Basal cell carcinoma (BCC) is the most common form of skin cancer, contributing to nearly a third of new cancer cases in Western countries. Most BCCs are considered low risk “routine” lesions that can either be excised through surgery or treated with chemotherapeutic agents. However, around 1–2% of BCC cases are locally aggressive, present a high risk of metastasis, and often develop chemoresistance, termed advanced BCC. There currently exists no animal model or cell line that can recapitulate advanced BCC, let alone intermediate-risk and high-risk early BCC. We previously found that aggressive BCC tumours presented a Th2 cytokine inflammation profile, mesenchymal stem cell properties, and macrophage-induced tumoral inflammation. In this study, we aimed to identify potential BCC “relatives” among solid-organ malignancies who present similar immune cell proportions in their microenvironment compositions. Using immune cell type deconvolution by CIBERSORTx, and cell type enrichment by xCell, we determined three cancers with the most similar tumour microenvironments as compared to BCC. Specifically, chromophobe renal cell carcinoma, sarcoma, and skin cutaneous melanoma presented significance in multiple cell types, namely in CD4+ T lymphocytes, gammadelta T lymphocytes, and NK cell populations. Consequently, further literature analysis was conducted to understand similarities between BCC and its “relatives”, as well as investigating novel treatment targets. By identifying cancers most like BCC, we hope to propose prospective druggable pathways, as well as to gain insight on developing a reliable animal or cell line model to represent advanced BCC.

Ophthalmic surgery requires micrometer-level precision due to the eye’s delicate anatomy, yet manual limitations and restricted 3D visualization make absolute accuracy challenging, driving interest in robotic and Artificial Intelligence technologies to enhance safety and precision. This is a narrative review of experimental and published studies on PubMed and Open Evidence to review the current advances, challenges, and translational potential of robotic-assisted needle insertion in corneal surgery. Topics include robotic corneal surgery platforms such as the da Vinci and custom microsurgical robots, telemanipulation, intraoperative optical coherence tomography (iOCT), and reinforcement learning applications. Recent advancements in the field have demonstrated enhanced needle insertion precision, tremor elimination, and improved visualization of needle trajectory in corneal procedures, including corneal lacerations, pterygium repairs and penetrating keratoplasties (PKs). Nonetheless, significant limitations in the state of the art persist, particularly concerning the integration of robotic systems into clinical practice in in vivo settings. Our results indicate that current studies are mostly conducted in an ex vivo setting, which introduces inherent biases and reduces the generalizability of findings to clinical practice. Additionally, the majority of these studies involve small sample sizes, limiting statistical power and the ability to draw robust conclusions. Together, these limitations highlight the need for larger, well-designed in vivo studies to validate and expand upon existing findings. This review bridges experimental innovation and clinical application, highlighting strategies to overcome current barriers in robotic corneal surgery.