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Development of a Novel Sphingolipid Signaling Pathway-Related Risk Assessment Model to Predict Prognosis in Kidney Renal Clear Cell Carcinoma

ABSTRACT: This study aimed to explore underlying mechanisms by which sphingolipid-related genes play a role in kidney renal clear cell carcinoma (KIRC) and construct a new prognosis-related risk model. We used a variety of bioinformatics methods and databases to complete our exploration. Based on the TCGA database, we used multiple R-based extension packages for data transformation, processing, and statistical analyses. First, on analyzing the CNV, SNV, and mRNA expression of 29 sphingolipid-related genes in various types of cancers, we found that the vast majority were protective in KIRC. Subsequently, we performed cluster analysis of patients with KIRC using sphingolipid-related genes and successfully classified them into the following three clusters with significant prognostic differences: Cluster 1, Cluster 2, and Cluster 3. We performed differential analyses of transcription factor activity, drug sensitivity, immune cell infiltration, and classical oncogenes to elucidate the unique roles of sphingolipid-related genes in cancer, especially KIRC, and provide a reference for clinical treatment. After analyzing the risk rates of sphingolipid-related genes in KIRC, we successfully established a risk model composed of seven genes using LASSO regression analysis, including SPHK1, CERS5, PLPP1, SGMS1, SGMS2, SERINC1, and KDSR. Previous studies have suggested that these genes play important biological roles in sphingolipid metabolism. ROC curve analysis results showed that the risk model provided good prediction accuracy. Based on this risk model, we successfully classified patients with KIRC into high- and low-risk groups with significant prognostic differences. In addition, we performed correlation analyses combined with clinicopathological data and found a significant correlation between the risk model and patient’s M, T, stage, grade, and fustat. Finally, we developed a nomogram that predicted the 5-, 7-, and 10-year survival in patients with KIRC. The model we constructed had strong predictive ability. In conclusion, we believe that this study provides valuable data and clues for future studies on sphingolipid-related genes in KIRC.

SUBMITTER: Sun Y

PROVIDER: S-EPMC9277577 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

Project description:Objective: We aimed to investigate the potential role of ERBB signaling pathway-related genes in kidney renal clear cell carcinoma (KIRC) and establish a new predictive risk model using various bioinformatics methods. Methods: We downloaded the KIRC dataset and clinicopathological information from The Cancer Genome Atlas database. Univariate Cox analysis was used to identify essential genes significantly associated with KIRC progression. Next, we used the STRING website to construct a protein-protein interaction network of ERBB signaling pathway-related molecules. We then used the least the absolute shrinkage and selection operator (LASSO) regression analysis to build a predictive risk model for KIRC patients. Next, we used multiple bioinformatics methods to analyze the copy number variation, single-nucleotide variation, and overall survival of these risk model genes in pan-cancer. At last, we used the Genomics of Drug Sensitivity in Cancer to investigate the correlation between the mRNA expression of genes associated with this risk model gene and drug sensitivity. Results: Through the LASSO regression analysis, we constructed a novel KIRC prognosis-related risk model using 12 genes: SHC1, GAB1, SOS2, SRC, AKT3, EREG, EIF4EBP1, ERBB3, MAPK3, transforming growth factor-alpha, CDKN1A, and PIK3CD. Based on this risk model, the overall survival rate of KIRC patients in the low-risk group was significantly higher than that in the high-risk group (p = 1.221 × 10-15). Furthermore, this risk model was associated with cancer metastasis, tumor size, node, stage, grade, sex, and fustat in KIRC patients. The receiver operating characteristic curve results showed that the model had better prediction accuracy. Multivariate Cox regression analysis showed that the model's risk score was an independent risk factor for KIRC. The Human Protein Atlas database was used to validate the protein expression of risk model-associated molecules in tumors and adjacent normal tissues. The validation results were consistent with our previous findings. Conclusions: We successfully established a prognostic-related risk model for KIRC, which will provide clinicians with a helpful reference for future disease diagnosis and treatment.

Project description:Background: Kidney renal clear cell carcinoma is the most prevalent subtype of renal cell carcinoma encompassing a heterogeneous group of malignancies. Accurate subtype identification and an understanding of the variables influencing prognosis are critical for personalized treatment, but currently limited. To facilitate the sub-classification of KIRC patients and improve prognosis, this study implemented a normalization method to track cancer progression by detecting the accumulation of genetic changes that occur throughout the multi-stage of cancer development. Objective: To reveal KIRC patients with different progression based on gene expression profiles using a normalization method. The aim is to refine molecular subtyping of KIRC patients associated with survival outcomes. Methods: RNA-sequenced gene expression of eighty-two KIRC patients were downloaded from UCSC Xena database. Advanced-stage samples were normalized with early-stage to account for differences in the multi-stage cancer progression's heterogeneity. Hierarchical clustering was performed to reveal clusters that progress differently. Two techniques were applied to screen for significant genes within the clusters. First, differentially expressed genes (DEGs) were discovered by Limma, thereafter, an optimal gene subset was selected using Recursive Feature Elimination (RFE). The gene subset was subjected to Random Forest Classifier to evaluate the cluster prediction performance. Genes strongly associated with survival were identified utilizing Cox regression analysis. The model's accuracy was assessed with Kaplan-Meier (K-M). Finally, a Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. Results: Three clusters were revealed and categorized based on patients' overall survival into short, intermediate, and long. A total of 231 DEGs were discovered of which RFE selected 48 genes. Random Forest Classifier revealed a 100% cluster prediction performance of the genes. Five genes were identified with significant diagnostic capacity. The downregulation of genes SALL4 and KRT15 were associated with favorable prognosis, while the upregulation of genes OSBPL11, SPATA18, and TAL2 were associated with favorable prognosis. Conclusion: The normalization method based on tumour progression from early to late stages of cancer development revealed the heterogeneity of KIRC and identified three potential new subtypes with different prognoses. This could be of great importance for the development of new targeted therapies for each subtype.

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Development of a Novel Sphingolipid Signaling Pathway-Related Risk Assessment Model to Predict Prognosis in Kidney Renal Clear Cell Carcinoma

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