Potential therapeutic target secretogranin II might cooperate with hypoxia-inducible factor 1α in sunitinib-resistant renal cell carcinoma

Therapeutic effects and underlying mechanism of poly (L-glutamic acid)-g-methoxy poly (ethylene glycol)/combretastatin A4/BLZ945 nanoparticles on Renca renal carcinoma

Introduction: The prognosis of advanced renal carcinoma is not ideal, necessitating the exploration of novel treatment strategies. Poly(L-glutamic acid)-g-methoxy poly(ethylene glycol)/Combretastatin A4 (CA4)/BLZ945 nanoparticles (CB-NPs) possess the dual capability of CA4 (targeting blood vessels to induce tumor necrosis) and BLZ945 (inducing M2 macrophage apoptosis), thereby inhibiting tumor growth. Methods: Here, the therapeutic effects and underlying mechanism was explored by CCK-8 cytotoxicity experiment, transwell cell invasion and migration experiment, H&E, western blot analysis, immunohistochemistry, flow cytometry, and other techniques. Results: These results demonstrated that CB-NPs could inhibit the growth of Renca cells and subcutaneous tumors in mice, with an impressive tumor inhibition rate of 88.0%. Results suggested that CB-NPs can induce necrosis in renal carcinoma cells and tissues, downregulate VEGFA expression, promote renal carcinoma cell apoptosis, and reduce the polarization of M2 macrophages. Discussion: These findings offer innovative perspectives for the treatment of advanced renal carcinoma.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

Post-Operative Care of the Cancer Patient: Emphasis on Functional Recovery, Rapid Rescue, and Survivorship

A cancer diagnosis and its subsequent treatments are life-changing events, impacting the patient and their family. Treatment options available for cancer care are developing at pace, with more patients now able to achieve a cancer cure. This is achieved through the development of novel cancer treatments, surgery, and modern imaging, but also as a result of better understanding treatment/surgical trauma, rescue after complications, perioperative care, and innovative interventions like pre-habilitation, enhanced recovery, and enhanced post-operative care. With more patients living with and beyond cancer, the role of survivorship and quality of life after cancer treatment is gaining importance. The impact cancer treatments can have on patients vary, and the "scars" treatments leave are not always visible. To adequately support patients through their cancer journeys, we need to look past the short-term interactions they have with medical professionals and encourage them to consider their lives after cancer, which often is not a reflection of life before a cancer diagnosis.