Non-small cell lung cancer and metabolism research from 2013 to 2023: a visual analysis and bibliometric study

Abstract

Background: As one of the most prevalent primary lung tumors, non-small cell lung cancer (NSCLC) has garnered considerable research interest due to its high metastasis rates and poor prognosis outcomes. Across different cancer types, metabolic processes are required for tumors progression and growth, thus interfering with such processes in NSCLC may therapeutically viable for limiting/halting disease progression. Therefore, comprehending how metabolic processes contribute to growth and survival mechanisms in cancers, including NSCLC, may elucidate key functions underpinning tumor cell metabolism. However, no bibliometric analyses have been published in this field, therefore we address this knowledge gap here.

Methods: Between 2013 and 2023 (December 28^th), articles related to the NSCLC and metabolism (NSCLC-Met) field were retrieved from the Web of Science Core Collection (WoSCC). To fully dissect NSCLC-Met research directions and articles, we used the Bibliometrix package in R, VOSviewer and CiteSpace software to visually represent global trends and hotspots.

Results: Between 2013 and 2023, 2,246 NSCLC-Met articles were retrieved, with a continuous upward trend and rapid development observed year on year. Cancers published the most articles, with Cancer Research recording the highest average citation numbers. Zhang Li from China was the most prolific author, but the highest number of authors came from the USA. China, USA, and Italy were the top three countries with the highest number of published articles, with close cooperation identified between countries. Recent hotspots and research directions were reflected by "lung adenocarcinoma", "immunotherapy", "nivolumab", "checkpoint inhibitors", "blockade", and "pembrolizumab", while "gut microbiome", "egfr" and "dose painting" were important topics for researchers.

Conclusion: From our analyses, scientists can now explore new hotspots and research directions in the NSCLC-Met field. Further in-depth research in this field will undoubtedly provide more new insights on disease diagnostics, treatment, and prognostics.

Keywords: NSCLC; bibliometric; checkpoint inhibitor; immunotherapy; metabolism; non-small cell lung cancer.

Figure 1

Annual article and average citation numbers (2013–2023) for non-small cell lung cancer and metabolism articles.

Figure 2

Co-authorship map. The size of the node reflects the number of papers published by the researcher, and the curves indicate collaborations between researchers. The nodes with the same color denote researchers who have collaborated on multiple projects or papers.

Figure 3

Institution collaboration map related to the non-small cell lung cancer and metabolism field. The node is represented in a concentric circle, and the node size reflects the number of articles published by the institution. Different colors of circles denote different years, while the width of each circle represents the number of articles published in the corresponding year. The purple circles outside some nodes reflect high betweenness centrality, indicating their importance as bridges in the network. The curves between nodes represent collaborative relationships among institution, with the thickness indicating the strength of connection and the color indicating the earliest year in which the connection was found.

Figure 4

Country/region map related to non-small cell lung cancer and metabolism. (A) Geographical distribution of global output. (B) Pie chart showing the top ten countries/regions in terms of article numbers. (C) The top ten most cited countries/regions. (D) Visual cluster diagram showing cooperation between different countries. The node size reflects the country’s research activity levels, and the curves indicate collaborative relationships between different countries. The nodes with the same color denote countries who have similar scientific collaboration patterns. “people r china” here is referred to as “China” in the text.

Figure 5

Keyword analyses related to the non-small cell lung cancer and metabolism (NSCLC-Met) field. (A) Network visualization of co-occurrence keywords. The node size reflects the keyword frequency of occurrence in the literature and the nodes with the same color display groups of keywords with similar research focus. (B) Keyword cluster map. The node size reflects the keyword frequency of occurrence in the literature, and the curves indicate the relevance between keywords. The nodes with the same color represent groups of keywords with similar research focus. (C) Top 20 highest-intensity keywords in the NSCLC-Met field. Each keyword burstiness is represented by the length of the line and the saturation of its color. Long and deeply colored lines indicate keywords with high burstiness, signifying a sharp increase in research interest in these keywords over a short period of time.

Figure 6

Cited reference analyses related to non-small cell lung cancer and metabolism (NSCLC-Met). (A) Reference cluster analyses. Each cluster represents a research topic, with its influence and relevance indicated by the size of the nodes and the connections. The larger the nodes, the more frequent the research activities related to the topic. (B) Cluster timeline distribution. Each cluster is labeled according to the year it first appeared, and different research topics are represented by lines of different colors.

Figure 7

Top 20 references with the strongest citation bursts related to non-small cell lung cancer and metabolism (NSCLC-Met). Each reference’s burstiness is represented by the length of the line and the saturation of its color. Long and deeply colored lines indicate references with high burstiness, signifying a sharp increase in research interest in these references over a short period of time.

Figure 8

Dual-map overlay showing the disciplinary distribution of non-small cell lung cancer and metabolism (NSCLC-Met) articles. The articles and cited results were visualized. Wider edges indicated higher value in occurrence. The left part of the plot indicated citing journals, the right part of the plot indicated cited journals, and the curves represent citation relationships.