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Purpose: This bibliometric research aims to delineate global publication trends and emerging research interests in the use of acupuncture for breast cancer (BC)-related symptoms treatment over the past three decades. Furthermore, it identifies influential institutions, potential collaborative partners, and future research trends, thereby providing guidance for relevant, novel research directions. Methods: Scientific publications related to acupuncture for BC-related symptoms were gathered from the Web of Science Core Collection (WoSCC) from 1993 to 2023. Four software applications were principally used to analyze the resulting data: the "bibliometrix" package in the R environment (version 4.2.3), VOSviewer, CiteSpace6.1.R6, and the bibliometrics website. These applications were employed to evaluate different parameters. Results: A total of 621 papers on acupuncture in BC-related symptoms treatment were analyzed. The United States, China, and South Korea contributed the most, with Memorial Sloan Kettering Cancer Center, and Columbia University leading institutions. It is interesting to mention that Mao, Jun J. and Molassiotis, A. feature among the top 10 authors and co-cited authors. JAMA is the leading journal, with an ongoing focus on acupuncture's effectiveness. Keywords show that the initial research focus was mainly on "vasomotor symptoms", but in recent years there has been a gradual shift towards "pain", "chemotherapy-induced peripheral neuropathy (CIPN)", "electroacupuncture", and "non-specific effects". Conclusion: Acupuncture has demonstrated a unique value in the process of adjuvant treatment of BC-related symptoms, and has been shown to be effective in reducing pain, eliminating fatigue, and improving quality of life. The study of the mechanisms of acupuncture and the application of electroacupuncture are possible future research priorities in this field. This study offers a deep perspective on acupuncture for BC research, highlighting key points and future trends.
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Unexplained recurrent spontaneous abortion (URSA) is a severe challenge to reproductive females worldwide, and its etiology and pathogenesis have not yet been fully clarified. Abnormal intercellular communication between macrophages (Mφ) and decidual stromal cells (DSCs) or trophoblasts has been supposed to be the key to URSA. However, the exact molecular mechanisms in the crosstalk are not yet well understood. This study aimed to explore the potential molecule mechanism that may be involved in the communication between Mφ and DSC or trophoblast cells and determine their diagnostic characteristics by using the integrated research strategy of bioinformatics analysis, machine learning and experiments. First, microarrays of decidual tissue (GSE26787, GSE165004) and placenta tissue (GSE22490) in patients with URSA, as well as microarrays involving induced decidualization (GSE94644) and macrophage polarization in vitro (GSE30595) were derived from the gene expression omnibus (GEO) database. And 721 decidua-differentially expressed genes (DEGs), 613 placenta-DEGs, 510 Mφ polarization DEGs were obtained in URSA by differential expression analysis. Then, the protein-protein interaction (PPI) network was constructed, and the hub genes were identified by CytoHubba in Cytoscape software and validated by real-time PCR assay. Subsequently, immune enrichment analysis on decidua-DEGs and placenta-DEGs by ClueGO verified their regulation effects on Mφ. Besides, functional enrichment analysis was performed on Mφ polarization DEGs and the essential module genes derived from the weighted gene co-expression network analysis (WGCNA) to uncover the biological function that were related to abnormal polarization of Mφ. Furthermore, we screened out 29, 43 and 22 secreted protein-encoding genes from DSC-DEGs, placenta-DEGs and Mφ polarization DEGs, respectively. Besides, the hub secreted-protein-encoding genes were screened by CytoHubba. Moreover, we conducted functional enrichment analysis on these genes. And spearman correlation analysis between hub secreted-protein-encoding genes from donor cells and hub genes in recipient cells was performed to further understand the molecular mechanism of intercellular communication further. Moreover, signature genes with diagnostic value were screened from secreted protein-encoding genes by machine learning and validated by immunofluorescence co-localization analysis with clinical samples. Finally, three biomarkers of DSCs (FGF9, IL1R2, NID2) and three biomarkers of Mφ (CFB, NID2, CXCL11) were obtained. In conclusion, this project provides new ideas for understanding the mechanism regulatory network of intercellular communication involving macrophages at the maternal-fetal interface of URSA. Also, it provides innovative insights for the diagnosis and treatment of URSA.