ArticleJournal of stem cell research & therapy2012
Systems and Network Pharmacology Approaches to Cancer Stem Cells Research and Therapy.
Article in Journal of stem cell research & therapy, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Flaxseed Lignans as Important Dietary Polyphenols for Cancer Prevention and Treatment: Chemistry, Pharmacokinetics, and Molecular Targets.Pharmaceuticals (Basel, Switzerland) · 2019Review
- How Can Synergism of Traditional Medicines Benefit from Network Pharmacology?Molecules (Basel, Switzerland) · 2017Review
- Primary cultures of human colon cancer as a model to study cancer stem cells.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016Article
- Novel Holistic Approaches for Overcoming Therapy Resistance in Pancreatic and Colon Cancers.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2016Review
- Designing a broad-spectrum integrative approach for cancer prevention and treatment.Seminars in cancer biology · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The cancer stem cell (CSC) hypothesis is increasingly being accepted as a model to explain for the functional heterogeneity that is commonly observed in solid tumors. According to this hypothesis, there exists a hierarchical organization of cells within the tumor, in which a differential subpopulation of stem-like cells is responsible for sustaining and recurrence of tumor growth. CSCs have been shown to exist in a variety of solid tumors especially those with known resistant phenotypes such as breast, prostate and pancreatic adenocarcinoma (PDAC). In all these models, the commonality of deregulation of three crucial pathways; Wnt, notch and hedgehog that maintain CSC self-renewal capacity is emerging. Collectively these major pathways and have been linked to the observed resistance of CSC to chemotherapy and radiotherapy. The existing lack of knowledge and our incomplete understanding of the molecular signatures associated with CSCs highlight the need for better approaches in both isolation and identification of unique pathways associated with these cells. In this direction, computational biology, especially systems and network approaches, have proven to be of great utility in unraveling pathway complexities such as those associated with CSCs. With highlights on the most up-to-date molecular, network, cellular, clinical, and therapeutic cancer research findings, this article tends to provide a wealth of insights on systems and network biology approaches to CSC marker identification, the mechanism through which they evade treatment as well as therapeutic approaches that will help in conquering these elusive cells in incurable and refractory malignancies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.