Xiaoshun Shi

1.8k total citations · 1 hit paper
46 papers, 1.2k citations indexed

About

Xiaoshun Shi is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, Xiaoshun Shi has authored 46 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 20 papers in Pulmonary and Respiratory Medicine and 18 papers in Cancer Research. Recurrent topics in Xiaoshun Shi's work include RNA modifications and cancer (17 papers), Lung Cancer Treatments and Mutations (12 papers) and Lung Cancer Diagnosis and Treatment (11 papers). Xiaoshun Shi is often cited by papers focused on RNA modifications and cancer (17 papers), Lung Cancer Treatments and Mutations (12 papers) and Lung Cancer Diagnosis and Treatment (11 papers). Xiaoshun Shi collaborates with scholars based in China, United States and Australia. Xiaoshun Shi's co-authors include Jianxing He, Wenhua Liang, Qiuhua Deng, Wenlong Shao, Deruo Liu, Gening Jiang, Qun Wang, Zheng Wang, Lunxu Liu and Zhihua Zhu and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and CHEST Journal.

In The Last Decade

Xiaoshun Shi

45 papers receiving 1.2k citations

Hit Papers

Development and Validation of a Nomogram for Predicting S... 2015 2026 2018 2022 2015 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaoshun Shi China 17 610 418 353 297 212 46 1.2k
Soichiro Funaki Japan 22 683 1.1× 616 1.5× 346 1.0× 212 0.7× 335 1.6× 146 1.7k
Marine Lefèvre France 18 424 0.7× 291 0.7× 314 0.9× 208 0.7× 214 1.0× 67 1.1k
Shanqing Li China 22 510 0.8× 365 0.9× 467 1.3× 419 1.4× 149 0.7× 115 1.3k
Mauro Loi Italy 23 716 1.2× 586 1.4× 207 0.6× 249 0.8× 284 1.3× 124 1.5k
Michael V. Di Maria United States 15 955 1.6× 746 1.8× 471 1.3× 197 0.7× 278 1.3× 40 1.7k
Caroline Geers Belgium 16 342 0.6× 389 0.9× 474 1.3× 264 0.9× 190 0.9× 43 1.1k
Atsushi Osoegawa Japan 17 390 0.6× 587 1.4× 334 0.9× 177 0.6× 130 0.6× 88 1.1k
Osamu Kawashima Japan 23 723 1.2× 464 1.1× 343 1.0× 162 0.5× 345 1.6× 125 1.4k

Countries citing papers authored by Xiaoshun Shi

Since Specialization
Citations

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

Fields of papers citing papers by Xiaoshun Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaoshun Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoshun Shi. A scholar is included among the top collaborators of Xiaoshun Shi 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 Xiaoshun Shi. Xiaoshun Shi 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.
Huang, Yisheng, et al.. (2022). Mining the Prognostic Role of DNA Methylation Heterogeneity in Lung Adenocarcinoma. Disease Markers. 2022. 1–11. 2 indexed citations
2.
Hu, Xiaoshan, et al.. (2022). 5-methylcytosine RNA methylation regulators affect prognosis and tumor microenvironment in lung adenocarcinoma. Annals of Translational Medicine. 10(5). 259–259. 24 indexed citations
3.
Shi, Xiaoshun, Xiguang Liu, Xiaoying Dong, Hua Wu, & Kaican Cai. (2022). Trends, Symptoms, and Outcomes of Resectable Giant Mediastinal Tumors. Frontiers in Oncology. 12. 820720–820720. 4 indexed citations
4.
Shi, Xiaoshun, et al.. (2022). Cancer susceptibility genes: update and systematic perspectives. The Innovation. 3(5). 100277–100277. 17 indexed citations
5.
Shi, Xiaoshun, Ruidong Li, Allen Chen, et al.. (2021). The first comprehensive database of germline pathogenic variants in East Asian cancer patients. Database. 2021(2021).
6.
Shi, Xiaoshun, et al.. (2021). Current Evidence of the Efficacy and Safety of Neoadjuvant EGFR-TKIs for Patients With Non-small Cell Lung Cancer. Frontiers in Oncology. 11. 608608–608608. 5 indexed citations
7.
Shi, Xiaoshun, Ruidong Li, Xiaoying Dong, et al.. (2020). IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis. Journal of Translational Medicine. 18(1). 55–55. 28 indexed citations
8.
Lu, Di, Xiguang Liu, He Wang, et al.. (2020). Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary. Frontiers in Genetics. 11. 614823–614823. 4 indexed citations
9.
Lu, Di, Siyang Feng, Xiaoying Dong, et al.. (2019). Beyond T Cells: Understanding the Role of PD-1/PD-L1 in Tumor-Associated Macrophages. Journal of Immunology Research. 2019. 1–7. 103 indexed citations
10.
Lu, Di, Zhizhi Wang, Xiguang Liu, et al.. (2019). <p>Differential effects of adjuvant EGFR tyrosine kinase inhibitors in patients with different stages of non-small-cell lung cancer after radical resection: an updated meta-analysis</p>. Cancer Management and Research. Volume 11. 2677–2690. 8 indexed citations
11.
Shi, Xiaoshun, Xiaobing Le, Xiaoxiang Li, et al.. (2018). An expression signature model to predict lung adenocarcinoma-specific survival. Cancer Management and Research. Volume 10. 3717–3732. 32 indexed citations
12.
Shi, Xiaoshun, Yan Wang, Di Lu, et al.. (2018). P2.12-08 Network Meta-Analysis of Angiogenesis Inhibitors on Survival of Patients with Small Cell Lung Cancer. Journal of Thoracic Oncology. 13(10). S793–S793. 1 indexed citations
13.
Shi, Xiaoshun, Allen Chen, Xiaobing Le, et al.. (2017). LncRNA TUSC7 affects malignant tumor prognosis by regulating protein ubiquitination: a genome-wide analysis from 10,237 pan-cancer patients. Translational Cancer Research. 6(4). 834–842. 1 indexed citations
14.
Shi, Xiaoshun, Fuxi Huang, Xiaobing Le, et al.. (2017). A practical prognostic lncRNA signature for lung squamous cell carcinoma. SHILAP Revista de lepidopterología. 2(1). 3 indexed citations
15.
Pan, Hui, Xiaoshun Shi, Dakai Xiao, et al.. (2017). Nomogram prediction for the survival of the patients with small cell lung cancer. Journal of Thoracic Disease. 9(3). 507–518. 43 indexed citations
16.
Zhang, Jiexia, Xiaoshun Shi, Di Cai, et al.. (2016). PS01.37: Comparison Between Sanger Sequencing and ARMS qPCR Method for EGFR Gene Mutation Detection of Non–Small Cell Lung Cancer. Journal of Thoracic Oncology. 11(11). S291–S292. 1 indexed citations
17.
Jiang, Long, Wenhua Liang, Jianfei Shen, et al.. (2015). The Impact of Visceral Pleural Invasion in Node-Negative Non-small Cell Lung Cancer. CHEST Journal. 148(4). 903–911. 72 indexed citations
18.
Li, Jin, Jie Ting Zhang, Xiaohua Jiang, et al.. (2015). The cystic fibrosis transmembrane conductance regulator as a biomarker in non-small cell lung cancer. International Journal of Oncology. 46(5). 2107–2115. 44 indexed citations
19.
Shi, Xiaoshun, et al.. (2015). Correlation of increased MALAT1 expression with pathological features and prognosis in cancer patients: a meta-analysis. Genetics and Molecular Research. 14(4). 18808–18819. 13 indexed citations
20.
Zhang, Xiaoxue, Rong Zhang, Jianfei Shen, et al.. (2013). Expression of gamma-aminobutyric acid receptors on neoplastic growth and prediction of prognosis in non-small cell lung cancer. Journal of Translational Medicine. 11(1). 102–102. 57 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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