RESUMEN
D-borneol is a double-loop monoterpene with a wide use in the pharmaceutical, food, and cosmetics industries. Natural D-borneol can be extracted from branches and leaves of D-borneol resource plants. With the widespread use of natural D-borneol, the identification of D-borneol resource plants and the protection of germplasm resources have become the focus of research. In this study, plant leaf morphology, chemical composition, and simple sequence repeat (SSR) molecular marker analysis were used to analyze and cluster 5 species of D-borneol resource plants and their closely related species. It was found that all three analysis methods could distinguish and cluster these D-borneol resource plants to some degree. The result of SSR analysis using capillary electrophoresis was the best, and it could distinguish Mei Pian tree from Yin Xiang as well as Longnao Zhang from An Zhang. The correlation analysis between SSR similarity matrix and leaf morphology analysis and between SSR similarity matrix and chemical composition similarity matrix revealed that they both had significant correlations (P < 0.0001) and the correlation (r = 0.588) between SSR and leaf morphology was a little higher than that (r = 0.519) between SSR and chemical composition. This indicated that the environment had a greater impact on the chemical composition than on leaf morphology. The research findings will offer efficient techniques to cluster natural D-borneol resource plants and establish a theoretical basis for their future development and utilization.
RESUMEN
Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes.