LINC00160 mediates sunitinib resistance in renal cell carcinoma via SAA1 that is implicated in STAT3 activation and compound transportation

Integration analysis of PLAUR as a sunitinib resistance and macrophage related biomarker in ccRCC, an in silicon and experimental study

Sunitinib remains the preferred systemic treatment option for specific patients with advanced RCC who are ineligible for immune therapy. However, it's essential to recognize that Sunitinib fails to elicit a favourable response in all patients. Moreover, most patients eventually develop resistance to Sunitinib. Therefore, identifying new targets associated with Sunitinib resistance is crucial. Utilizing multiple datasets from public cohorts, we conducted an exhaustive analysis and identified a total of 8 microRNAs and 112 mRNAs displaying significant expression differences between Sunitinib responsive and resistant groups. A particular set of six genes, specifically NIPSNAP1, STK40, SDC4, NEU1, TBC1D9, and PLAUR, were identified as highly significant via WGCNA. To delve deeper into the resistance mechanisms, we performed additional investigations using cell, molecular, and flow cytometry tests. These studies confirmed PLAUR's pivotal role in fostering Sunitinib resistance, both in vitro and in vivo. Our findings suggest that PLAUR could be a promising therapeutic target across various cancer types. In conclusion, this investigation not only uncovers vital genes and microRNAs associated with Sunitinib resistance in RCC but also introduces PLAUR as a prospective therapeutic target for diverse cancers. The outcomes contribute to advancing personalized healthcare and developing superior therapeutic strategies.Communicated by Ramaswamy H. Sarma.

Mechanisms of sunitinib resistance in renal cell carcinoma and associated opportunities for therapeutics

Sunitinib is the first-line drug for renal cell carcinoma (RCC) treatment. However, patients who received sunitinib treatment will ultimately develop drug resistance after 6-15 months, creating a huge obstacle to the current treatment of renal cell carcinoma. Therefore, it is urgent to clarify the mechanisms of sunitinib resistance and develop new strategies to overcome it. In this review, the mechanisms of sunitinib resistance in renal cell carcinoma have been summarized based on five topics: activation of bypass or alternative pathway, inadequate drug accumulation, tumour microenvironment, metabolic reprogramming and epigenetic regulation. Furthermore, present and potential biomarkers, as well as potential treatment strategies for overcoming sunitinib resistance in renal cell carcinoma, are also covered.

Crucial role of the transcription factors family activator protein 2 in cancer: current clue and views

The transcription factor family activator protein 2 (TFAP2) is vital for regulating both embryonic and oncogenic development. The TFAP2 family consists of five DNA-binding proteins, including TFAP2A, TFAP2B, TFAP2C, TFAP2D and TFAP2E. The importance of TFAP2 in tumor biology is becoming more widely recognized. While TFAP2D is not well studied, here, we mainly focus on the other four TFAP2 members. As a transcription factor, TFAP2 regulates the downstream targets directly by binding to their regulatory region. In addition, the regulation of downstream targets by epigenetic modification, posttranslational regulation, and interaction with noncoding RNA have also been identified. According to the pathways in which the downstream targets are involved in, the regulatory effects of TFAP2 on tumorigenesis are generally summarized as follows: stemness and EMT, interaction between TFAP2 and tumor microenvironment, cell cycle and DNA damage repair, ER- and ERBB2-related signaling pathway, ferroptosis and therapeutic response. Moreover, the factors that affect TFAP2 expression in oncogenesis are also summarized. Here, we review and discuss the most recent studies on TFAP2 and its effects on carcinogenesis and regulatory mechanisms.

The E3 ligase RBCK1 reduces the sensitivity of ccRCC to sunitinib through the ANKRD35-MITD1-ANXA1 axis

Despite the promise of targeted tyrosine kinase inhibitors (TKIs), such as sunitinib, in the extension of survival time in patients with clear cell renal cell carcinoma (ccRCC) progression or metastasis, the patients eventually succumb to inevitable drug resistance. Protein degradation executed by the ubiquitin-dependent proteasome system played an important role in determining the sensitivity of ccRCC to sunitinib. Here, we applied the bioinformatic analysis to identify that E3 ligase RBCK1 was elevated in the sunitinib-resistant renal cancer cell lines or patient specimens. The subsequent in vitro or in vivo studies demonstrated that RBCK1 contributed to decreasing the sensitivity of ccRCC to sunitinib. Then, we showed that inhibition of RBCK1 inactivated the AKT and MAPK signaling pathways, which might be one of the main reasons why RBCK1 induces sunitinib resistance in ccRCC cells. Mechanistically, our results indicated that RBCK1 promotes the degradation of ANKRD35 and that ANKRD35 destabilizes MITD1 by binding with SUMO2 in ccRCC cells. In addition, we showed that the RBCK1-ANKRD35-MITD1-ANXA1 axis regulates the phosphorylation of AKT and ERK and contributes to the dysregulation of sunitinib in ccRCC cells. Therefore, we identified a novel mechanism for regulating the sensitivity of sunitinib in ccRCC. Therefore, we elucidated a novel mechanism by which RBCK1 regulates sunitinib sensitivity in ccRCC.

Sunitinib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers

Currently, renal cell carcinoma (RCC) is the most prevalent type of kidney cancer. Targeted therapy has replaced radiation therapy and chemotherapy as the main treatment option for RCC due to the lack of significant efficacy with these conventional therapeutic regimens. Sunitinib, a drug used to treat gastrointestinal tumors and renal cell carcinoma, inhibits the tyrosine kinase activity of a number of receptor tyrosine kinases, including vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), c-Kit, rearranged during transfection (RET) and fms-related receptor tyrosine kinase 3 (Flt3). Although sunitinib has been shown to be efficacious in the treatment of patients with advanced RCC, a significant number of patients have primary resistance to sunitinib or acquired drug resistance within the 6-15 months of therapy. Thus, in order to develop more efficacious and long-lasting treatment strategies for patients with advanced RCC, it will be crucial to ascertain how to overcome sunitinib resistance that is produced by various drug resistance mechanisms. In this review, we discuss: 1) molecular mechanisms of sunitinib resistance; 2) strategies to overcome sunitinib resistance and 3) potential predictive biomarkers of sunitinib resistance.

Advances in Renal Cell Carcinoma Drug Resistance Models

Renal cell carcinoma (RCC) is the most common form of kidney cancer. Systemic therapy is the preferred method to eliminate residual cancer cells after surgery and prolong the survival of patients with inoperable RCC. A variety of molecular targeted and immunological therapies have been developed to improve the survival rate and prognosis of RCC patients based on their chemotherapy-resistant properties. However, owing to tumor heterogeneity and drug resistance, targeted and immunological therapies lack complete and durable anti-tumor responses; therefore, understanding the mechanisms of systemic therapy resistance and improving clinical curative effects in the treatment of RCC remain challenging. In vitro models with traditional RCC cell lines or primary cell culture, as well as in vivo models with cell or patient-derived xenografts, are used to explore the drug resistance mechanisms of RCC and screen new targeted therapeutic drugs. Here, we review the established methods and applications of in vivo and in vitro RCC drug resistance models, with the aim of improving our understanding of its resistance mechanisms, increasing the efficacy of combination medications, and providing a theoretical foundation for the development and application of new drugs, drug screening, and treatment guidelines for RCC patients.

TIMP1 Indicates Poor Prognosis of Renal Cell Carcinoma and Accelerates Tumorigenesis via EMT Signaling Pathway

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The mortality of advanced RCC remains high despite advances in systemic therapy of RCC. Considering the misdiagnosis of early-stage RCC, the identification of effective biomarkers is of great importance. Tissue inhibitor matrix metalloproteinase 1 (TIMP1), which belongs to TIMP gene family, is a natural inhibitor of the matrix metalloproteinases (MMPs). In this study, we found TIMP1 was significantly up-regulated in cell lines and RCC tissues. Kaplan-Meier analysis revealed that high expression of TIMP1 indicated a poor prognosis. Multivariate analysis further indicated that TIMP1 overexpression was an independent prognostic factor of RCC patients. Furthermore, knockdown of TIMP1 in vitro suppressed the proliferation, migration, and invasion of RCC cells, while upregulating TIMP1 accelerated the proliferation, migration, and invasion of RCC cells. In addition, we also found that TIMP1 prompted the progression of RCC via epithelial-to-mesenchymal transition (EMT) signaling pathway. In conclusion, the present results suggested that TIMP1 indicated poor prognosis of renal cell carcinoma and could serve as a potential diagnostic and prognostic biomarker for RCC.

M2-Polarization-related CNTNAP1 gene might be a novel immunotherapeutic target and biomarker for clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common malignancies, characterized by high mortality rate in urology. Unfortunately, reliable biomarkers for ccRCC diagnosis and prognosis remain lacking. Contactin Associated Protein 1 (CNTNAP1) has yet to be thoroughly investigated in cancer, especially its relationship with immune infiltration or clinical outcomes of ccRCC. Here, we explored the Cancer Genome Atlas Kidney Clear Cell Carcinoma database (TCGA-KIRC) for prognostic significance, differential expression, and probable mechanism of CNTNAP1. The aberrant CNTNAP1 expression was also validated by international Cancer Genome Consortium (ICGC) and ccRCC clinic samples. We used Database for Annotation, Visualization, and Integrated Discovery (DAVID) to performed the GO & KEGG enrichment. TIMER database was further utilized to assess its correlation with immune infiltration in ccRCC. The the CellMiner database was used to analyse the relationship between CNTNAP1 expression and drug sensitivity. Results showed CNTNAP1 was upregulated in TCGA-KIRC, ICGC and clinic samples. And CNTNAP1 expression was positively related to infiltration levels of cancer-associated fibroblast, regulatory T cells, and Myeloid-derived suppressor cells, while negatively related to eosinophils. Furthermore, we observed CNTNAP1 was appreciably positively associated with alternatively activated macrophage (M2) in ccRCC. Finally, high CNTNAP1 expression was negatively correlated with Nilotinib, Crizotinib, Eribulin mesylate, and Vinorelbine. Collectively, these results strongly suggest that CNTNAP1 might act as an immunotherapeutic target and a promising novel biomarker for ccRCC.

Development of a four-gene prognostic model for clear cell renal cell carcinoma based on transcriptome analysis

This study aimed to develop a prognostic model for clear cell renal cell carcinoma (ccRCC) based on transcriptome analysis. We screened Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database for gene expression data and clinical characteristics of ccRCC. After differentially expression analysis, we identified 533 key genes of the development of ccRCC. Next, a weighted gene co-expression network analysis (WGCNA) was executed to investigate the association between differentially expressed genes and clinical characteristics. Then, based on protein-protein interaction (PPI) network, least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a four-gene (COL4A5, ABCB1, NR3C2 and PLG) prognostic model has been constructed in TCGA training cohort. Finally, we examined the predictive power of this model with survival analysis and receiver operating characteristic (ROC) curve both in training cohort and in validation cohorts. And we found this model was significantly correlated with infiltrating immune cells. The four-gene prognosis model could be a potential prediction tool with high accuracy and reliability for ccRCC patients.