Bo Su

3.0k total citations · 1 hit paper
56 papers, 2.0k citations indexed

About

Bo Su is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, Bo Su has authored 56 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 16 papers in Pulmonary and Respiratory Medicine and 16 papers in Cancer Research. Recurrent topics in Bo Su's work include Lung Cancer Treatments and Mutations (13 papers), MicroRNA in disease regulation (8 papers) and RNA modifications and cancer (8 papers). Bo Su is often cited by papers focused on Lung Cancer Treatments and Mutations (13 papers), MicroRNA in disease regulation (8 papers) and RNA modifications and cancer (8 papers). Bo Su collaborates with scholars based in China, United States and Japan. Bo Su's co-authors include Mengchao Wu, Dan Cao, Qin Han, Liang Tang, Shanhua Tang, Le‐Xing Yu, Shanna Huang, Tian Fang, Ting Li and Guishuai Lv and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Genes & Development.

In The Last Decade

Bo Su

55 papers receiving 2.0k citations

Hit Papers

Tumor-derived exosomal miR-1247-3p induces cancer-associa... 2018 2026 2020 2023 2018 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Bo Su China 21 1.4k 1.1k 457 400 181 56 2.0k
Maya Kansara Australia 14 1.3k 0.9× 631 0.6× 574 1.3× 580 1.4× 229 1.3× 31 2.2k
Jacson Shen United States 28 981 0.7× 523 0.5× 689 1.5× 617 1.5× 205 1.1× 52 2.0k
Haiou Yang China 18 2.0k 1.4× 1.6k 1.5× 274 0.6× 516 1.3× 201 1.1× 32 2.4k
Xiaofeng Xue China 29 1.5k 1.1× 1.0k 0.9× 539 1.2× 124 0.3× 265 1.5× 67 2.2k
Naoomi Tominaga Japan 15 1.6k 1.1× 1.2k 1.1× 332 0.7× 183 0.5× 249 1.4× 27 2.1k
Yuanjie Niu China 24 982 0.7× 585 0.5× 250 0.5× 404 1.0× 86 0.5× 45 1.5k
Bigang Liu United States 21 2.0k 1.4× 1.5k 1.4× 813 1.8× 360 0.9× 367 2.0× 34 2.8k
Bo Shen China 23 1.2k 0.9× 956 0.9× 314 0.7× 189 0.5× 131 0.7× 73 1.6k
Wantao Chen China 24 1.2k 0.9× 948 0.9× 410 0.9× 147 0.4× 147 0.8× 46 1.8k
Hangwen Li China 13 1.4k 1.0× 1.1k 1.0× 814 1.8× 470 1.2× 188 1.0× 22 2.1k

Countries citing papers authored by Bo Su

Since Specialization
Citations

This map shows the geographic impact of Bo Su's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bo Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bo Su more than expected).

Fields of papers citing papers by Bo Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bo Su. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bo Su. The network helps show where Bo Su may publish in the future.

Co-authorship network of co-authors of Bo Su

This figure shows the co-authorship network connecting the top 25 collaborators of Bo Su. A scholar is included among the top collaborators of Bo Su based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bo Su. Bo Su is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Song, Fuxing, Fang Guo, Bo Su, et al.. (2025). METTL3 promotes infantile pneumonia-induced lung injury by the m6A-TBL1XR1-ACSL1 axis. Cellular Immunology. 411-412. 104944–104944.
2.
Huang, Bo, et al.. (2024). G-MBRMD: Lightweight liver segmentation model based on guided teaching with multi-head boundary reconstruction mapping distillation. Computers in Biology and Medicine. 178. 108733–108733. 2 indexed citations
3.
Cai, Bolei, Jiachen Dong, Bo Su, et al.. (2023). Construction of Multi‐Module RNA Nanoparticles Harboring miRNA, AIE, and CH6 Aptamer for Bone Targeting and Bone Anabolic Therapy. Advanced Functional Materials. 34(13). 9 indexed citations
5.
Xue, Bin, Chen-Hua Chuang, Haydn M. Prosser, et al.. (2021). miR-200 deficiency promotes lung cancer metastasis by activating Notch signaling in cancer-associated fibroblasts. Genes & Development. 35(15-16). 1109–1122. 52 indexed citations
6.
Sun, Wenwen, et al.. (2021). The frequency and dynamics of CD4+ mucosal‐associated invariant T (MAIT) cells in active pulmonary tuberculosis. Cellular Immunology. 365. 104381–104381. 4 indexed citations
7.
Ding, Xi, et al.. (2020). High expression level of interleukin-1β is correlated with poor prognosis and PD-1 expression in patients with lung adenocarcinoma. Clinical & Translational Oncology. 23(1). 35–42. 12 indexed citations
8.
Chen, Hongyuan, Jie Zhang, Hongyan Chen, Bo Su, & Daru Lu. (2020). Establishment of multiplex allele-specific blocker PCR for enrichment and detection of 4 common EGFR mutations in non-small cell lung cancer. Annals of Translational Medicine. 8(22). 1509–1509. 8 indexed citations
9.
Liu, Di, Ziyang Cao, Wen Xu, et al.. (2019). Enhancement of chemosensitivity by WEE1 inhibition in EGFR-TKIs resistant non-small cell lung cancer. Biomedicine & Pharmacotherapy. 117. 109185–109185. 14 indexed citations
10.
Fang, Tian, Hongwei Lv, Guishuai Lv, et al.. (2018). Tumor-derived exosomal miR-1247-3p induces cancer-associated fibroblast activation to foster lung metastasis of liver cancer. Nature Communications. 9(1). 191–191. 779 indexed citations breakdown →
11.
Ding, Xi, et al.. (2017). Magnetic resonance imaging of tumor angiogenesis using dual-targeting RGD10–NGR9 ultrasmall superparamagnetic iron oxide nanoparticles. Clinical & Translational Oncology. 20(5). 599–606. 20 indexed citations
12.
Lei, Yubin, Lingling Liu, Shujing Zhang, et al.. (2017). Hdac7 promotes lung tumorigenesis by inhibiting Stat3 activation. Molecular Cancer. 16(1). 170–170. 127 indexed citations
13.
Ding, Xi, Yue Yang, Yutong Sun, et al.. (2016). MicroRNA-585 acts as a tumor suppressor in non-small-cell lung cancer by targeting hSMG-1. Clinical & Translational Oncology. 19(5). 546–552. 15 indexed citations
14.
Zheng, Di, Xue-Rui Ye, Yun Sun, et al.. (2016). Plasma EGFR T790M ctDNA status is associated with clinical outcome in advanced NSCLC patients with acquired EGFR-TKI resistance. Scientific Reports. 6(1). 20913–20913. 182 indexed citations
15.
Xu, Wen, Di Liu, Yang Yang, et al.. (2016). Association of CHEK2 polymorphisms with the efficacy of platinum-based chemotherapy for advanced non-small-cell lung cancer in Chinese never-smoking women. Journal of Thoracic Disease. 8(9). 2519–2529. 7 indexed citations
17.
Zheng, Di, Jie Zhang, Jian Ni, et al.. (2015). Small nucleolar RNA 78 promotes the tumorigenesis in non-small cell lung cancer. Journal of Experimental & Clinical Cancer Research. 34(1). 49–49. 74 indexed citations
18.
Yang, Yang, Wen Xu, Di Liu, et al.. (2015). PTEN polymorphisms contribute to clinical outcomes of advanced lung adenocarcinoma patients treated with platinum-based chemotherapy. Tumor Biology. 37(6). 7785–7796. 7 indexed citations
19.
Xu, Qing, Jingyun Shi, Yuqing Sun, et al.. (2013). Comparison of tumor neovasculature-targeted paramagnetic nanoliposomes for MRI in mice xenograft models. Clinical & Translational Oncology. 16(4). 395–401. 5 indexed citations
20.
Su, Bo, Peng Zhang, Huikang Xie, et al.. (2012). Expression of miR-150 and miR-3940-5p is reduced in non-small cell lung carcinoma and correlates with clinicopathological features. Oncology Reports. 29(2). 704–712. 39 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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