Jun Su

577 total citations
21 papers, 407 citations indexed

About

Jun Su is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Jun Su has authored 21 papers receiving a total of 407 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 9 papers in Cancer Research and 3 papers in Genetics. Recurrent topics in Jun Su's work include Cancer, Hypoxia, and Metabolism (4 papers), Epigenetics and DNA Methylation (4 papers) and RNA modifications and cancer (4 papers). Jun Su is often cited by papers focused on Cancer, Hypoxia, and Metabolism (4 papers), Epigenetics and DNA Methylation (4 papers) and RNA modifications and cancer (4 papers). Jun Su collaborates with scholars based in China, United States and Thailand. Jun Su's co-authors include Haoyu Li, Zijin Zhao, Xianrui Yuan, Yiwei Liao, Qing Liu, Ming Wu, Qing Liu, Chao Zhang, Xiangyu Wang and Xiuzhi Jia and has published in prestigious journals such as Nature Communications, The Plant Cell and Scientific Reports.

In The Last Decade

Jun Su

20 papers receiving 403 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jun Su China 12 287 113 41 40 39 21 407
Chen‐Xue Mao China 11 180 0.6× 118 1.0× 38 0.9× 58 1.4× 12 0.3× 20 364
Esraa Mohamed Egypt 9 199 0.7× 141 1.2× 23 0.6× 22 0.6× 20 0.5× 24 314
Timothy C. Kenny United States 13 350 1.2× 98 0.9× 10 0.2× 50 1.3× 11 0.3× 21 520
Dian Hu China 12 241 0.8× 126 1.1× 32 0.8× 116 2.9× 12 0.3× 20 507
Miranda Menniti Italy 11 336 1.2× 85 0.8× 17 0.4× 58 1.4× 7 0.2× 13 464
Catherine Buquet France 13 170 0.6× 67 0.6× 15 0.4× 65 1.6× 15 0.4× 17 412
Tomomi Hashidate‐Yoshida Japan 9 254 0.9× 52 0.5× 16 0.4× 31 0.8× 10 0.3× 19 447
Yinyin Xie China 11 335 1.2× 70 0.6× 10 0.2× 48 1.2× 13 0.3× 30 468
Shuchen Gu China 10 240 0.8× 37 0.3× 13 0.3× 66 1.6× 11 0.3× 14 408

Countries citing papers authored by Jun Su

Since Specialization
Citations

This map shows the geographic impact of Jun 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 Jun Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Su more than expected).

Fields of papers citing papers by Jun Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun 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 Jun Su. The network helps show where Jun Su may publish in the future.

Co-authorship network of co-authors of Jun Su

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Su. A scholar is included among the top collaborators of Jun 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 Jun Su. Jun 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.
Liu, Shuyan, Qianquan Ma, Chong Zeng, et al.. (2025). Crosstalk between lactylation and RNA modifications in tumorigenesis: mechanisms and therapeutic implications. Biomarker Research. 13(1). 110–110.
2.
Li, Keke, Ruimin Wang, Zhengying Gu, et al.. (2025). Serum metabolic profiling enables diagnosis, prognosis, and monitoring for brainstem gliomas. Nature Communications. 16(1). 6108–6108. 2 indexed citations
3.
Su, Jun, Qin Xie, & Longlong Xie. (2024). Identification and validation of a metabolism-related gene signature for predicting the prognosis of paediatric medulloblastoma. Scientific Reports. 14(1). 7540–7540. 2 indexed citations
4.
Zhu, Qian, Jinzhou Huang, Hongyang Huang, et al.. (2021). RNF19A-mediated ubiquitination of BARD1 prevents BRCA1/BARD1-dependent homologous recombination. Nature Communications. 12(1). 16 indexed citations
5.
Chang, Liang, Lisheng Yin, Dongzhi Zhang, et al.. (2021). MicroRNA-221 promotes tumor progression by targeting HHIP in human glioblastoma. Translational Cancer Research. 10(2). 1073–1081. 5 indexed citations
6.
Li, Haoyu, Qing Liu, Zihua Chen, et al.. (2021). Hsa_circ_0110757 upregulates ITGA1 to facilitate temozolomide resistance in glioma by suppressing hsa-miR-1298-5p. Cell Death and Disease. 12(3). 252–252. 32 indexed citations
7.
Ma, Qianquan, Wenyong Long, Changsheng Xing, et al.. (2020). PHF20 Promotes Glioblastoma Cell Malignancies Through a WISP1/BGN-Dependent Pathway. Frontiers in Oncology. 10. 573318–573318. 10 indexed citations
8.
Su, Jun, et al.. (2020). Biological function of protein tyrosine phosphatase H-type receptor and its progress in tumor.. PubMed. 45(1). 61–67. 1 indexed citations
9.
Peng, Hao, Chaoying Qin, Chao Zhang, et al.. (2019). circCPA4 acts as a prognostic factor and regulates the proliferation and metastasis of glioma. Journal of Cellular and Molecular Medicine. 23(10). 6658–6665. 38 indexed citations
10.
Wu, Changwu, Jun Su, Xiangyu Wang, et al.. (2019). <p>Overexpression of the phospholipase A2 group V gene in glioma tumors is associated with poor patient prognosis</p>. Cancer Management and Research. Volume 11. 3139–3152. 18 indexed citations
11.
Jia, Yanan, Yalin Peng, Baoshun Zhang, et al.. (2018). Oxyresveratrol prevents lipopolysaccharide/d-galactosamine-induced acute liver injury in mice. International Immunopharmacology. 56. 105–112. 43 indexed citations
12.
Zhao, Zijin, Songhua Xiao, Xianrui Yuan, et al.. (2017). AHNAK as a Prognosis Factor Suppresses the Tumor Progression in Glioma. Journal of Cancer. 8(15). 2924–2932. 30 indexed citations
13.
Zheng, Youguang, Xiaoqing Wu, Jun Su, et al.. (2017). Design and synthesis of a novel photoaffinity probe for labelling EGF receptor tyrosine kinases. Journal of Enzyme Inhibition and Medicinal Chemistry. 32(1). 954–959. 6 indexed citations
14.
Zhang, Chi, Xianrui Yuan, Zhongliang Hu, et al.. (2017). Valproic Acid Protects Primary Dopamine Neurons from MPP+-Induced Neurotoxicity: Involvement of GSK3βPhosphorylation by Akt and ERK through the Mitochondrial Intrinsic Apoptotic Pathway. BioMed Research International. 2017. 1–12. 20 indexed citations
15.
Zhang, Chi, Songlin Liu, Xianrui Yuan, et al.. (2016). Valproic Acid Promotes Human Glioma U87 Cells Apoptosis and Inhibits Glycogen Synthase Kinase-3β Through ERK/Akt Signaling. Cellular Physiology and Biochemistry. 39(6). 2173–2185. 55 indexed citations
16.
Chang, Liang, Qin Yu, Xuexin Zhang, et al.. (2015). Expression and prognostic value of SFRP1 and β-catenin in patients with glioblastoma. Oncology Letters. 11(1). 69–74. 9 indexed citations
17.
18.
Ren, Huan, Liang Chang, Jun Su, & Xiuzhi Jia. (2014). Treating malignant glioma in Chinese patients: update on temozolomide. OncoTargets and Therapy. 7. 235–235. 31 indexed citations
19.
Zhang, Chi, Xianrui Yuan, Haoyu Li, et al.. (2013). Downregualtion of dynamin-related protein 1 attenuates glutamate-induced excitotoxicity via regulating mitochondrial function in a calcium dependent manner in HT22 cells. Biochemical and Biophysical Research Communications. 443(1). 138–143. 20 indexed citations
20.
Wang, Yan, et al.. (2008). [The effect of shRNA targeting hTERT on telomerase and the expression of PCNA and Caspase-3 in nasopharyngeal carcinoma cells].. PubMed. 22(9). 411–5. 4 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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