Back

Time- and Dose-dependent Effects of CIGB-300 in the lung squamous cell carcinoma proteomics.

Garcia, L. E.; Dai, L.; Rodriguez-Ulloa, A.; Yi, Y.; Gonzalez, L. J.; Besada, V.; Li, W.; Perea, S. E.; Perera, Y.

2024-12-06 cancer biology
10.1101/2024.12.03.626528 bioRxiv
Show abstract

Proteome-wide scale in a dose- and time-depending setting is crucial in drug-regulated proteomics to fully understand the pharmacological mechanism of anticancer drugs as well as the identification of proteins suitable for drug response biomarkers. Here, we investigated the effect of the CIGB-300 anticancer peptide at IC50 and IC80 dose levels during 1 and 4 hours of treatment on squamous lung cancer cell (NCI-H226) proteome. A dose-dependent inhibitory effect of the global proteomics while slight change of the upregulated proteins was observed, by increasing CIGB-300 dose level. The biological processes that were more represented among the modulated proteins were those related to small molecule biosynthetic process, pyridine-containing compound metabolic process and nucleobase-containing small molecule metabolism. Importantly, NSCLC previously described biomarkers and Protein Kinase CK2 substrates were significantly modulated by treating both doses of CIGB-300. These proteins, which are associated with both metabolism and cell survival, reinforce the hypothesis of the therapeutic potential of CIGB-300 as anticancer treatment for squamous non-small cell lung cancer and merits be validated as CIGB-300 response biomarkers in NSCLC patients. Overall, our proteomics-guided strategy based on time and drug dose served not only to deep into the mechanism of action of CIGB-300 but also to identify candidate proteins for pharmacodynamic and drug response biomarkers.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.