Shuang–Jian Qiu

11.8k total citations · 1 hit paper
174 papers, 8.6k citations indexed

About

Shuang–Jian Qiu is a scholar working on Hepatology, Oncology and Surgery. According to data from OpenAlex, Shuang–Jian Qiu has authored 174 papers receiving a total of 8.6k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Hepatology, 65 papers in Oncology and 56 papers in Surgery. Recurrent topics in Shuang–Jian Qiu's work include Hepatocellular Carcinoma Treatment and Prognosis (67 papers), Cholangiocarcinoma and Gallbladder Cancer Studies (47 papers) and Cancer Mechanisms and Therapy (21 papers). Shuang–Jian Qiu is often cited by papers focused on Hepatocellular Carcinoma Treatment and Prognosis (67 papers), Cholangiocarcinoma and Gallbladder Cancer Studies (47 papers) and Cancer Mechanisms and Therapy (21 papers). Shuang–Jian Qiu collaborates with scholars based in China, Ethiopia and United States. Shuang–Jian Qiu's co-authors include Jia Fan, Jian Zhou, Xin‐Rong Yang, Yang Xu, Yun‐Fan Sun, Bo Hu, Wei Guo, Xin Zhang, Ying‐Hong Shi and Chao Sun and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Shuang–Jian Qiu

168 papers receiving 8.5k citations

Hit Papers

Systemic Immune-Inflammation Index Predicts Prognosis of ... 2014 2026 2018 2022 2014 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shuang–Jian Qiu China 48 3.6k 2.7k 2.1k 2.0k 2.0k 174 8.6k
Toshiharu Sakurai Japan 45 2.4k 0.7× 2.8k 1.0× 2.1k 1.0× 1.5k 0.7× 2.0k 1.0× 191 8.1k
Markus Guba Germany 38 3.8k 1.1× 3.0k 1.1× 1.3k 0.6× 1.3k 0.6× 1.3k 0.7× 194 8.5k
Shoji Nakamori Japan 55 4.4k 1.2× 3.6k 1.3× 1.6k 0.8× 2.0k 1.0× 1.5k 0.8× 276 10.1k
Koji Umeshita Japan 51 2.2k 0.6× 2.6k 0.9× 3.4k 1.6× 1.8k 0.9× 1.6k 0.8× 290 8.5k
Ahmed O. Kaseb United States 42 2.9k 0.8× 1.8k 0.7× 3.7k 1.7× 1.5k 0.7× 1.8k 0.9× 263 8.1k
Hiroaki Nagano Japan 60 4.2k 1.2× 5.0k 1.8× 3.5k 1.6× 3.2k 1.6× 2.2k 1.1× 451 13.1k
Jian Zhou China 39 2.0k 0.6× 2.5k 0.9× 1.8k 0.8× 1.8k 0.9× 863 0.4× 225 6.6k
Hui‐Chuan Sun China 58 3.1k 0.9× 5.0k 1.9× 3.7k 1.7× 4.2k 2.1× 2.0k 1.0× 308 11.8k
Etsuro Hatano Japan 47 2.4k 0.7× 1.8k 0.7× 3.6k 1.7× 1.1k 0.6× 2.2k 1.1× 402 8.6k
Louis Libbrecht Belgium 48 2.0k 0.6× 2.1k 0.8× 3.5k 1.6× 976 0.5× 2.1k 1.1× 170 7.5k

Countries citing papers authored by Shuang–Jian Qiu

Since Specialization
Citations

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

Fields of papers citing papers by Shuang–Jian Qiu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shuang–Jian Qiu. 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 Shuang–Jian Qiu. The network helps show where Shuang–Jian Qiu may publish in the future.

Co-authorship network of co-authors of Shuang–Jian Qiu

This figure shows the co-authorship network connecting the top 25 collaborators of Shuang–Jian Qiu. A scholar is included among the top collaborators of Shuang–Jian Qiu 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 Shuang–Jian Qiu. Shuang–Jian Qiu 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.
Zhu, Ying, et al.. (2025). Multi-omics analysis of the anti-cancer effects of curcumol in endometrial carcinoma. Frontiers in Pharmacology. 16. 1565959–1565959. 1 indexed citations
3.
Guan, Ruo‐Yu, Zhangfu Yang, Bao‐Ye Sun, et al.. (2024). Effective Antiviral Therapy Improves Immunosuppressive Activities in the Immune Microenvironment of Hepatocellular Carcinoma by Alleviating Inflammation and Fibrosis. Cancer Medicine. 13(23). e70459–e70459. 1 indexed citations
4.
Sun, Bao‐Ye, Zhu-Tao Wang, Yang Song, et al.. (2024). Mobilization and activation of tumor-infiltrating dendritic cells inhibits lymph node metastasis in intrahepatic cholangiocarcinoma. Cell Death Discovery. 10(1). 304–304. 10 indexed citations
5.
Cheng, Jianwen, Xin‐Rong Yang, Xu Yang, et al.. (2023). The clinical efficacy and safety of sintilimab plus anlotinib for unresectable intrahepatic cholangiocarcinoma (ICC): A prospective, single-arm phase II study.. Journal of Clinical Oncology. 41(16_suppl). e16170–e16170. 1 indexed citations
6.
Li, Yueshuo, Feng Shi, Jianmin Hu, et al.. (2021). Stabilization of p18 by deubiquitylase CYLD is pivotal for cell cycle progression and viral replication. npj Precision Oncology. 5(1). 14–14. 14 indexed citations
7.
Jing, Chuyu, Yi‐Peng Fu, Cheng Zhou, et al.. (2021). Hepatic stellate cells promote intrahepatic cholangiocarcinoma progression via NR4A2/osteopontin/Wnt signaling axis. Oncogene. 40(16). 2910–2922. 17 indexed citations
8.
10.
Zhou, Zheng‐Jun, Zhi Dai, Shao‐Lai Zhou, et al.. (2014). HNRNPAB Induces Epithelial–Mesenchymal Transition and Promotes Metastasis of Hepatocellular Carcinoma by Transcriptionally Activating SNAIL. Cancer Research. 74(10). 2750–2762. 88 indexed citations
11.
Hu, Bo, Xin‐Rong Yang, Yang Xu, et al.. (2014). Systemic Immune-Inflammation Index Predicts Prognosis of Patients after Curative Resection for Hepatocellular Carcinoma. Clinical Cancer Research. 20(23). 6212–6222. 1558 indexed citations breakdown →
12.
Gao, Qiang, Yingjun Zhao, Xiaoying Wang, et al.. (2012). CXCR6 Upregulation Contributes to a Proinflammatory Tumor Microenvironment That Drives Metastasis and Poor Patient Outcomes in Hepatocellular Carcinoma. Cancer Research. 72(14). 3546–3556. 136 indexed citations
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14.
Chen, Xiaohong, Boheng Zhang, Xin Yin, et al.. (2011). Risk factors for residual tumor after resection of hepatocellular carcinoma. World Journal of Gastroenterology. 17(14). 1889–1889. 2 indexed citations
15.
Yang, Xin‐Rong, Yang Xu, Bin Yu, et al.. (2009). CD24 Is a Novel Predictor for Poor Prognosis of Hepatocellular Carcinoma after Surgery. Clinical Cancer Research. 15(17). 5518–5527. 129 indexed citations
16.
Fan, Jia, Guang-Shun Yang, Zhiren Fu, et al.. (2009). Liver transplantation outcomes in 1,078 hepatocellular carcinoma patients: a multi-center experience in Shanghai, China. Journal of Cancer Research and Clinical Oncology. 135(10). 1403–1412. 87 indexed citations
17.
Dai, Zhi, Jian Zhou, Shuang–Jian Qiu, Yinkun Liu, & Jia Fan. (2009). Lectin‐based glycoproteomics to explore and analyze hepatocellular carcinoma‐related glycoprotein markers. Electrophoresis. 30(17). 2957–2966. 62 indexed citations
18.
Ding, Zhen‐Bin, Ying‐Hong Shi, Jian Zhou, et al.. (2008). Association of Autophagy Defect with a Malignant Phenotype and Poor Prognosis of Hepatocellular Carcinoma. Cancer Research. 68(22). 9167–9175. 236 indexed citations
19.
Wang, Zheng, Jia Fan, Jian Zhou, et al.. (2008). Preventive chemotherapy for hepatocellular carcinoma exceeding Milan criteria after fiver transplantation. Zhōnghuá xiāohuà wàikē zázhì/Zhonghua xiaohua waike zazhi. 7(4). 268–270. 1 indexed citations
20.
Yang, Guo‐Huan, Jia Fan, Yang Xu, et al.. (2008). Osteopontin Combined with CD44, a Novel Prognostic Biomarker for Patients with Hepatocellular Carcinoma Undergoing Curative Resection. The Oncologist. 13(11). 1155–1165. 56 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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