Ryan L. Shuck

687 total citations
21 papers, 556 citations indexed

About

Ryan L. Shuck is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Ryan L. Shuck has authored 21 papers receiving a total of 556 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 9 papers in Cancer Research and 7 papers in Oncology. Recurrent topics in Ryan L. Shuck's work include interferon and immune responses (5 papers), Sarcoma Diagnosis and Treatment (5 papers) and Cancer-related Molecular Pathways (5 papers). Ryan L. Shuck is often cited by papers focused on interferon and immune responses (5 papers), Sarcoma Diagnosis and Treatment (5 papers) and Cancer-related Molecular Pathways (5 papers). Ryan L. Shuck collaborates with scholars based in United States, Belgium and Thailand. Ryan L. Shuck's co-authors include Jason T. Yustein, Lawrence A. Donehower, Lyazat Kurenbekova, Motonari Nomura, Nino Rainusso, Neha Parikh, Cristian Coarfa, Susan G. Hilsenbeck, Daniel G. Fuja and Wendy Allen‐Rhoades and has published in prestigious journals such as JNCI Journal of the National Cancer Institute, Cancer Research and Oncogene.

In The Last Decade

Ryan L. Shuck

21 papers receiving 553 citations

Peers

Ryan L. Shuck

ONCOL

PRM

PFM

Yan Qiu China

Wanjiang Xue China

Tracy C.M. Lau Hong Kong

Zongzhen Xu China

Carolina Bizama Chile

Honggang Yu China

Shogo Okazaki Japan

Veronica Henderson United States

Raquel Batlle Spain

Yan Qiu China

Ryan L. Shuck

315 ×0.9

282 ×1.4

227 ×1.3

ONCOL

97 ×0.6

PRM

38 ×0.8

PFM

Citations per year, relative to Ryan L. Shuck Ryan L. Shuck (= 1×) peers Yan Qiu

Countries citing papers authored by Ryan L. Shuck

Since Specialization

Citations

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

Fields of papers citing papers by Ryan L. Shuck

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryan L. Shuck. 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 Ryan L. Shuck. The network helps show where Ryan L. Shuck may publish in the future.

Co-authorship network of co-authors of Ryan L. Shuck

This figure shows the co-authorship network connecting the top 25 collaborators of Ryan L. Shuck. A scholar is included among the top collaborators of Ryan L. Shuck 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 Ryan L. Shuck. Ryan L. Shuck is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Kurenbekova, Lyazat, et al.. (2023). Abstract 6713: Myc-regulated miR17, 20a modulate RANK expression in osteosarcoma. Cancer Research. 83(7_Supplement). 6713–6713. 2 indexed citations

Kurenbekova, Lyazat, et al.. (2023). MYC regulates CSF1 expression via microRNA 17/20a to modulate tumor-associated macrophages in osteosarcoma. JCI Insight. 8(13). 13 indexed citations

Kurenbekova, Lyazat, et al.. (2022). Abstract 1668: Development and characterization of a c-Myc-driven preclinical mouse model of osteosarcoma to investigate the tumor immune microenvironment. Cancer Research. 82(12_Supplement). 1668–1668. 2 indexed citations

Nakahata, Kengo, Brian W. Simons, Ryan L. Shuck, et al.. (2022). K-Ras and p53 mouse model with molecular characteristics of human rhabdomyosarcoma and translational applications. Disease Models & Mechanisms. 15(2). 11 indexed citations

Sierra, Laura, Ryan L. Shuck, Lyazat Kurenbekova, et al.. (2021). p21-activated kinases as viable therapeutic targets for the treatment of high-risk Ewing sarcoma. Oncogene. 40(6). 1176–1190. 12 indexed citations

Sierra, Laura, Lyazat Kurenbekova, Ryan L. Shuck, et al.. (2020). Targeting PAK4 Inhibits Ras-Mediated Signaling and Multiple Oncogenic Pathways in High-Risk Rhabdomyosarcoma. Cancer Research. 81(1). 199–212. 29 indexed citations

Kurenbekova, Lyazat, et al.. (2020). Abstract 6147: Myc-driven osteosarcoma murine model for dissecting the molecular genetics and novel therapeutic strategies for OS. Cancer Research. 80(16_Supplement). 6147–6147. 1 indexed citations

Nomura, Motonari, Nino Rainusso, Yi‐Chien Lee, et al.. (2019). Tegavivint and the β-Catenin/ALDH Axis in Chemotherapy-Resistant and Metastatic Osteosarcoma. JNCI Journal of the National Cancer Institute. 111(11). 1216–1227. 83 indexed citations

Kurenbekova, Lyazat, et al.. (2018). In Vitro and In Vivo Characterization of a Preclinical Irradiation-Adapted Model for Ewing Sarcoma. International Journal of Radiation Oncology*Biology*Physics. 101(1). 118–127. 5 indexed citations

10.

Nomura, Motonari, Nino Rainusso, Ruolan Han, et al.. (2018). Abstract 3186: Tegavivint suppresses progression and metastasis of osteosarcoma via blockade of Wnt signaling/ALDH1 axis: Preclinical study of a novel Wnt/β-catenin pathway inhibitor. Cancer Research. 78(13_Supplement). 3186–3186. 1 indexed citations

11.

Fuja, Daniel G., Nino Rainusso, Ryan L. Shuck, et al.. (2018). Transglutaminase-2 promotes metastatic and stem-like phenotypes in osteosarcoma.. PubMed. 8(9). 1752–1763. 12 indexed citations

12.

Parikh, Neha, Ryan L. Shuck, Mihai Gagea, Lanlan Shen, & Lawrence A. Donehower. (2017). Enhanced inflammation and attenuated tumor suppressor pathways are associated with oncogene‐induced lung tumors in aged mice. Aging Cell. 17(1). 28 indexed citations

13.

Shuck, Ryan L., Lyazat Kurenbekova, Wendy Allen‐Rhoades, et al.. (2017). miR‐130b directly targets ARHGAP1 to drive activation of a metastatic CDC42‐PAK1‐AP1 positive feedback loop in Ewing sarcoma. International Journal of Cancer. 141(10). 2062–2075. 39 indexed citations

14.

Trucco, Matteo, Nino Rainusso, Ronald J. Bernardi, et al.. (2017). Metabolic modulation of Ewing sarcoma cells inhibits tumor growth and stem cell properties. Oncotarget. 8(44). 77292–77308. 24 indexed citations

15.

Gao, Yang, et al.. (2016). Secreted Frizzled-Related Protein 2 (sFRP2) promotes osteosarcoma invasion and metastatic potential. BMC Cancer. 16(1). 869–869. 41 indexed citations

16.

Nomura, Motonari, et al.. (2016). Cancer’s Achilles’ Heel: Apoptosis and Necroptosis to the Rescue. International Journal of Molecular Sciences. 18(1). 23–23. 69 indexed citations

17.

Roos, Alison, Shuying Zhao, Daniel G. Fuja, et al.. (2015). Loss of Runx2 sensitises osteosarcoma to chemotherapy-induced apoptosis. British Journal of Cancer. 113(9). 1289–1297. 24 indexed citations

18.

Allen‐Rhoades, Wendy, Lyazat Kurenbekova, Neha Parikh, et al.. (2015). Cross‐species identification of a plasma microRNA signature for detection, therapeutic monitoring, and prognosis in osteosarcoma. Cancer Medicine. 4(7). 977–988. 65 indexed citations

19.

Parikh, Neha, Ryan L. Shuck, Thuy‐Ai Nguyen, Alan J. Herron, & Lawrence A. Donehower. (2012). Mouse Tissues that Undergo Neoplastic Progression after K-Ras Activation Are Distinguished by Nuclear Translocation of phospho-Erk1/2 and Robust Tumor Suppressor Responses. Molecular Cancer Research. 10(6). 845–855. 15 indexed citations

20.

Somasundaram, Siva G., et al.. (2012). Citrus Limonin Lacks the Antichemotherapeutic Effect in Human Models of Breast Cancer. Lifestyle Genomics. 5(2). 106–114. 11 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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