Global bibliometric analysis of pleural mesothelioma biomarkers (2002–2024): trends and emerging frontiers

Introduction

Pleural mesothelioma (PM), an aggressive and rare malignancy arising from pleural mesothelial cells, accounts for only 0.3% of all cancer cases and remains largely incurable (1,2). Historically, the incidence of PM was predominantly higher in developed nations due to greater industrialization. However, in recent years, developing countries, particularly China, have witnessed a persistent rise in cases (3). PM constitutes the predominant form of mesothelioma, representing approximately 90% of all cases (4). Given PM’s insidious onset and prolonged latency, often spanning decades, patients are typically diagnosed at an advanced stage, rendering surgical resection challenging. Consequently, PM exhibits a high fatality rate, with a 5-year survival probability below 10% and a median survival duration of merely 9 to 12 months (5,6). This malignancy places a substantial psychological and financial burden on both patients and their families. The predominant risk factor for PM is asbestos exposure, with approximately 80% of cases linked to prior asbestos contact (7). Despite widespread asbestos restrictions since the 1870s, occupational mesothelioma incidence continues to rise, owing to its extended latency period of 15 to 60 years. PM is histopathologically classified into epithelial, sarcomatoid (fibrous), and mixed subtypes, with epithelial PM being the most prevalent (8). Surgery, chemotherapy, and radiotherapy demonstrate limited efficacy in treating PM.

Early diagnosis plays a crucial role in improving survival outcomes in PM; however, definitive clinical differential diagnostic criteria and robust prognostic biomarkers remain elusive. Bibliometrics is a methodological approach employed to evaluate and analyze the academic influence of systematic research methodologies, including publishing patterns, as well as the evolving trends and overall quality of research outcomes within associated disciplines (9,10). Despite the exponential growth in research on PM-associated biomarkers over the past two decades, a comprehensive bibliometric analysis of the most recent advancements in this domain remains lacking. Therefore, this study aims to comprehensively assess the current landscape of PM-related biomarker research utilizing two bibliometric tools, Vosviewer and Citespace. This analysis identifies key contributors, including influential authors, journals, institutions, and countries within the field, while also constructing collaborative network graphs and temporal evolution maps to delineate research trajectories and highlight critical hotspots over the past two decades for scholarly reference. Collaborative network maps and temporal evolution visualizations were developed to systematically examine emerging research trends and pinpoint pivotal hotspots over the past two decades, serving as a valuable reference for researchers. We present this article in accordance with the BIBLIO reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1171/rc).

Methods

Data sources and search strategy

A comprehensive search of the Web of Science Core Collection (WoSCC) was conducted for articles published between January 1, 2002, and December 31, 2024. To mitigate potential discrepancies arising from the dynamic inclusion or removal of records in subsequent database updates, the search was systematically completed within a single day using the following query: TS=(biomarkers) AND (“mesothelioma” OR “mesothelial tumor” OR “mesothelial neoplasm”). As this study employed a bibliometric approach focusing on publication trends and citation metrics, no formal quality assessment tools or checklists were applied. Therefore, the inclusion of studies was based solely on relevance to the research topic and compliance with the predefined eligibility criteria.

The search was limited to articles. To ensure the robustness and accuracy of the bibliometric analysis, all retrieved articles underwent a rigorous evaluation. The inclusion criteria for literature selection were as follows: (I) direct relevance to PM biomarker research and (II) publication in the English language. The exclusion criteria encompassed the following: (I) studies lacking direct relevance to PM biomarker research and (II) publications classified as reviews, conference abstracts, case reports, letters, or other non-primary research articles. The bibliometric data were subsequently extracted and exported in plain text format for further analysis. Ultimately, a total of 662 publications related to PM biomarker research were identified through this systematic search. The search process is shown in Figure 1.

Figure 1 The flowchart of this study. SSCI, Social Sciences Citation Index; SCIE, Science Citation Index Expanded; TS, topic search; WoSCC, Web of Science Core Collection.

Statistical analysis

We exported the downloaded literature data into a TXT file format and conducted bibliometric analysis and visualization using multiple software tools, including VOSviewer (version 1.6.18), CiteSpace (basic version 6.1.R2), and Microsoft Excel 2010. CiteSpace, developed by Dr. Chao-Mei Chen in the United States, is a visualization software specialized for scientific literature analysis. It enables researchers to explore citation networks, author collaboration patterns, and thematic developments, thereby providing insights into the evolving trends and research hotspots in a given field (11). In this study, CiteSpace was primarily utilized to create visual network maps depicting relationships among countries and institutions, authors, journals, and highly cited references (12).

VOSviewer is bibliometric software designed to map knowledge visually. It facilitates analyses such as co-word, co-citation, and document coupling, and allows the visualization of their results. In this study, VOSviewer was applied to visualize collaborative networks among countries, authors, and institutions, cited journals, keyword co-occurrence patterns, and to construct density maps (13). The main goal of VOSviewer is to give users a comprehensive view of the dynamics and structure of scientific research.

Results

Annual publication volume and trend analysis

In this study, a comprehensive systematic search was conducted in the WoSCC to identify 662 research articles on biomarkers for PM published between 2002 and 2024. The findings of Figure 2A on publication trends are set out below. The analysis revealed a substantial increase in global research on PM biomarkers over the past two decades, with overall development exhibiting a stable trajectory. Specifically, the annual publication output reached its peak in 2021, with 70 articles, and has remained consistently high thereafter. This trend clearly underscores the increasing academic interest in mesothelioma biomarker research in recent years, establishing it as a prominent focal area within thoracic oncology. However, despite the growing attention paid to the research on mesothelioma biomarkers in the academic community at present, clinically useful PM biomarkers remain quite scarce. This may be related to the limited clinical translation of PM biomarkers. The core reasons are as follows: first, early studies have methodological limitations such as small sample sizes and single-center data; second, the design of translational research does not prioritize clinical relevance, and there is a lack of key verification studies; third, the low incidence of the disease, coupled with the insufficient specificity and sensitivity of the biomarkers themselves, leads to difficulties in verification; meanwhile, systemic issues such as technical cost barriers in primary medical institutions, inconsistent detection protocols, and insufficient inclusion in clinical guidelines further hinder their routine clinical application.

Figure 2 Analysis of publication characteristics and collaboration networks in relevant research. (A) Annual number of published works; (B) national publication output trend line chart; (C) cross-country collaborative networks; (D) inter-institutional cooperation networks. CCs, connected components; CST, China standard time; LBY, look back years; L/N, links per node; LRF, link retaining factor.

Analysis of country/institution publications and collaborations

Globally, 56 countries/regions have conducted studies related to PM biomarkers. The yearly publishing trend of the top 10 nations in terms of total publications from 2002 to 2024 is displayed in Figure 2B. Analyzed from the dimension of the number of publications (Table 1), the United States (n=162), Italy (n=148), Australia (n=95), China (n=78), and the United Kingdom (n=47) were ranked among the top five countries, in which the United States and Italy accounted for 24.47% and 22.36% of the total number of publications, which were significantly ahead of other countries. It is worth noting that China’s research output in this field has shown significant growth, indicating strong development potential. In terms of academic impact indicators, the United States leads with 6,491 total citations and a centrality index of 0.33, and its average number of citations reaches 40.07, indicating that the country’s research output in this field has a high academic impact. In contrast, the total citations of Chinese scholars were 1,126, and the average number of citations was 14.44, which indicates that there is still much room for improvement in research influence. Through the analysis of international cooperation network, it is found that the United States is at the center node position of the cooperation network (centrality =0.33), and maintains close scientific cooperation with Italy, China, Australia, the United Kingdom, Germany and other countries (Figure 2C). Frequent academic collaborations among these countries have facilitated the output of high-quality research results and promoted the development of international research cooperation networks in this field.

In this study, we systematically revealed the characteristics of institutional distribution in the field of PM biomarker research by visualizing the collaborative networks of 416 research institutions during the period of 2010–2024 (Figure 2D), combined with bibliometric indicators (Table 2). The study data showed that the University of Western Australia (UWA), Sir Charles Gairdner Hospital, University of Pisa, and University of Sydney were the leading institutions in terms of research output in this area. Notably, the UWA was the most academically influential research institution with 1,329 total citations, while New York University (NYU) highlighted the strength of the academic quality of its research outputs with 59.53 citations per article. Further analysis of the international collaboration network revealed that the UWA and Sir Charles Gairdner Hospital form the core hub of the collaboration network and maintain extensive collaborative relationships with the international academic community. In addition, this study identified several sub-clusters: the Euro-Australian research community centered on the University of Pisa and the University of Sydney, and the North American-European research alliance represented by NYU, University of Milan, and Brigham and Women’s Hospital. These collaborative innovation networks across geographic boundaries strongly contribute to the output and dissemination of important scientific discoveries in the field. The output and dissemination of important scientific discoveries in the field is strongly facilitated by these collaborative innovation networks across geographic regions.

Analysis of journals and cited journals

In this study, we systematically revealed the journal distribution characteristics of mesothelioma biomarker research by using journal co-occurrence density mapping (Figure 3A) constructed by VOSviewer software, combined with bibliometric data (Tables 3,4). The data showed that Cancers [impact factor (IF) =4.5, Q1] ranked first with 38 publications, followed by Journal of Thoracic Oncology (IF =21.1, Q1) and Frontiers in Oncology (IF =2.9, Q1), which together constituted a core knowledge dissemination platform in the field The three together constitute a core knowledge dissemination platform in the field. In terms of academic impact, Journal of Thoracic Oncology topped the list with 1,819 citations, while PLoS One (IF =2.9, Q1) and Lung Cancer (IF =4.5, Q1) ranked the second and the third, and all of the above journals are in the journal citation reports (JCR) Q1 region, which highlights their academic leadership role in this field. The above journals are all JCR Q1 publications, emphasizing their academic leadership in the field. The study further analyzes the characteristics of subject knowledge flow through the double graph overlay analysis method (Figure 3B). The group of citing journals on the left side of the graph and the group of cited journals on the right side of the graph form two significant knowledge transfer paths: one shows that the research outputs of journals in the field of “Molecular Biology/Genetics” are mainly inherited by journals in the fields of “Molecular Biology/Immunology” and “Clinical Medicine”; and the other shows that the academic outputs of journals in the field of “Health Care/Medicine” serve the research advances in the field of “Clinical Medicine” more than in the field of “Health Care/Medicine”. This interdisciplinary citation pattern reveals that mesothelioma biomarker research has the dual attributes of basic research and clinical translation, and provides a theoretical basis for researchers to select target journals.

Figure 3 Analysis of journal publication distribution and citation correlation. (A) Density distribution map of journal articles; (B) journal dual-map visualization: colored paths indicate citation links, with citing journals displayed on the left and cited journals on the right.

Analysis of authors and cited authors

By analyzing the information of the authors with the highest number of publications and citations in the field of PM biomarker research (Table 5). The results were visualized by combining the author collaboration network and the citation relationship network (Figure 4A,4B). The node size in the author collaboration network graph has a positive correlation with how frequently writers appear, the connecting lines between nodes indicate the cooperation relationship between authors, and the degree of thickness of the connecting lines reflects the strength of cooperation. The study shows that Jenette Creaney (n=24), Alessandra Bonotti (n=16), and Bruce W. Robinson (n=16) are the three writers who have published the most in the discipline and who have significantly advanced the field’s research. In terms of scholarly impact, Jenette Creaney (n=888), Glen Reid (n=790), and Harvey I. Pass (n=784) are the three most frequently quoted academics whose contributions to the study of PM biomarkers have had a significant influence. Analysis of the author collaboration network revealed that Jenette Creaney is at the center of the research network and maintains close collaborations with numerous scholars. In addition, scholars such as Alessandra Bonotti, Alfonso Cristaudo, Bruce W. Robinson, and Jan P van Meerbeeck play important leadership roles in their respective research teams.

Figure 4 Analysis of author collaboration networks and co-citation networks. (A) Cooperation network of authors; (B) co-citation network of authors. CCs, connected components; CST, China standard time; LBY, look back years; L/N, links per node; LRF, link retaining factor.

Keyword network analysis

Keywords directly reflect the topic of the thesis and the core ideas of the paper, while keyword frequency reflects the researcher’s attention to a specific topic. In order to explore the knowledge structure in the field of PM biomarker research, this study constructed a keyword density and co-occurrence network visualization map based on VOSviewer software (Figure 5A,5B), and quantitatively analyzed the keyword frequency (Table 6). In the visualized network, node size and density depth reflect the frequency of keywords, respectively. The analysis results showed that in addition to the search terms, the high-frequency keywords were, in order, PM (n=86), malignant mesothelioma (n=65), asbestos (n=56), mesothelin (n=55), and immunotherapy (n=48), the distribution characteristics of these keywords were highly correlated with the study topic. In this study, 137 keywords with a frequency threshold greater than 3 were selected to construct the co-occurrence network. The structure of the network presents significant multicenter intersection characteristics, and the co-occurrence relationship between keywords is clearly discernible. In order to deeply reveal the distributional characteristics and evolutionary trends of the research topics, we generated a clustering view and a peak map based on the keyword co-occurrence network (Figure 5C,5D). The results of the cluster analysis showed that 11 major research directions were formed in the field, namely: “#0 immunotherapy”, “#1 mesothelin”, “#2 epigenetics”, “#3 immunohistochemistry”, “#4 malignant mesothelioma”, “#5 pleural mesothelioma”, “#6 asbestos”, “#7 arginine”, “#8 breast cancer cells”, “#9 gauge gene expression”, “#10 pleural effusion”, this clustering structure reflects the multidimensional nature of mesothelioma biomarker research. Notably, “epigenetics” and “immunohistochemistry” show significant fluctuations in the recent peaks, indicating that these two directions have become the latest research hotspots in the field. Subsequently generated timeline charts and time-axis graphs of research topics show that the trend in this research field has evolved from traditional exposure and chemotherapy research to a precision era centered on immunotherapy, supported by molecular/pathological stratification and multi-omics (Figure 5E,5F).

Figure 5 Analysis of keywords characteristics and evolution trends. (A) Keywords co-occurrence frequency chart; (B) keywords density distribution map; (C) keywords clustering visualization map; (D) keywords clustering peak diagram; (E) keywords change time zone map; (F) keywords variation timeline. CCs, connected components; CST, China standard time; LBY, look back years; L/N, links per node; LRF, link retaining factor.

Literature co-citation analysis

CiteSpace was used in this study to conduct a co-citation analysis of the literature network in order to investigate high-impact literature and co-occurrence interactions among the literature (Figure 6A, Table 7), Baas et al.’s article published in Lancet titled “First-line nivolumab plus ipilimumab in unresectable malignant pleural mesothelioma (CheckMate 743): a multicentre, randomised, open-label, phase 3 trial” (n=58), Hmeljaki et al. in the journal Cancer Discovery entitled “Integrative Molecular Characterization of Pleural Mesothelioma” (n=44) and Alley et al. in Lancet Oncology entitled “Clinical safety and activity of pembrolizumab in patients with PM (KEYNOTE-028): preliminary results from a non-randomised, open-label, phase 1b trial” (n=43) had the highest number of co-citations in the network, indicating that these seminal findings have a high impact in the field. Based on the literature co-citation network, we constructed a clustering map (Figure 6B,6C) to show the distribution characteristics of the research topics. The analysis results show that “immunotherapy” is the core research foundation, and several research directions are derived, including “microRNA”, “pemetrexed”, “drug resistance”, ‘predictive’, etc. In particular, “#0 immunotherapy” is the most important research theme. In particular, “#1 immune checkpoint inhibitors (ICIs)” formed a close network with “#4 calretinin” and “#10 drug resistance”. From the perspective of chronological evolution, the research focuses in the past 20 years show obvious stages. Early on, scholars focused on DNA-array (cluster 8) and pleural (cluster 9). Subsequently, mesothelin (cluster 2), microRNA (cluster 3), and pemetrexed (cluster 6) have gradually become the hot research directions. Immune checkpoint inhibitors (cluster 1), calretinin (cluster 4), and drug resistance (cluster 10) are the current hot research topics. Immune checkpoint inhibitors (cluster 1), calretinin (cluster 4), and drug resistance (cluster 10) are the current hotspots that have attracted much attention. Literature emergence analysis (Figure 6D) showed that the research results of Harvey I. Pass and Jenette Creaney published in New Engl J Med and Thorax, respectively, demonstrated the strongest emergence intensity (n=19.20, n=14.78), and continued to lead the direction of academic development during 2013–2019.

Figure 6 Analysis of co-cited literature characteristics and research hotspots. (A) Literature co-citation network; (B) co-cited literature clustering; (C) co-cited literature peak diagram; (D) cited literature burst list. CCs, connected components; CST, China standard time; LBY, look back years; L/N, links per node; LRF, link retaining factor.

Discussion

General information

In this study, 662 relevant publications on PM biomarker research were retrieved from the WoSCC database. Bibliometric and visualization analyses were conducted using CiteSpace and VOSviewer to explore research patterns in this field. The primary objective of this study was to examine the characteristics and research hotspots in the field of PM biomarker research. Additionally, this study aimed to forecast emerging trends that may provide valuable insights for researchers working in this domain.

PM presents with an insidious onset, typically manifesting as chest pain and pleural effusion, which are often the primary reasons for medical consultation. The absence of specific symptoms frequently results in diagnostic delays, leading to detection at an advanced stage. Consequently, the median survival following diagnosis is approximately one year (14). Current therapeutic options are palliative rather than curative. Although pathological biopsy remains the gold standard for diagnosing PM due to its high sensitivity (15,16), its invasive nature and dependence on the patient’s physical condition render it unsuitable for early detection. Thus, identifying effective and reliable biomarkers for both the diagnosis and treatment of PM is imperative. Through an extensive review and analysis of relevant literature, our study has yielded several key insights. Firstly, over the past two decades, there has been a substantial and continuous increase in research on PM biomarkers. As the demand for personalized PM treatment continues to expand, scholarly interest in the field has correspondingly intensified. This trend underscores the increasing recognition of biomarkers in PM research and the widespread global engagement in this domain. A country/region-based analysis reveals that the United States exhibits a significantly higher volume of publications and citations than other nations, establishing itself as the most influential player in PM biomarker research. Integration of the international collaboration network analysis reveals that the United States occupies a central node within the network, a position potentially linked to the disease’s incidence rate. According to data published by the World Health Organization (WHO), in 2020, there were 30,870 newly diagnosed PM cases worldwide, constituting 0.2% of all new malignant tumors. The disease resulted in 26,278 deaths, accounting for 0.3% of global cancer-related mortality. The highest incidence of PM was observed in Europe, as well as in Oceania—particularly in Australia and New Zealand—and in the United States (17). Australian institutions, notably the UWA and Sir Charles Gairdner Hospital, have emerged as leading contributors to scientific advancements in this domain. Notably, no Latin American or African institutions appeared in the analysis, implying that, despite the high disease burden in these regions, limited research infrastructure and resource constraints may hinder their scientific contributions. A journal analysis revealed that oncology-focused publications dominated the field in terms of article volume, with the Journal of Thoracic Oncology, Clinical Cancer Research, and Journal of Clinical Oncology ranking as the three most frequently cited journals, underscoring their substantial academic impact. These journals hold a strong academic presence in oncology, reaffirming their pivotal role in shaping research discourse within the field. The analysis revealed that Professor Jenette Creaney of the UWA ranked first in both publication count and citation impact, underscoring her seminal contributions to PM biomarker research. Notably, her 2011 Nature Genetics study demonstrated that BRCA1-associated protein 1 (BAP1) knockdown in PM cell lines induces transcriptional dysregulation, a key mechanism in PM pathogenesis (18). The second most prolific scholar, Professor Glen Reid, also hails from Australia, further highlighting the prominent role of Australian researchers in this domain.

Keywords and hotspots

Keyword analysis serves as a crucial tool for identifying research frontiers and emerging hotspots within a given field. Based on an extensive analysis of existing studies, our findings indicate that high-frequency keywords in this domain include “mesothelioma”, “biomarker”, “asbestos”, “immunotherapy”, “mesothelin”, “microRNA”, “prognosis”, “diagnosis”, and “PD-L1”, along with other terms intimately linked to the diagnosis and treatment of PM. Through a comprehensive review of keywords, co-cited references, and their temporal emergence patterns, our analysis reveals that the research hotspots in PM biomarker studies primarily revolve around the following key areas, which will be discussed in detail in subsequent sections. In addition, we have also summarized the current status of the clinical role of biomarkers for PM and their importance in recent research trends (Table 8).

Biomarkers of proteomics related to PM

Proteomics, a set of proteins expressed by organisms and systems over a certain period of time under uncertain physiological and pathological conditions, is a high-throughput method of generating protein signatures, and has recently been used to efficiently screen for biomarkers and to improve the diagnostic accuracy of different cancers (19,20). The use of proteomics to study PM is still somewhat limited due to small sample sizes, but in general, proteomics is an extremely promising diagnostic tool for PM. Currently, the classical proteomic biomarkers include soluble mesothelin-related peptide (SMRP), bone bridging protein, and programmed cell death ligand-1 (PD-L1). Mesothelin is one of the widely studied biomarkers of PM, which is expressed at low levels in normal cells and undetectable in normal tissues, but is abundantly expressed in malignant mesothelioma, pancreatic cancer, ovarian cancer, lung cancer, and other cancers (21). After a series of expressions, mesothelin can generate SMRP in serum. SMRP are the only biomarkers for PM approved by the U.S. Food and Drug Administration (FDA) (22). SMRP levels can be used to identify asbestos unexposed individuals, asbestos-exposed individuals, and patients with benign pleural disease (23,24). It should be noted that serum mesothelin levels are also elevated in patients with renal impairment. Therefore, elevated levels of SMRP in the absence of renal disease should raise suspicion of malignancy. Also, elevated levels of SMRP facilitate the clinical diagnosis of patients with pleural effusions at risk for PM, and elevated pleural effusion SMRP are more sensitive than elevated serum SMRP. Osteopontin (OPN), also known as secreted phosphoprotein 1 (SPP1), is a secreted extracellular matrix protein widely distributed in a variety of tissues and cells, which can participate in tissue repair and self-metabolism, and can be used as a marker for the diagnosis and screening of PM (25,26). OPN is involved in the regulation of the malignant behavior of tumor cells and exerts an important influence on the process of tumor development. Pass et al. (27) conducted a statistical analysis of asbestos-exposed patients without PM, asbestos-unexposed patients without PM, and asbestos-exposed patients with PM, and found that the concentration of OPN correlated with the duration of asbestos exposure, and that the average concentration of OPN was significantly increased in subjects who had been exposed for 10 or more years. The asbestos-exposed group with normal chest radiographs, plaques, and fibrosis had significantly lower OPN compared to the plaque and fibrosis-only group. This analysis found that duration of asbestos exposure and radiographic findings were independent influences on OPN. In addition, serum OPN was able to distinguish mesothelioma patients from asbestos-exposed healthy subjects. The PD-L1 protein plays a central role in the antitumor immune response (28). Carosio et al. (29) assessed the baseline expression level of the soluble PD-L1 (sPD-L1) in the pleural fluid of 84 patients with PM and correlated it with the PD-L1 status of the matched tumors and the overall survival (OS) of the patients, finding that sPD-L1 was expressed to varying degrees in all pleural fluid. In the non-epithelial subpopulation, sPD-L1 levels were slightly higher, suggesting that sPD-L1 levels may be involved in the poor prognosis of PM tissue types. Compared with patients with PD-L1-negative tumors, sPD-L1 levels tended to be higher in patients with PD-L1-positive tumors, and sPD-L1 concentrations were positively correlated with OS.

Genomic biomarkers related to PM

microRNAs (miRNA, miR) are endogenous, non-coding RNAs. Studies have shown that miRNAs are widely present in human circulating tissues and body fluids, and their levels have diagnostic value for a variety of human diseases (30,31). Han et al. (32) found that the accuracy of miRNA in tissues and body fluids as a diagnostic marker for PM was not satisfactory. However, circulating miRNAs are characterized by stability, non-invasiveness, easy detection, and economy, while certain miRNAs are differentially expressed in the developmental process of cancer. In recent years, a large number of studies have revealed the characteristic changes of miRNA expression in PM. miRNAs affect tumor growth, invasion and angiogenesis by binding to mRNAs, thereby influencing the expression of oncogenes and oncogenes and the activity of signaling pathways. In the development of PM, the expression level of some miRNAs increases and selectively binds to tumor suppressor genes, promoting cell proliferation, angiogenesis, metastasis and related processes (33). One of the tumorigenic miRNAs associated with PM development is miR-182-5p (34). miR-182-5p’s oncogenic function was first identified in melanoma, where the miRNA promotes tumor migration and invasion by inhibiting the activity of the forkhead box O3 (FOXO3) transcription factor (35). Increasing evidence suggests that dysregulation of miR-182 and miR-183 is associated with the development of human malignant tumors. The forkhead box O1 (FOXO1) transcription factor has been identified as a target of miR-182 and miR-183 regulation in PM cells. A study by Abd-Elmawla et al. (33) found that inhibition of miR-182 and miR-183 expression in PM cells resulted in overexpression of the target gene FOXO1 and its downstream gene p27. Moreover, inhibition of miR-182 and miR-183 expression also significantly reduced the invasive ability of PM cells. Therefore, targeting the miR-182/183-FOXO1 axis provides a potential strategy for the treatment of PM. miRNAs encoded by the let-7 family were the first miRNAs identified to regulate the expression of the proto-oncogene RAS proteins, and current studies have demonstrated that the expression profiles of the let-7 miRNAs differed significantly between tumor cells and normal cells, and therefore let-7 miRNAs are useful in the early diagnosis of tumors. miRNA may be important in the early diagnosis of tumors (36). Guled et al. (37) showed that in malignant mesothelioma, the expression of let-7b was upregulated, while at the same time the expression of let-7e was downregulated in a progressive manner. Furthermore, systematic evaluation and meta-analysis have demonstrated the effectiveness of miRNAs as diagnostic biomarkers for PM. Micolucci et al. (38) retrieved the published literature from 1994 to 2015 and reported that the expression of circulating miRNAs (miR-126-3p, miR-103a-3p, miR-625-3p) and tissue miRNAs (miR-16-5p, miR-126-3p, miR-143-3p, miR-145-5p, miR-192-5p, miR-193a-3p, miR-200b-3p, miR-203a-3p, miR-652-3p) are two widely used and valuable markers, which are more consistent with the instability and genetic characteristics of PM chromosomes. Tissue miRNAs are more suitable for the diagnosis of PM due to their potential consistency with oncogenes. The above studies indicate that miRNAs have the potential to become new biomarkers for tumor diagnosis and prognosis.

New diagnostic methods related to PM biomarkers

Currently, for the diagnosis and differential diagnosis of PM, traditional biomarkers lack sensitivity compared with the gold standard pathologic biopsy, so the search for new effective markers is urgent (39,40). Advances in conventional molecular biology have revealed the microscopic composition of PM and also facilitated the development and application of new biological markers (41). In addition, the development of bioinformatics technology plays a crucial role and the diagnosis of PM is moving towards informatization and automation with the application of classification algorithms to molecular data such as gene sequencing (42). DeRienzo et al. (43) used gene sequencing to sequentially and positively test 26 genes for the purpose of discriminating PM from other thoracic malignancies such as sarcomas, renal cell carcinomas, and thymomas, with specificity and sensitivity ranging from 90% to 100%. Subsequently, 113 genomic samples were tested and analyzed, and another 170 samples were used for sequence validation, from patients with different types of PM, healthy individuals and other thoracic malignancies. Several different biomarkers were found to be effective in identifying patients with PM and benign chest diseases, different types of PM and other chest malignancies. Gene sequencing technology can test for highly sensitive biological markers, but it requires high sampling and technical requirements and the testing process is complicated (43). Parodi et al. (44) used the logic learning machine (LLM) for differential diagnosis of PM and benign pleural diseases and other pleural tumors. Specifically, LLM was used for multivariate analysis to explore the correlation between cytology and biomarkers, with the commonly used immunohistochemical markers carcinoembryonic antigen (CEA), CYFRA 21-1, and SMRP incorporated into the model construction process. It was found that the diagnostic accuracy of LLM could reach 77.5%, and could effectively identify 79% of PM, 66% of thoracic malignant metastases, and 89% of benign lesions, suggesting that LLM was superior to standard data mining techniques. CEA and CYFRA21-1 were not the best markers for modeling, and more appropriate criteria should be selected for the differential diagnosis of PM. Tosun et al. (45) developed a computer-assisted diagnostic method for PM based on nuclear chromatin, which is based on the use of the K-nearest neighbor (KNN) algorithm to analyze digital images of cell samples. In addition, they developed computers that can identify benign and malignant nuclei, and found the method to be 100% accurate by diagnosing 16 cases of PM, 34 cases of benign pleural tumors, and 18 cases of healthy individuals. The method relies on experienced pathologists for image identification, making it difficult to apply on a large scale in the clinic until it is automated.

Clinical significance and cutting-edge applications of PM biomarkers

The core value of PM biomarkers currently lies in bridging fundamental research with clinical practice. Their clinical applications have expanded beyond single-purpose diagnostics to encompass multidimensional roles such as treatment guidance and prognostic assessment. This evolution presents both practical challenges in implementation and opportunities for pioneering research.

In terms of diagnostic and therapeutic stratification, SMRP, as the first FDA-approved PM-associated biomarker, has been preliminarily employed in clinical settings for screening high-risk populations and differential diagnosis. For individuals with a history of asbestos exposure, elevated serum SMRP levels may indicate the necessity for further pathological biopsy. Moreover, SMRP in pleural effusion demonstrates superior sensitivity compared to serum samples, providing non-invasive auxiliary diagnostic evidence for patients with effusions of unknown aetiology. Of greater clinical significance, the potential association between SMRP levels and response to ICI therapy has garnered attention. A recent clinical study retrospectively analyzed 125 PM patients receiving immune checkpoint inhibitor therapy (ICT) treatment, confirming that serum SMRP serves as a potential biomarker for predicting survival duration in PM patients undergoing ICT therapy (46). Furthermore, PD-L1, as a core biomarker for immunotherapy, has seen its expression levels in PM tumour tissue and pleural effusions incorporated into certain clinical decision-making frameworks: PD-L1-positive patients demonstrate greater likelihood of benefiting from ICI therapy, whilst sPD-L1 concentrations in pleural effusions correlate positively with OS, serving as a supplementary indicator for prognostic assessment and treatment efficacy monitoring (47). Currently, the latest Diagnostic and Treatment Guidelines for PM (2025 Edition) also recognise that clinically elevated levels of SMRP and PD-L1 may be associated with the development of PM. However, the clinical application of these biomarkers remains significantly constrained at present (48). For instance, SMRP levels may nonspecifically elevate in patients with renal insufficiency, resulting in a false-positive rate of approximately 15–20%. Consequently, underlying renal disease must be ruled out prior to clinical application. OPN, whilst capable of distinguishing asbestos-exposed individuals from PM patients, also exhibits abnormal expression in other tumours such as lung and ovarian cancers. Its specificity is insufficient when used alone, necessitating combined testing with other biomarkers. Asbestos exposure constitutes a significant risk factor for PM. Upon inhalation into the lungs, asbestos fibres may puncture alveolar walls with their sharp tips, causing tissue damage. When lung tissue is compromised, type II alveolar epithelial cells proliferate, leading to elevated levels of the characteristic protein KL-6 within the alveoli. This protein then leaks into the bloodstream through the damaged basement membrane, resulting in increased serum KL-6 concentrations (49). One study also found elevated serum KL-6 levels in occupational asbestosis patients compared to healthy controls, suggesting KL-6 may serve as a biomarker for asbestos-related pulmonary injury. However, existing studies suffer from small sample sizes and inconsistent conclusions, necessitating large-scale, multicentre research to validate its reliability and stability. From a clinical perspective, biomarker testing has yet to achieve universal adoption. Primary healthcare institutions, constrained by testing costs and technical limitations, continue to rely primarily on pathological biopsy for diagnosis. Compared to pathological biopsy, biomarker testing offers non-invasive, rapid, and repeatable advantages. However, its sensitivity (70–80%) remains lower than that of pathological biopsy (over 95%), preventing it from fully replacing the ‘gold standard’ status of pathological diagnosis. Consequently, it is more commonly employed for preoperative screening and postoperative monitoring (50).

In the frontier field of immunotherapy, applications targeting PM biomarkers are also being extensively pursued. For chimeric antigen receptor T-cell (CAR-T) therapy, mesothelin represents a relatively suitable target. It exhibits low expression in normal mesothelial tissue yet is overexpressed in the majority of epithelial-type PM. Mesothelin participates in tumour malignant transformation and is significantly correlated with tumour invasiveness, leading to local invasion and metastasis (51). A preliminary toxicity assessment study (NCT01355965) employed anti-mesothelin CAR-T cells incorporating CD3-ζ and 4-1BB signalling domains. Results demonstrated favourable safety profiles for the CAR-T cells (52). In recent years, the role of eosinophils in the tumor microenvironment has become a research hotspot. In PM, studies have also focused on the infiltration of eosinophils. Eosinophils can support local anti-tumor responses by producing cytotoxic molecules; however, they can also secrete cytokines that promote inhibitory macrophages. These inhibitory macrophages are not only a major component of the PM microenvironment but also a constraint on the success of Immune ICB therapy. Williams et al. demonstrated a correlation between a baseline absolute eosinophil count (AEC) of ≥220/µL and poorer outcomes in PM patients receiving chemotherapy or immunotherapy (53). Therefore, further prospective studies are necessary to verify blood AEC as a potential predictive biomarker for both therapies. Nevertheless, there are relatively few relevant research cases at present, and more large-scale, in-depth studies are required to confirm the possibility of eosinophils becoming a new biomarker. Moreover, microRNAs such as miR-182-5p and let-7b within the microRNA family not only serve as early diagnostic markers, but their expression profiles can also predict patients’ risk of developing resistance to ICIs. Patients exhibiting high miR-182-5p expression are more prone to immune therapy resistance, whilst suppressing this miRNA’s expression enhances tumour cells’ sensitivity to programmed cell death protein 1 (PD-1) inhibitors, thereby offering a potential therapeutic target for resistant patients (54). In the future, with the maturation of liquid biopsy technology and the integration of multi-omics data, biomarkers are expected to provide comprehensive clinical guidance throughout the entire process of diagnosis, treatment, and prognosis, thereby further improving treatment outcomes for patients with PM.

Conclusions

This study suffers from some limitations inherent in bibliometric studies. For example, our study was mainly based on bibliometric analysis, focusing on the number of publications and citations, and lacked in-depth assessment of the quality of the study, and the data of the study were only obtained from the WOSCC, with which we may miss relevant literature in other databases, especially non-English language literature, which may lead to one-sided results of the study. Nonetheless, our study presents the 2002 to 2024 PM biologic status and frontiers of marker research and provides potential directions for future research, and we believe that the analysis in this paper can help scholars to further understand the hotspots in the field of PM biomarker research and make a significant contribution to bibliometrics.

Acknowledgments

We would like to thank researcher Xingguo Liu from the Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, for his assistance in polishing the language of this manuscript.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1171/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1171/prf

Funding: This study was supported by the National Natural Science Foundation of China (No. 82160516), Yunnan Local Universities Joint Fund (No. 202401BA070001-093) and the Xingguo Liu Expert Workstation in Dali Bai Prefecture (No. 202402).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1171/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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