ArticlePloS one2018
Transcriptome-based signature predicts the effect of taxol in serous ovarian cancer.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 10 citations in OpenAlex.
- Translocon-associated Protein Subunit SSR3 Determines and Predicts Susceptibility to Paclitaxel in Breast Cancer and Glioblastoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Article
- Protein expression profiling identifies a prognostic model for ovarian cancer.BMC women's health · 2022Article
- Development and verification of an immune-related gene pairs prognostic signature in ovarian cancer.Journal of cellular and molecular medicine · 2021Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Taxol is a widely used chemotherapy drug used clinically for ovarian cancer, although the response to Taxol among individuals varies due to the heterogeneity among ovarian cancer patients. In this work, we analyzed differences in the prognostic effect of gene expression and Taxol usage in the Cancer Genome Atlas (TCGA) dataset and identified specific genes associated with the Taxol effect. Using the Cox regression model, a risk model (Taxol score) was developed to assess the outcome of ovarian cancer patients who underwent chemotherapy with Taxol. According to the results, survival was significantly associated with the Taxol score. Moreover, the patients in the high and low Taxol score group had different responses to Taxol. This result was further validated in another two independent datasets. The correlation between clinicopathological indicators was also analyzed, and we determined that the Taxol score is not associated with age, pathological stage, or Taxol treatment, while there was significant correlation with tumor size and grade. Gene Set Enrichment Analysis (GSEA) showed that various signaling pathways including ECM receptor, drug metabolism and ascorbate metabolism pathways were significantly enriched in the high Taxol score group. Collectively, these results indicate that the model is robust for predicting the effectiveness of Taxol by reflecting the various cell statuses of serous ovarian carcinoma.
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.