ArticleOncogene2021
Application of machine learning to large in-vitro databases to identify cancer cell characteristics: telomerase reverse transcriptase (TERT) expression.
Article in Oncogene, 2021. 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, 6 citations in OpenAlex.
- Research progress and application of the CRISPR/Cas9 gene-editing technology based on hepatocellular carcinoma.Asian journal of pharmaceutical sciences · 2023Review
- Genomic, epigenomic, and transcriptomic signatures for telomerase complex components: a pan-cancer analysis.Molecular oncology · 2023Article
- Associations between TERT Promoter Mutations and Survival in Superficial Spreading and Nodular Melanomas in a Large Prospective Patient Cohort.The Journal of investigative dermatology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in biotechnology and machine learning have created an enhanced environment for unearthing and exploiting previously unrecognized relationships between genomic and epigenetic data with potential therapeutic implications. We applied advanced algorithms to data from the Cancer Dependency Map to uncover increasingly complex relationships. Specifically, we investigate characteristics of tumor cell lines with varying levels of telomerase reverse transcriptase (TERT) expression in liver cancer. The findings indicate that the effect of CRISPR knockout of Histone Deacetylase 1 (HDAC1) and numerous individual respiratory complex I genes is strongly related to the level of TERT expression, with knockout being particularly efficacious at killing or inhibiting growth of tumor cells with low levels of TERT expression for HDAC1 and high levels for Complex I genes. These findings suggest key biomarkers for therapeutic efficacy and yield novel potential pathways for drug development and provide further proof of principle for the potential of artificial intelligence in oncology.
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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.