Establishment and validation of an eight-gene metabolic-related prognostic signature model for lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: Ma W
PROVIDER: S-EPMC8034925 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
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Establishment and validation of an eight-gene metabolic-related prognostic signature model for lung adenocarcinoma.
Ma Weishuang W Liang Jiaming J Liu Jinhui J Tian Dongbo D Chen Zisheng Z
Aging 20210222 6
In this study, we constructed an eight-gene metabolic related signature for LUAD. The eight-gene prognostic signature (including PLAUR, F2, UGT2B17, GNG7, IDO2, ST3GAL6, PIK3CG, and GLS2) exhibited a good prognostic value in the TCGA LUAD training dataset and testing dataset. In addition, the risk score based on the signature model was significantly correlated with immune cell infiltration and expression levels of immune markers in LUAD patients. LUAD cohorts from GEO were used to validate the m ...[more]
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Project description:BackgroundGiven that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs).MethodsThe gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD.ResultsA total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration.ConclusionsWe established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.
| S-EPMC8050921 | biostudies-literature
Project description:ObjectiveTo establish a prognostic signature for lung adenocarcinoma (LUAD) based on cuproptosis-related long non-coding RNAs (lncRNAs), and to study the immune-related functions of LUAD.MethodsFirst, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate COX analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate COX analysis were performed to analyze the cuproptosis-related lncRNAs, and a prognostic signature was established. Second, univariate COX analysis and multivariate COX analysis were performed for independent prognostic analyses. Receiver operating characteristic (ROC) curves, C index, survival curve, nomogram, and principal component analysis (PCA) were performed to evaluate the results of the independent prognostic analyses. Finally, gene enrichment analyses and immune-related function analyses were also carried out.Results(1) A total of 1,297 cuproptosis-related lncRNAs were screened. (2) A LUAD prognostic signature containing 13 cuproptosis-related lncRNAs was constructed (NIFK-AS1, AC026355.2, SEPSECS-AS1, AL360270.1, AC010999.2, ABCA9-AS1, AC032011.1, AL162632.3, LINC02518, LINC0059, AL031600.2, AP000346.1, AC012409.4). (3) The area under the multi-indicator ROC curves at 1, 3, and 5 years were AUC1 = 0.742, AUC2 = 0.708, and AUC3 = 0.762, respectively. The risk score of the prognostic signature could be used as an independent prognostic factor that was independent of other clinical indicators. (4) The results of gene enrichment analyses showed that 13 biomarkers were primarily related to amoebiasis, the wnt signaling pathway, hematopoietic cell lineage. The ssGSEA volcano map showed significant differences between high- and low-risk groups in immune-related functions, such as human leukocyte antigen (HLA), Type_II_IFN_Reponse, MHC_class_I, and Parainflammation (P < 0.001).ConclusionsThirteen cuproptosis-related lncRNAs may be clinical molecular biomarkers for the prognosis of LUAD.
| S-EPMC9990240 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. The main obstacle to early diagnosis or monitoring of patients at high risk of poor survival has been the lack of essential predictive biomarkers.MethodsRNA-sequencing was performed on LUAD affected tissue and paired adjacent to noncancerous tissue samples and Gene Expression Omnibus dataset GSE19188 and GSE33532 were used to obtain an intersection of differential expressed genes and construct a protein-protein interaction network to get hub genes. Then corresponding overall survival information of two cohorts of LUAD patients from our hospital and The Cancer Genome Atlas project-LUAD were included in the present study. An analysis of the Kyoto Encyclopedia of Genes and Genomes database and Gene Ontology were carried out to study the signature mechanism.ResultsIn our study, we identified eight candidate genes (DLGAP5, KIF11, RAD51AP1, CCNB1, AURKA, CDC6, OIP5 and NCAPG) closely related to survival in LUAD. A linear prognostic model of the eight genes was constructed and weighted by the regression coefficient (β) from the multivariate Cox regression analysis of The Cancer Genome Atlas-LUAD cohort to divide patients into low- and high-risk groups. The prognostic ability of the signature was validated in LUAD patients at our hospital. Patients assigned to the high-risk group exhibited poor overall survival compared to patients in the low-risk group. Finally, functional enrichment analysis showed that cell division played a vital role in the development of LUAD.ConclusionThe study identified an mRNA signature including eight genes, which may serve as a potential prognostic marker of LUAD.
| S-EPMC6138967 | biostudies-literature
Project description:Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival‑associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was used to derive a risk signature which is significantly negatively correlated with OS and divide samples into high-, medium- and low-risk group. Univariate and multivariate Cox analyses suggested that the level of risk group was an independent prognostic factor of the overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5-years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30219 of Gene Expression Omnibus (GEO) was used for verification. We further explored the differences among risk groups in Gene set enrichment analysis (GSEA), tumor mutation and tumor microenvironment. Furthermore, Single Sample Gene Set Enrichment Analysis (ssGSEA) and the results of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) suggested the status of immune cell infiltration was highly associated with risk groups. We demonstrated the prediction effect of CTLA-4 and PD-1/PD-L1 inhibitors in the low-risk group was better than that in the high-risk group using two methods of immune score include immunophenoscore from The Cancer Immunome Atlas (TCIA) and TIDE score from Tumor Immune Dysfunction and Exclusion (TIDE). In addition, partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. Our findings provide a foundation for future research targeting inflammation-related genes to predictive prognosis and some reference significance for the selection of immunotherapy and drug regimen for lung adenocarcinoma.
| S-EPMC9427773 | biostudies-literature
Project description:BackgroundLung cancer has been a prominent research focus in recent years due to its role in cancer-related fatalities globally, with lung adenocarcinoma (LUAD) being the most prevalent histological form. Nonetheless, no signature of lactate metabolism-related long non-coding RNAs (LMR-lncRNAs) has been developed for patients with LUAD. Accordingly, we aimed to develop a unique LMR-lncRNA signature to determine the prognosis of patients with LUAD.MethodThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to derive the lncRNA expression patterns. Identification of LMR-lncRNAs was accomplished by analyzing the co-expression patterns between lncRNAs and LMR genes. Subsequently, the association between lncRNA levels and survival outcomes was determined to develop an effective signature. In the TCGA cohort, Cox regression was enlisted to build an innovative signature consisting of three LMR-lncRNAs, which was validated in the GEO validation cohort. GSEA and immune infiltration analysis were conducted to investigate the functional annotation of the signature and the function of each type of immune cell.ResultsFourteen differentially expressed LMR-lncRNAs were strongly correlated with the prognosis of patients with LUAD and collectively formed a new LMR-lncRNA signature. The patients could be categorized into two cohorts based on their LMR-lncRNA signatures: a low-risk and high-risk group. The overall survival of patients with LUAD in the high-risk group was considerably lower than those in the low-risk group. Using Cox regression, this signature was shown to have substantial potential as an independent prognostic factor, which was further confirmed in the GEO cohort. Moreover, the signature could anticipate survival across different groups based on stage, age, and gender, among other variables. This signature also correlated with immune cell infiltration (including B cells, neutrophils, CD4+ T cells, CD8+ T cells, etc.) as well as the immune checkpoint blockade target CTLA-4.ConclusionWe developed and verified a new LMR-lncRNA signature useful for anticipating the survival of patients with LUAD. This signature could give potentially critical insight for immunotherapy interventions in patients with LUAD.
| S-EPMC9004472 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) remains an incurable disease with a poor prognosis. This study aimed to explore neutrophil‑related genes (NRGs) and develop a prognostic signature for predicting the prognosis of LUAD. NRGs were obtained by intersecting modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data and the marker genes of neutrophils identified from single-cell RNA-sequencing(scRNA-seq) data. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were run to construct a prognostic signature, follow by delineation of risk groups, and external validation. Analyses of ESTIMAT, immune function, Tumor Immune Dysfunction and Exclusion (TIDE) scores, Immune cell Proportion Score (IPS), and immune checkpoint genes between high- and low-risk groups were performed, and then analyses of drug sensitivity to screen for sensitive anticancer drugs in high-risk groups. A total of 45 candidate NRGs were identified, of which PLTP, EREG, CD68, CD69, PLAUR, and CYP27A1 were considered to be significantly associated with prognosis in LUAD and were used to construct a prognostic signature. Correlation analysis showed significant differences in the immune landscape between high- and low-risk groups. In addition, our prognostic signature was important for predicting drug sensitivity in the high-risk group. Our study screened for NRGs in LUAD and constructed a novel and effective signature, revealing the immune landscape and providing more appropriate guidance protocols in LUAD treatment.
| S-EPMC10600204 | biostudies-literature
Project description:BackgroundCuproptosis is a novel form of copper-induced cell death that targets lipoylated tricarboxylic acid (TCA) cycle proteins. However, its prognostic role in lung adenocarcinoma (LUAD) remains unclear. This study aimed to establish a cuproptosis-related prognostic signature for patients with LUAD.MethodsTranscriptome data of LUAD samples were extracted from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The prognostic value of cuproptosis-related genes (CRGs) was investigated using Cox regression analysis to develop a cuproptosis-related prognostic model. Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO) and gene set variation analysis (GSVA) were conducted to characterize different biological activities or pathways between high- or low-CRG groups. The expression pattern and prognostic values of CRGs were validated in 37 paired tumor-normal samples using quantitative PCR. Furthermore, in vitro experiments were performed to investigate the relationship between cuproptosis and CRG expression and to explore the function of target genes in cuproptosis.ResultsAmong the 36 CRGs, 17 genes were upregulated, and 3 genes were downregulated in LUAD. A total of 385 CRGs were identified using Pearson correlation analysis. A cuproptosis-related signature was constructed using least absolute shrinkage and selection operator (LASSO) analysis. The prognostic value of the cuproptosis-related signature was validated in six external validation cohorts and in LUAD specimens from our facility. Patients in the high-risk group based on the CRG signature score had shorter overall survival than those in the low-risk group in both the datasets and clinical specimens. In vitro experiments revealed that the expression of BARX1, GFRA3, and KHDRBS2 was upregulated after cuproptosis was induced by elesclomol-CuCL2, whereas the upregulation was suppressed on pretreatment with tetrathiomolybdate (TTM), a chelator of copper. Further, the cell proliferation assay revealed that the BARX1 and GFRA3 deficiency facilities the cuproptosis induced by elesclomol-CuCL2.ConclusionThis study established a new CRG signature that can be used to predict the OS of LUAD patients. Moreover, the knockdown of BARX1 and GFRA3 could increase the sensitivity of LUAD cells to the cuproptosis.
| S-EPMC9637654 | biostudies-literature
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: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:Lung adenocarcinoma (LUAD) is a commonly occurring histological subtype of lung cancer. Glutathione peroxidase 4 (GPX4) is an important regulatory factor of ferroptosis and is involved in the development of many cancers, but its prognostic significance has not been systematically described in LUAD. In this study, we focused on developing a robust GPX4-related prognostic signature (GPS) for LUAD. Data for the training cohort was extracted from The Cancer Genome Atlas, and that for the validation cohort was sourced from the GSE72094 dataset including 863 LUAD patients. GPX4-related genes were screened out by weighted gene coexpression network analysis and Spearman's correlation analysis. Then, Cox regression and least absolute shrinkage and selection operator regression analyses were employed to construct a GPS. The ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and GSEA were utilized to evaluate the relationship between GPS and the tumor microenvironment (TME). We constructed and validated a GPS premised on four GPX4-related genes (KIF14, LATS2, PRKCE, and TM6SF1), which could classify LUAD patients into low- and high-score cohorts. The high-risk cohort presented noticeably poorer overall survival (OS) as opposed to the low-risk cohort, meaning that the GPS may be utilized as an independent predictor of the OS of LUAD. The GPS was also adversely correlated with multiple tumor-infiltrating immune cells and immune-related processes and pathways in TME. Furthermore, greater sensitivity to erlotinib and lapatinib were identified in the low-risk cohort based on the GDSC database. Our findings suggest that the GPS can effectively forecast the prognosis of LUAD patients and may possibly regulate the TME of LUAD.
| S-EPMC9130018 | biostudies-literature