ArticleInternational journal of molecular sciences2023
Multi-Omics Analysis of NCI-60 Cell Line Data Reveals Novel Metabolic Processes Linked with Resistance to Alkylating Anti-Cancer Agents.
Article in International journal of molecular sciences, 2023. 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, 8 citations in OpenAlex.
- CNTN5 Promotes Invasion and Metastasis in Non-Small Cell Lung Cancer by Regulating YAP1 Nuclear Translocation.The journal of gene medicine · 2026Article
- Mendelian randomization analysis integrating GWAS and eQTL data identified potential regulatory genes associated with prostate cancer in neural cells.Discover oncology · 2025Article
- Article
- Molecular mechanisms explaining sex-specific functional connectivity changes in chronic insomnia disorder.BMC medicine · 2025Article
- Nutrient transporter-oriented nanoinhibitor counteracts intracellular metabolic reprogramming for RT-resistant HCC treatment.Materials today. Bio · 2025Article
- The genetic architecture of bone metastases: unveiling the role of epigenetic and genetic modifications in drug resistance.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- Unlocking the Molecular Secrets of Antifolate Drug Resistance: A Multi-Omics Investigation of the NCI-60 Cell Line Panel.Biomedicines · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
This study aimed to elucidate the molecular determinants influencing the response of cancer cells to alkylating agents, a major class of chemotherapeutic drugs used in cancer treatment. The study utilized data from the National Cancer Institute (NCI)-60 cell line screening program and employed a comprehensive multi-omics approach integrating transcriptomic, proteomic, metabolomic, and SNP data. Through integrated pathway analysis, the study identified key metabolic pathways, such as cysteine and methionine metabolism, starch and sucrose metabolism, pyrimidine metabolism, and purine metabolism, that differentiate drug-sensitive and drug-resistant cancer cells. The analysis also revealed potential druggable targets within these pathways. Furthermore, copy number variant (CNV) analysis, derived from SNP data, between sensitive and resistant cells identified notable differences in genes associated with metabolic changes (WWOX, CNTN5, DDAH1, PGR), protein trafficking (ARL17B, VAT1L), and miRNAs (MIR1302-2, MIR3163, MIR1244-3, MIR1302-9). The findings of this study provide a holistic view of the molecular landscape and dysregulated pathways underlying the response of cancer cells to alkylating agents. The insights gained from this research can contribute to the development of more effective therapeutic strategies and personalized treatment approaches, ultimately improving patient outcomes in cancer treatment.
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.