Background Nucleolar spindle-associated protein 1 (NUSAP1) is reported to be a useful diagnostic and prognostic marker for a variety of cancers, but relevant studies are lacking in papillary thyroid carcinoma (PTC). Methods The relationship between NUSAP1 expression and the overall survival (OS) of pan-cancer was examined by GEPIA and KMplot. We explored the relationship between NUSAP1 and clinical PTC data based on the THCA dataset of TCGA and the GEO dataset of NCBI; GO, KEGG analysis, and ceRNA networks were performed on co-expressed genes through LinkedOmics and Starbase. We assessed the relevance between NUSAP1 and the tumor microenvironment using ESTIMATE, correlations between NUSAP1 and immune cells with TIMER, the relationship between NUSAP1 and immunotherapy by TCIA, and small-molecule drugs targeting NUSAP1 that can be discovered using the CMap database. Results Higher expression of NUSAP1 in pan-cancer tissues was correlated with shorter OS. NUSAP1 was also significantly expressed in PTC tissues and was an independent prognostic risk factor. Compared to the NUSAP1 low expression group, the NUSAP1 high expression group was more likely to also have lymph node metastasis, pathological PTC type, shorter progression-free survival (PFS), and higher scores for immune checkpoint inhibitor treatment. The genes associated with NUSAP1 were mostly involved in the cell cycle, immune-related pathways, and AITD. Ten lncRNAs (GAS5, SNHG7, UCA1, SNHG1, HCP5, DLEU2, HOTAIR, TP53TG1, SNHG12, C9orf106), eleven miRNAs (hsa-miR-10a-5p, hsa-miR-10b-5p, hsa-miR-18a-5p, hsa-miR-18b-5p, hsa-miR-128-3p, hsa-miR-214-3p, hsa-miR-219a-2-3p, hsa-miR-339-5p, hsa-miR-494-3p, hsa-miR-545-3p, hsa-miR-769-5p), and one mRNA (NUSAP1) were constructed. NUSAP1 participated in the formation of the tumor microenvironment. CMap predicted the 10 most important small molecules about NUSAP1. Conclusions In PTC, NUSAP1 shows good diagnostic value and prognostic value; NUSAP1 impacts the cell cycle, immune-related pathways, and AITD and has a complex effect on the tumor microenvironment in PTC.

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Background/Aims. Diabetic nephropathy (DN) remained the leading driver of global end-stage renal disease (ESRD) incidence, and tens of thousands of studies to date have analyzed DN-related topics. However, no bibliometric analysis of DN-associated studies indexed in the PubMed database from 2001 to 2021 has been conducted to date. The present bibliometric study was thus developed to visualize and offer insight into the knowledge framework, research hotspots, and dynamic changes in the DN field over the course from 2001 to 2021. Methods. The PubMed database was searched to identify all studies related to DN that were published from 2001 to 2021, with these studies being separated into four time-based groups. The characteristics of these studies were analyzed and extracted using BICOMB. Biclustering analyses for each of these groups were then performed using gCLUTO, with these results then being analyzed and GraphPad Prism 5 being used to construct strategy diagrams. The social network analyses (SNAs) for each group of studies were conducted using NetDraw and UCINET. Results. In total, 18,889 DN-associated studies published from 2001 to 2021 and included in the PubMed database were incorporated into the present bibliometric analysis. Biclustering analysis and strategy diagrams revealed that active areas of research interest in the DN field include studies of the drug-based treatment, diagnosis, etiology, pathology, physiopathology, and epidemiology of DN. The specific research topics associated with these individual areas, however, have evolved over time in a dynamic manner. Strategy diagrams and SNA results revealed podocyte metabolism as an emerging research hotspot in the DN research field from 2010 to 2015, while DN-related microRNAs, signal transduction, and mesangial cell metabolism have emerged as more recent research hotspots in the interval from 2016 to 2021. Conclusion. Through analyses of PubMed-indexed studies pertaining to DN published since 2001, the results of this bibliometric analysis offer a knowledge framework and insight into active and historical research hotspots in the DN research space, enabling investigators to readily understand the dynamic evolution of this field over the past two decades. Importantly, these analyses also enable the prediction of future DN-related research hotspots, thereby potentially guiding more focused and impactful research efforts.