PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 1716465

Renal cell carcinoma.

J H Reese
View original source ↗Export metadata ↓

Abstract

Renal cell carcinoma represents a significant challenge to surgeons and oncologists treating urologic malignancies. Diagnostically, it is critically important to identify the precise extent of the tumor prior to therapeutic intervention. Therapeutically, a number of controversies continue to be debated, including the role of renal-conserving surgery and the role of surgery in patients with metastatic disease. New research is beginning to identify factors involved in the multidrug-resistant properties of these tumors that may allow us, in the future, to treat these tumors more effectively with systemic chemotherapy. Utilizing immunotherapy in the form of autolymphocytes, interferon, interleukin-2, or combinations of these regimens, a number of exciting advances have been made in the treatment of metastatic renal cell carcinoma. This review examines the most recent literature on each of the above-mentioned aspects in the treatment of this difficult and challenging tumor.

Explore related subjects

Carcinoma, Renal CellHumansImmunotherapy, AdoptiveInterferon-gammaInterferonsInterleukin-2Kidney NeoplasmsJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J H Reese. 1991. Renal cell carcinoma.. https://doi.org/10.1097/00001622-199106000-00016

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell↗
PubMed2026

Dual-Matrix Platform for Highly Specific Multi-Omics Profiling of Renal Cell Carcinoma.

Multiomics interrogation provides complementary information beyond single-omics approaches for improved disease characterization. To enable such multilayer profiling, we expanded the rapid functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform by designing two structurally homologous but functionally tailored fMNPs. This design enables efficient acquisition of both serum metabolic and peptide fingerprints from a total of only 2.05 μL of serum, with an LDI MS analysis time of approximately 90 s per sample, while addressing the limitation of single-matrix systems in simultaneously optimizing analytical performance for different biomolecular species. Through statistical analysis and machine learning-based feature selection, an integrated multiomics biomarker panel was established, comprising 5 peptides and 4 metabolites. Notably, this integrated panel outperformed both single-omics panels across all evaluation metrics in the validation set, improving the area under curve from 0.985 to 1.000 and increasing the classification accuracy from 0.947 (metabolites) and 0.930 (peptides) to 0.965, while showing consistent improvements in F1-score, precision, and recall. Collectively, these results demonstrate the robust performance of the dual-matrix design and multiomics integration for renal cell carcinoma classification, with potential relevance for broader applications in complex disease profiling.

Carcinoma, Renal Cell↗
PubMed2026

Enhancer-mediated DDIT4 activation by SMYD2-dependent H3K4me1 promotes pazopanib resistance in clear cell renal cell carcinoma.

BACKGROUND: The progression and resistance to targeted therapy, including pazopanib, frequently lead to poor prognosis in clear cell renal cell carcinoma (ccRCC) patients. However, the underlying molecular mechanisms of these processes remain unclear. METHODS: In this study, we first performed RNA-seq to identify genes that were differentially expressed in both SMYD2-knockdown and pazopanib-resistant cells, indicating their potential role in SMYD2-mediated drug resistance. We analyzed TCGA-KIRC data and 150 patient samples to identify the relationship between SMYD2 and DDIT4 expression levels, as well as the prognostic significance of DDIT4. In vitro functional assays and murine models were applied to evaluate the effects of SMYD2 and DDIT4 on tumor growth and on pazopanib resistance. CUT&Tag and chromosome conformation capture (4 C) assays were applied to identify enhancers associated with SMYD2-mediated regulation of DDIT4, while the JASPAR database was utilized to predict transcription factors involved in the enhancer regulation. CRISPR-mediated enhancer deletion and ChIP-qPCR were subsequently performed to validate the regulatory roles of the identified enhancer and the transcription factor SPI1 in DDIT4 expression. RESULTS: Our study revealed that the expression level of DDIT4 is positively correlated with SMYD2. DDIT4 is highly expressed in renal cell carcinoma and is associated with poorer survival outcomes. Further research revealed that SMYD2 regulates H3K4me1 in a DDIT4 distal enhancer (chr10:72830412-72830891), promoting the recruitment of the transcription factor SPI1, thereby activating DDIT4 expression. We found that DDIT4 promotes the proliferation, metastasis, and pazopanib resistance of ccRCC, and DDIT4 knockdown enhances drug sensitivity in both in vitro and in vivo experiments. Furthermore, the SMYD2-DDIT4 axis activates the downstream STAT3 signaling pathway, thereby promoting tumor progression. In addition, DDIT4-related prognostic features showed potential associations with patient survival and predicted drug sensitivity in computational analyses. CONCLUSIONS: Our study identifies a previously unrecognized SMYD2-enhancer-DDIT4 regulatory axis, which promotes tumor progression and pazopanib resistance in ccRCC. These findings may provide potential therapeutic implications to overcome pazopanib resistance and improve treatment outcomes in ccRCC by targeting the SMYD2-enhancer-DDIT4 axis.

Carcinoma, Renal Cell↗