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Topic · Cancer Research

NF-κB Signaling Pathways

NF-κB Signaling Pathways is a research topic within Cancer Research. Science Explorer counts 23k research works in it since 1950. 24.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers explores the role of NF-?B signaling in inflammation, immunity, and cancer. It covers various aspects such as the regulation of NF-?B activity, the link between NF-?B and inflammatory diseases, the impact of NF-?B on cancer development and progression, and the crosstalk with other signaling pathways. The papers also discuss potential therapeutic strategies targeting the NF-?B pathway.

  • NF-?B
  • signaling
  • inflammation
  • cancer
  • immunity
  • ubiquitination
  • transcription factors
  • TNF-a
  • IKK
  • regulation
Research works
23k
fractional, since 1950
In the world top 10%
5.6k
per year above
Top-10% rate
24.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-16%
the tick is no change

Which countries lead NF-κB Signaling Pathways research?

By volume, China and the United States publish the most (943 and 287 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 943 works
  2. 2 United States 287 works
  3. 3 South Korea 94 works
  4. 4 Japan 91 works
  5. 5 India 72 works
  6. 6 Germany 71 works
  7. 7 United Kingdom 43 works
  8. 8 Iran 31 works
  9. 9 Taiwan 29 works
  10. 10 Türkiye 29 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 55.8%United States: 17.0%South Korea: 5.6%Japan: 5.4%6 others listed: 16.2%56%largest
China943 · 55.8%United States287 · 17.0%South Korea94 · 5.6%Japan91 · 5.4%6 others listed274 · 16.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead NF-κB Signaling Pathways research?

By volume in 2022–2025, Chinese Academy of Medical Sciences & Peking Union Medical College publishes the most NF-κB Signaling Pathways research, followed by University of North Carolina at Chapel Hill and Nanjing Medical University.

By volume, 2022–2025

  1. 1 Chinese Academy of Medical Sciences & Peking Union Medical College China 13 works
  2. 2 University of North Carolina at Chapel Hill United States 11 works
  3. 3 Nanjing Medical University China 11 works
  4. 4 Wenzhou Medical University China 10 works
  5. 5 Sun Yat-sen University China 9 works
  6. 6 Shanghai Jiao Tong University China 9 works
  7. 7 Central South University China 9 works
  8. 8 Southern Medical University China 9 works
  9. 9 Sichuan University China 8 works
  10. 10 Nanjing University of Chinese Medicine China 8 works

Who are the leading researchers in NF-κB Signaling Pathways?

The most-cited researchers publishing on NF-κB Signaling Pathways include Alberto Ortíz, Bharat B. Aggarwal and Michael Karin.

  1. 1 Alberto Ortíz 5.9k citations
  2. 2 Bharat B. Aggarwal 4.2k citations
  3. 3 Michael Karin 4k citations
  4. 4 Shizuo Akira 3.6k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is NF-κB Signaling Pathways research done?

The largest centres of NF-κB Signaling Pathways research in 2022–2025 are Beijing (China), Shanghai (China), Guangzhou (China) and Nanjing (China).

Largest cities, 2022–2025

  1. 1 Beijing China 76 works
  2. 2 Shanghai China 75 works
  3. 3 Guangzhou China 64 works
  4. 4 Nanjing China 46 works
  5. 5 Hangzhou China 41 works
  6. 6 Wuhan China 36 works
  7. 7 Seoul South Korea 35 works
  8. 8 Jinan China 29 works
  9. 9 Changsha China 29 works
  10. 10 Chengdu China 26 works
See NF-κB Signaling Pathways on the map

Where is the best place to study NF-κB Signaling Pathways?

Among universities, judged by research, Nanjing University of Chinese Medicine, Nanjing Medical University and Chinese Academy of Medical Sciences & Peking Union Medical College score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 23.59%fractional works in this node (log) →share in the world top 10% →Nanjing University of Chinese Medicine: 8, 37.0%Nanjing Medical University: 11, 26.3%Chinese Academy of Medical Sciences & Peking Union Medical College: 13, 28.5%Wenzhou Medical University: 10, 22.3%Southern Medical University: 9, 15.9%Sun Yat-sen University: 10, 30.4%Central South University: 9, 32.3%Shanghai Jiao Tong University: 9, 27.3%University of North Carolina at Chapel Hill: 12, 1.8%Sichuan University: 8, 14.1%Nanjing University o…Chinese Academy of M…Nanjing Medical Univ…Wenzhou Medical Univ…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1Nanjing University of Chinese Medicine China 70.037.0%16.5×8 +334.1%
2Nanjing Medical University China 66.726.3%11.3×11 +39.8%
3Chinese Academy of Medical Sciences & Peking Union Medical College China 65.128.5%6.9×13 -11.7%
4Wenzhou Medical University China 63.922.3%14.6×10 +131.0%
5Southern Medical University China 52.015.9%13.1×9 +46.7%
6Sun Yat-sen University China 44.030.4%3.0×10 -5.0%
7Central South University China 41.332.3%2.8×9 -14.2%
8Shanghai Jiao Tong University China 41.227.3%2.2×9 +38.8%
9University of North Carolina at Chapel Hill United States 22.11.8%3.5×12 +1.5%
10Sichuan University China 21.514.1%2.5×8 +19.5%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is NF-κB Signaling Pathways research growing?

Output in 2018–2022 was 16% lower than in 2013–2017, peaking in 2010.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.