Evaluating the Relationship between Cathepsins and Papillary Thyroid Carcinoma: A Mendelian Randomization Study.

Background: Papillary Thyroid Carcinoma (PTC) is the most common thyroid cancer, with an etiology and progression that are not fully understood. Research suggests a link between cathepsins and PTC, but the causal nature of this link is unclear. This study uses Mendelian Randomization (MR) to investigate if cathepsins causally influence PTC risk.

Appraising the effectiveness of immune cells on thyroid cancer: a Mendelian randomization study.

Background: The intricate interplay between the immune system and tumor plays a pivotal role in thyroid cancer (TC) pathogenesis, potentially influencing both the causation and therapeutic outcomes. Despite extensive research, existing literature offers ambiguous insights regarding the association between immune cell traits and thyroid cancer progression.

Methods: To elucidate the potential causal relationships, we conducted an integrated two-sample Mendelian randomization (MR) analysis.

Identification of Potential Drug Therapy for Dermatofibrosarcoma Protuberans with Bioinformatics and Deep Learning Technology.

Background: Dermatofibrosarcoma protuberans (DFSP) is a rare mesenchymal tumor that is primarily treated with surgery. Targeted therapy is a promising approach to help reduce the high rate of recurrence. This study aims to identify the potential target genes and explore the candidate drugs acting on them effectively with computational methods.

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology.

Background: Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced melanomas, but resistance always occurs. This study is aimed at identifying the potential target genes and candidate drugs for drug-resistant melanoma effectively with computational methods.

Global Trends of Stem Cell Precision Medicine Research (2018-2022): A Bibliometric Analysis.

Background: Stem cells are a group of cells that can self-renew and have multiple differentiation capabilities. Shinya Yamanaka first discovered a method to convert somatic cells into pluripotent stem cells in 2006. Stem cell therapy can be summarized into three aspects (regenerative treatment, therapy targeted at stem cells, and establishment of disease models).