Xiaolin Zi

6.1k total citations · 1 hit paper
102 papers, 4.8k citations indexed

About

Xiaolin Zi is a scholar working on Molecular Biology, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Xiaolin Zi has authored 102 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Molecular Biology, 26 papers in Oncology and 24 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Xiaolin Zi's work include Wnt/β-catenin signaling in development and cancer (14 papers), Cancer-related Molecular Pathways (13 papers) and Cancer-related gene regulation (13 papers). Xiaolin Zi is often cited by papers focused on Wnt/β-catenin signaling in development and cancer (14 papers), Cancer-related Molecular Pathways (13 papers) and Cancer-related gene regulation (13 papers). Xiaolin Zi collaborates with scholars based in United States, China and Canada. Xiaolin Zi's co-authors include Michaël Pollak, Rajesh Agarwal, Anne R. Simoneau, Bang H. Hoang, Jun Xie, Desmond Mascarenhas, Yen‐Yu Lu, Yuqing Zhao, Yi Guo and Xuesen Li and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and JNCI Journal of the National Cancer Institute.

In The Last Decade

Xiaolin Zi

99 papers receiving 4.7k citations

Hit Papers

Insulin-Like Growth Factor-I Receptor Signaling and Resis... 2001 2026 2009 2017 2001 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaolin Zi United States 39 2.9k 1.3k 1.1k 663 454 102 4.8k
Franco O. Ranelletti Italy 44 2.4k 0.8× 1.2k 0.9× 338 0.3× 925 1.4× 421 0.9× 128 6.0k
Wei Yue United States 43 2.4k 0.8× 1.7k 1.2× 377 0.4× 908 1.4× 149 0.3× 124 5.3k
Michihiro Mutoh Japan 33 2.2k 0.7× 1.4k 1.0× 362 0.3× 954 1.4× 206 0.5× 168 4.8k
Zhi Shi China 42 2.4k 0.8× 1.6k 1.2× 454 0.4× 635 1.0× 305 0.7× 122 4.3k
Jiao Feng China 40 3.0k 1.0× 1.1k 0.8× 511 0.5× 1.8k 2.7× 184 0.4× 153 5.6k
Jing Zhou China 37 2.3k 0.8× 779 0.6× 638 0.6× 892 1.3× 160 0.4× 151 4.8k
Jiang‐Jiang Qin China 42 3.9k 1.3× 1.8k 1.3× 527 0.5× 1.6k 2.4× 394 0.9× 175 5.8k
Allen C. Gao United States 54 4.4k 1.5× 2.7k 2.0× 3.5k 3.3× 2.6k 3.9× 159 0.4× 161 8.4k
Ramzi M. Mohammad United States 41 2.9k 1.0× 1.2k 0.9× 218 0.2× 601 0.9× 280 0.6× 154 4.8k
Shangha Pan China 47 3.0k 1.0× 843 0.6× 341 0.3× 1.2k 1.7× 303 0.7× 121 5.3k

Countries citing papers authored by Xiaolin Zi

Since Specialization
Citations

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

Fields of papers citing papers by Xiaolin Zi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaolin Zi

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaolin Zi. A scholar is included among the top collaborators of Xiaolin Zi 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 Xiaolin Zi. Xiaolin Zi 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
2.
Zi, Xiaolin, et al.. (2023). Wnt Signaling and Therapeutic Resistance in Castration-Resistant Prostate Cancer. Current Pharmacology Reports. 9(5). 261–274. 8 indexed citations
3.
Du, Linna, Yang Han, Yufei Ren, et al.. (2023). Inhibition of LSD1 induces ferroptosis through the ATF4-xCT pathway and shows enhanced anti-tumor effects with ferroptosis inducers in NSCLC. Cell Death and Disease. 14(11). 716–716. 21 indexed citations
4.
Liu, Zhongbo, Jun Xie, Xue‐Ru Wu, et al.. (2023). Kavalactone Kawain Impedes Urothelial Tumorigenesis in UPII-Mutant Ha-Ras Mice via Inhibition of mTOR Signaling and Alteration of Cancer Metabolism. Molecules. 28(4). 1666–1666. 3 indexed citations
5.
Fu, Dong‐Jun, Jiajia Yang, Ping Li, et al.. (2018). Bioactive heterocycles containing a 3,4,5-trimethoxyphenyl fragment exerting potent antiproliferative activity through microtubule destabilization. European Journal of Medicinal Chemistry. 157. 50–61. 30 indexed citations
6.
Liu, Zhongbo, Noriko Yokoyama, Christopher Blair, et al.. (2016). High Sensitivity of an Ha-RAS Transgenic Model of Superficial Bladder Cancer to Metformin Is Associated with ∼240-Fold Higher Drug Concentration in Urine than Serum. Molecular Cancer Therapeutics. 15(3). 430–438. 13 indexed citations
7.
8.
Fu, Dong‐Jun, Li Zhang, Jian Song, et al.. (2016). Design and synthesis of formononetin-dithiocarbamate hybrids that inhibit growth and migration of PC-3 cells via MAPK/Wnt signaling pathways. European Journal of Medicinal Chemistry. 127. 87–99. 48 indexed citations
9.
Lee, Chung, Zhenyu Jia, Farah Rahmatpanah, et al.. (2014). Role of the Adjacent Stroma Cells in Prostate Cancer Development and Progression: Synergy between TGF-βand IGF Signaling. BioMed Research International. 2014. 1–8. 16 indexed citations
10.
Jandial, Danielle, Christopher Blair, Sai‐Yang Zhang, et al.. (2014). Molecular Targeted Approaches to Cancer Therapy and Prevention Using Chalcones. Current Cancer Drug Targets. 14(2). 181–200. 114 indexed citations
11.
Liu, Zhongbo, Xia Xu, Xuesen Li, et al.. (2013). KAVA Chalcone, Flavokawain A, Inhibits Urothelial Tumorigenesis in the UPII-SV40T Transgenic Mouse Model. Cancer Prevention Research. 6(12). 1365–1375. 30 indexed citations
12.
McQueen, Peter, et al.. (2011). The Wnt signaling pathway: implications for therapy in osteosarcoma. Expert Review of Anticancer Therapy. 11(8). 1223–1232. 73 indexed citations
13.
Tang, Yaxiong, Anne R. Simoneau, Yi Guo, et al.. (2009). WIF1, a Wnt pathway inhibitor, regulates SKP2 and c-myc expression leading to G1 arrest and growth inhibition of human invasive urinary bladder cancer cells. Molecular Cancer Therapeutics. 8(2). 458–468. 89 indexed citations
14.
Tang, Yaxiong, Anne R. Simoneau, Jun Xie, Babbak Shahandeh, & Xiaolin Zi. (2008). Effects of the Kava Chalcone Flavokawain A Differ in Bladder Cancer Cells with Wild-type versus Mutant p53. Cancer Prevention Research. 1(6). 439–451. 68 indexed citations
15.
Guo, Yi, Jun Xie, Elyssa Rubin, et al.. (2008). Frzb, a Secreted Wnt Antagonist, Decreases Growth and Invasiveness of Fibrosarcoma Cells Associated with Inhibition of Met Signaling. Cancer Research. 68(9). 3350–3360. 65 indexed citations
16.
Lee, Kyung-Woo, Makoto Anzo, Xiaolin Zi, et al.. (2006). Insulin-like growth factor-binding protein-3 inhibition of prostate cancer growth involves suppression of angiogenesis. Oncogene. 26(12). 1811–1819. 89 indexed citations
19.
Lu, Yuhong, Xiaolin Zi, Yunhua Zhao, & Michaël Pollak. (2003). Overexpression of ErbB2 receptor inhibits IGF-I-induced Shc–MAPK signaling pathway in breast cancer cells. Biochemical and Biophysical Research Communications. 313(3). 709–715. 28 indexed citations
20.
Lu, Yen‐Yu, Xiaolin Zi, Yuqing Zhao, Desmond Mascarenhas, & Michaël Pollak. (2001). Insulin-Like Growth Factor-I Receptor Signaling and Resistance to Trastuzumab (Herceptin). JNCI Journal of the National Cancer Institute. 93(24). 1852–1857. 699 indexed citations breakdown →

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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