Despite its complex molecular heterogeneity, cancer results from the integration of at least ten common pathways that drive tumor progression and therapy response 
. It is now becoming clear that pathway integration rather than single gene manipulation could represent the basis to understand malignant transformation and therapy response.
Based on this hypothesis, in the current study we exploited different transcriptomic (miRNA and GE array) and proteomic (protein array) platforms to generate, in a well defined in vitro model of tumor cells, an integration map of the key pathways possibly involved in the mechanism of resistance to trabectedin.
In this perspective, our study is one of the first attempts towards the combination of the three layers of gene regulation (transcriptional, post-transcriptional and translational) for the identification of those pathways and networks playing a critical role in trabectedin action.
In particular, in this work, we looked for changes in molecular behavior due to an intensive and prolonged treatment with trabectedin that was performed to induce drug resistance. Then, using a systems biology approach we systematically screened the possible sources of variability in mRNAs, miRNAs and protein expressions. Our integration strategy started by extracting individual results from the three different layers and then combining them to increase their power.
Gene expression data
Regarding gene expression data, a significant portion of the underexpressed (6%) and overexpressed (2%) genes are zinc finger (ZF) proteins. ZF proteins are multiple and play important role in the metabolism of normal cells. There are many reports on the inhibition of ZF transcription factors in early as well as advanced stages of oncogenesis, including the impairment of signal transduction 
. These results are also in agreement with the recent findings of Duan et al. 
who performed gene expression analysis on chondrosarcoma cell lines resistant to trabectedin. Remarkably, the authors found some ZF proteins to be differentially expressed and suggest that ZF proteins are involved in the mechanism of trabectedin resistance. However, differently from Duan et al. 
we did not find overexpression of ZNF93 and ZNF43 in the resistant 402-91/ET cell line.
Interestingly, we found BIRC2 (which encodes for c-IAP1 protein) overexpressed in 402-91/ET cells. c-IAP1 inhibits apoptosis by binding to TNF receptor-associated factors TRAF1 (that we found overexpressed in resistant cells) and TRAF2. It has recently been reported that BIRC2 is associated with resistance of esophageal squamous cell carcinomas to drug-induced apoptosis 
and that c-IAP1 could be a novel predictive marker for resistance to radiotherapy in cervical squamous cell carcinomas 
The 402-91/ET cell line seems to be characterized by a reduced apoptosis and an increased cell proliferation. It is noteworthy that pathways involved in cell adhesion are enriched in downregulated genes, suggesting a loss of 402-91/ET intracellular communications and altered cellular processes such as proliferation, migration and differentiation.
miRNA expression data
miRNAs are involved in processes such as development, carcinogenesis, cell survival, and apoptosis, as well as cellular sensitivity to anticancer drugs 
. However, opposite effects can be seen towards the same compound in different tumor types, suggesting a complex relationship between miRNAs and drug resistance 
Among differentially expressed miRNAs in 402-91/ET we found miR-21, let-7e, miR-192, miR-130a and miR-98, whose ability to affect the potencies of a number of anticancer agents have been recently reported. Several studies recently reported a relation between the overexpression of miR-21 and the resistance to a variety of anticancer drugs. In particular, Shi and colleagues 
demonstrated a role of miR-21 in temozolomide resistance in glioblastoma cells. Moreover, the overexpression of miR-21 has also been found to induce resistance against cytosine arabinoside (Ara-C) and arsenic trioxide. Targeting of PDCD4 (underexpressed in 402-91/ET cells) by miR-21 prevented Ara-C-induced apoptosis in the leukemic HL60 cell line 
, whereas in several myelogenous cell lines reduced expression of PDCD4 by miR-21 targeting prevented arsenic trioxide-induced apoptosis 
Let-7e, and miR-130a were found underexpressed in a panel of paclitaxel- and cisplatin-resistant cells lines 
. Furthermore, Herbert et al. 
predicted HGMA2 (overexpressed in microarray and validated with qRT-PCR) as a target for miR-98, and showed their involvement in promotion of resistance to doxorubicin and cisplatin, while Boni and colleagues suggested a role of miR-192 in 5-fluorouracil resistance in colorectal cancer 
In general, pathway analysis on microRNA target genes highlights a subset of the pathways previously found in gene expression analysis. This means that in some specific pathways both levels of transcription are co-involved in the development of resistance to trabectedin. Cell cycle, angiogenesis, regulation of transcription and apoptosis-survival are the mostly over-represented pathways.
CHOP transcription factor binding site
We investigated the presence of differentially expressed miRNAs, potentially transcriptionally regulated by FUS-CHOP chimera with an enriched CHOP motif in their promoter regions. We found three differentially expressed miRNAs (miR-21, miR-130a, miR-7) having either computationally or experimentally validated CHOP binding sites. Remarkably two of them, miR-21 and miR-130a, were previously discussed because of their documented involvement in drug resistance.
Among our differentially expressed genes we found 149 putative targets of miR-7 (with an anti-correlated expression profile), which are mainly involved in cell cycle, chromatin and cytoskeleton organization. Among them we found PIK3CD, RB1, CAMK2D, CUL5. There are emerging evidences about the involvement of PI3K signaling cascade in myxoid liposarcoma. The overexpression of receptor tyrosine kinases MET, IGF1R and IGF2 promote cell survival through both the PI3K/Akt and the Ras-Raf-ERK/MAPK pathways 
Mir-21 had 99 putative anti-correlated targets mostly involved in the regulation of transcription (CBX4, KLF13, PDCD4 and several ZF proteins).
Among the 118 anti-correlated putative targets of miR-130a, we identified genes involved in vasculature development (ROBO4, SIPR1, NRP1, ELK3, EDN1, THBS1) and cell motion, in particular FOSL1, PLCL2 and LYN. FOSL1 dimerizes with proteins of the JUN family (JUNB is overexpressed in resistant cell line) forming the transcription factor complex AP-1; FOS proteins have been implicated as regulators of cell proliferation, differentiation, and transformation. Furthermore, LYN has been recently shown to act as a mediator of tumor invasion.
Antibody array data
Gene and miRNA expression profiles are able to separate 402-91/ET from 402-91 cell lines (), while protein expression dendrogram () has a more heterogeneous behaviour. 402-91 cell line is divided into two groups; one 402-91/ET sample clusters with 402-91 ones. It is much more difficult to quantify protein expression than gene expression in a multiplex manner than for gene expression, due to the larger variability in the physico-chemical properties of proteins 
. Therefore, this discrepancy could be due to the higher variability of protein arrays than that of mRNA and miRNA assessment.
GO enrichment analysis on differentially expressed proteins seems to be much more focused than the same analysis on genes. In particular, apoptosis-survival and cell cycle are categories capable of collecting the greatest number of differences between sensitive and resistant cells. Among the over-represented pathways we found p53, apoptosis and MAPK.
In particular we found CCND1, CCND2, BBC3, CDKN2A, PERP involved in p53 signaling, RB1, E2F2, E2F3, E2F4, SMAD4, CDC14, CDC25A, CDC6, CDC7 involved in cell cycle. Some of these proteins are involved in the regulation of transcription in G1/S phase of the mitotic cell cycle. G1/S transition is a rate-limiting step in cell cycle progression 
The cyclin D1 proto-oncogene is a key regulator of G1 to S phase progression in several cell types. Together with CDK4 and CDK6, cyclin D1 forms active complexes that promote cell cycle progression by phosphorylating and inactivating the retinoblastoma protein (RB1). RB1 in turn leads to the release of the E2F family of transcription factors driving the expression of several genes associated with S phase progression 
. A number of therapeutic agents have been observed to induce cyclin D1 degradation in vitro
, indicating that such an induction may offer a useful avenue for therapeutic intervention.
Integrative analysis and network reconstruction
After the analysis and the discussion of each regulatory aspect (mRNAs, miRNAs and proteins) separately, we combined all the biological evidences within a systems biology approach in order to have a wider perspective of the regulatory phenomena acting during trabectedin resistance.
miRNAs act as post-transcriptional regulators of gene expression. The reduction of protein levels is the final result of the regulation that can be achieved through two strategies: (i) mRNA degradation or (ii) translational repression. The effects of the two modes of action can be evaluated at different points of the transcription-translation pathway. Focusing on the two main highly correlated pathways that seem to be involved in trabectedin resistance (negative regulation of apoptosis and cell cycle), we reconstructed two different networks: i) the network composed of differentially expressed mRNAs targeted by differentially expressed miRNAs that allow us to study the regulation at the transcriptional level, hereafter the “post-transcriptional regulatory network” and ii) the network composed by differentially expressed miRNAs leading to differentially expressed proteins, hereafter “pre-translational regulatory network”.
It is clear the central role that CCND1, RB1, E2F4, TNF, CDKN1C, ABL1, TNFRSF21 play in both post-transcriptional and pre-translational regulatory networks. In particular, they act as a bridge between the two parts of the network with opposite regulations: upregulated genes/protein and downregulated miRNA and downregulated genes/protein and upregulated miRNA.
In some cases the involvement of miRNAs can explain the discordant relation between the transcriptome and the proteome. In a seminal review Inui et al. 
describe the role of miRNAs in pathways as decision maker elements to discriminate real versus too weak or too transient signals. In this study we identified 3 loops involving differentially expressed miRNAs with roles in drug resistance; these loops have as potential targets MDC1 (overexpressed in 402-91/ET), BACE1 and BAP1 (underexpressed in 402-91/ET).
The present study is one of the first works that try to screen the behavior of genes, microRNAs (miRNAs) and proteins in order to reconstruct in silico the regulatory networks leading to resistance to anticancer agents such as trabectedin. Diverse sources of omic data have been used to describe altered regulatory mechanisms within a transcriptional, post-transcriptional and translational perspective. The combination of the three layers of regulation gave us the possibility to reconstruct putative gene-miRNA-protein circuits altered in trabectedin resistance. We found that transcriptome and proteome data agreed in recognizing anti-apoptosis and cell cycle proliferation as the two main biological processes characterizing the difference between 402-91 and 402-91/ET cell lines. All the hallmarks of cancer seem to be altered in the resistant cell line, raising the hypothesis that the mechanism of resistance is acting with more strength on the same molecular markers triggered by cancer development.
On the other hand, post-transcriptional analysis revealed two miRNAs (miR-21 and miR-130a) already identified as playing a role in drug resistance and putatively regulated by the FUS-CHOP chimera, which could be good markers for future functional studies. Most of miR-21 and miR-130a targets are known oncogenes or oncosuppressors highlighting their involvement in malignancies.
Once validated on in vivo models this approach might allow the identification of druggable targets for trabectedin-resistant malignancies.