A Novel Mitochondrial-Related Nuclear Gene Signature Predicts Overall Survival of Lung Adenocarcinoma Patients
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ABSTRACT:
SUBMITTER: Zhang X
PROVIDER: S-EPMC8573348 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Protein ubiquitination has been reported to be involved in many biological processes that affect cancer cell growth or death. In this study, we identified differentially expressed E3s/DUB-related genes associated with the prognosis of lung adenocarcinoma and then constructed an E3s/DUB enzyme signature prediction model for the training group and validated its accuracy for prognosis prediction in the validation group. According to our constructed model, all patients were divided into the high- or low-risk group, and a comparison of the two groups revealed that the high-risk group had poorer survival and higher mortality than the low-risk group. The calculated risk score was also an independent prognostic factor when analyzed together with other clinical factors. To explore the functions of the signature genes, we predicted the substrate proteins with which they interact and then performed enrichment analysis. Interestingly, we found that the signature genes were enriched in multiple treatment resistance and immune-related pathways. Therefore, we continued to analyze immune infiltration in the samples and found a variety of differences in immune cell infiltration. According to our constructed model, these differences in immune cell infiltration may predict different immune statuses after grouping and are associated with worse prognosis in high-risk patients.
| S-EPMC8034934 | biostudies-literature
Project description:Background: Lung adenocarcinoma (LUAD) is a highly malignant cancer with a bleak prognosis. Pyroptosis is crucial in LUAD. The present study investigated the prognostic value of a pyroptosis-related signature in LUAD. Methods: LUAD's genomic data were downloaded from TCGA and GEO databases. K-means clustering was used to classify the data based on pyroptosis-related genes (PRGs). The features of tumor microenvironment were compared between the two subtypes. Differentially expressed genes (DEGs) were identified between the two subtypes, and functional enrichment and module analysis were carried out. LASSO Cox regression was used to build a prognostic model. Its prognostic value was assessed. Results: In LUAD, genetic and transcriptional changes in PRGs were found. A total of 30 PRGs were found to be differentially expressed in LUAD tissues. Based on PRGs, LUAD patients were divided into two subgroups. Subtype 1 has a higher overall survival rate than subtype 2. The tumor microenvironment characteristics of the two subtypes differed significantly. Compared to subtype 1, subtype 2 had strong immunological infiltration. Between the two groups, 719 DEGs were discovered. WGCNA used these DEGs to build a co-expression network. The network modules were analyzed. A prognostic model based on seven genes was developed, including FOSL1, KRT6A, GPR133, TMPRSS2, PRDM16, SFTPB, and SFTA3. The developed model was linked to overall survival and response to immunotherapy in patients with LUAD. Conclusion: In LUAD, a pyroptosis-related signature was developed to predict overall survival and treatment responses to immunotherapy.
| S-EPMC9252528 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common malignant tumor that seriously affects human health. Previous studies have indicated that abnormal levels of glycosylation promote progression and poor prognosis of lung cancer. Thus, the present study aimed to explore the prognostic signature related to glycosyltransferases (GTs) for LUAD.MethodsThe gene expression profiles were obtained from The Cancer Genome Atlas (TCGA) database, and GTs were obtained from the GlycomeDB database. Differentially expressed GTs-related genes (DGTs) were identified using edge package and Venn diagram. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ingenuity pathway analysis (IPA) methods were used to investigate the biological processes of DGTs. Subsequently, Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct a prognostic model for LUAD. Kaplan-Meier (K-M) analysis was adopted to explore the overall survival (OS) of LUAD patients. The accuracy and specificity of the prognostic model were evaluated by receiver operating characteristic analysis (ROC). In addition, single-sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze the infiltrating immune cells in the tumor environment.ResultsA total of 48 DGTs were mainly enriched in the processes of glycosylation, glycoprotein biosynthetic process, glycosphingolipid biosynthesis-lacto and neolacto series, and cell-mediated immune response. Furthermore, B3GNT3, MFNG, GYLTL1B, ALG3, and GALNT13 were screened as prognostic genes to construct a risk model for LUAD, and the LUAD patients were divided into high- and low-risk groups. K-M curve suggested that patients with a high-risk score had shorter OS than those with a low-risk score. The ROC analysis demonstrated that the risk model efficiently diagnoses LUAD. Additionally, the proportion of infiltrating aDCs (p < 0.05) and Tgds (p < 0.01) was higher in the high-risk group than in the low-risk group. Spearman's correlation analysis manifested that the prognostic genes (MFNG and ALG3) were significantly correlated with infiltrating immune cells.ConclusionIn summary, this study established a novel GTs-related risk model for the prognosis of LUAD patients, providing new therapeutic targets for LUAD. However, the biological role of glycosylation-related genes in LUAD needs to be explored further.
| S-EPMC9793550 | biostudies-literature
Project description:Secreted proteins are important proteins in the human proteome, accounting for approximately one-tenth of the proteome. However, the prognostic value of secreted protein-related genes has not been comprehensively explored in lung adenocarcinoma (LUAD). In this study, we screened 379 differentially expressed secretory protein genes (DESPRGs) by analyzing the expression profile in patients with LUAD from The Cancer Genome Atlas database. Following univariate Cox regression and least absolute shrinkage and selection operator method regression analysis, 9 prognostic SPRGs were selected to develop secreted protein-related risk score (SPRrisk), including CLEC3B, C1QTNF6, TCN1, F2, FETUB, IGFBP1, ANGPTL4, IFNE, and CCL20. The prediction accuracy of the prognostic models was determined by Kaplan-Meier survival curve analysis and receiver operating characteristic curve analysis. Moreover, a nomogram with improved accuracy for predicting overall survival was established based on independent prognostic factors (SPRrisk and clinical stage). The DESPRGs were validated by quantitative real-time PCR and enzyme-linked immunosorbent assay by using our clinical samples and datasets. Our results demonstrated that SPRrisk can accurately predict the prognosis of patients with LUAD. Patients with a higher risk had lower immune, stromal, and ESTIMATE scores and higher tumor purity. A higher SPRrisk was also negatively associated with the abundance of CD8+ T cells and M1 macrophages. In addition, several genes of the human leukocyte antigen family and immune checkpoints were expressed in low levels in the high-SPRrisk group. Our results provided some insights into assessing individual prognosis and choosing personalized treatment modalities.
| S-EPMC9204015 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer which is the leading cause of death in cancer patients. Circadian clock disruption has been listed as a likely carcinogen. However, whether the expression of circadian genes affects overall survival (OS) in LUAD patients remains unknown. In this article, we identified a circadian gene signature to predict overall survival in LUAD.MethodsRNA sequencing (HTSeq-FPKM) data and clinical characteristics were obtained for a cohort of LUAD patients from The Cancer Genome Atlas (TCGA). A multigene signature based on differentially expressed circadian clock-related genes was generated for the prediction of OS using Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and externally validated using the GSE72094 dataset from the GEO database.ResultsFive differentially expressed genes (DEGs) were identified to be significantly associated with OS using univariate Cox proportional regression analysis (P < 0.05). Patients classified as high risk based on these five DEGs had significantly lower OS than those classified as low risk in both the TGCA cohort and GSE72094 dataset (P < 0.001). Multivariate Cox regression analysis revealed that the five-gene-signature based risk score was an independent predictor of OS (hazard ratio > 1, P < 0.001). Receiver operating characteristic (ROC) curves confirmed its prognostic value. Gene set enrichment analysis (GSEA) showed that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to cell proliferation, gene damage repair, proteasomes, and immune and autoimmune diseases were significantly enriched.ConclusionA novel circadian gene signature for OS in LUAD was found to be predictive in both the derivation and validation cohorts. Targeting circadian genes is a potential therapeutic option in LUAD.
| S-EPMC8272922 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.
| S-EPMC8183047 | biostudies-literature
Project description:We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA-miRNA-lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.
| S-EPMC10504370 | biostudies-literature
Project description:We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine-cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.
| S-EPMC6914845 | 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:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.
| S-EPMC9013292 | biostudies-literature