Yongzhong Yao

1.2k total citations
37 papers, 864 citations indexed

About

Yongzhong Yao is a scholar working on Cancer Research, Molecular Biology and Oncology. According to data from OpenAlex, Yongzhong Yao has authored 37 papers receiving a total of 864 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cancer Research, 14 papers in Molecular Biology and 11 papers in Oncology. Recurrent topics in Yongzhong Yao's work include RNA modifications and cancer (6 papers), Cancer-related molecular mechanisms research (5 papers) and Cancer, Hypoxia, and Metabolism (4 papers). Yongzhong Yao is often cited by papers focused on RNA modifications and cancer (6 papers), Cancer-related molecular mechanisms research (5 papers) and Cancer, Hypoxia, and Metabolism (4 papers). Yongzhong Yao collaborates with scholars based in China, Bangladesh and United States. Yongzhong Yao's co-authors include Jianfeng Sang, Xitai Sun, Weijie Zhang, Weijie Zhang, Yuzhen Wang, Daping Fan, Johnie Hodge, Xinyun Xu, Jinqiu Tao and Jiahui Ye and has published in prestigious journals such as Small, Genome biology and Experimental Cell Research.

In The Last Decade

Yongzhong Yao

37 papers receiving 856 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yongzhong Yao China 15 492 338 274 231 112 37 864
Mengyu Sun China 18 489 1.0× 290 0.9× 252 0.9× 225 1.0× 183 1.6× 45 916
Xiaohui Pan China 16 517 1.1× 306 0.9× 217 0.8× 188 0.8× 109 1.0× 27 853
Changjie Lou China 16 468 1.0× 337 1.0× 271 1.0× 123 0.5× 82 0.7× 33 820
Laurent Lessard Canada 17 811 1.6× 528 1.6× 359 1.3× 225 1.0× 259 2.3× 25 1.3k
Vijesh Kumar Yadav Taiwan 18 502 1.0× 310 0.9× 173 0.6× 104 0.5× 147 1.3× 41 814
Juan Moreno‐Rubio Spain 19 634 1.3× 393 1.2× 288 1.1× 86 0.4× 162 1.4× 35 1.1k
Dhiraj Kumar United States 15 735 1.5× 409 1.2× 423 1.5× 200 0.9× 98 0.9× 28 1.3k
Dong Hoon Shin South Korea 16 491 1.0× 311 0.9× 271 1.0× 66 0.3× 152 1.4× 40 830
Shiyue Sun China 11 507 1.0× 332 1.0× 202 0.7× 194 0.8× 40 0.4× 19 794
Yong-Nyun Kim South Korea 14 377 0.8× 206 0.6× 193 0.7× 156 0.7× 65 0.6× 23 732

Countries citing papers authored by Yongzhong Yao

Since Specialization
Citations

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

Fields of papers citing papers by Yongzhong Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yongzhong Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Yongzhong Yao. A scholar is included among the top collaborators of Yongzhong Yao 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 Yongzhong Yao. Yongzhong Yao 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.
Sun, Yulu, Hao Yu, Yin Zhang, et al.. (2025). Predictive model using systemic inflammation markers to assess neoadjuvant chemotherapy efficacy in breast cancer. Frontiers in Oncology. 15. 1552802–1552802. 2 indexed citations
2.
Cao, Meng, Xianglin Liu, Yue Liu, et al.. (2024). Development of Stable and Intensified Mixing Processes for the Precise and Scalable Production of Uniform Drug Delivery Nanocarriers. Small. 20(52). e2406521–e2406521. 2 indexed citations
3.
Zhao, Hongting, Meng Zhang, Jinghua Zhang, et al.. (2023). Hinokitiol-iron complex is a ferroptosis inducer to inhibit triple-negative breast tumor growth. Cell & Bioscience. 13(1). 87–87. 19 indexed citations
4.
Sun, Yulu, Yixin Zhao, Yin Zhang, et al.. (2023). Study on the Relationship Between Differentially Expressed Proteins in Breast Cancer and Lymph Node Metastasis. Advances in Therapy. 40(9). 4004–4023. 1 indexed citations
5.
Qu, Shuang, Zichen Jiao, Geng Lu, et al.. (2022). Human lung adenocarcinoma CD47 is upregulated by interferon-γ and promotes tumor metastasis. Molecular Therapy — Oncolytics. 25. 276–287. 9 indexed citations
6.
Wang, Wei, Tingting Zhu, Hao Chen, & Yongzhong Yao. (2022). The impact of HER2-low status on response to neoadjuvant chemotherapy in clinically HER2-negative breast cancer. Clinical & Translational Oncology. 25(6). 1673–1681. 10 indexed citations
7.
Shi, Xianbiao, Yulu Sun, Yin Zhang, et al.. (2021). MEX3A promotes development and progression of breast cancer through regulation of PIK3CA. Experimental Cell Research. 404(1). 112580–112580. 13 indexed citations
8.
Li, Yong, Johnie Hodge, Qing Liu, et al.. (2020). TFEB is a master regulator of tumor-associated macrophages in breast cancer. Journal for ImmunoTherapy of Cancer. 8(1). e000543–e000543. 57 indexed citations
9.
Liu, Qing, Johnie Hodge, Junfeng Wang, et al.. (2020). Emodin reduces Breast Cancer Lung Metastasis by suppressing Macrophage-induced Breast Cancer Cell Epithelial-mesenchymal transition and Cancer Stem Cell formation. Theranostics. 10(18). 8365–8381. 102 indexed citations
10.
Wang, Wei, Weijie Zhang, Lei Su, et al.. (2019). Plasma cell-free DNA integrity: a potential biomarker to monitor the response of breast cancer to neoadjuvant chemotherapy. Translational Cancer Research. 8(4). 1531–1539. 2 indexed citations
11.
Xu, Guifang, Bin Zhang, Jiahui Ye, et al.. (2019). Exosomal miRNA-139 in cancer-associated fibroblasts inhibits gastric cancer progression by repressing MMP11 expression. International Journal of Biological Sciences. 15(11). 2320–2329. 133 indexed citations
12.
Liu, Tong, Jing Sui, Bo Shen, et al.. (2018). Prognostic value of a two‐microRNA signature for papillary thyroid cancer and a bioinformatic analysis of their possible functions. Journal of Cellular Biochemistry. 120(5). 7185–7198. 16 indexed citations
13.
Zhou, Jing, Xiaohua Wang, Yixin Zhao, et al.. (2018). Cancer-Associated Fibroblasts Correlate with Tumor-Associated Macrophages Infiltration and Lymphatic Metastasis in Triple Negative Breast Cancer Patients. Journal of Cancer. 9(24). 4635–4641. 63 indexed citations
14.
15.
Yang, Sheng, Jing Sui, Wenjuan Wu, et al.. (2018). Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature. Cancer Management and Research. Volume 10. 4297–4310. 17 indexed citations
16.
Sang, Jianfeng, et al.. (2015). Role of survivin in the pathogenesis of papillary thyroid carcinoma. Genetics and Molecular Research. 14(4). 15102–15111. 9 indexed citations
17.
Yao, Yongzhong, et al.. (2007). Endoglin (CD105) expression in angiogenesis of primary hepatocellular carcinomas: analysis using tissue microarrays and comparisons with CD34 and VEGF.. PubMed. 37(1). 39–48. 54 indexed citations
18.
Yu, Decai, Xitai Sun, Jun Chen, et al.. (2007). Particular distribution and expression pattern of endoglin (CD105) in the liver of patients with hepatocellular carcinoma. BMC Cancer. 7(1). 122–122. 37 indexed citations
19.
Yao, Yongzhong, et al.. (2006). Caveolin-1 is important for nitric oxide-mediated angiogenesis in fibrin gels with human umbilical vein endothelial cells. Acta Pharmacologica Sinica. 27(12). 1567–1574. 21 indexed citations
20.

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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