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<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Explor Med</journal-id>
<journal-id journal-id-type="publisher-id">EM</journal-id>
<journal-title-group>
<journal-title>Exploration of Medicine</journal-title>
</journal-title-group>
<issn pub-type="epub">2692-3106</issn>
<publisher>
<publisher-name>Open Exploration Publishing</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.37349/emed.2023.00196</article-id>
<article-id pub-id-type="manuscript">1001196</article-id>
<article-categories>
<subj-group>
<subject>Original Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Cholesterol <italic>de novo</italic> biosynthesis: a promising target to overcome the resistance to aromatase inhibitors in postmenopausal patients with estrogen receptor-positive breast cancer</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3771-4341</contrib-id>
<name>
<surname>Coradini</surname>
<given-names>Danila</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
<role content-type="https://credit.niso.org/contributor-roles/investigation/">Investigation</role>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing—original draft</role>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing—review &amp; editing</role>
<xref ref-type="aff" rid="I1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1">
<sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-9358-011X</contrib-id>
<name>
<surname>Ambrogi</surname>
<given-names>Federico</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/investigation/">Investigation</role>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing—review &amp; editing</role>
<xref ref-type="aff" rid="I1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="I2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="editor">
<name>
<surname>Nicolini</surname>
<given-names>Andrea</given-names>
</name>
<role>Academic Editor</role>
<aff>University of Pisa, Italy</aff>
</contrib>
</contrib-group>
<aff id="I1">
<sup>1</sup>“Giulio A. Maccacaro” Laboratory of Medical Statistics and Biometry, Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy</aff>
<aff id="I2">
<sup>2</sup>Scientific Directorate, IRCCS Policlinico San Donato, 20097 San Donato Milanese, Italy</aff>
<author-notes>
<corresp id="cor1">
<bold>
<sup>*</sup>Correspondence:</bold> Danila Coradini, “Giulio A. Maccacaro” Laboratory of Medical Statistics and Biometry, Department of Clinical Sciences and Community Health, Campus Cascina Rosa, University of Milan, 20122 Milan, Italy. <email>danila.coradini@gmail.com</email></corresp>
</author-notes>
<pub-date pub-type="ppub">
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>29</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>4</volume>
<issue>6</issue>
<fpage>1079</fpage>
<lpage>1093</lpage>
<history>
<date date-type="received">
<day>31</day>
<month>07</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>26</day>
<month>10</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>© The Author(s) 2023.</copyright-statement>
<license xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Aim:</title>
<p>Cholesterol is an essential component of cell membranes and serves as a precursor for several bioactive molecules, including steroid hormones and isoprenoids. Generally supplied by the bloodstream, the <italic>de novo</italic> cholesterol biosynthesis is activated in response to an increased cell requirement due to normal tissue remodeling or tumor proliferation. In estrogen receptor (ER)-positive breast cancers, cholesterol biosynthesis may promote and sustain tumor growth and concur with the failure of the treatment with aromatase inhibitors.</p>
</sec>
<sec>
<title>Methods:</title>
<p>In this study, the comparison of gene compared the expression involved in cholesterol biosynthesis was conducted in ER-positive tumors that were responsive and nonresponsive to letrozole; besides, an exploration of their association with genes implicated in estrogen production, the Hippo pathway, and cell cycle control was performed.</p>
</sec>
<sec>
<title>Results:</title>
<p>In responsive tumors, letrozole significantly decreased the expression of five genes [acetyl-coenzyme A (CoA) acetyltransferase 2 (<italic>ACAT2</italic>), 3-hydroxy-3-methylglutaryl-CoA synthase 1 (<italic>HMGCS1</italic>), 3-hydroxy-3-methylglutaryl-CoA reductase (<italic>HMGCR</italic>), farnesyl diphosphate synthase (<italic>FDPS</italic>), and squalene epoxidase (<italic>SQLE</italic>)] crucial for the biosynthetic process. Conversely, in nonresponsive tumors, these genes were unaffected by letrozole but associated with several genes involved in estrogens production [cytochrome P450 family 19 subfamily A member 1 (<italic>CYP19A1</italic>), hydroxysteroid 17-beta dehydrogenase 2 (<italic>HSD17B2</italic>), and sulfotransferase family 1A member 1 (<italic>SULT1A1</italic>)], cell cycle [control cyclin dependent kinase 4 (<italic>CDK4</italic>) and <italic>CDK6</italic>], and Hippo pathway [Yes1 associated transcriptional regulator (<italic>YAP1</italic>) and baculoviral inhibitor of apoptosis (IAP) repeat containing 5 (<italic>BIRC5</italic>)].</p>
</sec>
<sec>
<title>Conclusions:</title>
<p>The findings corroborated the notion that the dysregulation of the mevalonate pathway may contribute to the resistance to letrozole and supported the use of statins to contrast this metabolic dysfunction.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Breast cancer</kwd>
<kwd>aromatase inhibitors</kwd>
<kwd>cholesterol biosynthesis</kwd>
<kwd>Hippo signaling pathway</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p id="p-1">Approximately 80% of postmenopausal breast cancer are estrogen receptor (ER)-positive and, for this reason, usually treated with endocrine therapy. Tamoxifen has been the conventional long-term treatment for several years. Then, to overcome the emergence of tumor resistance, it was first flanked and then replaced by aromatase inhibitors (AIs) both in operable and advanced ER-positive breast cancer achieving excellent results as progression-free and survival time [<xref ref-type="bibr" rid="B1">1</xref>–<xref ref-type="bibr" rid="B3">3</xref>].</p>
<p id="p-2">Despite the recognized superiority of AIs over tamoxifen, the response rate in the neoadjuvant setting is only 50% to 70% because of an acquired or <italic>de novo</italic> resistance to estrogen deprivation therapy. Several putative mechanisms of resistance, mainly based on <italic>in vitro</italic> experiments, have been proposed. They include intrinsic resistance of tumors to estrogen, aromatase-independent estrogenic hormones, signal transduction by non-endocrine pathways, and selection of hormone-insensitive clones during AI therapy [<xref ref-type="bibr" rid="B4">4</xref>, <xref ref-type="bibr" rid="B5">5</xref>], none of which are entirely convincing.</p>
<p id="p-3">Cholesterol is an essential component of cell membranes because required for membrane composition and fluidity. In addition to this activity at the membrane level, cholesterol is the precursor in several biochemical pathways, including the synthesis of steroid hormones. Generally supplied by the bloodstream, the <italic>de novo</italic> cholesterol biosynthesis can be activated in response to the increased cell requirement during the physiological tissue remodeling and the proliferation of cancer cells, especially if steroid hormone-dependent. In these latter, it concurs in promoting and sustaining cell growth as the precursor of steroid hormones and may contribute to the failure of the treatment with AIs.</p>
<p id="p-4">Recent findings [<xref ref-type="bibr" rid="B6">6</xref>] indicated that the mevalonate pathway, the core of the biosynthetic process leading to cholesterol, may interplay with the Hippo signaling pathway through the geranylgeranyl pyrophosphate (GG-PP) that plays an essential role in protein isoprenylation, a process which allows some proteins, including small guanosine triphosphatases (GTPases), such as cell division cycle 42 (Cdc42), ras-related C3 botulinum toxin substrate 1 (Rac1), and ras homolog family member A (RhoA), to be anchored at the plasma membrane and enzymatically activated.</p>
<p id="p-5">Cumulating evidence indicated that the augmented production of isoprenoids, due to the overexpression of the gene coding for the 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase and the following increase in mevalonic acid level, results in the isoprenylation and activation of the small GTPases that interplay with the Hippo signaling pathway by controlling the phosphorylation of Yes-associated protein (YAP)/transcriptional co-activator with PDZ-binding motif (TAZ), the main downstream effectors [<xref ref-type="bibr" rid="B7">7</xref>]. Once anchored to the plasma membrane by isoprenylation and activated, GTPases inhibit YAP/TAZ phosphorylation allowing the translocation into the nucleus, the binding to transcriptional enhancer factor (TEA) domain family member 4 (TEAD) transcription factors, and the expression of a wide variety of genes involved in essential physiological processes, such as stem cell functions, cell proliferation and differentiation, organ growth, tissue regeneration, and embryonic development [<xref ref-type="bibr" rid="B8">8</xref>–<xref ref-type="bibr" rid="B11">11</xref>].</p>
<p id="p-6">Therefore, the experimental findings indicating that the abnormal accumulation of YAP/TAZ into the nucleus may promote the neoplastic transformation by stimulating cell proliferation, inhibiting apoptosis, inducing the acquisition of additional stem cell properties, and supporting cancer cell dissemination by reducing cell contact inhibition, and triggering epithelial to mesenchymal transition, are expected [<xref ref-type="bibr" rid="B12">12</xref>–<xref ref-type="bibr" rid="B15">15</xref>]. Likewise, it is unsurprising that several solid tumors, including breast cancer (especially those poorly differentiated), show a nuclear accumulation of YAP/TAZ [<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B17">17</xref>].</p>
<p id="p-7">The studies also showed that, in tumor cells, the inhibition of the HMG-CoA reductase activity by statins, causing isoprenoid depletion and the unsuccessful activation of small GTPases, promoted the phosphorylation of YAP/TAZ, which is sequestered in cytoplasm and degraded, thus blocking gene transcription [<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>].</p>
<p id="p-8">The aim of this <italic>in silico</italic> study was to compare the expression of the genes involved in the biosynthesis of cholesterol in responsive and nonresponsive tumors from patients treated with a neoadjuvant third-generation AI (letrozole) and explore their association with the genes involved in estrogens production, Hippo signaling pathway, and cell cycle control to verify the potentiality of statins as an additional treatment to overcome the resistance to AIs.</p>
</sec>
<sec id="s2">
<title>Materials and methods</title>
<p id="p-9">The study was carried out using a publicly accessible dataset retrieved from the NCBI Gene Expression Omnibus (GEO) database (<uri xlink:href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</uri>), identified by the GEO accession number GSE5462, and that responded to the following selection criteria: an adequate number of sequential biopsies from the same tumor before and after a short treatment with letrozole; availability of information about the clinical response; gene expression profile obtained using a commercial and therefore reproducible platform.</p>
<p id="p-10">As described in the original articles [<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B21">21</xref>], postmenopausal patients with large operable or locally advanced breast cancer were recruited prospectively into a neoadjuvant study with the AI letrozole (2.5 mg/day, oral). Women with ER-positive breast cancer had tumor biopsies taken before and after 10 to 14 days of treatment and were assessed for clinical response (based on changes in tumor volume) at three months. Fifty patients had available paired tumor samples: 36 responsive and 14 nonresponsive tumors. All patients consented to provide excess tissues for research purposes, and the study was approved by the ethics committee of the University of Edinburgh, Edinburgh, UK (2001/W/BU/09 and 2001/W/BU/10).</p>
<p id="p-11">Gene expression data, obtained using Affymetrix Human Genome U133A Array (GEO accession GPL96), were provided as filtered and log<sub>2</sub> transformed expression estimates.</p>
<sec id="t2-1">
<title>Gene set selection</title>
<p id="p-12">According to the aim of the study, a panel of 37 genes was chosen that included: 13 genes coding for the enzymes involved in the biosynthesis of cholesterol (<xref ref-type="fig" rid="fig1">Figure 1</xref>), 9 involved in the synthesis of estrogens from cholesterol (<xref ref-type="sec" rid="s-suppl">Figure S1</xref>), 10 coding the essential elements of the Hippo signaling pathway and some representative YAP/TAZ target genes [namely, baculoviral inhibitor of apoptosis (IAP) repeat containing 5 (<italic>BIRC5</italic>), cellular communication network factor 1 (<italic>CCN1</italic>), and <italic>CCN2</italic>] [<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>] (<xref ref-type="sec" rid="s-suppl">Figure S2</xref>), and 5 [cyclin D1 (<italic>CCND1</italic>), cyclin dependent kinase 4 (<italic>CDK4</italic>), <italic>CDK6</italic>, CDK inhibitor 1A (<italic>CDKN1A</italic>), and <italic>CDKN1B</italic>] involved in cell cycle control. The latter genes were chosen on the notion that the estrogen-driven activation of the cyclin D-CDK4/CDK6 complex is essential for the physiologic mammary development and proliferation of ER-positive breast cancers [<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>], and CDKN1A (P21, Cip1) and CDKN1B (P27, Kip1) are pivotal players in the control of the G<sub>1</sub>-phase of the cell cycle (details on the selected genes in <xref ref-type="sec" rid="s-suppl">Table S1</xref>).</p>
<fig id="fig1" position="float">
<label>Figure 1</label>
<caption>
<p>Schematic diagram depicting the enzymes involved in cholesterol biosynthesis (in parenthesis, the corresponding gene). Cholesterol biosynthesis is regulated mainly by the SREBP2, which controls the expression of several genes involved in the biosynthetic pathway, including <italic>HMGCR</italic>, <italic>FDFT1</italic>, and <italic>SQLE</italic>. <italic>ACAT2</italic>: acetyl-coenzyme A (CoA) acetyltransferase 2; <italic>HMGCS1</italic>: HMG-CoA synthase 1; <italic>HMGCR</italic>: HMG-CoA reductase; <italic>MVK</italic>: mevalonate kinase; <italic>PMVK</italic>: phosphomevalonate kinase; <italic>MVD</italic>: mevalonate PP decarboxylase; <italic>FDPS</italic>: farnesyl diphosphate synthase; <italic>FDFT1</italic>: farnesyl-diphosphate farnesyltransferase 1; <italic>SQLE</italic>: squalene epoxidase; <italic>LSS</italic>: lanosterol synthase; <italic>GGPS1</italic>: GG diphosphate synthase 1; SREBP2: sterol regulatory element-binding protein-2; <italic>SREBF2</italic>: sterol regulatory element-binding factor-2; <italic>NSDHL</italic>: nicotinamide adenine dinucleotide (phosphate) dependent steroid dehydrogenase-like</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="em-04-1001196-g001.tif"/>
</fig>
</sec>
<sec id="t2-2">
<title>Statistical analysis</title>
<p id="p-13">As each gene may be recognized by several probes, only the best probes were considered for the analysis. Best probes were selected based on the sensitivity and specificity score, where the sensitivity score is the fraction of probes in a probe set that match the gene, and the specificity score indicates to what extent probes of a probe set bind to genes (GeneAnnot version 2.2). When a gene was recognized by two or more best probes, the mean was calculated to obtain a single value of the expression. The nonparametric Wilcoxon signed-rank test for paired samples was used to assess the differential expression of a given gene before and after treatment. The principal component analysis (PCA) was applied to summarize and visualize, as a biplot, the relationship among the different sets of genes (namely, cholesterol biosynthesis, estrogens production, hippo pathway, and proliferation) [<xref ref-type="bibr" rid="B26">26</xref>]. In each biplot, any vector represents the two-dimensional projection of the position of the corresponding variable in the multidimensional space. Results interpretation is based on the length, direction, and position of each vector. Specifically, the length of the vector approximates the quality of the representation of the variable: the longer the vector better the variable representation. The direction of the vector reflects the component to which the variable is most strongly related. The position of vectors represents their pairwise correlation: Vectors close to one another are positively correlated, whereas negatively correlated variables are positioned on the opposite side. If vectors are orthogonal, the variables have a small or null correlation. Spearman’s correlation coefficient was used to describe the correlation between genes. All analyses were performed using the open-source software R, version 4.1.2 (<ext-link xlink:href="http://www.r-project.org/" ext-link-type="uri">http://www.r-project.org</ext-link>), and, in particular, FactoMineR (for the analysis), factoextra (for data interpretation), and ggpubr (for data visualization) packages were used to perform PCA. <italic>P</italic> &lt; 0.05 was considered statistically significant.</p>
</sec>
</sec>
<sec id="s3">
<title>Results</title>
<sec id="t3-1">
<title>Expression of the genes involved in cholesterol biosynthesis in responsive and nonresponsive tumors</title>
<p id="p-14">Wilcoxon paired test showed a significant decrease in the expression level of several genes involved in cholesterol biosynthesis in responsive tumors. In particular, a decreased expression level was observed in <italic>ACAT2</italic>, which encodes the first enzyme of the biosynthetic chain, <italic>HMGCS1</italic> and <italic>HMGCR</italic>, which encode the two enzymes that trigger the mevalonate pathway, <italic>FDPS</italic>, which codes for the enzyme that catalyzes the synthesis of farnesyl PP (FPP), the precursor for the synthesis of isoprenoids, and <italic>SQLE</italic>, which encodes the key enzyme for cholesterol commitment (<xref ref-type="fig" rid="fig2">Figure 2</xref>). Conversely, no decrease was observed in nonresponsive tumors.</p>
<fig id="fig2" position="float">
<label>Figure 2</label>
<caption>
<p>Differential expression of <italic>ACAT2</italic>, <italic>HMGCS1</italic>, <italic>HMGCR</italic>, <italic>FDPS</italic>, and <italic>SQLE</italic> gene before and after a 2-week treatment with letrozole in responsive and nonresponsive tumors. Wilcoxon signed-rank test for paired samples was applied. N.S.: non-significant</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="em-04-1001196-g002.tif"/>
</fig>
</sec>
<sec id="t3-2">
<title>Basal association among the five genes modulated by letrozole and the other genes involved in cholesterol biosynthesis in responsive and nonresponsive tumors</title>
<p id="p-15">PCA indicated that the basal expression of the five genes modulated by letrozole showed a different pattern of expression in responsive and nonresponsive tumors (<xref ref-type="fig" rid="fig3">Figure 3</xref>).</p>
<fig id="fig3" position="float">
<label>Figure 3</label>
<caption>
<p>Biplots visualizing the relationship among the genes downregulated by the letrozole treatment (“downregulated”) and the other genes involved in the biosynthesis of the cholesterol (“unaffected”) in responsive and nonresponsive tumors. Correlation between variables is represented by arrows (vectors) within the correlation circle of radius 1. The length of each vector approximates the quality of the representation of the variable: the longer the vector better the variable representation. The position of vectors represents their pairwise correlation: Vectors close to one another are positively correlated, whereas negatively correlated variables are positioned on the opposite side. If vectors are orthogonal, the variables have a small or null correlation</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="em-04-1001196-g003.tif"/>
</fig>
<p id="p-16">Indeed, in responsive tumors, <italic>HMGCR</italic> was associated positively with <italic>HMGCS1</italic> (<italic>r</italic> = 0.56, <italic>P</italic> = 0.0004), and both with <italic>MVK</italic> (<italic>r</italic> = 0.54, <italic>P</italic> = 0.0007, and <italic>r</italic> = 0.59, <italic>P</italic> = 0.0002, respectively) and <italic>MVD</italic> (<italic>r</italic> = 0.35, <italic>P</italic> = 0.0386 and <italic>r</italic> = 0.49, <italic>P</italic> = 0.0023, respectively); besides, <italic>HMGCR</italic> and <italic>HMGCS1</italic> were associated negatively with <italic>NSDHL</italic> (<italic>r</italic> = –0.58, <italic>P</italic> = 0.0002 and <italic>r</italic> = –0.56, <italic>P</italic> = 0.0003, respectively). <italic>ACAT2</italic> was associated positively with <italic>SQLE</italic> (<italic>r</italic> = 0.41, <italic>P</italic> = 0.0123), while <italic>FDPS</italic> was positively associated with <italic>PMVK</italic> (<italic>r</italic> = 0.74, <italic>P</italic> &lt; 0.0001), <italic>GGPS1</italic> (<italic>r</italic> = 0.39, <italic>P</italic> = 0.0186) and <italic>NSDHL</italic> (<italic>r</italic> = 0.35, <italic>P</italic> = 0.0329).</p>
<p id="p-17">Conversely, in nonresponsive tumors, <italic>ACAT2</italic>, <italic>HMGCR</italic>, and <italic>FDPS</italic> were associated positively with each other (specifically, <italic>ACAT2 vs. HMGCR</italic>: <italic>r</italic> = 0.53, <italic>P</italic> = 0.0567; <italic>ACAT2 vs. FDPS</italic>: <italic>r</italic> = 0.46, <italic>P</italic> = 0.0972; <italic>HMGCR vs. FDPS</italic>: <italic>r</italic> = 0.52, <italic>P</italic> = 0.0591), and with <italic>SQLE</italic> (<italic>r</italic> = 0.43, <italic>P</italic> = 0.1239; <italic>r</italic> = 0.34, <italic>P</italic> = 0.2334; <italic>r</italic> = 0.44, <italic>P</italic> = 0.1198, respectively). Besides, <italic>ACAT2</italic> and <italic>HMGCR</italic> were associated negatively with <italic>FDFT1</italic> (<italic>r</italic> = –0.50, <italic>P</italic> = 0.0704 and <italic>r</italic> = –0.44, <italic>P</italic> = 0.1154, respectively). Regarding <italic>HMGCS1,</italic> it was associated positively with <italic>FDFT1</italic> (<italic>r</italic> = 0.59, <italic>P</italic> = 0.0271) and <italic>MVK</italic> (<italic>r</italic> = 0.39, <italic>P</italic> = 0.1625) and negatively with <italic>PMVK</italic> (<italic>r</italic> = –0.36, <italic>P</italic> = 0.2129), while <italic>FDPS</italic>, in addition to the association with <italic>ACAT2</italic> and <italic>HMGCR,</italic> was associated positively with <italic>PMVK</italic> (<italic>r</italic> = 0.44, <italic>P</italic> = 0.1195). <italic>SQLE</italic>, in addition to the association with <italic>ACAT2</italic>, <italic>HMGCR</italic>, and <italic>FDPS</italic>, was associated positively with <italic>NSDHL</italic> (<italic>r</italic> = 0.60, <italic>P</italic> = 0.0249) and negatively with <italic>MVK</italic> (<italic>r</italic> = –0.46, <italic>P</italic> = 0.1017) and <italic>FDFT1</italic> (<italic>r</italic> = –0.40, <italic>P</italic> = 0.1510).</p>
</sec>
<sec id="t3-3">
<title>Basal relationship between <italic>ACAT2</italic>, <italic>HMGCS1</italic>, <italic>HMGCR</italic>, <italic>FDPS</italic>, or <italic>SQLE</italic> and the genes involved in estrogens biosynthesis in responsive and nonresponsive tumors</title>
<p id="p-18">The basal relationship between the genes downregulated in responsive tumors and those involved in the production of estrogens differed in nonresponsive tumors compared to responsive ones (<xref ref-type="fig" rid="fig4">Figure 4</xref>). In responsive tumors, <italic>HMGCS1</italic> and <italic>HMGCR</italic> were associated positively with hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 2 (<italic>HSD3B2</italic>, <italic>r</italic> = 0.58, <italic>P</italic> = 0.0002 and <italic>r</italic> = 0.47, <italic>P</italic> = 0.0036, respectively) and steroid sulfatase (<italic>STS</italic>, <italic>r</italic> = 0.55, <italic>P</italic> = 0.0006 and <italic>r</italic> = 0.46; <italic>P</italic> = 0.0045, respectively); <italic>HMGCS1</italic> was also associated positively with cytochrome P450 family 11 subfamily A member 1 (<italic>CYP11A1</italic>, <italic>r</italic> = 0.49, <italic>P</italic> = 0.0023) and <italic>CYP17A1</italic> (<italic>r</italic> = 0.38, <italic>P</italic> = 0.0218); <italic>HMGCR</italic> was associated negatively, respectively, with sulfotransferase family 1A member 1 (<italic>SULT1A1</italic>, <italic>r</italic> = –0.61, <italic>P</italic> &lt; 0.0001) and <italic>FDPS</italic> was associated negatively, with <italic>STS</italic> (<italic>r</italic> = –0.58, <italic>P</italic> = 0.0002).</p>
<fig id="fig4" position="float">
<label>Figure 4</label>
<caption>
<p>Biplots visualize the relationship among the genes downregulated by the letrozole treatment (“cholesterol”) and the genes involved in the synthesis of estrogens (“estrogens”) in responsive and nonresponsive tumors. Correlation between variables is represented by arrows (vectors) within the correlation circle of radius 1. The length of each vector approximates the quality of the representation of the variable: the longer the vector better the variable representation. The position of vectors represents their pairwise correlation: Vectors close to one another are positively correlated, whereas negatively correlated variables are positioned on the opposite side. If vectors are orthogonal, the variables have a small or null correlation. HSD17B2: hydroxysteroid 17-beta dehydrogenase 2</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="em-04-1001196-g004.tif"/>
</fig>
<p id="p-19">Conversely, in nonresponsive tumors, <italic>ACAT2</italic> and <italic>HMGCR</italic> were associated positively with <italic>CYP19A1</italic> (<italic>r</italic> = 0.53, <italic>P</italic> = 0.0522 and <italic>r</italic> = 0.49, <italic>P</italic> = 0.0809, respectively) which, conversely, was associated negatively with <italic>HMGCS1</italic> (<italic>r</italic> = –0.59, <italic>P</italic> = 0.0249). Besides, while <italic>HMGCS1</italic> was associated positively with <italic>CYP11A1</italic> (<italic>r</italic> = 0.69, <italic>P</italic> = 0.0064) and <italic>CYP17A1</italic> (<italic>r</italic> = 0.61, <italic>P</italic> = 0.0199) similarly to responsive tumors, <italic>FDPS</italic> and <italic>SQLE</italic> were associated negatively with <italic>CYP17A1</italic> (<italic>r</italic> = –0.43, <italic>P</italic> = 0.1239 and <italic>r</italic> = –0.50, <italic>P</italic> = 0.0694, respectively) and <italic>SQLE</italic> was associated negatively also with <italic>CYP11A1</italic> (<italic>r</italic> = –0.48, <italic>P</italic> = 0.0840). Furthermore, <italic>HMGCR</italic> and <italic>FDPS</italic> were associated negatively with <italic>HSD17B2</italic> (<italic>r</italic> = –0.39, <italic>P</italic> = 0.1656, and <italic>r</italic> = –0.55, <italic>P</italic> = 0.0423, respectively), and <italic>FDPS</italic> was associated negatively also with <italic>STS</italic> (<italic>r</italic> = –0.43, <italic>P</italic> = 0.1281).</p>
</sec>
<sec id="t3-4">
<title>Basal relationship between <italic>ACAT2</italic>, <italic>HMGCS1</italic>, <italic>HMGCR</italic>, <italic>FDPS</italic>, or <italic>SQLE</italic> and the genes involved in the Hippo signaling pathway in responsive and nonresponsive tumors</title>
<p id="p-20">PCA showed that, in the nonresponsive tumors, the expression of the genes downregulated in responsive tumors had a different association profile with the genes involved in the Hippo pathway compared to responsive tumors (<xref ref-type="fig" rid="fig5">Figure 5</xref>).</p>
<fig id="fig5" position="float">
<label>Figure 5</label>
<caption>
<p>Biplots visualizing the relationship among the genes downregulated by the letrozole treatment (“cholesterol”) and the genes involved in the Hippo pathway (“hippo”) in responsive and nonresponsive tumors. Correlation between variables is represented by arrows (vectors) within the correlation circle of radius 1. The length of each vector approximates the quality of the representation of the variable: the longer the vector better the variable representation. The position of vectors represents their pairwise correlation: Vectors close to one another are positively correlated, whereas negatively correlated variables are positioned on the opposite side. If vectors are orthogonal, the variables have a small or null correlation. LATS1: large tumor suppressor kinase 1; MOB1A: Mps one binder (MOB) kinase activator 1A; SAV1: salvador family WW domain containing protein 1; STK3: serine/threonine kinase 3; WWTR1: WW domain containing transcription regulator 1; YAP1: Yes1 associated transcriptional regulator</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="em-04-1001196-g005.tif"/>
</fig>
<p id="p-21">In particular, in nonresponsive tumors, <italic>HMGCR</italic> and <italic>FDPS</italic> were associated positively with <italic>YAP1</italic> (<italic>r</italic> = 0.38, <italic>P</italic> = 0.1767 and <italic>r</italic> = 0.43, <italic>P</italic> = 0.1237, respectively), and <italic>HMGCR</italic> showed a positive association also with <italic>BIRC5</italic> (<italic>r</italic> = 0.49, <italic>P</italic> = 0.0764), one of most representative genes regulated by the YAP/TAZ complex. Besides, <italic>ACAT2</italic> and <italic>FDPS</italic> were associated positively with <italic>STK3</italic> (<italic>r</italic> = 0.70, <italic>P</italic> = 0.0058 and <italic>r</italic> = 0.43, <italic>P</italic> = 0.1216, respectively), and <italic>FDPS</italic> and <italic>SQLE</italic> were associated negatively with <italic>STK4</italic> (<italic>r</italic> = –0.46, <italic>P</italic> = 0.0945 and <italic>r</italic> = –0.39, <italic>P</italic> = 0.1635, respectively).</p>
<p id="p-22">Conversely, in responsive tumors, <italic>HMGCS1</italic> and <italic>HMGCR</italic> were associated positively with <italic>STK3</italic> (<italic>r</italic> = 0.62, <italic>P</italic> &lt; 0.0001 and <italic>r</italic> = 0.38, <italic>P</italic> = 0.0210, respectively), and <italic>LATS1</italic> (<italic>r</italic> = 0.61, <italic>P</italic> &lt; 0.0001 and <italic>r</italic> = 0.38, <italic>P</italic> = 0.0211, respectively).</p>
</sec>
<sec id="t3-5">
<title>Basal relationship between <italic>ACAT2</italic>, <italic>HMGCS1</italic>, <italic>HMGCR</italic>, <italic>FDPS</italic>, or <italic>SQLE</italic> and the genes involved in cell cycle control in responsive and nonresponsive tumors</title>
<p id="p-23">As shown in <xref ref-type="fig" rid="fig6">Figure 6</xref>, in nonresponsive tumors, all but the <italic>HMGCS1</italic> gene were associated negatively with <italic>CDK6</italic> (specifically, <italic>ACAT2</italic>: <italic>r</italic> = –0.56, <italic>P</italic> = 0.0377; <italic>HMGCR</italic>: <italic>r</italic> = –0.52, <italic>P</italic> = 0.0546; <italic>FDPS</italic>: <italic>r</italic> = –0.54, <italic>P</italic> = 0.0477; <italic>SQLE</italic>: <italic>r</italic> = –0.62, <italic>P</italic> = 0.0184). Besides, in addition to the positive association with <italic>CDK6</italic> (<italic>r</italic> = 0.47, <italic>P</italic> = 0.0918), <italic>HMGCS1</italic> was associated negatively with <italic>CCND1</italic> (<italic>r</italic> = –0.49, <italic>P</italic> = 0.0756) and <italic>CDK4</italic> (<italic>r</italic> = –0.44, <italic>P</italic> = 0.1129). In responsive tumors, <italic>FDPS</italic> was associated positively with <italic>CDK4</italic> (<italic>r</italic> = 0.40, <italic>P</italic> = 0.0145).</p>
<fig id="fig6" position="float">
<label>Figure 6</label>
<caption>
<p>Biplots visualizing the relationship among the genes downregulated by the letrozole treatment (“cholesterol”) and the genes involved in cell cycle control (“proliferation”) in responsive and nonresponsive tumors. Correlation between variables is represented by arrows (vectors) within the correlation circle of radius 1. The length of each vector approximates the quality of the representation of the variable: the longer the vector better the variable representation. The position of vectors represents their pairwise correlation: Vectors close to one another are positively correlated, whereas negatively correlated variables are positioned on the opposite side. If vectors are orthogonal, the variables have a small or null correlation</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="em-04-1001196-g006.tif"/>
</fig>
</sec>
</sec>
<sec id="s4">
<title>Discussion</title>
<p id="p-24">Present results indicated that in responsive tumors, the treatment with letrozole was associated with a statistically significant decrease in the expression level of five genes that play a crucial role in the biosynthesis of cholesterol: <italic>ACAT2,</italic> which encodes the first enzyme of the biosynthetic chain; <italic>HMGCS1</italic> and <italic>HMGCR</italic>, which encode the two enzymes that initiate the mevalonate pathway; <italic>FDPS</italic>, which codes for the enzyme that catalyzes the two-steps condensation of dimethylallyl PP with two molecules of isopentenyl PP (IPP) to form FPP, the precursor of GG-PP; <italic>SQLE</italic>, which encodes the enzyme that commits cholesterol biosynthesis irreversibly.</p>
<p id="p-25">This finding could appear unexpected given the specific mechanism of action of letrozole, which inhibits the aromatase by competitively binding to the heme of the cytochrome P450 of the enzyme. However, because some of these genes (especially <italic>HMGCR</italic> and <italic>FDPS</italic>) [<xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B28">28</xref>] are regulated by estrogen through a functional estrogen response element (ERE) in the promoter region, the decrease in the expression level observed in responsive tumors can be ascribed to the inhibition of the aromatase activity that reduced the estradiol availability. This hypothesis agrees with the finding that, in nonresponsive tumors, the five genes had no evidence of modulation but, taken as a whole, they were associated with several genes involved in estrogen biosynthesis. Indeed, they associated positively with <italic>CYP19A1</italic>, the gene coding for the aromatase, and negatively with <italic>HSD17B2</italic> and <italic>SULT1A,</italic> which code, respectively, the enzyme that converts the estradiol to the less active estrone and the enzyme that neutralizes the estradiol by converting it into estradiol-sulfate. That is in agreement with the functional cooperation of cholesterol biosynthesis and estradiol production in the promotion and support of cancer cell proliferation: Estradiol acts as a potent growth factor for ER-positive cancer cells, and cholesterol plays the double role of an essential component of cell membranes and precursor for estrogen biosynthesis. Therefore, it is not surprising that, in nonresponsive tumors, the expression of the five genes was associated with that of the genes involved in cell cycle control, and in particular, with <italic>CDK4</italic> and <italic>CDK6,</italic> the genes coding the two CDKs that drive the G<sub>1</sub>-phase of the cell cycle. Indeed, all but <italic>HMGCS1</italic> genes were associated positively with <italic>CDK4</italic> and negatively with <italic>CDK6</italic>, recently recognized as a novel downregulated estrogen-responsive gene that prevents cell proliferation and regulates cell cycle exit during cell differentiation [<xref ref-type="bibr" rid="B29">29</xref>].</p>
<p id="p-26">In addition to the association with the genes involved in cell proliferation, <italic>HMGCR</italic>, and <italic>FDPS</italic> were associated positively with <italic>YAP1</italic>, which codes for the main transcriptional coactivator regulated by the Hippo signaling pathway (<xref ref-type="fig" rid="fig7">Figure 7</xref>), and <italic>BIRC5</italic>, one of the most representative YAP/TAZ-regulated gene that codes for survivin, an anti-apoptosis protein, overexpressed in most tumors including breast cancer [<xref ref-type="bibr" rid="B30">30</xref>].</p>
<fig id="fig7" position="float">
<label>Figure 7</label>
<caption>
<p>Correlation between <italic>HMGCR</italic> or <italic>FDPS</italic> and <italic>YAP1</italic>, <italic>BIRC5</italic>, <italic>STK4</italic>, or <italic>LATS1</italic> in nonresponsive tumors (evaluated by using the nonparametric Spearman’s correlation coefficient test)</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="em-04-1001196-g007.tif"/>
</fig>
<p id="p-27">The positive association between <italic>HMGCR</italic> or <italic>FDPS</italic> and <italic>YAP1</italic> is of particular interest because, on one side, confirms the functional role of the mevalonate pathway on the activity of the YAP coactivator, through the production of isoprenoids that, anchoring to the membrane and activating the small GTPases, inhibits the phosphorylation of YAP and the following sequestration in the cytoplasm; on the other side, they suggest a relationship between mevalonate and Hippo pathways also at the genomic level. Indeed, the expression of <italic>FDPS</italic>, which is the regulatory node in the production of isoprenoids, other than associated positively with that of <italic>YAP1</italic>, was associated negatively with that of <italic>STK4</italic> and <italic>LATS1</italic>, which code for the two STKs that concur in the inactivation of YAP by phosphorylation and can be inhibited by prenylated GTPases (<xref ref-type="fig" rid="fig7">Figure 7</xref>). This result is in agreement with the evidence that the expression levels of the kinases involved in the Hippo signaling pathway are downregulated in human breast cancer tissues compared with normal breast tissues [<xref ref-type="bibr" rid="B31">31</xref>], and high expression levels of STKs are associated with longer disease-free and overall survival [<xref ref-type="bibr" rid="B32">32</xref>]. Besides, some recent studies [<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>] showed that LATS1 kinase controls human breast cell fate by promoting the ER ubiquitination and ubiquitin-mediated proteasomal degradation and that the YAP/TEAD complex can activate the expression of estrogen-regulated genes by binding to the enhancer region of ERα, thus provide solid pieces of evidence of a close interrelationship among cholesterol biosynthesis, estrogens production, and the Hippo signaling pathway.</p>
<p id="p-28">Remarkable is also the finding that, in nonresponsive tumors, the <italic>HMGCS1</italic> gene, which codes the enzyme turning acetoacetyl-CoA into HMG-CoA (the direct precursor of the mevalonic acid), lost the positive association with <italic>HMGCR</italic> while retaining the positive association with <italic>CDK6</italic> and <italic>MOB1A</italic> and the negative association with <italic>CCND1</italic> and <italic>CDK4</italic>. That corroborates the notion that the dysregulation of the gene coding for the rate-limiting enzyme HMG-CoA reductase causes, on one side, the abnormal production of bioactive estrogens and, on the other side, the synthesis of the isoprenoids necessary for the isoprenylation of the small GTPases and the subsequent inhibition of YAP inactivation, provides a convincing explanation for the tumor resistance to AIs and supports the use of statins to contrast this metabolic dysfunction. In agreement with this idea, a recent population-based cohort study [<xref ref-type="bibr" rid="B35">35</xref>] showed improved patient survival and reduced risk for contralateral breast cancer in postmenopausal women assuming statins for more than five years with conventional AI therapy.</p>
<p id="p-29">Several treatments have been investigated to overcome tumor resistance to AIs, and the CDK4/CDK6 inhibitors provided the most encouraging results [<xref ref-type="bibr" rid="B36">36</xref>–<xref ref-type="bibr" rid="B39">39</xref>]. Accordingly, specific CDK4/CDK6 inhibitors such as palbociclib and abemaciclib have become the standard of care in either the adjuvant or second-line setting for patients with ER-positive breast cancer in combination with AI therapy [<xref ref-type="bibr" rid="B40">40</xref>–<xref ref-type="bibr" rid="B42">42</xref>]. However, despite the substantial improvement in disease-free and overall survival, most tumors fail to respond, and patients ultimately experience disease progression because of acquired resistance [<xref ref-type="bibr" rid="B43">43</xref>]. Currently, excluding chemotherapy, no alternative strategies are available to overcome the resistance to CDK4/CDK6 inhibitors.</p>
<p id="p-30">The cumulating evidence shows that the inhibition of the mevalonate pathway by statins caused the accumulation of the YAP coactivator in the cytoplasm hampering its nuclear translocation and the subsequent transcription of the YAP/TAZ-dependent genes [<xref ref-type="bibr" rid="B6">6</xref>, <xref ref-type="bibr" rid="B44">44</xref>, <xref ref-type="bibr" rid="B45">45</xref>] and that statins were effective in preventing and treating several tumors, including breast cancer [<xref ref-type="bibr" rid="B46">46</xref>–<xref ref-type="bibr" rid="B49">49</xref>], increased the interest for this class of molecules, initially developed as cholesterol-lowering drugs, for the treatment of ER-positive breast cancer with the double advantage of reducing the production of bioactive estrogens in an AI-independent manner and inhibiting the YAP/TAZ-dependent transcriptional activity.</p>
<p id="p-31">In conclusion, although obtained on a limited number of patients, present results indicated that in nonresponsive tumors, the expression of <italic>ACAT2</italic>, <italic>HMGCS1</italic>, <italic>HMGCR</italic>, <italic>FDPS</italic>, and <italic>SQLE</italic> genes, which play a crucial role in the biosynthesis of cholesterol and isoprenoids production was unaffected by the treatment with letrozole and associated with that of some genes involved in estrogens production (<italic>CYP19A1</italic>, <italic>HSD17B2</italic>, and <italic>SULT1A</italic>), cell cycle control (<italic>CDK4</italic> and <italic>CDK6</italic>), and Hippo signaling pathway (<italic>YAP1</italic> and <italic>BIRC5</italic>), corroborating the notion that the dysregulation of the mevalonate pathway contributes to the resistance to letrozole and supporting the clinical use of statins to contrast this metabolic dysfunction.</p>
</sec>
</body>
<back>
<glossary>
<title>Abbreviations</title>
<def-list>
<def-item>
<term>
<italic>ACAT2</italic>
</term>
<def>
<p>acetyl-coenzyme A acetyltransferase 2</p>
</def>
</def-item>
<def-item>
<term>AI</term>
<def>
<p>aromatase inhibitor</p>
</def>
</def-item>
<def-item>
<term>
<italic>BIRC5</italic>
</term>
<def>
<p>baculoviral inhibitor of apoptosis repeat containing 5</p>
</def>
</def-item>
<def-item>
<term>
<italic>CCND1</italic>
</term>
<def>
<p>cyclin D1</p>
</def>
</def-item>
<def-item>
<term>
<italic>CDK4</italic>
</term>
<def>
<p>cyclin dependent kinase 4</p>
</def>
</def-item>
<def-item>
<term>
<italic>CDKN</italic>
</term>
<def>
<p>cyclin dependent kinase inhibitor</p>
</def>
</def-item>
<def-item>
<term>
<italic>CYP11A1</italic>
</term>
<def>
<p>cytochrome P450 family 11 subfamily A member 1</p>
</def>
</def-item>
<def-item>
<term>ER</term>
<def>
<p>estrogen receptor</p>
</def>
</def-item>
<def-item>
<term>
<italic>FDFT1</italic>
</term>
<def>
<p>farnesyl-diphosphate farnesyltransferase 1</p>
</def>
</def-item>
<def-item>
<term>
<italic>FDPS</italic>
</term>
<def>
<p>farnesyl diphosphate synthase</p>
</def>
</def-item>
<def-item>
<term>GG</term>
<def>
<p>geranylgeranyl</p>
</def>
</def-item>
<def-item>
<term>
<italic>GGPS1</italic>
</term>
<def>
<p>geranylgeranyl diphosphate synthase 1</p>
</def>
</def-item>
<def-item>
<term>GTPases</term>
<def>
<p>guanosine triphosphatases</p>
</def>
</def-item>
<def-item>
<term>HMG-CoA</term>
<def>
<p>3-hydroxy-3-methylglutaryl-coenzyme A</p>
</def>
</def-item>
<def-item>
<term>
<italic>HMGCR</italic>
</term>
<def>
<p>3-hydroxy-3-methylglutaryl-coenzyme A reductase</p>
</def>
</def-item>
<def-item>
<term>
<italic>HMGCS1</italic>
</term>
<def>
<p>3-hydroxy-3-methylglutaryl-coenzyme A synthase 1</p>
</def>
</def-item>
<def-item>
<term>
<italic>HSD17B2</italic>
</term>
<def>
<p>hydroxysteroid 17-beta dehydrogenase 2</p>
</def>
</def-item>
<def-item>
<term>
<italic>HSD3B2</italic>
</term>
<def>
<p>hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 2</p>
</def>
</def-item>
<def-item>
<term>LATS1</term>
<def>
<p>large tumor suppressor kinase 1</p>
</def>
</def-item>
<def-item>
<term>MOB1A</term>
<def>
<p>Mps one binder kinase activator 1A</p>
</def>
</def-item>
<def-item>
<term>
<italic>MVD</italic>
</term>
<def>
<p>mevalonate pyrophosphate decarboxylase</p>
</def>
</def-item>
<def-item>
<term>
<italic>MVK</italic>
</term>
<def>
<p>mevalonate kinase</p>
</def>
</def-item>
<def-item>
<term>
<italic>NSDHL</italic>
</term>
<def>
<p>nicotinamide adenine dinucleotide (phosphate) dependent steroid dehydrogenase-like</p>
</def>
</def-item>
<def-item>
<term>PCA</term>
<def>
<p>principal component analysis</p>
</def>
</def-item>
<def-item>
<term>
<italic>PMVK</italic>
</term>
<def>
<p>phosphomevalonate kinase</p>
</def>
</def-item>
<def-item>
<term>PP</term>
<def>
<p>pyrophosphate</p>
</def>
</def-item>
<def-item>
<term>
<italic>SQLE</italic>
</term>
<def>
<p>squalene epoxidase</p>
</def>
</def-item>
<def-item>
<term>
<italic>STK</italic>
</term>
<def>
<p>serine/threonine kinase</p>
</def>
</def-item>
<def-item>
<term>
<italic>STS</italic>
</term>
<def>
<p>steroid sulfatase</p>
</def>
</def-item>
<def-item>
<term>
<italic>SULT1A1</italic>
</term>
<def>
<p>sulfotransferase family 1A member 1</p>
</def>
</def-item>
<def-item>
<term>TAZ</term>
<def>
<p>transcriptional co-activator with PDZ-binding motif</p>
</def>
</def-item>
<def-item>
<term>YAP</term>
<def>
<p>Yes-associated protein</p>
</def>
</def-item>
<def-item>
<term>YAP1</term>
<def>
<p>Yes1 associated transcriptional regulator</p>
</def>
</def-item>
</def-list>
</glossary>
<sec id="s-suppl" sec-type="supplementary-material">
<title>Supplementary materials</title>
<p>The supplementary material for this article is available at: <uri xlink:href="https://www.explorationpub.com/uploads/Article/file/1001196_sup_1.pdf">https://www.explorationpub.com/uploads/Article/file/1001196_sup_1.pdf</uri>.</p>
<supplementary-material id="SD1" content-type="local-data">
<media xlink:href="1001196_sup_1.pdf" mimetype="application" mime-subtype="pdf"/>
</supplementary-material>
</sec>
<sec id="s6">
<title>Declarations</title>
<sec>
<title>Author contributions</title>
<p>DC: Conceptualization, Investigation, Writing—original draft, Writing—review &amp; editing. FA: Investigation, Writing—review &amp; editing. Both authors read and approved the submitted version.</p>
</sec>
<sec sec-type="COI-statement">
<title>Conflicts of interest</title>
<p>The authors declare that they have no conflicts of interest.</p>
</sec>
<sec>
<title>Ethical approval</title>
<p>The original study was approved by the local ethics committee of the University of Edinburgh, Edinburgh, UK (2001/W/BU/09 and 2001/W/BU/10).</p>
</sec>
<sec>
<title>Consent to participate</title>
<p>Informed consent to participate in the study was obtained from all participants.</p>
</sec>
<sec>
<title>Consent to publication</title>
<p>Not applicable.</p>
</sec>
<sec sec-type="data-availability">
<title>Availability of data and materials</title>
<p>Original data are available through the NCBI Gene Expression Omnibus (GEO) database (<ext-link xlink:href="https://www.ncbi.nlm.nih.gov/geo/" ext-link-type="uri">https://www.ncbi.nlm.nih.gov/geo/</ext-link>). Supplementary material associated with this article can be found, in the online version.</p>
</sec>
<sec>
<title>Funding</title>
<p>Not applicable.</p>
</sec>
<sec>
<title>Copyright</title>
<p>© The Author(s) 2023.</p>
</sec>
</sec>
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