Identification of 4 immune cells and a 5-lncRNA risk signature with prognosis for early-stage lung adenocarcinoma.
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ABSTRACT: Background
Lung cancer is the most common cancer and cause of cancer-related mortality worldwide, increasing evidence indicated that there was a significant correlation between tumors and the long non-coding RNAs (lncRNAs), as well as tumor immune infiltration, but their role in early lung adenocarcinoma (LUAD) are still unclear.Methods
Gene expression data and corresponding clinical data of early-stage LUAD patients were downloaded from GEO and TCGA databases. 24 kinds of tumor-infiltrating immune cells were analyzed by quantity analysis and univariate cox regression analysis, we divided patients into two subgroups using consensus clustering, recognized the differentially expressed genes (DEGs) in the subgroups, then, established lncRNA risk signature by least absolute shrinkage and selection operator (LASSO) regression.Results
A total of 718 patients were enrolled in this study, including 246 from GSE31210 dataset, 127 from GSE50081 dataset and 345 from TCGA-LUAD. We identified that Th2 cells, TFH, NK CD56dim cells and Mast cells were prognosis-related(p < 0.05), then established a 5-lncRNA risk signature (risk score = 0.374600616* LINC00857 + 0.173825706* LINC01116 + (- 0.021398903)* DRAIC + (- 0.113658256)* LINC01140 + (- 0.008403702)* XIST), and draw a nomogram showed that the signature had a well prediction accuracy and discrimination.Conclusions
We identified 4 immune infiltrating cells related to the prognosis of early-stage LUAD, and established a novel 5 immune-related lncRNA signature for predicting patients' prognosis.
SUBMITTER: Mu L
PROVIDER: S-EPMC8004399 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major type of lung cancer. This study aimed to establish a signature based on immune related genes that can predict patients' OS for LUAD. METHODS:The expression data of 976 LUAD patients from The Cancer Genome Atlas database (training set) and the Gene Expression Omnibus database (four testing sets) and 1534 immune related genes from the ImmPort database were used for generation and validation of the signature. The glmnet Cox proportional hazards model was used to find the best gene model and construct the signature. To assess the independently prognostic ability of the signature, the Kaplan-Meier survival analysis and Cox's proportional hazards model were performed. RESULTS:A gene model consisting of 30 immune related genes with the highest frequency after 1000 iterations was used as our signature. The signature demonstrated robust prognostic ability in both training set and testing set and could serve as an independent predictor for LUAD patients in all datasets except GSE31210. Besides, the signature could predict the overall survival (OS) of LUAD patients in different subgroups. And this signature was strongly associated with important clinicopathological factors like recurrence and TNM stage. More importantly, patients with high risk score presented high tumor mutation burden. CONCLUSIONS:This signature could predict prognosis and reflect the tumor immune microenvironment of LUAD patients, which can promote individualized treatment and provide potential novel targets for immunotherapy.
| S-EPMC6399972 | 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: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.
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Project description:BackgroundThe incidence of lung cancer is the highest of all cancers, and it has the highest death rate. Lung adenocarcinoma (LUAD) is a major type of lung cancer. This study is aimed at identifying the prognostic value of immune-related long noncoding RNAs (lncRNAs) in LUAD.Materials and methodsGene expression profiles and the corresponding clinicopathological features of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was performed on the prognostic immune-related lncRNAs to calculate the risk scores, and a risk signature was constructed. Survival analysis was performed to assess the prognostic value of the risk signature. A nomogram was also constructed based on the clinicopathological features and risk signature.ResultsA total of 437 LUAD patients with gene expression data and clinicopathological features were obtained in this study, which was considered the combination set. They were randomly and equally divided into a training set and a validation set. Seven immune-related lncRNAs (AC092794.1, AL034397.3, AC069023.1, AP000695.1, AC091057.1, HLA-DQB1-AS1, and HSPC324) were identified and used to construct a risk signature. The patients were divided into the low- and high-risk groups based on the median risk score of -0.04074. Survival analysis suggested that patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (p = 1.478e - 02). A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients (C-index of the nomogram was 0.755, and the AUCs for the 1-, 3-, and 5-year survivals were 0.826, 0.719, and 0.724, respectively). The validation and combination sets confirmed these results.ConclusionOur study identified seven novel immune-related lncRNAs and generated a risk signature, as well as a nomogram, that could predict the prognosis of LUAD patients.
| S-EPMC7273488 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.
| S-EPMC8260838 | biostudies-literature
Project description:Recent evidence suggests that aberrant expression of long non-coding RNA (lncRNA) can drive the initiation and progression of malignancies. However, little is known about the prognostic potential of lncRNA. We aimed at constructing a lncRNA-based signature to improve the prognosis prediction of pancreatic adenocarcinoma (PAAD). The PAAD samples with clinical information were obtained from The Cancer Genome Atlas and International Cancer Genome Consortium. We established an eight-IRlncRNA signature in a training cohort. The prognostic value of eight-IRlncRNA signature was validated in two distinct cohorts when compared to other four prognostic models. We continued to analyze its independence in subgroups by univariate and multivariate Cox regression. We constructed a nomogram for clinicopathologic features and 1-, 3-, and 5-year overall survival performance. Moreover, Gene set enrichment analysis and Gene Set Variation Analysis distinguished the typical functions between high- and low-risk groups. In addition, we further observed the different correlations of immune cell between eight IRlncRNAs. Eight-IRlncRNA signature appears to be a good performer to predict the survival capability of PAAD patients, and the nomogram will enable PAAD patients to be more accurately managed in clinical practice.
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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:Identification of effective markers for outcome is expected to improve the clinical management of non-small cell lung cancer (NSCLC). Here, we assessed in NSCLC the prognostic efficacy of genes, which we had previously found to be differentially expressed in an in vitro model of human lung carcinogenesis.Prediction algorithms and risk-score models were applied to the expression of the genes in publicly available NSCLC expression data sets. The prognostic capacity of the immunohistochemical expression of proteins encoded by these genes was also tested using formalin-fixed paraffin-embedded (FFPE) tissue specimens from 156 lung adenocarcinomas and 79 squamous cell carcinomas (SCCs).The survival of all-stages (P < 0.001, HR = 2.0) or stage-I (P < 0.001, HR = 2.84) adenocarcinoma patients that expressed the five-gene in vitro lung carcinogenesis model (FILM) signature was significantly poorer than that of patients who did not. No survival differences were observed between SCCs predicted to express or lack FILM signature. Moreover, all stages (P < 0.001, HR = 1.95) or stage-I (P = 0.001, HR = 2.6) adenocarcinoma patients predicted to be at high risk by FILM transcript exhibited significantly worse survival than patients at low risk. Furthermore, the corresponding protein signature was associated with poor survival (all stages, P < 0.001, HR = 3.6; stage-I, P < 0.001, HR = 3.5; stage-IB, P < 0.001, HR = 4.6) and mortality risk (all stages, P = 0.001, HR = 4.0; stage-I, P = 0.01, HR = 3.4; stage-IB, P < 0.001, HR = 7.2) in lung adenocarcinoma patients.Our findings highlight a gene and corresponding protein signature with effective capacity for identification of stage-I lung adenocarcinoma patients with poor prognosis that are likely to benefit from adjuvant therapy.
| S-EPMC3079395 | biostudies-other
Project description:BackgroundSuppressive tumor microenvironment is closely related to the progression and poor prognosis of lung adenocarcinoma (LUAD). Novel individual and universal immune-related biomarkers to predict the prognosis and immune landscape of LUAD patients are urgently needed. Two-gene pairing patterns could integrate and utilize various gene expression data.MethodsThe RNA-seq and relevant clinicopathological data of the LUAD project from the TCGA and well-known immune-related genes list from the ImmPort database were obtained. Co-expression analysis followed by an analysis of variance was performed to identify differentially expressed immune-related lncRNA (irlncRNA) (DEirlncRNA) between tumor and normal tissues. Two arbitrary DEirlncRNAs (DEirlncRNAs pair) in a tumor sample underwent pairwise comparison to generate a score (0 or 1). Next, Univariate analysis, Lasso regression and Multivariate analysis were used to screen survival-related DEirlncRNAs pairs and construct a prognostic model. The Acak information standard (AIC) values of the receiver operating characteristic (ROC) curve for 3 years are calculated to determine the cut-off point for high- or low-risk score. Finally, we evaluated the relationship between the risk score and overall survival, clinicopathological features, immune landscape, and chemotherapy efficacy.ResultsData of 54 normal and 497 tumor samples of LUAD were enrolled. After a strict screening process, 15 survival-independent-related DEirlncRNA pairs were integrated to construct a prognostic model. The AUC value of the 3-year ROC curve was 0.828. Kaplan-Meier analysis showed that patients with low risk lived longer than patients with high risk (p <0.001). Univariate and Multivariate Cox analysis suggested that the risk score was an independent factor of survival. The risk score was negatively associated with most tumor-infiltrating immune cells, immune score, and microenvironment scores. The low-risk group was correlated with increased expression of ICOS. The high-risk group had a connection with lower half inhibitory centration (IC50) of most chemotherapy drugs (e.g., etoposide, paclitaxel, vinorelbine, gemcitabine, and docetaxel) and targeted medicine-erlotinib, but with higher IC50 of methotrexate.ConclusionThe established irlncRNA pairs-based model is a promising prognostic signature for LUAD patients. Furthermore, the prognostic signature has great potential in the evaluation of tumor immune landscape and guiding individualized treatment regimens.
| S-EPMC8784752 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, but the prognosis of LUAD patients remains unsatisfactory. Here, we retrieved the RNA-seq data of LUAD cohort from The Cancer Genome Atlas (TCGA) database and then identified differentially expressed immune-related lncRNAs (DEirlncRNAs) between LUAD and normal controls. Based on a new method of cyclically single pairing along with a 0-or-1 matrix, we constructed a novel prognostic signature of 8 DEirlncRNA pairs in LUAD with no dependence upon specific expression levels of lncRNAs. This prognostic model exhibited significant power in distinguishing good or poor prognosis of LUAD patients and the values of the area under the curve (AUC) were all over 0.70 in 1, 3, 5 years receiver operating characteristic (ROC) curves. Moreover, the risk score of the model could serve as an independent prognostic factor for patients with LUAD. In addition, the risk model was significantly associated with clinicopathological characteristics, tumor-infiltrating immune cells, immune-related molecules and sensitivity of anti-tumor drugs. This novel signature of DEirlncRNA pairs in LUAD, which did not require specific expression levels of lncRNAs, might be used to guide the administration of patients with LUAD in clinical practice.
| S-EPMC8373953 | biostudies-literature