Ashok G, Ramaiah S. FN1 and cancer-associated fibroblasts markers influence immune microenvironment in clear cell renal cell carcinoma.
J Gene Med 2023;
25:e3556. [PMID:
37358013 DOI:
10.1002/jgm.3556]
[Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/18/2023] [Accepted: 06/08/2023] [Indexed: 06/27/2023] Open
Abstract
BACKGROUND
Altered tumor microenvironment (TME) is characterized in clear cell renal cell carcinoma (ccRCC) as a result of the heterogeneity observed in the TME. Modulations in TME have shown tumor metastasis promotion; hence, identifying TME-based biomarkers can be critical for theranostics application.
METHODS
Here, we performed an integrated systems biology approach utilizing differential gene expression, network metrics and clinical samples cohorts to prioritize the major deregulated genes and their associated pathways specific for metastasis.
RESULTS
The gene expression profiling of 140 ccRCC samples resulted in 3657 differentially expressed genes, from which a network of 1867 up-regulated genes were further computed using network metrics for screening hub-genes. The specific pathways of ccRCC entailed through functional enrichment analysis of the hub-gene clusters indicated the role of the identified hub-genes in the enriched pathways, further validating the functional significance of the hub-genes. The positive correlation of TME cells, namely cancer-associated fibroblasts (CAFs) and its biomarkers (FAP and S100A4) with FN1, signified the role of hub-gene signaling for promoting metastasis in ccRCC. Thereafter, comparative expression, differential methylation, genetic alteration and overall survival analysis were analyzed to validate the screened hub-genes.
CONCLUSIONS
The hub-genes were validated and prioritized by correlating with expression-based parameters, including histological grades, tumor, metastatic and pathological stages (based on median transcript per million; analysis of variance [ANOVA], P ≤ 0.05) from a clinically curated ccRCC dataset to further substantiate the translational benefits of the screened hub-genes as potential diagnostic biomarkers for ccRCC.
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