Evidence map›Paper›PMID 36068463›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

The Yin-Yang Dynamics in Cancer Pharmacogenomics and Personalized Medicine.

Qing Yan

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Qing YanPharmTao, Santa Clara, CA, USA. qyan@pharmtao.com.
American Pharmacists Association · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The enormous heterogeneity of cancer systems has made it very challenging to overcome drug resistance and adverse reactions to achieve personalized therapies. Recent developments in systems biology, especially the perception of cancer as the complex adaptive system (CAS), may help meet the challenges by deciphering the interactions at various levels from the molecular, cellular, tissue-organ, to the whole organism. The ubiquitous Yin-Yang interactions among the coevolving components, including the genes and proteins, decide their spatiotemporal features at various stages from cancer initiation to metastasis. The Yin-Yang imbalances across different systems levels, from genetic mutations to tumor cells adaptation, have been related to the intra- and inter-tumoral heterogeneity in the micro- and macro-environments. At the molecular and cellular levels, dysfunctional Yin-Yang dynamics in the cytokine networks, mitochondrial activities, redox systems, apoptosis, and metabolism can contribute to tumor cell growth and escape of immune surveillance. Up to the organism and system levels, the Yin-Yang imbalances in the cancer microenvironments can lead to different phenotypes from breast cancer to leukemia. These factors may be considered the systems-based biomarkers and treatment targets. The features of adaptation and nonlinearity in Yin-Yang dynamical interactions should be addressed by individualized drug combinations, dosages, intensities, timing, and frequencies at different cancer stages. The comprehensive "Yin-Yang dynamics" framework would enable powerful approaches for personalized and systems medicine strategies.

Indexed as

NeoplasmsYin-YangHumansMedicine, Chinese TraditionalPharmacogeneticsPrecision MedicineSystems BiologyTumor MicroenvironmentBiomarkersCancerComplex adaptive systems (CAS)EpigeneticsImmunePersonalized medicinePharmacogenomicsSystems biologyTumorYin-Yang

Identifiers

PMID36068463
OpenAlexW4294804687

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.