free survival (HR=45.728, 95% CI=6.241-335.1; p=1.69e-04). The prognostic value of this relapse-related lncRNA signature was confirmed in the testing dataset and other two independent datasets. Multivariable Cox regression analysis and stratified analysis showed that the relapse-related lncRNA signature was independent of other clinical variables. Integrative in silico functional analysis suggested that these nine relapse-related lncRNAs revealed biological relevance to disease relapse, such as cell cycle, DNA repair and damage and cell death. Our study demonstrated that the relapse-related lncRNA signature may not only help to identify LUAD patients at high risk of relapse benefiting from adjuvant therapy but also could provide novel insights into the understanding of molecular mechanism of recurrent disease.
| S-EPMC5045428 | biostudies-literature
Project description:BackgroundFerroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. This study aims to investigate the potential correlation between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).MethodsRNA-seq data were collected from the LUAD dataset of The Cancer Genome Atlas (TCGA) database. Based on ferroptosis-related genes, differentially expressed genes (DEGs) between LUAD and paracancerous specimens were identified. The univariate Cox regression analysis was performed to screen key genes associated with the prognosis of LUAD. LUAD patients were divided into the training set and validation set. Then, we screened out key genes and built a prognostic prediction model involving 5 genes using the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation and the multivariate Cox regression analysis. After dividing LUAD patients based on the median level of risk score as cut-off value, the generated prognostic prediction model was validated in the validation set. Moreover, we analyzed the somatic mutations, and estimated the scores of immune infiltration in the high-risk and low-risk groups. Functional enrichment analysis of DEGs was performed as well.ResultsHigh-risk scores indicated the worse prognosis of LUAD. The maximum area under curve (AUC) of the training set and the validation set in this study was 0.7 and 0.69, respectively. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of LUAD cases with the survival time of 1, 3 and 5 years was 0.698, 0.71 and 0.73, respectively. In addition, the mutation frequency of LUAD patients in the high-risk group was significantly higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.ConclusionsThis study constructs a novel LUAD prognosis prediction model involving 5 ferroptosis-related genes, which can be used as a promising tool for decision-making of clinical therapeutic strategies of LUAD.
| S-EPMC8276441 | biostudies-literature
Project description:As the malignancy with the highest global incidence, breast cancer represents a significant threat to women's health. Recent advances have shed light on the importance of mitochondrial function in cancer, particularly in metabolic reprogramming within tumors. Recognizing this, we developed a novel risk signature based on mitochondrial-related genes to improve prognosis prediction and risk stratification in breast cancer patients. In this study, transcriptome data and clinical features of breast cancer samples were extracted from two sources: the TCGA, serving as the training set, and the METABRIC, used as the independent validation set. We developed the signature using LASSO-Cox regression and assessed its prognostic efficacy via ROC curves. Furthermore, the signature was integrated with clinical features to create a Nomogram model, whose accuracy was validated through clinical calibration curves and decision curve analysis. To further elucidate prognostic variations between high and low-risk groups, we conducted functional enrichment and immune infiltration analyses. Additionally, the study encompassed a comparison of mutation landscapes and drug sensitivity, providing a comprehensive understanding of the differing characteristics in these groups. Conclusively, we established a risk signature comprising 8 mitochondrial-related genes-ACSL1, ALDH2, MTHFD2, MRPL13, TP53AIP1, SLC1A1, ME3, and BCL2A1. This signature was identified as an independent risk predictor for breast cancer patient survival, exhibiting a significant high hazard ratio (HR = 3.028, 95%CI 2.038-4.499, P < 0.001). Patients in the low-risk group showed a more favorable prognosis, with enhanced immune infiltration, distinct mutation landscapes, and greater sensitivity to anti-tumor drugs. In contrast, the high-risk group exhibited an adverse trend in these aspects. This risk signature represents a novel and effective prognostic indicator, suggesting valuable insights for patient stratification in breast cancer.
| S-EPMC10838276 | biostudies-literature