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Overdue treatment and prognostic evaluation lead to low survival rates in patients with lung adenocarcinoma (LUAD). To date, effective biomarkers for prognosis are still required. The aim of the present study was to screen differentially expressed genes (DEGs) as biomarkers for prognostic evaluation of LUAD. DEGs in tumor and normal samples were identified and analyzed for Kyoto Encyclopedia of Genes and Genomes/Gene Ontology functional enrichments. The common genes that are up and downregulated were selected for prognostic analysis using RNAseq data in The Cancer Genome Atlas. Differential expression analysis was performed with 164 samples in GSE10072 and GSE7670 datasets. A total of 484 DEGs that were present in GSE10072 and GSE7670 datasets were screened, including secreted phosphoprotein 1 (SPP1) that was highly expressed and DEGs ficolin 3, advanced glycosylation end-product specific receptor (AGER), transmembrane protein 100 that were lowly expressed in tumor tissues. These four key genes were subsequently verified using an independent dataset, GSE19804. The gene expression model was consistent with GSE10072 and GSE7670 datasets. The dysregulation of highly expressed SPP1 and lowly expressed AGER significantly reduced the median survival time of patients with LUAD. These findings suggest that SPP1 and AGER are risk factors for LUAD, and these two genes may be utilized in the prognostic evaluation of patients with LUAD. Additionally, the key genes and functional enrichments may provide a reference for investigating the molecular expression mechanisms underlying LUAD. [ABSTRACT FROM AUTHOR]

Background: With the improved knowledge of disease biology and the introduction of immune checkpoints, there has been significant progress in treating renal cell carcinoma (RCC) patients. Individual treatment will differ according to risk stratification. As the clinical course varies in RCC, it has developed different predictive models for assessing patient's individual risk. However, among other prognostic scores, no transparent preference model was given. MicroRNA as a putative marker shown to have prognostic relevance in RCC, molecular analysis may provide an innovative benefit in the prophetic prediction and individual risk assessment. Therefore, this study aimed to establish a prognostic-related microRNA risk score model of RCC and further explore the relationship between the model and the immune microenvironment, immune infiltration, and immune checkpoints. This practical model has the potential to guide individualized surveillance protocols, patient counseling, and individualized treatment decision for RCC patients and facilitate to find more immunotherapy targets.Methods: Downloaded data of RCC from the TCGA database for difference analysis and divided it into a training set and validation set. Then the prognostic genes were screened out by Cox and Lasso regression analysis. Multivariate Cox regression analysis was used to establish a predictive model that divided patients into high-risk and low-risk groups. The ENCORI online website and the results of the RCC difference analysis were used to search for hub genes of miRNA. Estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment (TME) and immune infiltration. Based on Kaplan-Meier survival analysis, search for immune checkpoints related to the prognosis of RCC.Results: There were nine miRNAs in the established model, with a concordance index of 0.702 and an area under the ROC curve of 0.701. Nine miRNAs were strongly correlated with the prognosis (P < 0.01), and those with high expression levels had a poor prognosis. We found a common target gene PDGFRA of hsa-miR-6718, hsa-miR-1269b and hsa-miR-374c, and five genes related to ICGs (KIR2DL3, TNFRSF4, LAG3, CD70 and TNFRSF9). The immune/stromal score, immune infiltration, and immune checkpoint genes of RCC were closely related to its prognosis and were positively associated with a risk score.Conclusions: The established nine-miRNAs prognostic model has the potential to facilitate prognostic prediction. Moreover, this model was closely related to the immune microenvironment, immune infiltration, and immune checkpoint genes of RCC. [ABSTRACT FROM AUTHOR]