Assessment of the progression of kidney renal clear cell carcinoma using transcriptional profiles revealed new cancer subtypes with variable prognosis.

Autor:	Livesey M; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa., Eshibona N; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa., Bendou H; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa.; Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2023 Nov 24; Vol. 14, pp. 1291043. Date of Electronic Publication: 2023 Nov 24 (Print Publication: 2023).
DOI:	10.3389/fgene.2023.1291043
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Livesey, Eshibona and Bendou.)
Databáze:	MEDLINE
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