Transcriptome profiling reveals Silibinin dose-dependent response network in non-small lung cancer cells

Jagan Mohan Kaipa; Vytaute Starkuviene; Holger Erfle; Roland Eils; Evgeny Gladilin

doi:10.7717/peerj.10373

Transcriptome profiling reveals Silibinin dose-dependent response network in non-small lung cancer cells

PeerJ. 2020 Dec 16:8:e10373. doi: 10.7717/peerj.10373. eCollection 2020.

Authors

Jagan Mohan Kaipa^{1

2

3}, Vytaute Starkuviene^{2

4}, Holger Erfle², Roland Eils^{5

6}, Evgeny Gladilin^{2

7

8}

Affiliations

¹ Helmholtz Center for Infection Research, Braunschweig, Germany.
² BioQuant, University Heidelberg, Heidelberg, Germany.
³ Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany.
⁴ Institute of Biosciences, Vilnius University Life Science Center, Vilnius, Lithuania.
⁵ Center for Digital Health, Berlin Institute of Health and Charité Universitätsmedizin Berlin, Berlin, Germany.
⁶ Health Data Science Unit, Heidelberg University Hospital, Heidelberg, Germany.
⁷ Leibniz Institute of Plant Genetics and Crop Plant Research, Seeland, Germany.
⁸ Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany.

Abstract

Silibinin (SIL), a natural flavonolignan from the milk thistle (Silybum marianum), is known to exhibit remarkable hepatoprotective, antineoplastic and EMT inhibiting effects in different cancer cells by targeting multiple molecular targets and pathways. However, the predominant majority of previous studies investigated effects of this phytocompound in a one particular cell line. Here, we carry out a systematic analysis of dose-dependent viability response to SIL in five non-small cell lung cancer (NSCLC) lines that gradually differ with respect to their intrinsic EMT stage. By correlating gene expression profiles of NSCLC cell lines with the pattern of their SIL IC50 response, a group of cell cycle, survival and stress responsive genes, including some prominent targets of STAT3 (BIRC5, FOXM1, BRCA1), was identified. The relevancy of these computationally selected genes to SIL viability response of NSCLC cells was confirmed by the transient knockdown test. In contrast to other EMT-inhibiting compounds, no correlation between the SIL IC50 and the intrinsic EMT stage of NSCLC cells was observed. Our experimental results show that SIL viability response of differently constituted NSCLC cells is linked to a subnetwork of tightly interconnected genes whose transcriptomic pattern can be used as a benchmark for assessment of individual SIL sensitivity instead of the conventional EMT signature. Insights gained in this study pave the way for optimization of customized adjuvant therapy of malignancies using Silibinin.

Keywords: Adjuvant cancer therapy; Custom drug response; Drug susceptibility network; Silibinin; Transcriptome profiling.

Grants and funding

Jagan Mohan Kaipa was supported by the DKFZ-MOST Grant c-160. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.