Introduction

Osteosarcoma (OS) is the most common malignant bone tumor, accounting for approximately 20% of all bone tumors and about 5% of pediatric tumors overall (Tang et al. 2008). Despite the fact that the precise identity of the cell at the origin of the tumor remains unknown, OS is characterized by the presence of transformed osteoblastic cells that produce osteoid matrix. The current treatment combines surgery with preoperative and postoperative multi-drug chemotherapy using three or four cytotoxic agents (cisplatin, doxorubicin, high-dose methotrexate/ifosfamide). However, the survival rate has not notably improved in the past 50 years after the introduction of chemotherapy. Patients with localized OS have a 5-year survival rate of 70–75%, while patients with metastatic disease or recurrence have a long-term survival rate of only 30% (Meltzer et al. 2021). Therefore, revealing the molecular mechanisms and improving the prognosis of OS remains a constant and major goal for many worldwide research and clinical groups.

Omics technology is used to study a series of molecules and their interactions involved in the whole process of gene expression from a systems-level, mainly including genomics, transcriptomics, proteomics and metabolomics (Jeong et al. 2023). More specifically, the study of genomes involves understanding the structure, function, and inheritance of an organism’s entire genome. Transcriptomics evaluates all messenger RNA molecules in a single cell, tissue, or organism in terms of their quality or quantity. In contrast, proteomics was created from genomics and is centered on measuring proteins/peptides, modifications, and interactions in various sample types using MS-based methods or high-throughput analyses. Metabolomics involves the large-scale study of many small molecules, including amino acids, fatty acids, organic acids, and ketones, which are the end products of complex biochemical processes. On their respective scales, each type of omics science is used to identify, characterize, and quantify all biological molecules associated with diseases.With the advances in the technologies and tools for generating and processing large omics data, and the application of artificial intelligence methodologies for deciphering complex multi-omics interaction, omics technology becomes a powerful approach to decipher the mechanistic details of gene expression (Lee et al. 2022). Omics technology is expected to complement current clinical and pathology evaluations and guide personalized cancer management by discovering previously obscure sub-types with clinical implications and identifying patients’ prognoses, which help in revealing the molecular mechanisms and heterogeneity of OS, thereby improving the prognosis of patients (Pan et al. 2021).

Annually, a large number of original research articles on omics in OS were published and many conventional reviews had been conducted in this field to elucidate the research status and trends too. However, most of these review studies focused on the application or the outcome of a certain kind of omics technologies to attempt to answer a specific research question. For example, Dylan C Dean and colleagues searched the articles on metabolomics of OS and discussed the new founding of metabolic pathways only (Esperança-Martins et al. 2021). In another simple example, based on the outcome of genomics, Fuloria S and colleagues analyzed the intricate interplay between ncRNAs and the Wnt/β-catenin cascade in OS and proposed ncRNAs as biomarkers and therapeutics approaches in their review (Fuloria et al. 2024). Thus it can be seen that the inclusion–exclusion criteria of these conventional reviews were formulated according to the specific research question and were particularly stringent, the coverage was not comprehensive. Compared with conventional narrative reviews by experts, bibliometric analysis was an objectively quantitative method which applied mathematical and statistical tools to extract and analyze the metrics of each publication including author, institution, country and keywords, in order to evaluate their inter-relationships and impacts (Donthu et al. 2021). More important, the results of bibliometric analysis can be displayed in a more intuitive and comprehensible way. Due to these benefits, bibliometric analysis had gained considerable popularity in biomedical research in recent years. In the field of OS, some bibliometric analyses had been published regarding to limb salvage surgery (Raj et al. 2023), immunotherapy (Shen et al. 2024), the application of graphene oxide (Barba-Rosado et al. 2024), prognosis (Yin et al. 2024), immune microenvironment (Zhang et al. 2023), extracellular vesicles (Pei et al. 2024), and non-coding RNA (Chen et al. 2024). However, none has conducted bibliometric analysis on omics in OS. Therefore, in this study, we carried out the bibliometric analysis method analyze articles on omics in OS for the following three purposes: (1) identifying the cooperation and impact of various authors, countries, institutions, and journals, (2) displaying the basic knowledge and development trends through a co-cited reference analysis, and (3) detecting research frontiers through a keyword analysis. Our bibliometric analysis will give researchers an all-encompassing view of omics in OS research over the past twenty years, and lay a foundation for future research.

Methods

Data source and search strategy

Fig. 1
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The flowchart of publications screening

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The literature was obtained from the Web of Science Core Collection (WoSCC) database (https://www.webofscience.com/wos/woscc/basic-search). The publication date was restricted from January 1,1999 to October 12, 2023. The search strategy was TS = (genom* OR transcriptom* OR proteom* OR metabolom* OR metabonom* OR microbiom* OR “multi omic*”) AND TS = (osteosarcoma OR “bone sarcoma”) and LA = (English). In this study, only research and review articles were selected, and editorial materials, corrections, letters, news items, meeting abstracts, and retractions were eliminated from the search results. Due to the daily update to the database, all searches were conducted on the same day to avoid bias.All records were saved and kept in plain text and tab-delimited file for the purpose of drawing and analyzing the scientific atlas.The literature screening procedure was depicted in Fig. 1.

Bibliometric analysis

The literature’s year, authors, organizations, titles, abstracts, keywords, journals, and cited references were downloaded in plain text. An Excel spreadsheet was used to collect the following data as bibliometric indicators: total number of publications, year of publication, publication types, top ten countries, top ten institutions, top ten journals, and top ten citations.

Visualize analysis

The VOSviewer software tool (version 1.6.16) was used to explore the co-authorship (authors, organizations, and countries), co-occurrence (author and keywords), bibliographic coupling (sources), and co-citation (cited references, cited sources, and cited authors), was applied to create network visualization maps of the most co-occurrence terms to analysis the research hotspots and the most co-authorship of countries (Song et al. 2022).In this map, items were called nodes reflecting author, country, organization, and keywords, and links reflecting the degree of collaboration of each item were represented by edges (Zhang et al. 2023).

Simultaneously, the dual-map overlay of journals and citation bursts were built based on CiteSpace(version 6.2.R4), which helped to identify emerging trends and the distribution of academic journals in real time (Chen et al. 2019). In this dual-map, network nodes usually represent authors, and the size of the nodes was proportional to the number of studies posted by them. Link colors varied with the years articles were published, and link clusters represented author cooperation relationships. In the analysis of keywords, selection criteria were set as follows: g-index(k = 8), LRF = 2.0, L/N = 10, LBY = 8, e = 2.0.

In particular, R package “Bibliometrix”(version 3.2.1) (https://www.bibliometrix.org) was adopted to put out the popularity of key words each year. The 2022 impact factor (IF) and Journal Citation Reports (JCR) were also gained from the Web of Science group.

Results

Literature search results

After scanning, a total of 1581 English articles related to omics in OS were included, of which 1406 research articles and 175 reviews.These were from 64 countries, 2082 constitutions, 540 journals and 9832 authors with 61,361 references from 5048 journals.

The annual number of publication and citation from 1999 to 2023 were presented in Fig. 2, the average h-index is 94, and the average citation is 28.88. The growth of the publication and citation showed two stage: the first (1999–2017), which had a very slow and unstable growth, and the second (2018–2022), which had a explosive growth. The number of published paper reached 179 in 2022, exactly 10 times the previous amount in 1999.

Fig. 2
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Annual number of publication and citation in omics in OS

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Distribution of countries

In total, 64 countries were involved in the research of omics in OS, of which top 10 leading countries were shown in Table 1. China had the largest number of publications and the number was 569, accounting 36.0%, followed by United States (523, 33.1%). But Germany, in third place, had only 106 articles, a sharp drop, followed by Italy (104, 6.6%). The network of countries in this field of omics in OS were presented in Fig. 3. This map offered a clear imagine of eight clusters in these countries, with the strongest links showing the frequent association among China, United States, Australia and Canada.

Table 1 The top 10 countries on the research of omics in OS
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Fig. 3
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Country collaboration map

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Institution and authors analysis

Table 2 Showed the most productive research areas, institutions and authors. In the field of omics in OS, articles on oncology (558, 35.29%) were more than twice as many as articles on biochemistry molecular biology. Among the 2082 institutions, the University of Texas MD Anderson Cancer Center published the greatest number of article (44, 2.78%), followed by Baylor College of Medicine (40, 2.53%). A total of 9832 authors was involved in omics research in OS. Based on the number of publications, the number of records and the proportion of the top 6 authors were shown in the table 2. The top three authors were Cleton-Jansen AM (20, 1.27%), Baumhoer D (18, 1.14%) and Modiano JF (18, 1.14%). The relationship between affiliations, authors and keywords in the field of omics in OS is shown in Fig. 4.

Table 2 The top 5 research areas, 7 affiliations and 6 authors on the research of omics in OS
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Fig. 4
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The alluvial diagram of author, institution and article subject

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Journals and co-cited journals

Publications related to omics in OS were published in 540 journals, and the top 10 journals and co-cited journals were shown in Table 3. Plos One published the highest number of articles, 46, and also had the highest number of citations, 1256. Of all the top 10 journals, only the International Journal of Molecular Sciences and Cancer Research appeared in the Web of Science’s 2023 edition of Journal Citation Reports Category Quartile (JCR-c) Q1. The co-cited journals shown in the Table 3 had an impact factor (IF) ranging from 3.7 to 64.8, and four out of five were included in JCR-c Q1. The double map overlay of the journals (Fig. 5) showed the distribution of the journals with regard to the topic. The target (citing journals) was placed on the left side and the source (cited journals) on the other side, with the coloured paths indicating the citation correlations. An orange path was clearly identified in the figure.

Table 3 The top 10 journals and co-cited journals on the research of omics in OS
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Fig. 5
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Dual-map overlay of journals

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Cited references

The citation analysis of journals was applied to assess the influence usually within a particular field. In the research, articles related to omics in OS cited 61,361 references from 5048 journals. The top 10 cited references were presented in Table 4, and all of these articles had no less than 283 citations. Four out of five journals belong to JCR Q1, and one appeared in JCR Q3. The article with the most citation (n = 818, IF = 78.5, JCR-c = Q1) entitled “Translational biology of osteosarcoma” was published in Nature Reviews Cancer in 2014. The second article (n = 732, IF = 4.8, JCR-c = Q2) entitled “Role of Poly (ADP-ribose) Polymerase (PARP) Cleavage in Apoptosis” was published in Journal of Biological Chemistry in 1999.

Table 4 Top 10 cited articles related to omics inOS
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Keywords

To capture the research hotspots, a keyword co-occurrence map was created using Citespace, as shown in Fig. 6A. The high-frequency terms included “expression”, “gene”, “proliferation”, “identification”, “apoptosis”, “survival”, “metastasis” and “comparative genomic hybridization”. In addition, the cluster of the keywords was analyzed to gain further insight into the hotspots in the field of omics in OS. The results of the keyword cluster analysis were presented in Fig. 6B, and a total of ten clusters were obtained, with the largest cluster being Cluster #0 named “prognosis”, followed by “cancer”, “comparative genomic hybridization”, “gene expression” and so on. A timeline plot of the keyword clusters was shown in Fig. 7, “prognosis” is the essential topic consistently, and “6-methoxyflavone” aroused the attention of scholars.

More specifically, the top 25 keywords with the strongest citation bursts were presented in Fig. 8. Keyword with the burst strength (32.14) and longest duration (1999–2012) was “comparative genomic hybridization”. Latest keywords in the outbreak contained"resistance” (2020–2023), “target therapy“(2020–2023) and “progression“(2021–2023). The R Studio heatmap (Fig. 9) verifying these results showed that the keywords"chondrosarcoma”, “gene expression” and “cell cycle"were the earliest burst keywords in 2011, with “gene expression”, “p53” and “microarray” having the longest citation duration, and the recent outbreaks were associated with “immune infiltration”, “tumor microenvironment” and “biomarkers”, indicating the latest trends of the study.

Fig. 6
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Visualization of keyword clustering map from 1999 to 2023. A: keyword co-occurrence map, B: keyword cluster map

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Fig. 7
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Keywords clustering timeline view between 1999 and 2023

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Fig. 8
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Top 25 keywords with the strongest citation bursts

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Fig. 9
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Heatmap and accumulated frequency heatmap of keywords from 1999 to 2023

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Discussion

With the rapid development of research in various fields, it has become increasingly important for researchers to understand the current advancements in their respective research areas. Compared to meta-analysis and systematic review methods, bibliometric analysis provides a more objective and simpler visualization method to validate and analyze existing literature (Donthu et al. 2021). Our research is the first bibliometric study to evaluate and visualize research of omics in OS. A total of 1581 articles from the WoSCC database were analyzed to identify the hotspots and global trends. According to our study, the annual publications from 1999 to 2017 were relatively rare, with an average annual publication of 41 articles, indicating that the research of omics in OS was not adequate. The number of related publications and the citation frequency had increased rapidly since 2018, indicating that omics technology developed rapidly and captured more and more researchers’ attention in the past 5 years, which is consistent with the overall trend of biological technology development. Especially, the recent traction of integrated multi-omics analysis had seen the focus of research, which had empowered to characterize different molecular layers at unprecedented scale and resolution, fueling OS precision medicine.

Regarding the distribution of research, the data indicated that the majority of studies on omics in OS were concentrated in China and the United States, with over five times as many papers in each nation as in the third country, pointing to an uneven global development. Besides, we noticed that China, the United States and Europe (including Germany, Italy, United Kingdom and so forth) were the gathering place of the current related research, and the cooperative research in this field was relatively extensive, and many authors had participated in international cooperation, which indicated a well-established international framework. The top 7 leading institutions were listed in Table 2, hoping to recommend platforms for collaboration and further learning. A diverse group of experts in the field and a large quantity of financial assistance for researchers were key factors in the success of research in these nations and institutions.

To further understand the research of omics in OS, the top 10 cited articles, including 7 research articles and 3 reviews are summarized. The multiple somatic chromosomal lesions was the primary topic. The most cited paper was a study by Kansara M et al. in Nature Reviews Cancer as a review (Kansara et al. 2014), with 818 total citations, which discusses normal bone biology relevant to OS, and argued that genetic features of OS were characterized by chromosomal instability, so that the effect of targeted therapy was uncertain and immunotherapy may be more suitable for OS patients. The forth most cited paper was by Chen X et al. from Cell Report (Chen et al. 2014). This study reported that chromosomal lesions, rather than single-nucleotide variations (SNVs), were the major mechanism of recurrent mutations in OS, and many of the most significant chromosomal lesions were found in known cancer genes, including TP53, RB1, and ATRX. The paper by Ma X et al. (Ma X et al. 2018) and the paper by Pierron G et al.(Pierron et al. 2012) discussed somatic chromosomal lesions from different perspectives. The immune microenvironment was another hot topic. The article was Buddingh EP et al.(Buddingh et al. 2011), published in Clinical Cancer Research in 2011, with 313 total citations. This study reported that tumor-infiltrating macrophages were associated with metastasis suppression in high-grade OS. All three reviews (Kansara et al. 2014; Gianferante et al. 2017; Lindsey et al. 2017) summarized the characteristics of immune microenvironment in OS and suggested that newer immune-based treatments may offer a more comprehensive approach to battling cancer pleomorphism. From the perspective of the “seed and soil” theory (Fidler 2003), it was easy to understand the reasons why “multiple somatic chromosomal lesions” and “ immune microenvironment” had become hot topics. Genetic aberrations cell was “seed”, and the immune microenvironment was “soil”. The crosstalk established between them fueled the tumor growth by inducing a local immunosuppressive environment. For this reason, there has been a long-standing interest in targeting this interaction and modulating the host’s immune response as a strategy to eliminate cancer. Targeting immune checkpoints, such as cytotoxic T-lymphocyte- associated antigen 4 (CTLA-4) and programmed cell death 1 (PD-1)/ligand 1 (PD-L1), has been an overwhelmingly successful step forward for immunotherapy in the treatment of cancer, but clinical trials in OS was disappointing. Osteosarcoma demonstrates significant genetic complexity and genome instability with resultant high levels of genomic rearrangements and the highest point mutation burden as compared to other pediatric cancers, suggesting that these genomics factors may yield neoantigens capable of eliciting an immune response. However, the rsults of clinical trials did not match this rationale, indicating the existence of other unknown factors. In the MDACC OS cohort, Wu cc and Livingston JA further found that copy number loss has a significant negative correlation with the immune scores, but such a correlation was not observed between copy number gains and the immune scores (Wu et al. 2020). The copy number loss may lead to permanent loss of many genes and eventually impact immune response and the effect of immunotherapy in OS. Therefore, the future challenge in OS will be to comprehensively describe the relationship between genetic aberrations and immune microenvironment, and clarify the reasons for disappointing clinical trials in order to break immunosuppressive mechanisms and enhance antitumor immune responses.

Keyword co-occurrence analysis helped to understand the distribution and evolution of multiple research hotspots in a certain field. In co-occurrence clustering analysis, expression, gene, proliferation, identification, apoptosis, survival, metastasis, comparative genomic hybridization and other high-frequency keywords ranked in the top ten, indicating that prognostic evaluation and pathogenesis were research hotspots in OS. Furthermore, “prognosis” was the largest cluster, followed by “cancer”, “comparative genomic hybridization”, “gene expression” and so on, according to the keyword cluster analysis. OS was characterized by marked instability of its somatic genome, which frequently featured chromothripsis, chromosomal aneuploidy and chromosomal rearrangements. The structural variations produced by frequent chromosomal rearrangements contribute to the majority of genetic lesions in OS (Liao et al. 2020) and also the fundamental reason for the lack of significant improvement in the treatment strategy and long-term survival of OS in the past 50 years. Therefore, the discovery of genetic abnormalities in OS through various omics sequencing techniques to improve the prognosis of OS patients was a hot topic for researchers, which was consistent with our research findings. However, few recurrent genetic alterations was identified with the exception of the tumor suppressors TP53 and RB1. As was well known, somatic mutations in TP53 were one of the most frequent alterations in human cancers, with the majority of these alterations being missense mutation (Petitjean et al. 2007). It had been estimated previously that only 20-50% of OS carried TP53 mutations, and other portion were wild type(Kovac et al. 2015; Chen et al. 2016). With further research of sequencing techniques, more and more TP53 structural variations were identified (Chen et al. 2014). Therefore, it now was suspected that up to 75–90% of OS patients harbored various types of TP53 genetic alterations. Loss of the p53 pathways that disabled the cell’s ability to respond to DNA damage mediated genome instability and triggered OS oncogenesis. RB1 was a key regulator of cell cycle progression by controlling the G1/S phase transition. RB1 alterations had been identified in 50–78% of OS by sequencing studies (Wu et al. 2020). Unlike TP53, the depletion of RB1 alone was not sufficient to induce OS formation in animal models. Therefore, it was speculated that Rb1 alterations may synergize with TP53 inactivation during OS oncogenesis. The model of the natural history of OS, proposed by Kovac M and colleagues, theorized that a mutation of TP53 and/or RB1 led to secondary genetic aberrations, which in turn resulted in the emergence of OS (Kovac et al. 2015). So, it was not difficult to understand that TP53 was constantly mentioned in the majority of articles (Liu et al. 2013; Sorimachi et al. 2023).

CiteSpace for burst detection for high-frequency keywords showed that the focus of research was gradually shifting from “comparative genomic hybridization” (1999–2012) to “tumor microenvironment” (2021–2023) and “progression”(2021–2023). Comparative genomic hybridization allowed detection of DNA sequence copy number changes throughout the genome in a single hybridization and it mapped these sometimes very complex changes onto normal metaphase chromosomes, which was an ideal screening tool for instable somatic genome of OS. However, the current and future research hotspots were not about discovering new molecular target in targeted therapy from omics sequencing in the past, but turning to reveal the characteristics of tumor microenvironment of OS and how to activate the immune system to fight cancer cells. The poor therapeutic effects with side effects of targeted therapy for OS in many clinical trials can partially explain this transitions (Wang et al. 2024). The keyword visualization view over time showed that the frequency of use of the main keywords was increasing year by year, which was consistent with the increase in the number of related publications.

Limitations

This study has limitations inherent in bibliometrics. Firstly, not all relevant articles were in the WoSCC database, though WoSCC database was a core journal citation index database and its concept of scientific citation indexing (SCI) was relatively normalized and provided metadata with further distributive refinement (Qiu et al. 2018). Secondly, from the filters, only articles published in English were included, which may indicate that non-English publications were underestimated. Thirdly, from the way of analyzing, lots of information in the articles was ignored. For example, the publications with high frequency and citations may be cited for both negative and positive reasons which our research cannot distinguish, and the recent publications were underrepresented due to time constraints. Another example was that some articles on omics in other fields were also imported into software tools for further analysis just because they mentioned the function of the gene in OS, which may have an effect on the outcome. Despite these, our study still provided excellent objective information and insights, with the aim of facilitating the research of omics in OS.

Conclusions

This study showed the current status of the research of omics in OS on a global level and the hottest directions. According to the pattern in recent years, there will be a explosive rise in the number of publications in this field. Until now, China and the United States had made the most significant contributions in this field. The research hotspots had shifted from the discovering of new molecular target to revealing the characteristics of tumor microenvironment of OS and how to activate the immune system to fight cancer cells. In the future, research on the immune microenvironment and its relationship with genetic aberrations of OS will be a priority.