ArticleBriefings in bioinformatics2016
A literature mining-based approach for identification of cellular pathways associated with chemoresistance in cancer.
Article in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Microarray-based detection and expression analysis of drug resistance in an animal model of peritoneal metastasis from colon cancer.Clinical & experimental metastasis · 2024Article
- Quantitative Analysis of Isopimpinellin fromMolecules (Basel, Switzerland) · 2024Article
- A Novel HOXA10-Associated 5-Gene-Based Prognostic Signature for Stratification of Short-term Survivors of Pancreatic Ductal Adenocarcinoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Recent Advances and Implication of Bioengineered Nanomaterials in Cancer Theranostics.Medicina (Kaunas, Lithuania) · 2021Review
- Characterizing Cancer Drug Response and Biological Correlates: A Geometric Network Approach.Scientific reports · 2018Article
- Biological Networks for Cancer Candidate Biomarkers Discovery.Cancer informatics · 2016Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Chemoresistance is a major obstacle to the successful treatment of many human cancer types. Increasing evidence has revealed that chemoresistance involves many genes and multiple complex biological mechanisms including cancer stem cells, drug efflux mechanism, autophagy and epithelial-mesenchymal transition. Many studies have been conducted to investigate the possible molecular mechanisms of chemoresistance. However, understanding of the biological mechanisms in chemoresistance still remains limited. We surveyed the literature on chemoresistance-related genes and pathways of multiple cancer types. We then used a curated pathway database to investigate significant chemoresistance-related biological pathways. In addition, to investigate the importance of chemoresistance-related markers in protein-protein interaction networks identified using the curated database, we used a gene-ranking algorithm designed based on a graph-based scoring function in our previous study. Our comprehensive survey and analysis provide a systems biology-based overview of the underlying mechanisms of chemoresistance.
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.