Dataset Information

Establishment of an Autophagy-Related Clinical Prognosis Model for Predicting the Overall Survival of Osteosarcoma.

ABSTRACT:

Purpose

Osteosarcoma is the most common primary and highly invasive bone tumor in children and adolescents. The purpose of this study is to construct a multi-gene expression feature related to autophagy, which can be used to predict the prognosis of patients with osteosarcoma. Materials and methods. The clinical and gene expression data of patients with osteosarcoma were obtained from the target database. Enrichment analysis of autophagy-related genes related to overall survival (OS-related ARGs) screened by univariate Cox regression was used to determine OS-related ARGs function and signal pathway. In addition, the selected OS-related ARGs were incorporated into multivariate Cox regression to construct prognostic signature for the overall survival (OS) of osteosarcoma. Use the dataset obtained from the GEO database to verify the signature. Besides, gene set enrichment analysis (GSEA) were applied to further elucidate the molecular mechanisms. Finally, the nomogram is established by combining the risk signature with the clinical characteristics.

Results

Our study eventually included 85 patients. Survival analysis showed that patients with low riskScore had better OS. In addition, 16 genes were included in OS-related ARGs. We also generate a prognosis signature based on two OS-related ARGs. The signature can significantly divide patients into low-risk groups and high-risk groups, and has been verified in the data set of GEO. Subsequently, the riskScore, primary tumor site and metastasis status were identified as independent prognostic factors for OS and a nomogram were generated. The C-index of nomogram is 0.789 (95% CI: 0.703~0.875), ROC curve and calibration chart shows that nomogram has a good consistency between prediction and observation of patients.

Conclusions

ARGs was related to the prognosis of osteosarcoma and can be used as a biomarker of prognosis in patients with osteosarcoma. Nomogram can be used to predict OS of patients and improve treatment strategies.

SUBMITTER: Li J

PROVIDER: S-EPMC8485141 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundTargeting cancer stem cells (CSC) may represent a future therapeutic direction for osteosarcoma (OS), which mainly relies on the identification of CSC markers. This study aimed to classify OS based on messenger ribonucleic acid (mRNA) stemness indices (mRNAsi) and construct a mRNAsi-related risk model to predict the prognosis of OS.MethodsThe one-class logistic regression (OCLR) algorithm was applied to the RNA- sequencing (seq) data of human embryonic stem cells (hESC) and induced pluripotent stem cell (iPSC) lines to calculate mRNAsi. Weighted gene co-expression network analysis (WGCNA) was performed on data obtained from the TARGET database to screen the mRNAsi-related genes. Univariate Cox regression analysis was implemented to screen mRNAsi-related genes with prognostic significance for consensus clustering of OS. The least absolute shrinkage and selection operator (LASSO) and COX regression analysis were conducted to construct a risk model based on mRNAsi-related genes.ResultsSix gene modules were identified in the TARGET database. The yellow module showed the strongest negative correlation with mRNAsi and the strongest significant positive correlation with the immune score and stromal score. OS was divided into three molecular subtypes with significant survival differences based on 73 mRNAsi-related genes with prognostic value for OS. The survival rate was ranked as C3 < C1 < C2 from low to high. The levels of immune components in C2 was significantly higher than those in C1 and C3. HSD11B2, GBP1, RNF130, APBB1IP, and NPC2 in the yellow module were used as variables for building the mRNAsi-related risk model. The survival rate of the high-risk group (as defined by this model) was significantly higher than that of the low-risk group, and it had significant survival prediction ability in 28 types of cancer. In addition, the mRNAsi-related risk model was superior to the Tumor Immune Dysfunction and Exclusion (TIDE) model in predicting the prognosis and immunotherapy response in all three immunotherapy cohorts.ConclusionsThis study classified OS and constructed a mRNAsi-related risk model based on mRNAsi-related genes, which provides a potential tool for more accurate risk stratification of OS and prediction of immunotherapy response.

Project description:Background:Gastric cancer (GC) is one of the high-risk cancers that lacks effective methods for prognosis prediction. Therefore, we searched for immune cells related to the prognosis of GC and studied the role of related genes in GC prognosis. Methods:In this study, we collected the mRNA data of GC from The Cancer Genome Atlas (TCGA) database and studied the immune cells that were closely related to the prognosis of GC. Spearman correlation analysis was performed to show the association between immune cell-related genes and the differentially expressed genes (DEGs) of GC. Univariate and multivariate Cox regression analyses were conducted on the immune cell-related genes with a high correlation with GC. A prognostic risk score model was constructed and the most significant feature genes were identified. Kaplan-Meier method was then used to compare the overall survival (OS) of patients with high-risk and low-risk, and receiver operating characteristic (ROC) analysis was used to assess the accuracy of the risk model. In addition, GC patients were grouped according to the median expression of the features genes, and survival analysis was further carried out. Results:It was noted that regulatory T cells (Tregs) were significantly correlated with the prognosis of GC, and 172 genes related to Tregs were found to be closely associated with GC. An optimal prognostic risk model was constructed, and a 5-gene (including LRFN4, ADAMTS12, MCEMP1, HP and MUC15) signature-based risk score was established. Survival analysis showed significant difference in OS between low-risk and high-risk samples. ROC analysis results indicated that the risk model had a high accuracy for the prognosis prediction of samples (AUC?=?0.717). The results of survival analysis on each feature gene based on expression levels were consistent with the results of multivariate Cox analysis for predicting the risk rate of the 5 genes. Conclusion:These results proved that the 5-gene signature-based risk score could be used to predict the survival of GC patients, and these 5 genes were closely related to Tregs. These findings are of great significance for studying the role of immune cells and related immune factors in regulating the prognosis of GC.

Project description:Numerous studies have shown that microRNA (miRNA) serves as key regulatory factors in the origin and development of cancers. However, the biological mechanisms of miRNAs in kidney renal clear cell carcinoma (KIRC) are still unknown. It is necessary to construct an effective miRNA-clinical model to predict the prognosis of KIRC. In this study, 94 differentially expressed miRNAs were found between para-tumor and tumor tissues based on the Cancer Genome Atlas (TCGA) database. Seven miRNAs (hsa-miR-21-5p, hsa-miR-3613-5p, hsa-miR-144-5p, hsa-miR-376a-5p, hsa-miR-5588-3p, hsa-miR-1269a, and hsa-miR-137-3p) were selected as prognostic indicators. According to their cox coefficient, a risk score formula was constructed. Patients with risk scores were divided into high- and low-risk groups based on the median score. Kaplan-Meier curves analysis showed that the low-risk group had a better survival probability compared to the high-risk group. The area under the ROC curve (AUC) value of the miRNA model was 0.744. In comparison with clinical features, the miRNA model risk score was considered as an independent prognosis factor in multivariate Cox regression analysis. In addition, we built a nomogram including age, metastasis, and miRNA prognostic model based on the results of multivariate Cox regression analysis. The decision curve analysis (DCA) revealed the clinical net benefit of the prognostic model. Gene set enrichment analysis (GSEA) results suggested that several important pathways may be the potential pathways for KIRC. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the target genes of 7 miRNAs revealed that miRNAs may participate in KIRC progression via many specific pathways. Additionally, the levels of seven prognostic miRNAs showed a significant difference between KIRC tissues and adjacent non-tumorous tissues. In conclusion, the miRNA-clinical model provides an effective and accurate way to predict the prognosis of KIRC.