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SNX5 suppresses clear cell renal cell carcinoma progression by inducing CD44 internalization and epithelial-to-mesenchymal transition.

ABSTRACT: Aberrant expression of SNX5 can contribute to tumorigenesis, invasion, and metastasis of several human cancers. However, the clinicopathological and biological significance of SNX5 in clear cell renal cell carcinoma (ccRCC) remain unclear. In this study, we found that SNX5 expression was downregulated and negatively correlated with tumor size, American Joint Committee on Cancer stage, tumor thrombus of inferior vena cava, and poor prognosis in human ccRCC. Ectopic expression of SNX5 inhibited ccRCC cell proliferation and metastasis, whereas knockdown of SNX5 increased these activities both in vitro and in vivo. Mechanistically, overexpression of SNX5 blocked internalization and intracellular trafficking of CD44 in ccRCC cells. Knockdown of SNX5 was associated with epithelial-to-mesenchymal transition (EMT) in ccRCC cells. Overexpression of SNX5 inhibited TGF-β-induced migration, invasion, and EMT in ccRCC cells. KLF9 directly bound to the SNX5 promoter and increased SNX5 transcription. Moreover, we found that the combination of SNX5 and CD44 or E-cadherin or KLF9 was a more powerful predictor of poor prognosis than either parameter alone. Collectively, our data reveal a mechanism that KLF9-mediated SNX5 expression was associated with poor prognosis via trafficking of CD44 and promoting EMT in ccRCC. SNX5 may be a potential prognostic biomarker and therapeutic target for patients with ccRCC.

SUBMITTER: Zhou Q

PROVIDER: S-EPMC8717386 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA rising amount of data demonstrates that the epithelial-mesenchymal transition (EMT) in clear cell renal cell carcinomas (ccRCC) is connected with the advancement of the cancer. In order to understand the role of EMT in ccRCC, it is critical to integrate molecules involved in EMT into prognosis prediction. The objective of this project was to establish a prognosis prediction model using genes associated with EMT in ccRCC.MethodsWe acquired the mRNA expression profiles and clinical information about ccRCC from TCGA database. In this study, we measured differentially expressed EMT-related genes (DEEGs) by two comparison groups (tumor versus normal tissues; "stages I-II" versus "stages III-IV" tumor tissues). Based on classification and regression random forest models, we identified the most important DEEGs in predicting prognosis. Afterwards, a risk-score model was created using the identified important DEEGs. The prediction ability of the risk-score model was calculated by the area under the curve (AUC). A nomogram for prognosis prediction was built using the risk-score in combination with clinical factors.ResultsAmong the 72 DEEGs, the classification and regression random forest models identified six hub genes (DKK1, DLX4, IL6, KCNN4, RPL22L1, and SPDEF), which exhibited the highest importance values in both models. Through the expression of these six hub genes, a novel risk-score was developed for the prognosis prediction of ccRCC. ROC curves showed the risk-score performed well in both the training (0.749) and testing (0.777) datasets. According to the survival analysis, individuals who were separated into high/low-risk groups had statistically different outcomes in terms of prognosis. Besides, the risk-score model also showed outstanding ability in assessing the progression of ccRCC after treatment. In terms of nomogram, the concordance index (C-index) was 0.79. Additionally, we predicted the differences in response to chemotherapy drugs among patients from low- and high-risk groups.ConclusionGene signatures related to EMT could be useful in predicting ccRCC prognosis.

Project description:Epithelial-mesenchymal transition (EMT), a reversible cellular program, is critically important in tumor progression and is regulated by a family of transcription factors, induction factors, and an array of signaling pathway genes. The prognostic role and biological functions of EMT-related lncRNAs in ccRCC are largely unknown. In the present study, we analyzed the gene expression data and clinical information retrieved from The Cancer Genome Atlas (TCGA) database (N=512) and International Cancer Genome Consortium (ICGC) database (N=90) which served as training and external validation dataset, respectively. Then, we constructed an EMT-related lncRNA risk signature based on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information. The Kaplan-Meier curve analysis revealed that patients in the low-risk and high-risk groups exhibited significant divergence in the overall survival (OS) and disease-free survival (DFS) of ccRCC, as was confirmed in the validation dataset. The Cox regression analysis of the clinical factors and risk signature in the OS and DFS demonstrated that the risk signature can be utilized as an independent prognostic predictor. Moreover, we developed an individualized prognosis prediction model relying on the nomogram and receive operator curve (ROC) analysis based on the independent factors. The Gene Set Enrichment Analysis (GSEA) indicated that patients in the low-risk group were associated with adherens junction, focal adhesion, MAPK signaling pathway, pathways in cancer, and renal cell carcinoma pathway. In addition, we identified three robust subtypes (named C1, C2 and C3) of ccRCC with distinct clinical characteristics and prognostic role in the TCGA dataset and ICGC dataset. Among them, C1 was associated with a better survival outcome, whereas C2 and C3 was associated with a worse survival outcome and have more advanced-stage patients. Moreover, C2 was more likely to respond to immunotherapy and was sensitive to chemo drugs, this may provide insights to clinicians to develop an individualized treatment. Collectively, this work developed a reliable EMT-related lncRNA risk signature that can independently predict the OS and DFS of ccRCC. Besides, we identified three stable molecular subtypes based on the EMT-related lncRNA expression, which may comprehensively be vital in elucidating the underlying molecular mechanism of ccRCC.

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SNX5 suppresses clear cell renal cell carcinoma progression by inducing CD44 internalization and epithelial-to-mesenchymal transition.

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