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Background: It is well known that hypoxia and ferroptosis are intimately connected with tumor development. The purpose of this investigation was to identify whether they have a prognostic signature. To this end, genes related to hypoxia and ferroptosis scores were investigated using bioinformatics analysis to stratify the risk of lung adenocarcinoma. Methods: Hypoxia and ferroptosis scores were estimated using The Cancer Genome Atlas (TCGA) database-derived cohort transcriptome profiles via the single sample gene set enrichment analysis (ssGSEA) algorithm. The candidate genes associated with hypoxia and ferroptosis scores were identified using weighted correlation network analysis (WGCNA) and differential expression analysis. The prognostic genes in this study were discovered using the Cox regression (CR) model in conjunction with the LASSO method, which was then utilized to create a prognostic signature. The efficacy, accuracy, and clinical value of the prognostic model were evaluated using an independent validation cohort, Receiver Operator Characteristic (ROC) curve, and nomogram. The analysis of function and immune cell infiltration was also carried out. Results: Here, we appraised 152 candidate genes expressed not the same, which were related to hypoxia and ferroptosis for prognostic modeling in The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort, and these genes were further validated in the GSE31210 cohort. We found that the 14-gene-based prognostic model, utilizing MAPK4, TNS4, WFDC2, FSTL3, ITGA2, KLK11, PHLDB2, VGLL3, SNX30, KCNQ3, SMAD9, ANGPTL4, LAMA3, and STK32A, performed well in predicting the prognosis in lung adenocarcinoma. ROC and nomogram analyses showed that risk scores based on prognostic signatures provided desirable predictive accuracy and clinical utility. Moreover, gene set variance analysis showed differential enrichment of 33 hallmark gene sets between different risk groups. Additionally, our results indicated that a higher risk score will lead to more fibroblasts and activated CD4 T cells but fewer myeloid dendritic cells, endothelial cells, eosinophils, immature dendritic cells, and neutrophils. Conclusion: Our research found a 14-gene signature and established a nomogram that accurately predicted the prognosis in patients with lung adenocarcinoma. Clinical decision-making and therapeutic customization may benefit from these results, which may serve as a valuable reference in the future.
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Background: As a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques. Methods: We obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs. Results: We identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues. Conclusion: In summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.
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Background: The most common subtype of lung cancer, called lung adenocarcinoma (LUAD), is also the largest cause of cancer death in the world. The aim of this study was to determine the importance of the METTL7A gene in the prognosis of patients with LUAD. Methods: This particular study used a total of four different LUAD datasets, namely TCGA-LUAD, GSE32863, GSE31210 and GSE13213. Using RT-qPCR, we were able to determine METTL7A expression levels in clinical samples. Univariate and multivariate Cox regression analyses were used to identify factors with independent effects on prognosis in patients with LUAD, and nomograms were designed to predict survival in these patients. Using gene set variation analysis (GSVA), we investigated differences in enriched pathways between METTL7A high and low expression groups. Microenvironmental cell population counter (MCP-counter) and single-sample gene set enrichment analysis (ssGSEA) methods were used to study immune infiltration in LUAD samples. Using the ESTIMATE technique, we were able to determine the immune score, stromal score, and estimated score for each LUAD patient. A competing endogenous RNA network, also known as ceRNA, was established with the help of the Cytoscape program. Results: We detected that METTL7A was down-regulated in pan-cancer, including LUAD. The survival study indicates that METTL7A was a protective factor in the prognosis of LUAD. The univariate and multivariate Cox regression analyses revealed that METTL7A was a robust independent prognostic indicator in survival prediction. Through the use of GSVA, several immune-related pathways were shown to be enriched in both the high-expression and low-expression groups of METTL7A. Analysis of the tumor microenvironment revealed that the immune microenvironment of the group with low expression was suppressed, which may be connected to the poor prognosis. To explore the ceRNA regulatory mechanism of METTL7A, we finally constructed a regulatory network containing 1 mRNA, 2 miRNAs, and 5 long non-coding RNAs (lncRNAs). Conclusion: In conclusion, we presented METTL7A as a potential and promising prognostic indicator of LUAD. This biomarker has the potential to offer us with a comprehensive perspective of the prediction of prognosis and treatment for LUAD patients.
