Feng Ji

1.8k total citations · 1 hit paper
45 papers, 1.5k citations indexed

About

Feng Ji is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Feng Ji has authored 45 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 19 papers in Cancer Research and 7 papers in Oncology. Recurrent topics in Feng Ji's work include MicroRNA in disease regulation (7 papers), Autophagy in Disease and Therapy (5 papers) and RNA modifications and cancer (5 papers). Feng Ji is often cited by papers focused on MicroRNA in disease regulation (7 papers), Autophagy in Disease and Therapy (5 papers) and RNA modifications and cancer (5 papers). Feng Ji collaborates with scholars based in China, Singapore and Macao. Feng Ji's co-authors include Jing Zhou, Guo‐Dong Lu, Yue Xie, Yong Wu, Shouguo Wang, Han‐Ming Shen, Hui-Qi Dai, Xiaowei Huang, Xinyu Li and Zhongjie Li and has published in prestigious journals such as Journal of Agricultural and Food Chemistry, Biochemical and Biophysical Research Communications and Phytochemistry.

In The Last Decade

Feng Ji

43 papers receiving 1.5k citations

Hit Papers

Full-coverage regulations of autophagy by ROS: from induc... 2021 2026 2022 2024 2021 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Feng Ji China 20 1.0k 547 311 210 136 45 1.5k
Yu Yan China 17 814 0.8× 452 0.8× 521 1.7× 104 0.5× 183 1.3× 48 1.3k
Wanpeng Yu China 25 1.6k 1.6× 950 1.7× 505 1.6× 149 0.7× 221 1.6× 48 2.3k
Sanhong Liu China 24 1.0k 1.0× 570 1.0× 152 0.5× 108 0.5× 231 1.7× 60 1.6k
Fan Feng China 26 945 0.9× 625 1.1× 156 0.5× 126 0.6× 334 2.5× 66 1.8k
Lajos Ráduly Romania 20 1.2k 1.2× 669 1.2× 170 0.5× 86 0.4× 330 2.4× 61 1.9k
Yan Jiao China 24 1.2k 1.2× 757 1.4× 489 1.6× 109 0.5× 221 1.6× 95 2.1k
Xiaotong Hu China 24 1.0k 1.0× 416 0.8× 151 0.5× 215 1.0× 248 1.8× 82 1.7k
Wenjie Li China 22 1.0k 1.0× 267 0.5× 126 0.4× 87 0.4× 154 1.1× 75 1.5k
Yongde Luo United States 29 1.9k 1.9× 403 0.7× 197 0.6× 227 1.1× 203 1.5× 62 2.5k

Countries citing papers authored by Feng Ji

Since Specialization
Citations

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

Fields of papers citing papers by Feng Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Feng Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Feng Ji. A scholar is included among the top collaborators of Feng Ji 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 Feng Ji. Feng Ji 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.
Lu, Wen‐Min, Zhongfan Liu, Yong Wu, et al.. (2025). Clonorchis sinensis-driven hepatocarcinogenesis via E2F1-CD24 transcriptional axis: mechanistic and therapeutic implications. Parasites & Vectors. 18(1). 353–353.
3.
Ma, Kai, et al.. (2024). Antiaging Effects of Human Fecal Transplants with Different Combinations of Bifidobacterium bifidum LTBB21J1 and Lactobacillus casei LTL1361 in d-Galactose-Induced Mice. Journal of Agricultural and Food Chemistry. 72(17). 9818–9827. 3 indexed citations
4.
Meng, Chuang, et al.. (2024). Putative Probiotic Ligilactobacillus salivarius Strains Isolated from the Intestines of Meat-Type Pigeon Squabs. Probiotics and Antimicrobial Proteins. 17(5). 3338–3357. 4 indexed citations
5.
Ji, Feng, Zixuan Wang, Yongxin Chen, et al.. (2024). Pharmacological approaches for targeting lysosomes to induce ferroptotic cell death in cancer. Cancer Letters. 587. 216728–216728. 14 indexed citations
6.
Ji, Feng, Xiwen Liao, Yong Wu, et al.. (2024). The prognostic role of ACSL4 in postoperative adjuvant TACE-treated HCC: implications for therapeutic response and mechanistic insights. Journal of Experimental & Clinical Cancer Research. 43(1). 306–306. 4 indexed citations
7.
Wang, Yuanyuan, et al.. (2024). YTHDF1’s grip on CRC vasculature: insights into LINC01106 and miR-449b-5p-VEGFA axis. Cancer Cell International. 24(1). 195–195. 2 indexed citations
9.
Cui, Xuan, Yong Wu, Feng Ji, et al.. (2023). Andrographolide causes p53-independent HCC cell death through p62 accumulation and impaired DNA damage repair. Phytomedicine. 121. 155089–155089. 9 indexed citations
10.
Peng, Xiao, et al.. (2021). OGT regulated O-GlcNacylation promotes migration and invasion by activating IL-6/STAT3 signaling in NSCLC cells. Pathology - Research and Practice. 225. 153580–153580. 29 indexed citations
11.
Shi, Huan, Xinyu Li, Yao Chen, et al.. (2020). Quercetin Induces Apoptosis via Downregulation of Vascular Endothelial Growth Factor/Akt Signaling Pathway in Acute Myeloid Leukemia Cells. Frontiers in Pharmacology. 11. 534171–534171. 41 indexed citations
12.
Ji, Feng, Guangzhi Zhu, Shing Chuan Hooi, et al.. (2020). ACSL4 is a predictive biomarker of sorafenib sensitivity in hepatocellular carcinoma. Acta Pharmacologica Sinica. 42(1). 160–170. 126 indexed citations
13.
Li, Zhongjie, Hui-Qi Dai, Xiaowei Huang, et al.. (2020). Artesunate synergizes with sorafenib to induce ferroptosis in hepatocellular carcinoma. Acta Pharmacologica Sinica. 42(2). 301–310. 241 indexed citations
14.
Deng, Jing-Huan, Zhongjie Li, Zixuan Wang, et al.. (2020). Electron Microscopy-Based Comparison and Investigation of the Morphology of Exosomes Derived from Hepatocellular Carcinoma Cells Isolated at Different Centrifugal Speeds. Microscopy and Microanalysis. 26(2). 310–318. 13 indexed citations
15.
Hu, Xiaoxiao, Feng Ji, Xiaowei Huang, et al.. (2019). Histone deacetylases up-regulate C/EBPα expression through reduction of miR-124-3p and miR-25 in hepatocellular carcinoma. Biochemical and Biophysical Research Communications. 514(3). 1009–1016. 8 indexed citations
16.
Wang, Zixuan, Jing Zhou, Yue Tang, et al.. (2018). Quercetin induces P53-independent G2/M arrest and apoptosis in cancer cells. 32(10). 790–796. 1 indexed citations
17.
Ji, Feng, et al.. (2017). Activation of AMP-activated protein kinase by compound 991 protects osteoblasts from dexamethasone. Biochemical and Biophysical Research Communications. 495(1). 1014–1021. 17 indexed citations
18.
Guo, Shiguang, Caiyun Chen, Feng Ji, Li Mao, & Yue Xie. (2017). PP2A catalytic subunit silence by microRNA-429 activates AMPK and protects osteoblastic cells from dexamethasone. Biochemical and Biophysical Research Communications. 487(3). 660–665. 44 indexed citations
19.
Chen, Caiyun, et al.. (2016). MHY1485 activates mTOR and protects osteoblasts from dexamethasone. Biochemical and Biophysical Research Communications. 481(3-4). 212–218. 33 indexed citations
20.
Guo, Shiguang, et al.. (2015). α-Melanocyte stimulating hormone attenuates dexamethasone-induced osteoblast damages through activating melanocortin receptor 4-SphK1 signaling. Biochemical and Biophysical Research Communications. 469(2). 281–287. 32 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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