Chang S. Chan

4.3k total citations · 3 hit papers
26 papers, 2.8k citations indexed

About

Chang S. Chan is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Chang S. Chan has authored 26 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 13 papers in Oncology and 10 papers in Cancer Research. Recurrent topics in Chang S. Chan's work include Epigenetics and DNA Methylation (10 papers), Cancer-related Molecular Pathways (10 papers) and Autophagy in Disease and Therapy (5 papers). Chang S. Chan is often cited by papers focused on Epigenetics and DNA Methylation (10 papers), Cancer-related Molecular Pathways (10 papers) and Autophagy in Disease and Therapy (5 papers). Chang S. Chan collaborates with scholars based in United States, France and China. Chang S. Chan's co-authors include Eileen White, Janice M. Mehnert, Saurabh V. Laddha, Arnold J. Levine, Hua Zhu Ke, Evan H. Baugh, Richard Bonneau, Joshua D. Rabinowitz, Jessie Yanxiang Guo and Xin Teng and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Genes & Development.

In The Last Decade

Chang S. Chan

25 papers receiving 2.8k citations

Hit Papers

Autophagy, Metabolism, an... 2014 2026 2018 2022 2015 2014 2017 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chang S. Chan United States 18 1.8k 1.3k 983 667 240 26 2.8k
Mathias T. Rosenfeldt Germany 20 1.4k 0.8× 930 0.7× 613 0.6× 505 0.8× 341 1.4× 42 2.2k
Kirsteen H. Maclean United States 25 2.6k 1.5× 684 0.5× 761 0.8× 1.1k 1.6× 167 0.7× 30 3.6k
Fangping Zhao United States 12 2.2k 1.2× 592 0.5× 1.5k 1.5× 516 0.8× 155 0.6× 21 3.0k
Kwang Woon Kim United States 23 1.2k 0.7× 608 0.5× 314 0.3× 596 0.9× 202 0.8× 32 1.8k
José Palacios Spain 26 1.5k 0.9× 365 0.3× 441 0.4× 870 1.3× 288 1.2× 58 2.9k
Brandon Nicolay United States 19 1.5k 0.8× 443 0.4× 707 0.7× 582 0.9× 229 1.0× 34 2.2k
Monica Buzzai United States 13 3.1k 1.7× 508 0.4× 1.8k 1.8× 966 1.4× 182 0.8× 13 4.0k
Jessica L. Yecies United States 10 2.0k 1.1× 468 0.4× 926 0.9× 319 0.5× 204 0.8× 13 3.0k
Twan van den Beucken Netherlands 22 1.6k 0.9× 735 0.6× 940 1.0× 228 0.3× 112 0.5× 45 2.5k
Elizabeth S. Henson Canada 18 1.5k 0.9× 513 0.4× 798 0.8× 465 0.7× 677 2.8× 34 2.3k

Countries citing papers authored by Chang S. Chan

Since Specialization
Citations

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

Fields of papers citing papers by Chang S. Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chang S. Chan. 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 Chang S. Chan. The network helps show where Chang S. Chan may publish in the future.

Co-authorship network of co-authors of Chang S. Chan

This figure shows the co-authorship network connecting the top 25 collaborators of Chang S. Chan. A scholar is included among the top collaborators of Chang S. Chan 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 Chang S. Chan. Chang S. Chan 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.
Shi, Fuqian, Maria Gomez-Jenkins, Zhixian Hu, et al.. (2025). Respiration defects limit serine synthesis required for lung cancer growth and survival. Nature Communications. 16(1). 7621–7621.
2.
Shi, Fuqian, Zhixian Hu, Christian S. Hinrichs, et al.. (2024). Immune Checkpoint Blockade Delays Cancer Development and Extends Survival in DNA Polymerase Mutator Syndromes. Cancer Research. 85(6). 1130–1144. 2 indexed citations
3.
Tong, Kevin, Anshuman Panda, Lanjing Zhang, et al.. (2021). SMAD4 is critical in suppression of BRAF-V600E serrated tumorigenesis. Oncogene. 40(41). 6034–6048. 17 indexed citations
4.
Poillet-Perez, Laura, Daniel W. Sharp, Yang Yang, et al.. (2020). Autophagy promotes growth of tumors with high mutational burden by inhibiting a T-cell immune response. Nature Cancer. 1(9). 923–934. 78 indexed citations
5.
Chan, Chang S., Yvonne Sun, Hua Ke, et al.. (2020). Genetic and stochastic influences upon tumor formation and tumor types in Li-Fraumeni mouse models. Life Science Alliance. 4(3). e202000952–e202000952. 3 indexed citations
6.
Laddha, Saurabh V., Edaise M. da Silva, Kenneth Robzyk, et al.. (2019). Integrative Genomic Characterization Identifies Molecular Subtypes of Lung Carcinoids. Cancer Research. 79(17). 4339–4347. 50 indexed citations
7.
Feng, Xing, Huimei Lu, Jingyin Yue, et al.. (2019). Loss of Setd4 delays radiation-induced thymic lymphoma in mice. DNA repair. 86. 102754–102754. 5 indexed citations
8.
Chan, Chang S., Saurabh V. Laddha, Peter W. Lewis, et al.. (2018). ATRX, DAXX or MEN1 mutant pancreatic neuroendocrine tumors are a distinct alpha-cell signature subgroup. Nature Communications. 9(1). 4158–4158. 148 indexed citations
9.
Chan, Chang S.. (2017). Prevalence and penetrance of Li-Fraumeni cancer predisposition syndrome. Current Opinion in Systems Biology. 1. 48–53. 7 indexed citations
10.
Guo, Jessie Yanxiang, Xin Teng, Saurabh V. Laddha, et al.. (2016). Autophagy provides metabolic substrates to maintain energy charge and nucleotide pools in Ras-driven lung cancer cells. Genes & Development. 30(15). 1704–1717. 294 indexed citations
11.
Joshi, Shilpy, Denis Tolkunov, Hana Aviv, et al.. (2015). The Genomic Landscape of Renal Oncocytoma Identifies a Metabolic Barrier to Tumorigenesis. Cell Reports. 13(9). 1895–1908. 104 indexed citations
12.
Levine, Arnold J., Chang S. Chan, Crissy Dudgeon, Anna M. Puzio‐Kuter, & Pierre Hainaut. (2015). The Evolution of Tumors in Mice and Humans with Germline p53 Mutations. Cold Spring Harbor Symposia on Quantitative Biology. 80. 139–145. 15 indexed citations
13.
White, Eileen, Janice M. Mehnert, & Chang S. Chan. (2015). Autophagy, Metabolism, and Cancer. Clinical Cancer Research. 21(22). 5037–5046. 544 indexed citations breakdown →
14.
Laddha, Saurabh V., Shridar Ganesan, Chang S. Chan, & Eileen White. (2014). Mutational Landscape of the Essential Autophagy Gene BECN1 in Human Cancers. Molecular Cancer Research. 12(4). 485–490. 157 indexed citations
15.
Karsli-Uzunbas, Gizem, Jessie Yanxiang Guo, Sandy M. Price, et al.. (2014). Autophagy Is Required for Glucose Homeostasis and Lung Tumor Maintenance. Cancer Discovery. 4(8). 914–927. 437 indexed citations breakdown →
16.
Zhang, Cen, Juan Liu, Xiaolong Wang, et al.. (2014). MicroRNA-339-5p inhibits colorectal tumorigenesis through regulation of the MDM2/p53 signaling. Oncotarget. 5(19). 9106–9117. 44 indexed citations
17.
Zhang, Cen, Juan Liu, Rui Wu, et al.. (2014). Tumor suppressor p53 negatively regulates glycolysis stimulated by hypoxia through its target RRAD. Oncotarget. 5(14). 5535–5546. 85 indexed citations
18.
Hu, Wenwei, Chang S. Chan, Rui Wu, et al.. (2010). Negative Regulation of Tumor Suppressor p53 by MicroRNA miR-504. Molecular Cell. 38(5). 689–699. 252 indexed citations
19.
Chan, Chang S. & Jun S. Song. (2008). CCCTC-Binding Factor Confines the Distal Action of Estrogen Receptor. Cancer Research. 68(21). 9041–9049. 29 indexed citations
20.
Chan, Chang S., Akikazu Hashimoto, & Herman Verlinde. (2001). Duality Cascade and Oblique Phases in Non-Commutative Open String Theory. 8 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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