Skip to main content
Download PDF

Computational investigation of the functional landscape of the protective role that extra virgin olive oil consumption may have on chronic lymphocytic leukemia

Journal of Translational Medicine volume 22, Article number: 869 (2024) Cite this article

Abstract

Background

The health benefits of the Mediterranean diet are partially attributed to the polyphenols present in extra virgin olive oil (EVOO), which have been shown to have anti-cancer properties. However, the possible effect that EVOO could have on Chronic Lymphocytic Leukemia (CLL) has not been fully explored.

Methods

 This study investigates the anti-CLL activity of EVOO through a computational multi-level data analysis procedure, focusing on the identification of shared biological functions between them. Specifically, publicly available data from genomics, transcriptomics and proteomics related to EVOO consumption and CLL were collected from several resources and analyzed through a computational pipeline, highlighting common molecular mechanisms and biological processes. Computational verification of a number of the highlighted functional terms associating CLL and EVOO has been performed as well.

Results

Our investigation revealed four molecular pathways and three biological processes that overlap between mechanisms associated with CLL and those impacted by the consumption of EVOO. To further investigate the common biological functions, we focused on AKT1-related terms, aiming to investigate the potential importance of AKT1 in the anti- CLL effects associated with EVOO.

Conclusions

Overall, the results provide valuable insights into the potential beneficial effect of EVOO in CLL and highlight EVOO’s bioactive compounds as promising candidates for future investigations.

Graphical Abstract

Background

Leukemia is a type of blood cancer in which abnormal white blood cells are produced in the bone marrow. In 2020, it was estimated to be the 11th most cancer-related mortality worldwide, accounting for 31154 deaths [1]. Leukemia is categorized based on the type of blood cells affected and the disease rate in: Acute Lymphoblastic Leukemia (ALL), Chronic Lymphocytic Leukemia (CLL), Acute Myeloid Leukemia (AML) and Chronic Myeloid Leukemia (CML). This work focuses on CLL, a type of slow-growing leukemia that affects the development of B-lymphocytes (B-cells) [2].

The main treatments for leukemia often involve chemotherapy and radiation, both of which can be associated with many side effects, such as hair loss, diarrhea, liver damage, neurological disorders, etc. [3]. Additionally, conventional anticancer drugs are known to cause hepatotoxicity and myelosuppression [4]. Therefore, the need for the development of new therapeutic agents and strategies is even greater.

Geographical differences in cancer incidence rates indicate that environmental factors, particularly nutrition, are pivotal pillars in the development of cancer [5]. Among the different dietary compounds that have been related to cancer in a protective manner, olive oil and specifically Extra Virgin Olive Oil (EVOO) have been shown to be among the most significant ones[6]. Accumulated evidence has pointed out a reduction in various types of cancer in Mediterranean populations largely due to high consumption of olive oil [7]. EVOO is a functional food with a major contribution to health-promoting effects. It contains a group of complex phenol-conjugated compounds that are characterized by a wide range of biological mechanisms including antioxidant activity [8]. Studies have shown that olive oil phenolic components exhibit an anticancer effect [9,10,11,12]. In a recent pilot study on 22 patients, researchers found that consuming olive oil with high concentrations of oleocanthal and oleacein significantly improved biomarkers for early stage CLL [13].

Recent studies suggest that some EVOO components act on pathways responsible for cell cycle regulation and metabolism, exerting a defensive mechanism against the development of malignancy [14, 15]. These effects are mediated through complex mechanisms that involve changes in multiple molecular levels, including genome, transcriptome, and proteome, which modulate various biological processes [16]. The molecular targets of EVOO bioactive compounds are considered to be significantly involved in the interplay among these complex mechanisms [16]. However, further research is needed to fully understand the functional association between the beneficial effects of EVOO in the prevention of the progression of cancer.

In this context, the main aim of this study is the investigation of the anti-CLL activity of EVOO through a computational analysis on multi-level molecular data to highlight functional associations. Specifically, we collected from several resources publicly available genomics, transcriptomics and proteomics data related to EVOO consumption and CLL and we designed a computational pipeline to identify common biological functions between them. The validity of our findings was explored against a priori knowledge from the literature and against the pathways targeted from known drugs for CLL treatment and from drugs structurally similar to EVOO’s bioactive compounds.

Overall, our analysis identified 4 molecular pathways and 3 biological processes common to CLL-related mechanisms and those affected by EVOO consumption. We further concentrated on terms associated with AKT1 to delve deeper into the possible counteracting role of EVOO consumption on CLL. We expect that this work will provide the research community with a deeper insight into the protective role that EVOO consumption may have against CLL, triggering further research and clinical translation in this specific domain.

Materials and methods

Methodology overview

The workflow of the proposed methodology is described in the following diagram (Fig. 1):

Fig. 1
figure 1

Overview of the workflow. Multi-level data selection-Data sources: multi-level data for CLL and EVOO were collected from various sources to identify differentially expressed genes, repurposed drugs, differentially expressed microRNAs, related variants and proteins. Enrichment analysis: multi-level data were used for the identification of the corresponding KEGG pathways and GO biological processes. Intersection Analysis: common biological functions were highlighted. Computational Verification: the validity of the common functions was explored using i) literature information ii) information from known drugs for CLL treatment, and drugs structurally similar to EVOO’s bioactive compounds

Full size image

  • Multi-level data selection and preprocessing: multi-level data were collected from various available resources in order to identify differentially expressed genes (DEGs) from CLL and EVOO consumption conditions, common repurposed drugs, differentially expressed microRNAs, related variants to CLL and regulatory interactions between their corresponding genes and EVOO consumption-related DEGs, as well as related proteins to CLL and to EVOO consumption including the physical interactions between them.

  • Enrichment Analysis: KEGG pathways and GO biological processes were identified based on the analysis of the multi-level data.

  • Intersection Analysis: common KEGG pathways and GO biological processes from the aforementioned enrichment analysis results were highlighted.

  • Computational Verification: computational validity of our functional findings was performed in a two steps meta-analysis: 1) using literature information 2) using pathways targeted from known drugs for CLL treatment, as well as from drugs structurally similar to EVOO’s bioactive compounds.

Data collection and preprocessing

Gene expression data selection

We searched in the GEO Signatures of Differentially Expressed Genes for Diseases of the Harmonizome database (accessed on October 2022) [17] using the query “chronic lymphocytic leukaemia” and we found one transcriptomic dataset with title “B-Cell Chronic Lymphocytic Leukaemia-Small Lymphocytic Lymphoma Peripheral Blood Mononuclear Cell” from a Gene Expression Omnibus study (GEO) [18] with GSE ID: GSE8835 (https://maayanlab.cloud/Harmonizome/gene_set/B-cell+chronic+lymphocytic+leukaemia-small+lymphocytic+lymphoma_Peripheral+blood+mononuclear+cell_GSE8835/GEO+Signatures+of+Differentially+Expressed+Genes+for+Diseases) [19]. Harmonizome is a comprehensive resource of knowledge about genes and proteins, enabling researchers to discover novel relationships between biological entities. The dataset design involved obtaining CD4 T cells and CD8 T cells from the peripheral blood mononuclear cells of previously untreated patients with CLL and healthy individuals. Gene expression profiling was conducted using total RNA, and the data was analyzed to compare the gene expression profiles of T cells from patients with CLL to those from healthy individuals. From the 600 differentially expressed genes (DEGs) provided by Harmonizome, we selected the top 300 DEGs based on their standardized values (StVs). As described in Harmonizome, StVs indicate the relative strength of the functional associations and are related to empirical p-values as |StV|= -log10(p-value). Specifically, we identified the top 150 over-expressed and the top 150 under-expressed genes based on their StVs.

We also searched in Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) (accessed on October 2022) using the query “extra virgin olive oil” and we found one microarray study with gene expression profiles of PBMCs in healthy subjects following extra virgin olive oil intake (GSE id: GSE75025) [20]. Gene expression profiles of PBMCs in healthy subjects following extra virgin olive oil intake. The aim of the study was to investigate the whole-genome gene and miRNA profiles (miRNA profiles were described in the below section – MicroRNA data selection) of after EVOO intake. Specifically, RNA obtained from PBMCs of the same subjects before and after extra virgin olive oil intake (two time points T0 vs. T1). The dataset was normalized and log2 transformed. Overall analysis was done in R statistical environment (http://www.R-project.org/). The dataset was processed using the Limma R package [21], a linear model that calculates a moderated t-statistic from gene expression experiments. After the dataset pre-processing, differential expression analysis was performed and we kept the DEGs based on |log2FC|≥ 0.5849 (FC ≥ 1.5 & FC ≤ 0.67) with adj. p-values < 0.05. Finally, we concluded to 83 over and 98 under expressed genes.

MicroRNA data selection

As described above, microRNA expression profiles of PBMCs in healthy subjects following extra virgin olive oil consumption were also downloaded from GEO (GSE ID- GSE75026)[20]. Following the same procedure as in gene expression, the dataset was normalized and log2 transformed and the overall analysis was also performed in R using the Limma R package. As we do not have statistically significant microRNAs with adj. p-values < 0.05, we employed the selection criteria of p-value < 0.05 (recognizing this as a limitation in our study) and |log2FC|> = 1 for identifying differentially expressed microRNAs. We ended up with 3 over-expressed and 3 under expressed microRNAs.

In order to find CLL-related microRNAs, we searched the Human microRNA Disease Database—HMDD v3.2 (https://www.cuilab.cn/hmdd) (accessed on February 2023) [22]. HMDD is a database that curated experiment-supported evidence for human microRNA (miRNA) and disease associations. We retrieved the miRNAs that are associated with the disease term “Leukemia, Lymphocytic, Chronic, B-Cell” and finally, we kept86microRNAs for further analysis.

Extra virgin olive oil bioactive compounds

We searched in the literature to find the EVOO’s bioactive compounds [23,24,25]. We found sixteen compounds and we categorized them as presented in Table 1. We downloaded all the available EVOO-related compound structures in the form of the Simplified Molecular Input Line Entry Systems (SMILES) from PubChem (https://pubchem.ncbi.nlm.nih.gov/)) [26] (keeping the compounds annotated as “best match”) and from the Drug Repurposing Hub database (https://clue.io/repurposing#download-data) [27].

Table 1 Bioactive compounds of EVOO
Full size table

Protein–protein interaction network construction

The STRING Version 11.5 database (https://string-db.org/cgi/input?sessionId=bXiXy194kmi7&input_page_show_search=on) [28] was utilized to construct a protein–protein interaction (PPI) network. STRING is a comprehensive database that includes known and predicted interactions among proteins. These interactions encompass both direct (physical) and indirect (functional) associations, derived from computational predictions, knowledge transfer between organisms, and data aggregated from other primary databases. In constructing the PPI network, the edges indicate that the proteins are part of a physical complex. For this study, the minimum interaction score threshold was set at 0.7 to ensure high-confidence interactions.

Transcriptomic based drug repurposing

DEGs from both conditions were used as input in a transcriptomic-based drug repurposing tool (DR) tool called L1000CDS2 (https://maayanlab.cloud/L1000CDS2/#/index)) [29]. Developed by the Ma'ayan Laboratory at the Icahn School of Medicine, this tool leverages LINCS L1000 data from the Broad Institute's Connectivity Map team. It helps users find matching small molecule signatures by comparing input data using either a gene-set method or a cosine distance method. For up/down gene lists, it returns the top 50 matches by comparing them to differentially expressed genes. For the CLL case, we selected the “reverse” option to find drugs that reverse the disease signature. For the EVOO case, we chose the "mimic" option to find drugs that mimic the gene signature. We retained all 50 entries returned by L1000CDS2 for each signature.

Structural similarity

We also downloaded 6877 structures, in the form of SMILES, from a curated and annotated collection of FDA-approved drugs, clinical trial drugs, and pre-clinical compounds with a companion information resource, namely The Drug Repurposing Hub database. In combination with the EVOO’s bioactive compound structures, we converted the SMILES into two Structure data file (SDF) library files using the software Open Babel [30]. Open Babel is a versatile chemical toolbox designed to handle various types of chemical data. As an open, collaborative project, it allows users to search, convert, analyze, and store data from fields such as molecular modeling, chemistry, solid-state materials, and biochemistry. Using the Rcpi R package [31] we calculated the Tanimoto Similarity between the EVOO compounds and the 6877 chemical compounds from The Drug Repurposing Hub. Rcpi calculates the compounds’ similarity derived from their molecular fingerprints. A molecular fingerprint is a series of bits that represent the presence or absence of chemical substructures in a molecule.

Results

Transcriptomic-based associations between CLL and EVOO

Gene expression

The first part of this study included the gene expression-based associations between CLL and EVOO consumption. We firstly investigated this association by finding the commonalities between the over expressed and under expressed genes in CLL and EVOO respectively as well as the under expressed and over expressed genes in CLL and EVOO respectively. Nevertheless, we did not find any overlap between the aforementioned sets of DEGS.

Enrichment analysis of EVOO and CLL related DEGs was carried out using KEGG version 2021 and Gene Ontology (GO) version 2021 to investigate possible associations in terms of molecular mechanisms and biological processes. Specifically, the over and under expressed genes in each condition were used separately as input in the web tool of Enrichr (https://maayanlab.cloud/Enrichr/)) [32]. Enrichr is a robust resource that compiles curated gene sets, serving as a search engine to gather biological knowledge and aid in advancing biological research. As we didn’t find any statistically significant molecular mechanisms and biological processes using the selection criteria of adj. p-values < 0.05, we chose to focus on those with p-values < 0.05 recognizing this as a limitation in our study. The complete over-representation enrichment analyses result of the DEGs can be found in Supplementary File1.

Intersection analyses were performed to the over expressed enriched pathways of CLL with the under expressed enriched pathways of EVOO and to the under expressed enriched pathways of CLL with the over expressed enriched pathways of EVOO. Four KEGG pathways were found to be common from the analysis of over expressed genes of CLL and under expressed genes of EVOO and another four pathways from the analysis of under expressed genes of CLL and over expressed genes of EVOO respectively. Following the same procedure for GO-BP we found 18 common GO-BP from the analysis of the over expressed genes of CLL and the under expressed genes of EVOO and 21 common GO-BP from the analysis of the under expressed genes of CLL and the over expressed genes of EVOO. These findings from KEGG and GO-BP are presented in Figs. 2 and 3 respectively.

Fig. 2
figure 2

Common significantly enriched KEGG pathways between CLL and EVOO. A Common KEGG pathways from the over-expressed genes in CLL and under-expressed genes in EVOO B Common KEGG pathways from the under-expressed genes in CLL and over-expressed genes in EVOO. Color hues are proportional to the p-values of the overrepresentation results, with darker hues corresponding to lower p-values

Full size image
Fig. 3
figure 3

Common significantly enriched GO-BP between CLL and EVOO. A Common GO-BP from the over-expressed genes in CLL and under-expressed genes in EVOO B Common GO-BP from the under-expressed genes in CLL and over-expressed genes in EVOO. Color hues are proportional to the p-values of the overrepresentation results, with darker hues corresponding to lower p-values

Full size image

Drug repurposing-based overlap results

The two gene signatures from the analysis of CLL and EVOO consumption were used also as input in a transcriptomic-based DR tool called L1000CDS2 (see Transcriptomic Based Drug Repurposing in Materials and Methods section). For the case of CLL we collected the 50 returned drugs that reverse the disease gene expression profile and for the case of EVOO consumption, the 50 compounds that mimic the gene signature. By considering the unique drugs from each list, we obtained 38 unique drugs for CLL and 23 unique drugs for EVOO, respectively. Comparing the two drug lists, two drugs were found to be common between CLL and EVOO: geldanamycin and mitoxantrone. Chemical Information and properties of the two drug lists are presented in Supplementary File 2.

Geldanamycin is a benzoquinone antineoplastic antibiotic isolated from the bacterium Streptomyces hygroscopicus. It is an HSP inhibitor and targets the HSP90AA1. Furthermore, mitoxantrone is atopoisomerase inhibitor that targets TOP2A used for the treatment of secondary progressive, progressive relapsing, or worsening relapsing–remitting multiple sclerosis. Then we looked in which pathways the target genes of each drug are involved using the web tool Enrichr with the selection of KEGG 2021 HUMAN database and GO Biological Process 2021 database. Due to the limited number of drug targets present in the two drugs, we did not impose a threshold for pathway selection and we performed a simple membership analysis finding that HSP90AA1 is implicated in 15 KEGG pathways and 93 GO-BP pathways, while TOP2A is associated with 17 GO-BP pathways. The comprehensive lists of significantly enriched KEGG pathways and GO-BP are presented in Supplementary File 3.

MicroRNAs

We explored also the association between EVOO consumption and CLL by investigating their functional commonalities in terms of microRNAs. Firstly, we performed a differential expression analysis in a microRNA expression dataset of PBMCs in healthy subjects following EVOO intake. The microRNA expression signature consists of 3 over- and 3 under-expressed microRNAs. We also searched in the HMDD database in order to find microRNAs associated with CLL and we resulted to 86 microRNAs. The total lists with the associated microRNAs are presented in Supplementary File 4. We compared the two microRNAs lists and we found that they share two common microRNAs: hsa-miR-23band hsa-miR-15a. The first one was found as over-expressed in the EVOO-analysis and the second one as under-expressed.

KEGG and GO Enrichment analysis for the two common microRNAs was performed using the DIANA-miRPath v3 (https://dianalab.e-ce.uth.gr/html/mirpathv3/index.php?r=mirpath) [33]. DIANA-miRPath v3.0 is an online software suite designed to evaluate miRNA regulatory roles and identify the pathways they control. Thirty-eight significantly enriched KEGG pathways and 206 GO terms were found (Supplementary File 4).

Genomic-based associations between CLL and EVOO

In the fourth part of this study, we explored the functional association between EVOO and CLL through a genomic-based approach. We searched the related variants of CLL in the DisGeNet database (https://www.disgenet.org/)[34]. To identify the most significant variants in CLL, we utilized the Variant-Disease Associations (VDA) score with a threshold of ≥ 0.7. This score, which ranges from 0 to 1, depends on the quantity and quality of the sources/publications (degree of curation, organisms) that support the association. Using as threshold in the VDA (variant-disease associations) score ≥ 0.7, we obtained 183 CLL related variants that correspond to 90 unique genes. These genes in combination with the previously found EVOO-related DEGs were used as input in the TRRUST database (https://www.grnpedia.org/trrust/)) [35] in order to investigate their regulatory relationships. TRUST contains 8444 regulatory relationships between transcription factors (TFs) and their targets for 800 human TFs, and 6552 relationships for 828 mouse TFs. These were identified from 11,237 PubMed articles focused on transcriptional regulation studies. Sentence-based text mining method was used to extract the regulatory relationships from over 20 million PubMed articles. As presented in Fig. 4, 300 regulatory interactions were retrieved from 137 genes. From these genes,20 are CLL related variant genes including 2 (ATM and TP53) transcription factors (TFs) and 34 are DEGs from the analysis of EVOO including 6 TFs (HIF1A, JUN, ZBTB16, PARP1, STAT1 and FLI1). The rest are key regulators of the CLL and EVOO related genes.

Fig. 4
figure 4

Regulatory network of CLL- related variant genes and EVOO—related DEGs. The CLL related variants/ genes are presented with orange color and the EVOO—related DEGs with green color. Nodes with rhombus shape correspond to transcription factors

Full size image

Enrichment analysis with KEGG 2021 HUMAN database and GO Biological Process 2021 database using Enrichr tool was also performed in these 137 genes in order to investigate their underlying functional mechanisms in which they are involved. 160 KEGG pathways and 1072 GO-BP were found as significantly enriched (p-value < 0.05) respectively (Supplementary File 5).

Proteomic-based associations between CLL and EVOO

In this part of the study, we used a network-based approach to investigate the proteomic associations between CLL and EVOO. We started by collecting available data on human proteins associated with the bioactive compounds of EVOO from the FooDisNET (http://140.113.120.248/FooDisNET/) [36] database, which provides information on food-compound-protein-disease associations. FooDisNET comprises an extensive collection of foods, compounds, and target proteins, allowing users to explore associations from four perspectives: food, compound, target, and disorder. We also searched for human proteins associated with EVOO’s bioactive compounds in the Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/)) [37]. CTD is a comprehensive, publicly accessible database aimed at improving the understanding of how environmental exposures affect human health. It provides manually curated data on chemical–gene/protein interactions, chemical–disease associations, and gene–disease relationships. We identified associated proteins for six EVOO bioactive compounds: alpha-tocopherol, apigenin, caffeic acid, hydroxytyrosol, luteolin and pinoresinol.

We also collected proteins associated with CLL from the Human Protein Atlas (https://www.proteinatlas.org/)) [38]. Specifically, we selected six proteins that are suggested from the Human Protein Atlas as the most important for predicting immune cell-related cancer CLL: TCL1A, STC1, CD22, FCRL2, FCER2, and CD6 (https://www.proteinatlas.org/humanproteome/disease/chronic+lymphocytic+leukemia).

Next, we used the aforementioned associated proteins to EVOO bioactive compounds and to CLL as input in the STRING database to investigate the protein–protein interactions between them (see Protein–Protein Interaction Network Construction in Materials and Methods section). As shown in Fig. 5, TCL1A, the most important protein for predicting CLL [39], interacts with AKT1, an associated protein to apigenin, a bioactive flavonoid of EVOO. Additionally, CD22, another important associated protein of CLL, interacts with SYK protein, which is associated with luteolin and apigenin, another bioactive flavonoid of EVOO. Finally, CD22 interacts also with PTPRC protein, which is associated with alpha-tocopherol.

Fig. 5
figure 5

Ιnteraction network between proteins associated with EVOO’s bioactive compounds and CLL

Full size image

With the aim to highlight the interconnected pathways and functions of these proteins, we conducted KEGG and GO-BP enrichment analyses, using KEGG 2021 HUMAN database and GO Biological Process 2023 database, to explore the molecular mechanisms and biological processes associated with only the interacting proteins of both EVOO and CLL. We found four significantly enriched KEGG pathways and 24 significantly enriched GO-BP terms (see Supplementary File 6).

Summarizing the enrichment analyses from all layers

We summarized the aforementioned enrichment analysis results to investigate the common functional terms between different omics layers. As we described above, examining the gene expression-based approach, 4 common KEGG pathways were found from the analysis of the over expressed enriched pathways of CLL and the under expressed enriched pathways of EVOO and also 4 common KEGG pathways from the analysis of the under expressed enriched pathways of CLL with the over expressed enriched pathways of EVOO. For the case of GO-BP we found 18 common GO-BP from the analysis of the over expressed genes of CLL and the under expressed genes of EVOO and 21 common GO-BP from the analysis of the under expressed genes of CLL and the over expressed genes of EVOO. Examining the functional terms that are targeted from the two common repurposed drugs related to CLL and EVOO, we found 15 KEGG pathways and 93 GO-BP that are targeted from geldamycin and 17 GO-BP that are targeted from mitoxantrone (no associated KEGG pathways were identified for mitoxantrone). In the next part we investigated the functional associations under the prism of protein- protein interactions between the associated proteins of CLL and EVOO. Two KEGG pathways were found as significantly enriched and 24 GO-BP. Additionally, two microRNAs were found to be associated with both CLL and EVOO. 38 significantly enriched KEGG pathways were found and 206 GO terms in which the two microRNAs are involved. Finally, from the genomic based approach we highlighted 160 KEGG pathways and 1072 GO-BP respectively.

Through an intersection analysis we investigated the functional terms that were found to be common in at least 3 out of the 7 aforementioned approaches. As presented in Table 2, 8 KEGG pathways were highlighted and 16 GO-BPs.

Table 2 KEGG and GO-BP intersection analysis results. Functional terms that were found to be common in at least 3 out of the 7 approaches are presented
Full size table

Computational verification of the highlighted functional terms associating CLL and EVOO

To gain insights into the 8 KEGG pathways and 16 GO-BP terms resulting from our analysis, we performed a two-step meta-analysis procedure. Firstly, using information from the Comparative Toxicogenomics Database (CTD), we searched for KEGG pathways and GO-BP terms that are associated with CLL in order to compare them with our findings. In the second step, we explored the commonalities between our findings and the functional terms targeted by drugs structurally similar to EVOO bioactive compounds as well as by known drugs that are used for CLL treatment.

As presented in Fig. 6, 6 out of the 8 common KEGG pathways (Protein processing in endoplasmic reticulum, Endocytosis, PI3K-Akt signaling pathway, Salmonella infection, Prostate cancer, and Pathways in cancer) were also associated with CLL according to CTD. Additionally, 10 out of the 16 common GO-BP terms (positive regulation of apoptotic process, positive regulation of protein phosphorylation, regulation of apoptotic process, positive regulation of protein-containing complex assembly, regulation of programmed cell death, DNA metabolic process, innate immune response, positive regulation of type I interferon production, regulation of cellular response to heat, and toll-like receptor signaling pathway) were also found to be associated with CLL according to CTD.

Fig. 6
figure 6

Circos plot that summarizes the KEGG and GO-BP intersection analysis results from the different approaches. The KEGG pathways are represented in red color, while the GO-BPs are represented in green color. The KEGG pathways and GO-BP that were found to be associated with CLL from the CTD database are represented with a black rectangular border. Functional terms that are targeted 1) from known drugs used to CLL treatment are represented with blue rectangular border and 2) from structurally similar drugs to EVOO’s bioactive compounds with orange text color

Full size image

In the second step, we also used the curated and annotated collection of drugs from the Drug Repurposing Hub database to identify drugs with initial indication for CLL and investigated their drug-specific information. Our analysis led us to identify nine drugs that are used for CLL treatment: bendamustine, chlorambucil, cyclophosphamide, cytarabine, fludarabine, fludarabine-phosphate, ibrutinib, idelalisib, and venetoclax. The gene targets of these drugs were found from the Drug Repurposing Hub database and were used as input in the Enrichr web tool in order to find the KEGG pathways and the GO-BP that they are involved. We ended up with 133 KEGG pathways and 383 GO-BP (Supplementary File 7) including 7 out of the 8 intersection KEGG pathways (Prostate cancer, Salmonella infection, Estrogen signaling pathway, PI3K-Akt signaling pathway, Pathways in cancer, Necroptosis and Protein processing in endoplasmic reticulum)and 7 out of the 16 GO-BP (DNA metabolic process (GO:0006259), innate immune response (GO:0045087), cellular response to cytokine stimulus (GO:0071345), Fc-gamma receptor signaling pathway involved in phagocytosis (GO:0038096), toll-like receptor signaling pathway (GO:0002224), cellular protein modification process (GO:0006464) and regulation of apoptotic process (GO:0042981))(Fig. 6).

Furthermore, the collection of drugs from the Drug Repurposing Hub database was also used for the investigation of the most structurally similar drugs and small molecules with the bioactive compounds of EVOO. The top 20 structural similarities between drugs and EVOO compounds, based on the Tanimoto similarity are presented in Table 3.

Table 3 Top 20 structural similarities between drugs and EVOO bioactive compounds, based on the Tanimoto scores
Full size table

The gene targets of the top structurally similar drugs were also found from the Drug Repurposing Hub database and were used for the enrichment analysis procedure. Finally, 154 KEGG pathways and 895 GO-BP were found (Supplementary File 7) that are targets of the most structurally similar drugs to EVOO’s bioactive compounds. This list includes 6 out of the 8 intersection KEGG pathways (Estrogen signaling pathway, Pathways in cancer, Necroptosis, PI3K-Akt signaling pathway, Prostate cancer and Salmonella infection) and 11 out of the 16 intersection GO-BP (regulation of apoptotic process (GO:0042981), cellular response to cytokine stimulus (GO:0071345), innate immune response (GO:0045087), positive regulation of apoptotic process (GO:0043065), regulation of protein-containing complex assembly (GO:0043254), positive regulation of protein-containing complex assembly (GO:0031334), response to unfolded protein (GO:0006986), positive regulation of protein phosphorylation (GO:0001934), cellular protein modification process (GO:0006464), regulation of programmed cell death (GO:0043067) and DNA metabolic process (GO:0006259)) (Fig. 6).

Overall, from the 8 KEGG pathways that were highlighted from the intersection analysis, 4 pathways were also highlighted from both steps of the meta-analysis procedure: Pathways in cancer, PI3K-Akt signaling pathway, Prostate cancer and Salmonella infection. Moreover, from the 16 GO-BP resulted from the intersection analysis, 3 were also highlighted from our computational verification procedure: DNA metabolic process (GO:0006259), innate immune response (GO:0045087) and regulation of apoptotic process (GO:0042981).

A deeper view on the protective role that EVOO consumption may have on CLL by focusing on the role of AKT1

Among the limited group of computationally verified KEGG pathways and GO-BPs we have narrowed down, we emphasize the significance of the PI3K-Akt signaling pathway (KEGG) and the regulation of the apoptotic process (GO-BP). These biological functions involve AKT1. Studies suggest that a sudden surge in AKT1 activity could be detrimental to CLL cells[40]. More specifically, for the case of the KEGG pathway, we compiled the elements of the enrichment analysis across the three distinct omics layers, pinpointing the PI3K-Akt signaling pathway as a significant shared functional term. All these components were fed into SIGNOR 3.0 (The SIGnaling Network Open Resource) database (https://signor.uniroma2.it/)( [41], a repository with manually curated causal relationships among human proteins, biologically relevant chemicals, stimuli, and phenotypes. This step allowed us to explore the regulatory interactions among the selected entities. Additionally, we employed the STRING database Version 12 [26] to delve further into the protein–protein interactions using as threshold the interaction score ≥ 0.7.

As shown in Fig. 7, TCL1A, a protein linked to CLL, has been identified as an up-regulator of AKT1. Consequently, AKT1 up-regulates the activity of the transcription factor CREB1. Through its expression, CREB1 positively influences the levels of BCL2, another variant gene associated with CLL and recognized for its high expression in CLL [42].Conversely, AKT1 down-regulates the expression of BCL2L11, which in turn negatively impacts the CLL-related BCL2 gene. Considering these interconnections centered around AKT1 (Fig. 7-shadow), it becomes crucial to explore the potential of EVOO's bioactive compounds in deactivating AKT1 expression levels. Specifically, studies have suggested that apigenin deactivates AKT, prompting apoptosis in human prostate cancer [43]. Moreover, research indicates that three other polyphenols found in EVOO—oleocanthal, oleacein, and hydroxytyrosol—exhibit significant and selective effectiveness against human melanoma and hepatocellular carcinoma cells by inactivating AKT [44,45,46].

Fig. 7
figure 7

A) Interaction network of the elements of the enrichment analysis across the three distinct omics layers, pinpointing the PI3K-Akt signaling pathway. Shadow represents the associations that directly affect and are affected by AKT1

Full size image

Following the same procedure as for the PI3K-Akt signaling pathway, for the case of GO-BP and the regulation of apoptotic process, we gathered information from five different omics layers. The elements that were highlighted from these approaches were fed into SIGNOR 3.0 (The SIGnaling Network Open Resource) database (https://signor.uniroma2.it/) in order to investigate their regulatory interactions. As depicted in Fig. 8, once again, AKT1 occupies a central role among these elements. As in the PI3K-Akt signaling pathway the oncogene TCL1A up-regulates the AKT1 which up-regulates the activity of the transcription factor CREB1, which positively influences the expression of BCL2. Moreover, AKT1 down-regulates the expression of BCL2L11, which in turn negatively regulates the BCL2 gene. Furthermore, AKT1 down-regulates the quantity of CFLAR, also known as FLIP, a major apoptotic regulator frequently over-expressed in solid and hematological cancers, in which its high expression is often correlated with poor prognosis. In addition, CFLAR, together with CASP8, also plays a key role (together with caspase-8) in regulating another form of cell death termed necroptosis [45]. AKT1 also up-regulates the activity of RPS3, a CLL related gene that was found as under-expressed in our transcriptomic analysis. Finally, AKT1 up-regulates FAS, a CLL-related gene, which has been proposed as clinical predictor for the progression of B-CLL. As described above, the bioactive compounds found in EVOO might have the potential to inactivate AKT1 [43,44,45,46,47]. Inactivating AKT1 may potentially lead to changes in the regulation of the CLL-related genes involved in this biological process. However, further investigation is needed to understand the direction of this effect.

Fig. 8
figure 8

Regulatory interaction network of the elements of the enrichment analysis across the five distinct omics layers, pinpointing the GO-BP regulation of apoptotic process. Shadow represents the associations that directly affect and are affected by AKT1

Full size image

Discussion

There is growing evidence supporting the anticancer role of several bioactive compounds such as Oleuropein aglycone, Oleacein, Oleocanthal and others derived from EVOO [48,49,50].Natural products and molecules play an important role in drug discovery and development. A significant portion of small antitumor drugs approved between 1930 and 2012 were either inspired by or derived from natural products [51, 52]. The consumption of certain foods and beverages containing phenols and polyphenols, has been associated with the protection or triggering of various types of cancers, including CLL [53, 54].However, more research is needed that will deepen in the understanding of the molecular activity of EVOO in patients with CLL.

In this work, we performed a comprehensive multi-level analysis including genomics, transcriptomics and proteomics information from several resources to investigate the association between olive oil consumption and CLL, based on their common functional terms. We analyzed publicly available gene expression data to find DEGs related to CLL and EVOO consumption. Enrichment analyses were performed to identify the significantly enriched KEGG pathways and GO-BPs for each condition and through an intersection analysis we highlighted common functional terms. Furthermore, through an in silico drug repurposing pipeline we identified drugs that reverse the input disease signature for the case of CLL and mimic the EVOO gene signature. Comparing the output drugs lists, we found two common drugs, geldanamycin and mitoxantrone. The targeted functional terms of these two drugs were investigated through their gene targets. We also searched for the associated proteins of EVOO and CLL and their physical interactions and we identified the functional terms that both are involved. The associated microRNAs to both conditions were explored to highlight the underlying molecular mechanisms and biological processes that they are involved. Within the genomics level of information, we firstly searched for variants related to CLL. Then we used the corresponding genes of these variants in combination with EVOO DEGs to explore their regulatory relationships by investigating their common key regulators. Finally, genes highlighted from the regulatory network were used for enrichment analysis to find the significant KEGG pathways and GO-BPs that they are involved.

Summarizing the highlighted KEGG pathways and GO-BPs from each aforementioned approach, we applied an intersection analysis to keep the functional terms that were highlighted in at least 3 out of the 7 approaches. Finally, our analysis identified 8 KEGG pathways and 16 GO-BPs. Computational verification regarding these findings was performed in a two-steps meta-analysis including information i) from a widespread drug database ii) from drugs already used for CLL treatment and from structurally similar drugs and small molecules to EVOO’s bioactive compounds.

From these shortlisted pathways, 4 out of 8 KEGG pathways were also computationally verified: Salmonella infection, PI3K-Akt signaling pathway, Prostate cancer and Pathways in cancer. Interestingly, it is known that CLL cells are vulnerable to sudden and continuous activation of the PI3K/AKT signaling pathway [40]. Additionally, it has been reported that hydroxytyrosol inhibits the PI3K signaling pathway in acute human leukemia T cells (Jurkat and HL60) [55]. Regarding the shortlisted GO-BPs, 3 out of 16 were also highlighted from our computational verification meta-analysis: regulation of apoptotic process (GO:0042981), innate immune response (GO:0045087)and DNA metabolic process (GO:0006259). The malfunction of apoptosis has been linked to leukemia, and alterations in the expression of apoptosis-related proteins have been identified at all stages of CLL. It has been reported that high Oleocanthal/Oleacein -EVOO action may influence this dysfunction or survival of cancer cells in CLL patients [13].

In order to delve deeper into the common biological functions, our focus was directed towards terms involving AKT1. Our objective was to further explore the potential pivotal role of AKT1 in the anti-CLL activity associated with EVOO. This investigation led us to analyze the PI3K-Akt signaling KEGG pathway and the GO-BP for the regulation of the apoptotic process. Studies have consistently shown the presence of active AKT in CLL cells, indicating its crucial role in their survival. This suggests that targeting the AKT pathway could serve as a therapeutic approach for treating CLL [38]. Examining the PI3K-Akt signaling pathway and the regulation of the apoptotic process (Fig. 7–8), we observed that AKT1 directly and indirectly regulates the expression of several CLL-related genes such as BCL2, BCL2L11, CFLAR, FAS, RSP3 etc. Inactivation of AKT1 could potentially disrupt these signaling pathways, which may lead to changes in the regulation of CLL-related genes. This could affect various processes involved in the pathogenesis of CLL and consequently, the expression or activity of these genes may be affected. Certain compounds present in EVOO, such as polyphenols like oleocanthal, oleacein, hydroxytyrosol, and apigenin, have demonstrated effects on the activation of AKT1. For instance, research suggests that apigenin deactivates AKT, leading to induced apoptosis in human prostate cancer [41]. Luteolin and apigenin were among the strongest identified inhibitors in a comprehensive ELISA screening where they assessed the impact of 44 polyphenols on pAkt Ser473. These findings were corroborated by Western blot analysis and additional experiments conducted in Human umbilical vein endothelial cells HUVEC cells [56].Moreover, an in vivo experiment using an orthotopic model of human hepatocellular carcinoma (HCC) supported the hypothesis of hydroxytyrosol's protective activity through the inhibition of AKT [44]. Additionally, the ability of oleacein and oleocanthal to inhibit the AKT activity was also evaluated on cutaneous squamous cell carcinoma cells and on immortalized human keratinocytes stimulated with epidermal [46]. Another experiment in MCF-7 breast cancer cells demonstrates that hydroxytyrosol inhibits the PI3K/Akt/mTOR pathway [57].Finally, cell viability experiments using the WST-1 assay showed that oleocanthal exhibited notable and selective activity against human melanoma cells compared to normal dermal fibroblasts by a significant inhibition of AKT phosphorylation [58].

While our study is limited by a lack of experimental evidence supporting our findings, many of our results have already been associated with the beneficial effects of extra-virgin olive oil (EVOO), highlighting the initial validity of our approach. Building on this implicit validation, further investigation is needed to explore additional associations. As far as we know, this is the first time that the activity of EVOO in CLL has been investigated through a comprehensive computational methodology using a multi-level molecular landscape, under the prism of their common biological functions.

Availability of data and materials

The original contributions presented in the study are included in the article/supplementary material.

References

  1. Du M, Chen W, Liu K, Wang L, Hu Y, Mao Y, et al. The global burden of leukemia and its attributable factors in 204 countries and territories: findings from the global burden of disease 2019 study and projections to 2030. J Oncol. 2022;2022:1612702.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Depto DS, Rizvee MM, Rahman A, Zunair H, Rahman MS, Mahdy MRC. Quantifying imbalanced classification methods for leukemia detection. Comput Biol Med. 2023;152: 106372.

    Article  CAS  PubMed  Google Scholar 

  3. Crossnohere NL, Richardson DR, Reinhart C, O’Donoghue B, Love SM, Smith BD, et al. Side effects from acute myeloid leukemia treatment: results from a national survey. Curr Med Res Opin. 2019;35(11):1965–70.

    Article  PubMed  Google Scholar 

  4. Vincenzi B, Armento G, SpalatoCeruso M, Catania G, Leakos M, Santini D, et al. Drug-induced hepatotoxicity in cancer patients - implication for treatment. Expert Opin Drug Saf. 2016;15(9):1219–38.

    Article  CAS  PubMed  Google Scholar 

  5. Glade MJ. Food, nutrition, and the prevention of cancer: a global perspective. American Institute for Cancer Research/World Cancer Research Fund, American Institute for Cancer Research, 1997. Nutrition. 1999;15(6):523–6.

  6. Escrich E, Moral R, Grau L, Costa I, Solanas M. Molecular mechanisms of the effects of olive oil and other dietary lipids on cancer. Mol Nutr Food Res. 2007;51(10):1279–92.

    Article  CAS  PubMed  Google Scholar 

  7. Markellos C, Ourailidou ME, Gavriatopoulou M, Halvatsiotis P, Sergentanis TN, Psaltopoulou T. Olive oil intake and cancer risk: a systematic review and meta-analysis. PLoS ONE. 2022;17(1): e0261649.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cuyas E, Gumuzio J, Lozano-Sanchez J, Segura-Carretero A, Verdura S, Bosch-Barrera J, et al. Mimetics of extra virgin olive oil phenols with anti-cancer stem cell activity. Aging (Albany NY). 2020;12(21):21057–75.

    Article  CAS  PubMed  Google Scholar 

  9. Mourouti N, Panagiotakos DB. The beneficial effect of a Mediterranean diet supplemented with extra virgin olive oil in the primary prevention of breast cancer among women at high cardiovascular risk in the PREDIMED Trial. Evid Based Nurs. 2016;19(3):71.

    Article  PubMed  Google Scholar 

  10. Pampaloni B, Mavilia C, Fabbri S, Romani A, Ieri F, Tanini A, et al. In vitro effects of extracts of extra virgin olive oil on human colon cancer cells. Nutr Cancer. 2014;66(7):1228–36.

    Article  CAS  PubMed  Google Scholar 

  11. Papakonstantinou A, Koumarianou P, Diamantakos P, Melliou E, Magiatis P, Boleti H. A Systematic Ex-Vivo Study of the Anti-Proliferative/Cytotoxic Bioactivity of Major Olive Secoiridoids’ Double Combinations and of Total Olive Oil Phenolic Extracts on Multiple Cell-Culture Based Cancer Models Highlights Synergistic Effects. Nutrients. 2023;15:11.

    Article  Google Scholar 

  12. Papakonstantinou A, Koumarianou P, Rigakou A, Diamantakos P, Frakolaki E, Vassilaki N, et al. New Affordable Methods for Large-Scale Isolation of Major Olive Secoiridoids and Systematic Comparative Study of Their Antiproliferative/Cytotoxic Effect on Multiple Cancer Cell Lines of Different Cancer Origins. Int J Mol Sci. 2022;24:1.

    Article  Google Scholar 

  13. Rojas Gil AP, Kodonis I, Ioannidis A, Nomikos T, Dimopoulos I, Kosmidis G, et al. The effect of dietary intervention with high-oleocanthal and oleacein olive oil in patients with early-stage chronic lymphocytic leukemia: a pilot randomized trial. Front Oncol. 2021;11: 810249.

    Article  PubMed  Google Scholar 

  14. Piroddi M, Albini A, Fabiani R, Giovannelli L, Luceri C, Natella F, et al. Nutrigenomics of extra-virgin olive oil: A review. BioFactors. 2017;43(1):17–41.

    Article  CAS  PubMed  Google Scholar 

  15. Toric J, Markovic AK, Brala CJ, Barbaric M. Anticancer effects of olive oil polyphenols and their combinations with anticancer drugs. Acta Pharm. 2019;69(4):461–82.

    Article  CAS  PubMed  Google Scholar 

  16. Moral R, Escrich E. Influence of olive oil and its components on breast cancer: molecular mechanisms. Molecules. 2022;27:2.

    Article  Google Scholar 

  17. Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford). 2016;2016.

  18. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gorgun G, Holderried TA, Zahrieh D, Neuberg D, Gribben JG. Chronic lymphocytic leukemia cells induce changes in gene expression of CD4 and CD8 T cells. J Clin Invest. 2005;115(7):1797–805.

    Article  PubMed  PubMed Central  Google Scholar 

  20. D’Amore S, Vacca M, Cariello M, Graziano G, D’Orazio A, Salvia R, et al. Genes and miRNA expression signatures in peripheral blood mononuclear cells in healthy subjects and patients with metabolic syndrome after acute intake of extra virgin olive oil. Biochim Biophys Acta. 2016;1861(11):1671–80.

    Article  CAS  PubMed  Google Scholar 

  21. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7): e47.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, et al. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2019;47(D1):D1013-D7.

  23. D. B. Olive Oil: Minor Constituents and Health (1st ed.). Florida: CRC Press; 2009.

  24. Jimenez-Lopez C, Carpena M, Lourenco-Lopes C, Gallardo-Gomez M, Lorenzo JM, Barba FJ, et al. Bioactive compounds and quality of extra virgin olive oil. Foods. 2020;9:8.

    Article  Google Scholar 

  25. Geana EI, Ciucure CT, Apetrei IM, Clodoveo ML, Apetrei C. Discrimination of olive oil and extra-virgin olive oil from other vegetable oils by targeted and untargeted HRMS profiling of phenolic and triterpenic compounds combined with chemometrics. Int J Mol Sci. 2023;24:6.

    Article  Google Scholar 

  26. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):D1388–95.

    Article  CAS  PubMed  Google Scholar 

  27. Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med. 2017;23(4):405–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–12.

    Article  CAS  PubMed  Google Scholar 

  29. Duan Q, Reid SP, Clark NR, Wang Z, Fernandez NF, Rouillard AD, et al. L1000CDS(2): LINCS L1000 characteristic direction signatures search engine. NPJ Syst Biol Appl. 2016;2:16015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open babel: an open chemical toolbox. J Cheminform. 2011;3:33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cao DS, Xiao N, Xu QS, Chen AF. Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions. Bioinformatics. 2015;31(2):279–81.

    Article  CAS  PubMed  Google Scholar 

  32. Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL, et al. Gene set knowledge discovery with enrichr. Curr Protoc. 2021;1(3): e90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015;43(1):460–6.

    Article  Google Scholar 

  34. Pinero J, Sauch J, Sanz F, Furlong LI. The DisGeNET cytoscape app: exploring and visualizing disease genomics data. Comput Struct Biotechnol J. 2021;19:2960–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018;46(D1):D380–6.

    Article  CAS  PubMed  Google Scholar 

  36. C. -Y. Lin J-YL, S. -H. Huang, Y. -C. Hsu, N. -Y. Hsu and J. -M. Yang. FooDisNET: a database of food-compound-protein-disease associations. 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE); Cincinnati, OH, USA2020. p. 190–5.

  37. Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. Comparative toxicogenomics database (CTD): update 2023. Nucleic Acids Res. 2023;51(D1):D1257–62.

    Article  CAS  PubMed  Google Scholar 

  38. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.

    Article  PubMed  Google Scholar 

  39. Virgilio L, Lazzeri C, Bichi R, Nibu K, Narducci MG, Russo G, et al. Deregulated expression of TCL1 causes T cell leukemia in mice. Proc Natl Acad Sci USA. 1998;95(7):3885–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ecker V, Stumpf M, Brandmeier L, Neumayer T, Pfeuffer L, Engleitner T, et al. Targeted PI3K/AKT-hyperactivation induces cell death in chronic lymphocytic leukemia. Nat Commun. 2021;12(1):3526.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lo Surdo P, Iannuccelli M, Contino S, Castagnoli L, Licata L, Cesareni G, et al. SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update. Nucleic Acids Res. 2023;51(1):631–7.

    Article  Google Scholar 

  42. Majid A, Tsoulakis O, Walewska R, Gesk S, Siebert R, Kennedy DB, et al. BCL2 expression in chronic lymphocytic leukemia: lack of association with the BCL2 938A>C promoter single nucleotide polymorphism. Blood. 2008;111(2):874–7.

    Article  CAS  PubMed  Google Scholar 

  43. Kaur P, Shukla S, Gupta S. Plant flavonoid apigenin inactivates Akt to trigger apoptosis in human prostate cancer: an in vitro and in vivo study. Carcinogenesis. 2008;29(11):2210–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhao B, Ma Y, Xu Z, Wang J, Wang F, Wang D, et al. Hydroxytyrosol, a natural molecule from olive oil, suppresses the growth of human hepatocellular carcinoma cells via inactivating AKT and nuclear factor-kappa B pathways. Cancer Lett. 2014;347(1):79–87.

    Article  CAS  PubMed  Google Scholar 

  45. Fogli S, Arena C, Carpi S, Polini B, Bertini S, Digiacomo M, et al. Cytotoxic activity of oleocanthal isolated from virgin olive oil on human melanoma cells. Nutr Cancer. 2016;68(5):873–7.

    Article  CAS  PubMed  Google Scholar 

  46. Polini B, Digiacomo M, Carpi S, Bertini S, Gado F, Saccomanni G, et al. Oleocanthal and oleacein contribute to the in vitro therapeutic potential of extra virgin oil-derived extracts in non-melanoma skin cancer. Toxicol In Vitro. 2018;52:243–50.

    Article  CAS  PubMed  Google Scholar 

  47. Liu M, Wang J, Huang B, Chen A, Li X. Oleuropein inhibits the proliferation and invasion of glioma cells via suppression of the AKT signaling pathway. Oncol Rep. 2016;36(4):2009–16.

    Article  CAS  PubMed  Google Scholar 

  48. Castejon ML, Montoya T, Alarcon-de-la-Lastra C, Sanchez-Hidalgo M. Potential Protective Role Exerted by Secoiridoids from Olea europaea L. in Cancer, Cardiovascular, Neurodegenerative, Aging-Related, and Immunoinflammatory Diseases. Antioxidants (Basel). 2020;9:2.

  49. Fabiani R. Anti-cancer properties of olive oil secoiridoid phenols: a systematic review of in vivo studies. Food Funct. 2016;7(10):4145–59.

    Article  CAS  PubMed  Google Scholar 

  50. Rigacci S, Miceli C, Nediani C, Berti A, Cascella R, Pantano D, et al. Oleuropein aglycone induces autophagy via the AMPK/mTOR signalling pathway: a mechanistic insight. Oncotarget. 2015;6(34):35344–57.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Newman DJ, Cragg GM. Natural product scaffolds as leads to drugs. Future Med Chem. 2009;1(8):1415–27.

    Article  CAS  PubMed  Google Scholar 

  52. Carpi S, Polini B, Manera C, Digiacomo M, Salsano JE, Macchia M, et al. miRNA modulation and antitumor activity by the extra-virgin olive oil polyphenol oleacein in human melanoma cells. Front Pharmacol. 2020;11: 574317.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bakrim S, El Omari N, El Hachlafi N, Bakri Y, Lee LH, Bouyahya A. Dietary phenolic compounds as anticancer natural drugs: recent update on molecular mechanisms and clinical trials. Foods. 2022;11:21.

    Article  Google Scholar 

  54. Ahmad Salimi JP. Chapter 16 - Role of Natural Compounds in Prevention and Treatment of Chronic Lymphocytic Leukemia. Polyphenols: Prevention and Treatment of Human Disease (Second Edition) ed2018.

  55. Parra-Perez AM, Perez-Jimenez A, Gris-Cardenas I, Bonel-Perez GC, Carrasco-Diaz LM, Mokhtari K, et al. Involvement of the PI3K/AKT Intracellular Signaling Pathway in the AntiCancer Activity of Hydroxytyrosol, a polyphenol from olea europaea, in hematological cells and implication of hsp60 levels in its anti-inflammatory activity. Int J Mol Sci. 2022;23:13.

    Article  Google Scholar 

  56. Dirimanov S, Hogger P. Screening of inhibitory effects of polyphenols on Akt-phosphorylation in endothelial cells and determination of structure-activity features. Biomolecules. 2019;9:6.

    Article  Google Scholar 

  57. Calahorra J, Martinez-Lara E, Granadino-Roldan JM, Marti JM, Canuelo A, Blanco S, et al. Crosstalk between hydroxytyrosol, a major olive oil phenol, and HIF-1 in MCF-7 breast cancer cells. Sci Rep. 2020;10(1):6361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shuyan HH, Yiren Z, Zhongxiang F, Yun L, Le Z, Yandan W, Shijia Z, Hui Z, et al. Oleuropein and its hydrolysate from extra virgin olive oil inhibit breast cancer cells proliferation interfering with the PI3K-AKT signal pathway. J Funct Foods. 2023;110:1.

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge World Olive Center for Health (https://worldolivecenter.com/en/ ) and Leventis Foundation for the scholarship provided to M.M.B.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

    Marilena M. Bourdakou & George M. Spyrou

  2. Laboratory of Pharmacognosy and Natural Products Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece

    Eleni Melliou & Prokopios Magiatis

Authors
  1. Marilena M. Bourdakou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eleni Melliou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prokopios Magiatis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. George M. Spyrou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Supervision of the study: G.M.S. Conception and design of the study: M.M.B. and G.M.S. Collection, analysis and interpretation of data: M.M.B. and G.M.S. Drafting the article/ revising it critically: M.M.B., E.M., P.M. and G.M.S.

Corresponding authors

Correspondence to Prokopios Magiatis or George M. Spyrou.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bourdakou, M.M., Melliou, E., Magiatis, P. et al. Computational investigation of the functional landscape of the protective role that extra virgin olive oil consumption may have on chronic lymphocytic leukemia. J Transl Med 22, 869 (2024). https://doi.org/10.1186/s12967-024-05672-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12967-024-05672-z

Keywords

Journal of Translational Medicine

ISSN: 1479-5876

Contact us