Renal cell carcinoma (RCC) is distinguished by a highly inflamed tumor microenvironment (TME) that offers both opportunities and challenges for immunotherapy. This review synthesizes current insights into the immunological landscape of RCC, highlighting robust cluster of differentiation 8‑positiv (CD8⁺) T-cell infiltration, unconventional antigen sources such as endogenous retroviruses and frameshift neoantigens, and the heterogeneity of immune niches revealed by single-cell and spatial profiling. We then examine the clinical impact and mechanisms of immune checkpoint inhibitors —including programmed cell death protein 1 (PD‑1), programmed death‑ligand 1 (PD‑L1), and cytotoxic T‑lymphocyte‑associated protein 4 (CTLA‑4)—tumor vaccines, cellular therapies such as chimeric antigen receptor T cell (CAR‑T) therapy and tumor‑infiltrating lymphocytes (TILs) and bispecific antibody constructs, emphasizing advances in dosing, engineering, and combination regimens. Combination strategies—including dual checkpoint blockade, integration with anti-angiogenic tyrosine kinase inhibitors, radiotherapy, metabolism-targeted agents such as adenosine and poly (ADP‑ribose) polymerase (PARP) inhibitors, and hypoxia modulators—are reviewed for their capacity to overcome resistance and remodel the microenvironment. We further explore intrinsic and acquired resistance mechanisms, the immunosuppressive roles of myeloid and stromal elements, and emerging biomarker approaches spanning genomic, transcriptomic, spatial, and circulating analytes. Finally, we discuss current limitations—such as variable clinical response, toxicities, and biomarker gaps—and outline future prospects, including personalized combination regimens, next-generation engineered cell products, and artificial intelligence (AI)-driven precision monitoring. Together, these insights chart a path toward more effective, individualized immunotherapy in RCC.

Background: Lipomas are the most common benign tumours, but some deep lipomas are technically difficult to remove surgically. Early diagnosis and treatment of lipomas can be facilitated by early genetic biomarkers; however, the key genes and signalling pathways that influence lipoma development are not well understood. The aim of this study was to identify hub genes and signalling pathways associated with the development of lipomas. Methods: A dataset of human lipomas (GSE141027) was first downloaded from Gene Expression Omnibus, differential genes (DEGs) for expression profiles were analysed in R software via the edgeR package, and a protein‒protein interaction network was constructed. Based on preliminary data, further modular analysis, neighbour node analysis and Hubba analysis were performed using Cystoscope to identify intersecting genes and display them in a Venn diagram to obtain key hub genes. Enrichment analysis was then carried out using the ClueGO plugin in Cytoscape (v3.9). In addition, weighted gene coexpression network analysis (WGCNA) was used to identify coexpression modules positively and negatively associated with the clinicopathological features of lipoma in the whole dataset, and enrichment analysis was performed on the module genes to obtain the signalling pathways associated with the clinicopathological features of lipoma by intersecting with the signalling pathway enrichment of DEGs. All data were then used for GSEA enrichment to further validate the signalling pathways related to the clinicopathological features of lipoma. Results: A total of 418 DEGs were identified, of which 176 were upregulated and 242 downregulated. Seventeen hub genes were identified by MCODE and hubba plug-in and collateral node analysis, including CKM, ATP2A1, MYLPF, TNNI2, MYL1, ACTN3, ACTN2, ACTG2, MYH11, NEB, MYBPC2, MYOZ1, MYH2, MYBPC1, TNNC2, ACTA1 and TCAP. TCAP. The enrichment functions and signalling pathways of the DEGs were subsequently analysed by the ClueGO plugin. A Venn diagram revealed the 15 most clinically relevant modular gene-enriched signalling pathways for lipoma (including the calcium signalling pathway and ECM-receptor interaction). In addition, 9 key signalling pathways associated with lipoma were identified using GSEA. Conclusion: This study analysed hub genes and signalling pathways of lipoma by bioinformatics to provide potential targets and signalling pathways for early diagnosis and treatment.

Xenotransplantation, leveraging genetically engineered porcine donors, represents a promising solution to the global organ shortage crisis. Recent breakthroughs in genome editing have enabled the creation of pigs with multiple modifications, including knockout of key xenoantigens (GGTA1, CMAH, B4GALNT2) and insertion of human transgenes that regulate complement, coagulation, and innate immunity (e.g., hCD46, hCD55, hTBM, hCD47). Building on this genetic foundation, landmark clinical achievements have recently emerged. However, while the first porcine liver xenotransplants into human recipients demonstrated initial function, significant hurdles such as profound thrombocytopenia and coagulopathy—driven by factors like porcine vWF-human GPIb interactions and immune cell-mediated clearance—persist. Similarly, cardiac and renal xenotransplantation have seen milestones like the first pig-to-human heart transplants and extended kidney graft survival (up to 130 days) in a living recipient, yet delayed rejection and thrombotic microangiopathy remain critical challenges. Advanced strategies, including potent immunosuppression centered on anti-CD40 blockade, improved coagulation management (e.g., via hTFPI, hEPCR, hCD39), and emerging tolerance protocols, are actively being developed to overcome these barriers. This review synthesizes these pivotal 'bench-to-bedside' advancements, critically evaluating the current immunological and genetic innovations driving the progress of cardiac, renal, and hepatic xenotransplantation, and outlining the future directions necessary for successful clinical translation.