SPARC is a key mediator of TGF‐β‐induced renal cancer metastasis

Bao, Jingjing; Dang, Qiang; Lin, Ching-Yu; Lo, U‐Ging; Feldkoren, Boris; Dang, Andrew; Hernández, Elizabeth; Li, Fēi; Panwar, Vandana; Lee, Cheng‐Fan; Cen, Junjie; Guan, Bing; Margulis, Vitaly; Kapur, Payal; Brekken, Rolf A.; Luo, Junhang; Hsieh, Jer‐Tsong; Tan, Wanlong

doi:10.1002/jcp.29975

Cited by 34 publications

(26 citation statements)

References 45 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 D) and newly identified RANBP3L dependent genes like Sparc , Col4a1 and Col4a2 are upregulated, a feature already reported for RCC development and progression [ 57 , 58 ]. Interestingly, SPARC was described as key mediator of TGF-β-induced metastasis in RCC cell lines [ 57 ] corroborating the morphological and phenotypical changes we observed in RANBP3L-deficient cells. Further, the ectopic expression of Ranbp3l in the KIRC cell lines 786-0, Caki-1 and Caki-2 attenuated their cell migration behavior and reduced the cell proliferation in Caki-1 and Caki-2.…”

Section: Discussionmentioning

confidence: 89%

Loss of RANBP3L leads to transformation of renal epithelial cells towards a renal clear cell carcinoma like phenotype

Chernyakov

Groß

Fischer

et al. 2021

J Exp Clin Cancer Res

View full text Add to dashboard Cite

Background Renal cell carcinomas (RCC) are characterized by the deregulation of several hundred hyperosmolality-responsive genes. High expression of a subset of these genes including the Ran binding protein 3 like (RANBP3L) is linked to a favorable prognostic outcome in RCC. However, the cellular function of RANBP3L remains largely unknown. Methods We used CRISPR/Cas9-mediated gene editing to generate functional deletions of the Ranbp3l and nuclear factor of activated T cells 5 (Nfat5) gene loci in a murine renal cell line. The NFAT5-KO cells were used to assess the regulation of Ranbp3l by NFAT5 using immunofluorescence, RNA-Seq and promoter assays. RANBP3L-deficient cells were analyzed for changes in cell morphology, proliferation, migration and colony-forming capacity using immunofluorescence and live cell imaging. RANPB3L-dependent changes in gene expression were identified by RNA-Seq. Results We show that NFAT5 directly regulates Ranpb3l under hyperosmotic conditions by binding its promoter. Functional analysis of RANBP3L-deficient cells revealed a loss of epithelial structure, an increased cell migration behavior and colony forming capacity, accompanied by massive alterations in gene expression, all of which are hallmarks for tumor cells. Strikingly, a RANBP3L dependent signature of 60 genes separated samples with clear cell carcinoma (KIRC) from papillary (KIRP), chromophobe renal carcinoma (KICH) and healthy tissue. Conclusions Loss of RANBP3L induces a tumor like phenotype resembles RCC, especially KIRC, on the morphological and gene expression level and might promote tumor development and progression. Therapeutic reconstitution or elevation of osmoregulated RANBP3L expression might represent a novel treatment strategy for RCC or KIRC.

show abstract

Section: Discussionmentioning

confidence: 89%

Loss of RANBP3L leads to transformation of renal epithelial cells towards a renal clear cell carcinoma like phenotype

Chernyakov

Groß

Fischer

et al. 2021

J Exp Clin Cancer Res

View full text Add to dashboard Cite

show abstract

“…FN1 is a glycoprotein expressing gene, encoding an important component of the extracellular matrix, which could interact with the integrin receptor[ 19 , 20 ]. SPARC is a protein-coding gene rich in cysteine and could be expressed in fibroblasts, osteoblasts, chondrocytes, epithelial cells, and platelets[ 21 ]. Previous studies have shown a significant role of the two proteins, FN1 and SPARC, in the regulation of various physiological and pathological processes, including tissue reconstruction, cell migration, and morphogenesis[ 22 - 24 ].…”

Section: Resultsmentioning

confidence: 99%

Evaluation of a five-gene signature associated with stromal infiltration for diffuse large B-cell lymphoma

Nan¹,

Zhang²,

Huang³

et al. 2021

WJCC

View full text Add to dashboard Cite

BACKGROUND Diffuse large B-cell lymphoma (DLBCL) is a common non-Hodgkin lymphoma. The development of immunotherapy greatly improves the patient prognosis but there are some exceptions. Thus, screening for better biomarkers for prognostic evaluation could contribute to the treatment of DLBCL patients. AIM To screen the novel mediators involved in the development of DLBCL. METHODS The GSE60 dataset was applied to identify the differentially expressed genes (DEGs) in DLBCL, and the principal components analysis plot was used to determine the quality of the included samples. The protein-protein interactions were analyzed by the STRING tool. The key hub genes were entered into to the GEPIA database to determine their expressions in DLBCL. Furthermore, these hub gene alterations were analyzed in cBioportal. The UALCAN portal was employed to analyze the expression of the hub genes in different stages of DLBCL. The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Score was conducted to evaluate the correlation between the gene expression and tumor purity. The gene-gene correlation analysis was conducted in the GEPIA. The stromal score analysis was conducted in TIMER to confirm the correlation between the gene expression and infiltrated stromal cells. The correlation between the indicated genes and infiltration level of cancer-associated fibroblasts (CAFs) was also completed in TIMER with two methods, MCP-Counter and Tumor immune dysfunction and exclusion. The correlation between fibronectin (FN1) protein level and secreted protein acidic and cysteine-rich (SPARC) messenger ribonucleic acid expression was confirmed in the cBioportal. RESULTS The top 20 DEGs in DLBCL were identified, and the principal components analysis plot confirmed the quality of the significant DEGs. The pairwise correlation coefficient analysis among all samples showed that these DEGs have a certain co-expression pattern. The DEGs were subjected to STRING to identify the hub genes, alpha-2-macroglobulin ( A2M ), cathepsin B ( CTSB ), FN1 , matrix metallopeptidase 9 ( MMP9 ), and SPARC . The five hub genes were confirmed to be overexpressed in DLBCL. The cBioportal portal detected these five hub genes that had gene alteration, including messenger ribonucleic acid high amplification and missense mutation, and the gene alteration percentages of A2M , FN1 , CTSB , MMP9 , and SPARC were 5%, 8%, 5%, 2.7%, and 5%, respectively. Furthermore, the five hub genes had a potential positive correlation with tumor stage. The correlation analysis between the five genes and tumor purity confirmed that the five genes were overexpressed in DLBCL and had a positi...

show abstract

“…These results suggest that SPARC can induce brain infiltration during BM. Bao et al reported that SPARC was a key mediator of the TGF-β signaling pathway and promoted invasion and metastasis of renal cell carcinoma [76]. In lung squamous cell carcinoma, COL1A1 overexpression in the microenvironment is highly correlated with lymph node metastasis [77].…”

Section: Discussionmentioning

confidence: 99%

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Haam

Han

Lee

et al. 2021

Cancers

View full text Add to dashboard Cite

Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

show abstract

SPARC is a key mediator of TGF‐β‐induced renal cancer metastasis

Cited by 34 publications

References 45 publications

Loss of RANBP3L leads to transformation of renal epithelial cells towards a renal clear cell carcinoma like phenotype

Loss of RANBP3L leads to transformation of renal epithelial cells towards a renal clear cell carcinoma like phenotype

Evaluation of a five-gene signature associated with stromal infiltration for diffuse large B-cell lymphoma

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Contact Info

Product

Resources

About