A scientometrics and visualization analysis of oxidative stress modulator Nrf2 in cancer profiles its characteristics and reveals its association with immune response

Abstract

Background: Nrf2, an essential and fascinating transcription factor, enjoys a dual property in the occurrence and development of inflammation and cancer. For over two decades, numerous studies regarding Nrf2 in cancer have been reported, whereas there is still a lack of a scientometrics and visualization analysis of Nrf2 in cancer. Hence, a scientometric study regarding the oxidative stress modulator Nrf2 was implemented.

Methods: After the quality screening, we defined 7168 relevant studies from 2000 to 2021. CiteSpace, VOSviewer, R software, and GraphPad Prism were used for the following scientometric study and visualization analysis, including field profiles, research hotspots, and future predictions.

Results: The total number of publications and citations are 1058 and 54,690, respectively. After polynomial fitting curve analysis, two prediction functions of the annual publication number (y = 3.3909x² - 13585x + 1 E+07) and citation number (185.45x² - 743669x + 7 E+08) were generated. After scientometric analysis, we found that Biochemistry Molecular Biology correlates with Nrf2 in cancer highly, and Free Radical Biology and Medicine is a good choice for submitting Nrf2-related manuscripts. The current research hotspots of Nrf2 in cancer mainly focus on cancer therapy and its cellular and molecular mechanisms. "antioxidant response element (87.5)", "gene expression (43.98)", "antioxidant responsive element (21.14)", "chemoprevention (20.05)", "carcinogenesis (19.2)", "cancer chemoprevention (18.45)", "free radical (17.15)", "response element (14.17)", and "chemopreventive agent (14.04)" are important for cancer therapy study. In addition, "glutathione-S-transferase (47)", "keap1 (15.39)", and "heme oxygenase 1 gene (24.35)" are important for inflammation and cell fate study. More interestingly, by performing an "InfoMap" algorithm, the thematic map showed that the "immune response" is essential to oxidative stress modulator Nrf2 but not well developed, indicating it deserves further exploration.

Conclusion: This study revealed field profiles, research hotspots, and future directions of oxidative stress modulator Nrf2 in inflammation and cancer research, and our findings will offer a vigorous roadmap for further studies in this field.

Keywords: Cancer; Immune response; Nrf2; Oxidative stress; Scientometric; Visualization.

Fig. 1

Timelines and prediction functions of publication number and global citation score about Nrf2 in cancer. The annual publication number (orange) and its corresponding global citation score (blue) were obtained from WOSCC, and then a polynomial fitting curve analysis was performed to establish the prediction functions. R² represents the correlation coefficient.

Fig. 2

Scientometrics and visualization analysis of top ten highest cited articles and top ten most productive authors. (A) Radar chart was used to exhibit the total cited number and rank of the top ten highest cited articles. (B) The average citation number of each highest cited article was obtained by the ratio of its total citation number and time from publication to 2022. (C) The annual citation number of each highest cited article was visualized by a matrix heatmap. (D) The total publication number and rank of the top ten authors with the highest production. (E) The total cited number and rank of the top ten authors with the highest production. (F) The average publication number of each most productive author was obtained by the ratio of the total publication number and time from publication starting point to 2021. (G) The average citation number of each most productive author was obtained by the ratio of the total cited number and time from citation starting point to 2022. (H) The total citation number and rank of the top ten authors with the highest production. (I) The h-index and rank of the top ten authors with the highest production. (J, K) The annual publication or citation number of each highest cited author. Rectangle chart with less light green (C, K) or yellow (J) means less citation number and rectangle chart with more dark blue (C, K) or red (J) means more citation number.

Fig. 3

Scientometrics and visualization analysis of top ten most productive countries or affiliations. (A, D) Radar chart was used to exhibit the total publication number and rank of the top ten highest productive countries (A) or affiliations (D). (B, J) The total cited number and rank of the top ten most productive countries (B) or affiliations (J). (C, K) The average publication number of each most productive country (C) or affiliation (K) was obtained by the ratio of the total publication number and time from publication starting point to 2021. (D, L) The average citation number of each most productive country (D) or affiliation (L) was obtained by the ratio of the total cited number and time from citation starting point to 2022. (E, M) The total citation number and rank of the top ten most productive country (E) or affiliation (M). (F, N) The h-index and rank of the top ten most productive country (F) or affiliation (N). (G, O) The annual publication number of each most productive country (G) or affiliation (O) was visualized by a matrix heatmap. (H, P) The annual citation number of each most productive country (H) or affiliation (P). Rectangle chart with less light yellow (G, O) or green (H, P) means less citation number and rectangle chart with more dark red (G, O) or blue (H, P) means more citation number.

Fig. 4

Scientometrics and visualization analysis of top ten most productive research fields or journals. (A, D) Radar chart was used to exhibit the total publication number and rank of the top ten highest productive research fields (A) or journals (D). (B, J) The total cited number and rank of the top ten most productive research fields (B) or journals (J). (C, K) The average publication number of each most productive research field (C) or journal (K) was obtained by the ratio of the total publication number and time from publication starting point to 2021. (D, L) The average citation number of each most productive research field (D) or journal (L) was obtained by the ratio of the total cited number and time from citation starting point to 2022. (E, M) The total citation number and rank of the top ten most productive research field (E) or journal (M). (F, N) The h-index and rank of the top ten most productive research field (F) or journal (N). (G, O) The annual publication number of each most productive research field (G) or journal (O) was visualized by a matrix heatmap. (H, P) The annual citation number of each most productive research field (H) or journal (P). Rectangle chart with less light yellow (G, O) or green (H, P) means less citation number and rectangle chart with more dark red (G, O) or blue (H, P) means more citation number.

Fig. 5

Multi-network visualization analysis of authors, countries and affiliations. (A) Visualized cluster analysis of cooperation among authors. Of the 34,653 authors, 906 published at least five documents, and 679 items were chosen for further connection cluster analysis. (B) Timeline distribution of cluster analysis of the author. (C) Cooperation heatmap visualization of author with other authors. (D) Citation heatmap visualization of author. (E) Visualized cluster analysis of cooperation among countries. Of the 105 countries, 60 published at least five documents, and 103 items were chosen for further connection cluster analysis. (F) Timeline distribution of cluster analysis of the country. (G) Cooperation heatmap visualization of country with other countries. (H) Citation heatmap visualization of country. (I) Visualized cluster analysis of cooperation among affiliations. Of the 5046 affiliations, 701 published at least five documents, and 693 items were chosen for further connection cluster analysis. (J) Timeline distribution of cluster analysis of the affiliation. (K) Cooperation heatmap visualization of affiliation with other affiliations. (L) Citation heatmap visualization of affiliation. For the visualized cluster analysis, the size of node indicates cooperation strength with other authors. Cluster 1: Red; Cluster 2: Green; Cluster 3: Blue; Cluster 4: Yellow; Cluster 5: Purple. For the timeline distribution of cluster analysis, yellow node comes later than purple node.

Fig. 6

Multi-network visualization analysis of research hotspots of Nrf2 in cancer. (A) Visualized cluster analysis of co-occurrence among author keywords. Of the 10,111 author keywords, 97 keywords appeared at least 26 times and 97 items were chosen for further occurrence cluster analysis. The size of node indicates occurrence strength with other keywords. Cluster 1 (Red): Cancer Cell Fate Study. Cluster 2 (Green): Inflammation and Oxidative Stress Study. Cluster 3 (Blue): Cancer Therapy Study. Cluster 4 (Yellow): Toxicity Study. Cluster 5 (Purple): Metabolism Study. (B) Timeline distribution of the author keywords. Yellow node comes later than purple node. (C) Connection density visualization of keyword with other keywords. (D) Occurrence density visualization of keyword. (E) The top twenty-five keywords with the strongest citation burst. The keywords were ranked by the citation burst strength. The years between the “beginning” and “end” are marked in red, indicating the more influential period for the author keyword. Years to the left of green indicate that the author keyword is still not present, while years to the right indicate that the author keyword is of less influence.

Fig. 7

The status and hotspot prediction of research themes of Nrf2 in cancer. 1700 author's keywords were selected to perform an “InfoMap” cluster algorithm. The x-axis represents the centrality of a specific topic, indicating the level of interactivity with other keywords and reflecting its importance in the field. The y-axis represents topic density, which is considered a metric for the development level. Keywords “immune response” in quadrant IV (Important but under-developed theme).