# Pharmacomodulation of G-quadruplexes in long non-coding RNAs dysregulated in colorectal cancer

Shubham Sharma, Jérémie Mitteaux, Angélique Pipier, Marc Pirrotta, Marie-José Penouilh, David Monchaud, Bhaskar Datta

► To cite this version:

Shubham Sharma, Jérémie Mitteaux, Angélique Pipier, Marc Pirrotta, Marie-José Penouilh, et al. Pharmacomodulation of G-quadruplexes in long non-coding RNAs dysregulated in colorectal cancer. BMC Biology, 2025, 23, pp.249. 10.1186/s12915-025-02322-8. hal-05208812

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RESEARCH

Open Access

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# Pharmacomodulation of G-quadruplexes in long non-coding RNAs dysregulated in colorectal cancer

Shubham Sharma¹, Jérémie Mitteaux², Angélique Pipier², Marc Pirrotta², Marie-José Penouilh², David Monchaud²* and Bhaskar Datta¹,³*

**Abstract**

**Background** Non-coding RNAs (ncRNAs) in human cells constitute a substantial portion of the transcriptome but do not lead to protein synthesis. Among them, long non-coding RNAs (lncRNAs, > 200 nucleotides long) are fascinating in their ability to orchestrate critical cellular functions that govern cell development, differentiation, and metabolism. Therefore, the dysregulation of lncRNAs has been linked with several diseases, chiefly cancers.

**Results** We focused here on colorectal cancer (CRC), the second-highest cause of mortalities related to cancer world-wide, and more particularly on three lncRNAs, i.e., *LINC01589*, *MELTF-AS1*, and *UXT-AS1*, known to be dysregulated in CRC. We identified a vulnerability in these lncRNAs that could be exploited from a therapeutic point of view: a part of their sequence folds into a secondary structure referred to as G-quadruplex (G4), which is suspected to play active roles in the lncRNA functions. We demonstrate here that these sequences do fold into G4s both in vitro and in CRC cells, and that these G4s can be modulated using PHP, a prototype molecule for destabilizing G4s.

**Conclusion** We describe an innovative anticancer strategy that fully abides by the rules of chemical biology. We indeed modulate the formation of G4s in cells using ad hoc molecular tools in the aim of disturbing the homeostasis and inner functioning of lncRNAs. By exploiting cellular outcomes, we infer how this pharmacomodulation affects CRC biology and, beyond this, the fate of CRC cells owing to the flawed repertoire of correction and/or compensatory mechanisms in cancer cells.

**Keywords** Colorectal cancer, Long non-coding RNA, G-quadruplex, Ligands, Molecular helicases

**Background**

Long non-coding RNAs (lncRNAs) represent an important class of ncRNAs within the human transcriptome, encompassing pivotal functions in both normal cellular physiology and disease pathogenesis [1, 2]. These RNAs are characterized by their length (> 200 nucleotides), translationally silent nature (with a few exceptions), and tissue-specific expression patterns. Their unique and intricate three-dimensional structures, in conjunction with finely orchestrated spatiotemporal expression patterns, make lncRNAs key players in the regulation of cell growth, differentiation, and developmental processes,

<footnote>*Correspondence:
David Monchaud
david.monchaud@cnrs.fr
Bhaskar Datta
bdatta@iitgn.ac.in
¹ Department of Biological Sciences and Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382055, India
² Institut de Chimie Moléculaire de l'Université de Bourgogne (ICMUB), Université Bourgogne Europe (UBE), CNRS UMR6302, Dijon 21000, France
³ Department of Chemistry, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382055, India</footnote>
acting at both genetic and epigenetic levels [3–6]. These regulatory roles also explain why lncRNAs are often dysregulated in various pathologies, including cancers [7–10].

Among the structural motifs lncRNAs are replete with, G-quadruplexes (G4s) rank high. G4s are thermodynamically stable secondary structures that fold from guanine (G)-rich sequences owing to the ability of Gs to self-associate to form G-quartets, which then self-stack to form the G4 (Fig. 1A) [11–18]. The ncRNA G4s are involved in several ncRNA-related processes, from the docking

of RNA binding proteins (RBPs) to miRNA maturation, from the control of the ncRNA stability to that of its cellular localization. While in silico transcriptome-wide analyses showed the high G4 content of lncRNAs [19], the demonstration of their existence in cells was challenging, owing to their dynamic regulation by chaperones and helicases, which make G4 formation transient [15, 20]. The flagship examples of G4-containing lncRNAs are undoubtedly NEAT1 and MALAT1, both of which have been detected during transcriptome-wide profiling in vitro by the rG4-seq method [21] and in vivo by the

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<figure><img src="image_5.png" /><figcaption>**Fig. 1** A Schematic representation of the folding of a G-quadruplex (G4) from a guanine (G)-rich RNA sequence. B Selected putative G4-forming sequences (PQSS) in *LINC01589*, *MELTF-AS1*, and *UXT-AS1* lncRNAs along with the sequence of *NRAS*, used as a positive RNA G4 control herein (sequences from 5' to 3'). C Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analyses performed with RNAs isolated from different CRC cell lines (HT-29, HCT-116, HCT-15, COLO205, COLO320DM, and SW620) to assess the relative *LINC01589*, *MELTF-AS1*, and *UXT-AS1* expression with respect to β-Actin ($2^{-\Delta^{Ct}}$) ± SD. D CD spectroscopy performed with folded synthetic RNAs (1 µM). E N-TASQ fluorescence enhancement assay performed with folded synthetic RNAs (2 µM) against increasing concentrations of N-TASQ (1 to 5 µM). F Förster Resonance Energy Transfer-Melting Competition (FRET-MC) assay performed with folded F21T (0.2 µM) in the presence of N-TASQ (1 µM) and an excess of folded synthetic RNAs (3 µM). The results are collected from triplicates (n = 3) across three independent studies (n = 3). Panels F and D: a representative, unique experiment (D) or technical triplicate (F) is represented, for the sake of clarity. Two-way ANOVA was employed for the statistical analyses with \*: $P \le 0.05$, \*: $P \le 0.01$, \*: $P \le 0.001$; non-significant P-values are not represented</figcaption></figure>
G4RP-seq method [20, 22]. While the biological func-
tions of *NEAT1* and *MALAT1* are well-known [23], the
roles that their G4s play remain to be fully understood:
a conventional and global approach to do this is to use
G4-interacting compounds and exploit the cellular out-
comes of their incubation for mechanistic interpreta-
tions. For instance, TMPyP4 [24] and PDS [25] were used
to disrupt the interaction of *NEAT1* [26] and *MALAT1*
G4s [27], respectively, with their protein partners, in
both cases here NONO (for non-POU domain-contain-
ing octamer binding protein), known to play key roles
in gene regulation and DNA repair [28]. It is intriguing
that the same goal (impairing G4/NONO interaction) is
achieved through two opposite strategies, as TMPyP4
is used to distort G4 (thus modifying the protein bind-
ing site) while PDS is used to stabilize it (thus preclud-
ing the access of the protein). The strategy we propose
herein aims at benefitting from the advantages of these
two approaches, without suffering from their limitations:
on one hand, even if TMPyP4 is documented as a possi-
ble G4-unfolder [26, 29–32], its well-established G4-sta-
bilizing properties [33, 34] blur the relevance of the first
approach; on the other hand, even if PDS is used to target
RNA G4s [35–37], its well-established DNA-interacting
properties [38–41], which strongly jeopardizes genetic
stability, reduce the relevance of the second approach.
That is why we decided herein to further investigate the
relevance of the first strategy which allows for harness-
ing the roles of lncRNA G4s without triggering genetic
instability, using a more optimized molecular tool (*vide
infra*). To better imbue this strategy with therapeutic
value, we also decided to focus on G4s that may play
regulatory roles in lncRNAs found to be dysregulated in
colorectal cancer (CRC). This is justified because CRC is
the second-highest cause of mortalities related to cancer
worldwide, with > 900,000 deaths each year. While recent
improvements in therapies and surgery have resulted
in a 5-year survival rate of ~ 64%, the molecular basis of
CRC progression remains unclear at present [42, 43].
LncRNAs play a prominent role in CRC progression and
their dysregulation and/or mutations have been stud-
ied for both fundamental (tumorigenesis or metastasis)
and applied research (diagnosis and prognosis) [44, 45].
In spite of these appealing characteristics, the dysregu-
lation of lncRNAs in CRC is less explored compared to
other RNAs, and the questions pertaining to the roles
that G4s may have in their mechanism of action remain
to be further investigated [44–46]. It has been recently
shown that G4s in CRC-dysregulated lncRNAs *GSEC*,
*REG1CP*, and *LUCAT1* might be involved in CRC patho-
genesis [47–49]; however, their role in the regulation of
other lncRNAs known to be dysregulated in CRC such
as *LINC01589* (*CTA-941F9.9*), *MELTF-AS1* (*MFI2-AS1*),

and *UXT-AS1* [50–52], with implications in cell prolifera-
tion, migration, and apoptosis, remain to be understood
and pharmacologically exploited, which is precisely the
aim of our current investigations.

In this work, following a thorough in silico assess-
ment of G4 content in lncRNAs dysregulated in CRC,
we first assessed the expression of *LINC01589*, *MELTF-
AS1*, and *UXT-AS1* in a series of CRC cells and then
showed that G4s can fold within these lncRNAs both
in vitro (*via* a series of established techniques includ-
ing circular dichroism (CD), thermal difference spectra
(TDS), fluorescence titrations, melting assays (FRET-
MC) and mass spectrometry) and in CRC cells (via the
G4-RNA-specific precipitation (G4RP) technique) [22,
53–55]. Next, we demonstrated that these G4s are tar-
getable in CRC cells, ironing them out using PhpC, a
molecule currently under study for its G4-destabilizing
properties [56–58], which represents an unprecedented
strategy to fight against the cancer-specific upregula-
tion of these G4-containing lncRNAs. Our results thus
demonstrate the potential of these lncRNA G4s to be
used as targets for therapeutic interventions.

**Results and discussion**

The G4 content of lncRNAs dysregulated in CRC

We started our investigations by assessing the putative G-quadruplex-forming sequence (PQS) content of lncRNAs dysregulated in CRC (Additional file 1: Fig. S1). The list of these lncRNAs was obtained from Lnc2Cancer 3.0, and the corresponding FASTA sequences were retrieved from the NCBI Nucleotide database [59, 60]. Out of 952 entries, only 611 (corresponding to 260 unique lncRNAs) had validated or reviewed RefSeq sequences. The PQS content of these sequences was analyzed using QGRS mapper (max length: 45; min G-group: 2; loop size: 0 to 36) [61]: most of these lncRNAs (257 out of 260 lncRNAs, 99%) had at least one PQS bearing two G-quartets (2G-PQS, 99%), 120 had at least one PQS bearing three G-quartets (3G-PQS, 46%) and 8 had at least one PQS bearing four G-quartets (4G-PQS, 3%). We also evaluated the PQS content using G4Hunter (window size: 45; threshold: 0.9) [62, 63]: 134 had at least one 2G-PQS (51%), 101 had at least one 3G-PQS (39%) and 7 had at least one 4G-PQS (3%). Quite satisfyingly, *LINC01589* (NR_131244.1, 593-nt), *MELTF-AS1* (NR_038285.1, 951-nt), and *UXT-AS1* (NR_028119.1, 565-nt) were found to be highly G4-rich. The high-scoring 2G-, 3G-, and 4G-PQSs within *LINC01589*, *MELTF-AS1*, and *UXT-AS1*, respectively, were selected for subsequent experiments (Fig. 1B, Additional file 1: Fig. S1; Additional file 2: Table S1).
## Expression of *LINC01589*, *MELTF-AS1*, and *UCT-AS1* lncRNAs in CRC

We next assessed the expression levels of *LINC01589*, *MELTF-AS1*, and *UCT-AS1* lncRNAs in different CRC cells (also in different cancer cell lines, Additional file 1: Fig. S2). RT-qPCR analyses (Additional file 3: Table S2) were performed using RNA isolated from CRC cells, i.e., HT-29, HCT-116, HCT-15, COLO205, COLO320DM, and SW620: the relative lncRNA expression (with respect to β-actin) ranged from 46.4 to 784.1 for *LINC01589* (lowest: HT-29, highest: SW620); 59.3 to 2251.8 for *MELTF-AS1* (lowest: HT-29, highest: COLO320DM); 15.8 to 81.7 for *UCT-AS1* (lowest: HCT-116, highest: SW620) (Fig. 1C). These results showed the cell line-dependent variations in the level of *LINC01589*, *MELTF-AS1*, and *UCT-AS1* lncRNAs but also confirmed that they are bona fide good targets for assessing the existence of G4s in CRC-dysregulated lncRNAs.

### *LINC01589*, *MELTF-AS1*, and *UCT-AS1* lncRNAs form stable G4s in vitro

Several techniques were then implemented to confirm the formation of G4s in the above-mentioned PQSs selected within *LINC01589* (45-nt), *MELTF-AS1* (41-nt), and *UCT-AS1* (41-nt) lncRNAs in vitro (Fig. 1B; Additional file 4: Table S3). For simplicity, these PQSs are referred to hereafter by the name of their cognate lncRNAs. CD spectroscopy [64] indicated that the signatures obtained for these lncRNAs could be compatible with a parallel G4 fold (maxima at *ca.* 265 and minima at *ca.* 240 nm, Fig. 1D) [18] but this rather global analysis must be confirmed by complementary techniques. To this end, we performed TDS investigations [65] and the signatures obtained (Additional file 1: Fig. S3) confirmed unambiguously the G4 fold of *MELTF-AS1* and *UCT-AS1*, while that of *LINC01589* might indicate a possible mixture of structures. To go a step further, fluorescence titrations were performed using NaphthoTASQ, or N-TASQ (Fig. 1E, Additional file 1: Fig. S4). N-TASQ is a biomimetic ligand that assembles into a template-assembled synthetic G-quartet, or TASQ [66, 67], its template being a fluorogenic naphthalene. N-TASQ is also a twice-as-smart G4 ligand, being both a smart ligand (whose conformation is modified upon interaction with the G4 targets) and a smart fluorescent probe (whose fluorescence is switched on upon interaction with the G4 targets) [68]. It has been used both in live and fixed cells, notably for assessing the modulation of G4 landscapes upon incubation with G4-interacting molecules such as BRACO-19, TMPyP4 [69] and PHPC (*vide infra*) [57], but also for discriminating between different tumor types (ALT *versus* telomerase-positive cancers) [70]. Its turn-on fluorescence properties originate in a disruption of the

electronic communication between the TASQ template and its guanine arms (which restores the fluorescence of the naphthalene template), triggered by the conformational switch that the probe experiences when binding to G4s [71, 72]. All TASQs interact with G4s via their intramolecular synthetic G-quartet [67], which makes them pan-G4 ligands; they interact with DNA and RNA G4s with a similar affinity with an apparent affinity constant (aPPK<sub>D</sub>) lies between 0.5 and 1.0 mM [57, 73], determined by fluorescence quenching assay (FQA; Additional file 1: Fig. S5) [74]. We further showed here that it interacts efficiently with *LINC01589*, *MELTF-AS1*, and *UCT-AS1* G4s in vitro by both CD-melting (with ΔT<sub>1/2</sub> up to 9.8 °C; Additional file 1: Fig. S6) and UV-melting (with ΔT<sub>1/2</sub> up to 5.9 °C; Additional file 1: Fig. S7). N-TASQ is thus G4-affinic but also highly G4-specific (being uniquely actively selective for G4s owing to their smart nature); the results seen in Fig. 1E (and Additional file 1: Figure S4), with a ratio of fluorescence F/F<sub>0</sub> between 4 and 6 in all instances confirmed the G4 formation in these lncRNAs (even *LINC01589*, which shows that this possible mixture contains majorly G4s). These results align with those recently collected in a similar experimental setup (96-well format) obtained with both DNA and RNA G4s (SRC and VEGF, respectively), for which F/F<sub>0</sub> up to 10 were obtained [75]. These results were confirmed using another well-established G4 turn-on probe thioflavin T (ThT) [76, 77]: as seen in Additional file 1: Fig. S8, the high F/F<sub>0</sub> ratios obtained during these titrations provide a strong argument in support of the G4 fold of *UCT-AS1* (F/F<sub>0</sub>=162-fold), *MELTF-AS1* (161-fold), and *LINC01589* (96-fold). Next, we implemented Förster resonance energy transfer-melting competition (FRET-MC) assay [78], using the firmly established G4 F21T (a 21-nucleotide, human telomere-mimicking sequence d[5'-G<sub>3</sub>(T<sub>2</sub>AG<sub>3</sub>)<sub>3</sub>'] labeled with a fluorescein (F) on its 5'-end and a tetramethylrhodamine (T) on its 3'-end), stabilized by N-TASQ: the mid-transition temperature (T<sub>1/2</sub>) of N-TASQ-stabilized F21T is 67.9 °C; in the presence of a competitive G4, this T<sub>1/2</sub> value should decrease, as a result of the distribution of N-TASQ on both labeled and unlabeled G4s. The strong decrease observed in the presence of *LINC01589*, *MELTF-AS1*, and *UCT-AS1* lncRNAs (ΔT<sub>1/2</sub> = -17.3, -18.1, and -18.2 °C, respectively) fully supports the formation of G4s in these lncRNAs (Fig. 1F). The thermal stability of these G4s was also confirmed by both CD-melting and UV-melting experiments, with T<sub>1/2</sub>=54.9, 60.0, and 72.4 °C (Additional file 1: Fig. S6) and 50.9, 61.3, and 70.2 °C (Additional file 1: Fig. S7) for the G4s formed in *LINC01589*, *MELTF-AS1*, and *UCT-AS1* lncRNAs, respectively, in line with their cognate 2G-, 3G-, and 4G-G4 nature. Of note, the CD-melting profile of *UCT-AS1* exhibited a biphasic
pattern with a weak first transition, suggesting the presence of two distinct structural species in solution. Finally, the molecularity of these G4s was assessed by mass spectrometry (MS), using a combination of hydrophilic interaction liquid chromatography (HILIC) and electrospray ionization mass spectrometry (ESI-MS) analyses [79, 80] and peaks detected corresponded to the molecular weight of intramolecular G4s (14.5, 13.6 and 13.6 kDa for *LINC01589*, *MELTF-AS1*, and *UXT-AS1*, respectively) (Additional file 1: Fig. S9). Of note, the MS profile of *UXT-AS1* showed an additional peak, confirming the CD-melting results implying the presence of two distinct structural species. This set of results thus concurred in demonstrating the G4-folding of the PQSs found in the *LINC01589*, *MELTF-AS1*, and *UXT-AS1* lncRNAs.

### *LINC01589*, *MELTF-AS1*, and *UXT-AS1* lncRNAs fold into G4s in CRC cells

We next investigated the formation of *LINC01589*, *MELTF-AS1*, and *UXT-AS1* lncRNA G4s in CRC cells.

We selected HT-29 as a model cell line for our investigations, as the expression levels of these three lncRNAs were found to be similar (Fig. 1C): this selection indeed removed a possible bias related to the initial abundance of lncRNA in our subsequent analyses, bearing in mind that it does not prejudge in any way that their G4s do actually fold in cells.

To this end, we implemented the G4RP protocol coupled with RT-qPCR analyses (G4RP-RT-qPCR, Fig. 2A, Additional file 3: Table S2) [54, 55]: the HT-29 cells were fixed with paraformaldehyde (to capture transiently folded G4s), lysed, and the resulting lysate was incubated with BioCyTASQ, which serves as a molecular bait for pulling G4s down. BioCyTASQ also belongs to the family of TASQs, its central template being a biotinylated macrocycle (Fig. 2B). As above, it interacts with DNA and RNA G4s with a $^{aPP}K_D$ in between 0.5 and 1.0 mM (Additional file 1: Fig. S5) [57, 73, 81] and we further showed here that it interacts with *LINC01589*, *MELTF-AS1*, and *UXT-AS1* G4s in vitro by both CD-melting

<figure><img src="image_6.png" /><figcaption>**Fig. 2** A Principle of the G4-RNA-specific precipitation (G4RP) protocol coupled with RT-qPCR (G4RP-RT-qPCR). Created with BioRender (Monchaud, D. (2025) https://BioRender.com/7if50um). B Chemical structure of the molecular helicase PhpC, the molecular bait BioCyTASQ and biotin, used as control. C. Results of the G4RP-RT-qPCR performed with HT-29 cells either untreated or treated with PhpC (90 µM). Mean ± SD fold-change in the level of RNAs [5 * {2(Mean Ct input - Ct G4RP or biotin)}]. The results are collected from triplicates (n = 3) across three independent studies (n = 3). Two-way ANOVA was applied for the statistical analyses, with *: P ≤ 0.05, **: P ≤ 0.01, ***: P ≤ 0.001, ****: P ≤ 0.0001; non-significant P-values are not represented</figcaption></figure>
and UV-melting: as seen in Additional file 1: Figs. S6 and S7, a fair-to-good stabilization was obtained ($ΔT_{1/2}$ up to 11.0 °C in CD, and to 12.4 °C in UV), confirming its good G4-stabilization. For the G4RP investigations, we used biotin as a negative molecular bait control, and *NRAS* mRNA as a positive RNA G4 control because (i) HT-29 cells are known to be *NRAS* wild-type [82], (ii) *NRAS* mRNA is known to be G4-rich [83], and (iii) the G4RP protocol was calibrated using *NRAS* G4 [53, 55, 57]. The RT-qPCR analyses of the BioCyTASQ-precipitated and purified RNAs revealed a 2.56-, 0.75-, and 3.30-fold change for the *LINC01589*, *MELTF-AS1*, and *UXT-AS1*, respectively, along with a 3.56-fold change for *NRAS* mRNA, as previously reported [55, 57], and no fold change when using biotin (Fig. 2C). These values, which are normalized versus the input (that is, untreated RNA samples), cannot be exploited to infer the natural abundance of the lncRNA in HT-29 cells per se but solely the modulation of their abundance upon cell treatment with G4-interacting compounds (vide infra). These results thus strongly support the existence of G4s in the *LINC01589* and *UXT-AS1* lncRNAs in CRC cells; the question of the existence of cellular *MELTF-AS1* G4 remains open at this stage.

**LINC01589, MELTF-AS1, and UXT-AS1 G4s are targetable in CRC cells**

With the existence of some of these lncRNA G4s established in cells, a strategy to fight against CRC would be to perturb their biological functions by targeting their G4s: however, contrarily to classical approaches using G4 stabilizers (ligands) [84, 85], we believe that ironing out RNA G4s using G4 destabilizers could be a more efficient approach [86, 87]. The reason is twofold: first, because of the serious adverse effects related to the use of G4 ligands, chiefly an indiscriminate genetic instability (DNA damage) [39–41, 88], which is good for treating cancer cells but must be avoided for surrounding cells and tissues. Second, because if these G4s present in dysregulated lncRNAs are detectable in CRC cells (vide supra), they likely play an active role in the oncogenic process (e.g., acting as binding hubs for RBPs): therefore, an original way to impair this pathological activity would be to unfold them and assess whether this treatment might disrupt the tumorigenic development. However, as discussed above [26], while the relevance of this G4-unfolding strategy was cleared using TMPyP4, its debatable G4-stabilizing versus G4-destabilizing properties make this validation questionable [86]. This is why, among the handful of G4-destabilizers reported to date [86, 87], we used PhpC as its properties have already been investigated in different human cell lines, including cancer (MCF7 [57], HeLa [89], and A549 cells [90])

and central nervous system (CNS) cells (patient-derived astrocytes [58] and neural progenitor cells [91]). We first checked by both CD-melting and UV-melting the interaction between PhpC and lncRNA G4s, having in mind that variable-temperature techniques are not fully suited to this purpose (being generally used to discard G4-stabilizers) [56]: as seen in Additional file 1: Figs. S6 and S7, a thermal destabilization was observed only for *UXT-AS1* in UV-melting ($ΔT_{1/2} = -1.0$ °C). Next, we used a non-toxic concentration of PhpC (90 µM, its $IC_{50}$ established by the SRB assay [92] being 242 µM after a 72-h treatment in HT-29, Additional file 1: Fig. S10) and assessed the resulting modulation of RNAs by G4RP-RT-qPCR analyses (Additional file 3: Table S2): no significant effects were observed with PhpC against *LINC01589* and *MELTF-AS1*, while PhpC triggered a significant 1.85- and 2.05-fold decrease against *UXT-AS1* and *NRAS*, respectively (Fig. 2C), the latter being in line with previously reported results. These results thus show that the cellular abundance of highly stable G4s (such as those found in *UXT-AS1* lncRNA and *NRAS* mRNA) can be modulated by ad hoc molecular effectors: while indirect effects cannot be ruled out, these results embrace a dynamic of G4-modulation by PhpC, which has found applications in both human cancer and CNS cells (vide supra). This emerging strategy of G4-destabilization, which remains to be consolidated, opens dazzling perspectives to mitigate G4-associated diseases. However, the fact that PhpC operates on some RNA G4s (*UXT-AS1* and *NRAS*) but not on other G4s (*LINC01589* and *MELTF-AS1*) indicate that caution must be exercised when interpreting these data, as PhpC might modulate their abundance upon interaction with yet-to-be-discovered structural features. This was confirmed during our exploration of G4-stabilization strategy using the well-established G4 ligand BRACO-19, selected here as it was used in the initial G4RP investigations [22, 53, 55, 57]. We first checked by both CD-melting and UV-melting the interaction between BRACO-19 and lncRNA G4s: as seen in Additional file 1: Figs. S6 and S7, a notable stabilization was obtained in all instance ($ΔT_{1/2}$ up to 7.1 °C in CD and to 9.8 °C in UV), confirming its good G4-stabilization. Next, we used a non-toxic concentration of BRACO-19 (10 µM, its $IC_{50}$ being 20.2 µM after a 72-h treatment in HT-29 cells, Fig. S10) and assessed the resulting modulation of RNAs by G4RP-RT-qPCR analyses (Fig. S11): as above, only the abundance of *UXT-AS1* and the control *NRAS* was significantly modulated, confirming that these two RNA G4s are more responsive to small molecule treatment than *LINC01589* and *MELTF-AS1*. However, while the results obtained with *NRAS* were in line with previously reported data (BRACO-19 triggers a 1.58-fold increase in *NRAS* abundance) [55, 57], the
decrease observed with *UXT-AS1* (1.48-fold) indicated that both G4-interacting molecules, BRACO-19 and BioCyTASQ, might compete for binding to this particu- lar—and unique—G4 structure (they display comparable affinity for DNA/RNA G4s (with $^{aPP}K_D=0.4–1.0$ mM, determined by FQA, Additional file 1: Fig. S5) [57, 73], which is not the case with PhpC ($^{aPP}K_D>100$ mM)). How- ever, combined with its ability to trigger genetic insta- bility (notably at telomeres) [93], the use of BRACO-19 might thus be avoided, as its antiproliferative activity might originate in a series of non-predictable, certainly pleiotropic effects: this could yield real therapeutic div- idends [94] (if a therapeutic window can be found) but makes the elucidation of its mechanism of action quite challenging.

## Conclusions

Advancements in high-throughput techniques for ana- lysing human transcriptome have revealed the involve- ment of lncRNAs in key biological processes and their association with various pathophysiological conditions [95]. Despite the established connection between lncR- NAs dysregulation and diseases, such as cancers, the role of their secondary structures in modulating disease pro- gression remains relatively unexplored [7–10]. Although CRC ranks high in cancer-related mortalities, not much is known about the molecular basis of its carcinogen- esis. This explains why, over the last ten years, numer- ous studies have focused on dysregulated lncRNAs in CRC [44–46]. Recently, a few G4-harboring lncRNAs have been linked with CRC modulation, highlighting the possible roles of G4s as hubs for various CRC-associated regulatory entities [47–49]. However, the lack of under- standing regarding G4-formation in other dysregulated lncRNAs and the overall influence of their G4s on CRC progression led us to explore the G4-formation in addi- tional CRC-dysregulated lncRNAs. Using in silico tools along with in vitro and cell-based assays, we demonstrate that the CRC-dysregulated *LINC01589*, *MELTF-AS1*, and *UXT-AS1* lncRNAs (i) are expressed in CRC, and other cancers as well, suggesting their probable role in diverse cancers; (ii) form stable G4s in vitro; (iii) harbor G4s that are detectable in CRC cells, notably for *LINC01589* and *UXT-AS1*; (iv) harbor G4s that can be efficiently targeted by the prototype molecule PhpC in CRC cells, notably for *UXT-AS1*. This latter point opens interesting therapeu- tic opportunities: indeed, a classical strategy would have been to target lncRNA G4s using G4 stabilizers (ligands). However, we discouraged this approach as we showed that G4 ligands can be responsible for serious adverse effects when crossing the blood–brain barrier and trigger- ing DNA damage in central nervous system (CNS) cells for instance [96–99]. Instead, we have highlighted

an innovative strategy hinging on the destabilization of G4s using PhpC, taking into account that if G4s are folded in a tumorous context, their unfolding might dis- turb the inner functions of cancer cells. We must stress that the results gathered in this study have demonstrated the existence of G4s in the selected lncRNAs, but the roles that these structures may play in lncRNA biology in general, and in the oncogenetic process in particular, remain to be deciphered. Should these structures serve as docking sites for key protein effectors (thus controlling subsequent effects), their disruption with ad hoc mol- ecules would represent a novel and, above all, safe way to perturb tumor proliferation, as it does not rely on the use of genotoxic compounds, as is the case for the G4 ligands (PDS, TMPyP4, BRACO-19) used in aforementioned studies. We have demonstrated here that such a strat- egy is feasible using PhpC but have also emphasized that massive efforts must now be invested to uncover the way it acts both in vitro (e.g., what are the structural features that drive PhpC/G4 interactions? How could it distort the structure of a G4-prone sequence?) and in cells (e.g., what is the necessary dynamic for a G4 to be unfolded in a cel- lular context? Is a coordination with G4-binding proteins required for G4 destabilization?). It is critical to note that PhpC could interact with many cellular G4s, which is precisely at the origin of its genoprotective effects in CNS cells (for instance, it affects the expression of > 300 pro- teins in patient-derived astrocytes, 94% of which being upregulated) [58]. We do believe that ironing out cancer- specific RNA G4s might strengthen its sheltering effect; however, a limitation of this approach is that it requires longer timescales to be efficient, in a manner that is remi- niscent of the cancer cell remortalization mechanism that underlies the therapeutic use of drugs targeting telomer- ase for instance (which requires > 20 days of treatment to be effective) [100]. We thus believe that triggering small defects in cancer cells, rather than extensive DNA dam- age, might be the surest way to put a damper on their fate selectively, owing to their flawed repertoire of correction and/or compensatory mechanisms. Studies are currently ongoing (i.e., dose, schedule, treatment duration, etc.) to assess whether this new way of disrupting the homeosta- sis of cancer cells might pave a *hitherto* uncharted path of anticancer strategies, and we hope to report on these results at the earliest opportunity.

## Methods

*In silico identification of putative G-quadruplex-forming sequences (PQS) in CRC-dysregulated lncRNAs*

The Lnc2Cancer 3.0 database was used to obtain the list of all the lncRNAs dysregulated in CRC [41]. Sub- sequently, the FASTA sequences of all these lncRNAs and their transcript variants were retrieved from the
NCBI Nucleotide database (genomic context: Genome Reference assembly GRCh38.p14 (GCF_000001405.40-RS_2024_08, 2022), Aug 23, 2024) [60]. The QGRS mapper tool was used to identify PQS within the sequences of the selected lncRNAs and their transcript variants. The parameters used for QGRS mapper were max length: 45; min G-group: 2; loop size: 0 to 36 [61]. The G4Hunter tool was used with the following parameters: window size: 45; threshold: 0.9 [62, 63]. The highest-scoring PQS within the selected lncRNAs were chosen for in vitro investigations (Additional file 2: Table S1; Additional file 4: Table S3).

### RNA isolation and reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

RNA isolation from the mentioned cell lines was performed using the conventional TRIzol method. Following the RNA purification and DNase treatment of the purified RNAs, reverse transcription was carried out using the SuperScript™ III Reverse Transcriptase, Random Hexamers and Oligo(dT)₂₀ Primer, following the manufacturer's protocol. The resulting cDNAs were used to perform the quantitative Polymerase Chain Reaction (qPCR) with the iTaq Universal SYBR Green Supermix, as per the manufacturer's protocol. The qPCR was carried out in the presence of primer pairs specific for the selected lncRNAs and housekeeping genes (GAPDH and β-Actin) (Additional file 3: Table S2) using the Mx3005P qPCR System. The data were recorded in triplicates across two independent studies to determine lncRNA expression levels in CRC (HT-29, HCT-116), breast cancer (HCC-1954, MDA-MB-231, MCF7), prostate cancer (PC3) and cervical cancer (HeLa) cell lines. The mean relative lncRNA expression (2⁻ΔCt) with respect to GAPDH was plotted with standard error of mean against different cancer cell lines using GraphPad Software. For assessing lncRNA expression levels in different CRC cell lines (HT-29, HCT-116, HCT-15, COLO205, COLO320DM and SW620), the data were recorded in triplicate from three independent studies. The mean relative lncRNA expression (2⁻ΔCt) with respect to β-Actin were plotted with standard deviation against different CRC cell lines using GraphPad Software. RT-qPCR from different cancer and CRC cell lines was performed in two separate laboratories, employing the housekeeping genes GAPDH and β-Actin, available in the respective laboratories.

### Circular dichroism (CD) and UV spectroscopy

For CD and UV experiments, spectra were recorded on a JASCO J-815 spectropolarimeter in a 10-mm path-length quartz semi-micro cuvette (Starna). Experiments were recorded at the wavelengths range from 210 to 350 nm at 25 and then 95 °C (sensitivity: standard, DIT: 1 s,

bandwidth: 0.5 nm, data pitch: 1.0 nm, scanning speed: 100 nm/min, accumulations: 2) and performed with folded RNAs (1 µM) in 10 mM lithium cacodylate buffer (pH 7.2) plus 10 mM KCl and 90 mM LiCl. The data were zeroed at 320 nm. The CD data were smoothened (with the Savitzky-Golay method and 20 points of window), using OriginPro, Version 2018 (OriginLab Corporation).

### CD-melting and UV-melting

For CD and UV-melting experiments, spectra were recorded on a JASCO J-815 spectropolarimeter in a 10-mm path-length quartz semi-micro cuvette (Starna). Experiments were recorded at several wavelengths (see below) (starting: 25 °C, sampling: 2.0 °C, target: 95 °C, ramp rate: 2.0 °C/min, wait: 0 s, sensitivity: standard, DIT: 1 s, bandwidth: 0.5 nm) and performed with folded RNAs (1 µM) in the presence of G4-interacting compounds (PhpC, N-TASQ, BioCyTASQ and BRACO-19, 5 µM) in 10 mM lithium cacodylate buffer (pH 7.2) plus 10 mM KCl and 90 mM LiCl. The CD signal was monitored at 268 nm for *LINC01589* and 263 nm for *MELTF-AS1* and *UXT-AS1*; the UV signal was monitored at 258, 257, and 256 nm for *LINC01589*, *MELTF-AS1*, and *UXT-AS1*, respectively). The data were smoothened (with the Savitzky-Golay method and 20 points of window) and normalized (0–100) (for CD-melting) or only normalized (0–100) (for UV-melting), using OriginPro, Version 2018 (OriginLab Corporation). The mid-transition temperatures (ΔT₁/₂) were determined manually in order to characterize the ligands' ability to interact with G4s via a modification of the ΔT₁/₂.

### Thermal difference spectra

TDS signatures were calculated by subtracting the UV spectra collected at 25 °C from those collected at 95 °C and then zeroed at 320 nm. Spectra were recorded on a JASCO V630Bio spectrophotometer in a 10 mm path-length quartz semi-micro cuvette (Starna). Spectra of 1 µM of RNA sample in 100 µL (final volume) of 10 mM lithium cacodylate buffer (pH 7.2) plus 10 mM KCl and 90 mM LiCl. Final data were treated with OriginPro, Version 2018 (OriginLab Corporation).

### N-TASQ fluorescence enhancement assay

RNAs (2 µM) folded in the supplementation of 10 mM KCl were titrated with N-TASQ (1 µM), reaching a final concentration of 5 µM. After each titration, the fluorescence emission spectrum of N-TASQ was recorded from 335 to 600 nm, along with endpoint fluorescence emission at 393 nm, with excitation at 280 nm using a CLARI-Ostar® Plus multimode plate reader (BMG LABTECH SARL, France) [68]. The data were recorded in duplicate as part of an independent study (due to RNA scarcity).
The mean fluorescence intensities (arbitrary units) of emission spectra were plotted against the wavelength after smoothening the data with the Savitzky-Golay method and 20 points of window using Origin (Pro). The mean N-TASQ fluorescence fold-enhancements (end-point fluorescence in the presence of RNA/absence of RNA: F/F₀) were plotted with standard deviation against respective RNAs using GraphPad Software. Two-way ANOVA were employed for the statistical analyses.

### Thioflavin T (ThT) fluorescence enhancement assay

Folded RNAs (2 µM) were mixed with ThT (2 µM) and the ThT fluorescence emission spectra from 470 to 650 nm were recorded with excitation at 445 nm using a BioTek Cytation Hybrid Multimode Reader (Agilent Technologies International Pvt. Ltd., Manesar, India). The data were recorded in triplicates across two independent studies. The mean fluorescence intensities (arbitrary units) of emission spectra were plotted against the wavelength after smoothening the data with the Savitzky-Golay method and 20 points of window using Origin (Pro), Version 2017 (OriginLab Corporation, USA).

### Forster resonance energy transfer-melting competition (FRET-MC) assay

RNAs (3 µM) folded in the supplementation of 10 mM KCl were mixed with the folded F21T (0.2 µM) and N-TASQ (1 µM) in folding buffer: 10 mM Tris-HCl (pH 7.5) and 0.1 mM EDTA (pH 8.0), supplemented with 10 mM KCl. The thermal denaturation of this mixture was carried out from 25 °C to 90 °C, with the intensity of the FAM emission simultaneously recorded at intervals of 1 °C when excited at 488 nm, using a Mx3005P qPCR System (Agilent Technologies, France) [78]. The data were recorded in duplicates across two independent studies. The means of normalized FAM emission at maxima from N-TASQ-stabilized F21T in the presence or absence of RNAs were plotted with standard error of mean against the temperature for respective RNAs using GraphPad Software. The plot was fitted using the Boltzmann sigmoidal model of non-linear regression to determine the melting temperatures ($T_{1/2}$) of the respective RNAs.

### G4-RNA-specific precipitation coupled with reverse transcription-quantitative polymerase chain reaction (G4RP-RT-qPCR)

The HT-29 cells were seeded at $8 \times 10^6$ density in a T-175 tissue culture flask and cultured at 37 °C in the presence of 5% CO₂ for 72 h. The cells were treated with PHP (90 µM) or left untreated after 24 h of seeding. After 48 h of PHP treatment or no treatment, the G4RP-RT-qPCR procedure was carried out as mentioned in the

recent reports [22, 53–55, 58]. Subsequently, the cells were trypsinized, fixed with 1% paraformaldehyde, and lysed using a 27G (0.4 mm) needle and syringe. The cell lysate was centrifuged to collect the supernatant of the cell lysate. The resulting cell lysate (supernatant) was then incubated with biotinylated TASQ (BioCyTASQ) [81] (100 µM) or biotin (negative control) (100 µM) in the presence of Streptavidin MagneSphere® Paramagnetic Particles (150 µg) at 4 °C for 120 min. After washing the BioCyTASQ or Biotin-conjugated streptavidin beads, reverse crosslinking was carried out for both the input lysate and the bound streptavidin beads at 70 °C for 120 min. Following this, the input lysate and the bound streptavidin beads were snap-frozen in TRIzol™ Reagent. RNA isolation from the input lysate and the bound streptavidin beads was performed using the conventional TRIzol method. Following the in-column RNA purification and DNase treatment of the purified RNAs, RT-qPCR was carried out in the presence of primer pairs specific for the selected RNAs (Additional file 3: Table S2), as described earlier. The data were recorded in triplicates across three independent studies. The mean fold-change in the level of respective RNAs in Bio-CyTASQ (G4RP) precipitated or biotin samples relative to the input (5%) cell lysate in the presence or absence of PHP treatment [5 * {2(Mean Ct input - Ct G4RP or biotin)}] was plotted with standard error of the mean using GraphPad Software. Two-way ANOVA was applied for the statistical analyses.

### Statistical analyses

Data were analyzed with Excel (Microsoft Corp.) and GraphPad Prism version 9.5.1 for Mac OS (GraphPad software) or OriginPro, version 2018 (OriginLab Corporation) and subjected to two-way ANOVA variance tests, with the statistical significance ($P$-values) $\le 0.05$, $P \le 0.01$, $P \le 0.001$, and $P \le 0.0001$ denoted with one asterisk (*), two asterisks (**), three asterisks (***) and four asterisks (****), respectively. Non-significant $P$-values are not represented.

### Abbreviations

<table><tr><td>CD</td><td>Circular dichroism</td></tr><tr><td>CNS</td><td>Central nervous system</td></tr><tr><td>CRC</td><td>Colorectal cancer</td></tr><tr><td>ESI-MS</td><td>Electrospray ionization mass spectrometry</td></tr><tr><td>FRET</td><td>Förster resonance energy transfer</td></tr><tr><td>G4</td><td>G-quadruplex</td></tr><tr><td>G4RP</td><td>G4-RNA-specific precipitation</td></tr><tr><td>HILIC</td><td>Hydrophilic interaction liquid chromatography</td></tr><tr><td>IncRNA</td><td>Long non-coding RNA</td></tr><tr><td>NCBI</td><td>National Center for Biotechnology Information</td></tr><tr><td>NONO</td><td>Non-POU domain-containing octamer binding protein</td></tr><tr><td>PDS</td><td>Pyridostatin</td></tr><tr><td>PhPC</td><td>Phenylpyrrolocytosine</td></tr><tr><td>PQS</td><td>Putative G-quadruplex-forming sequence</td></tr><tr><td>QGRS</td><td>Quadruplex forming G-rich sequences</td></tr></table>
RBP RNA binding protein

RT-qPCR Reverse transcriptase quantitative polymerase chain reaction

SD Standard deviation

TASQ Template-assembled synthetic G-quartet

TDS Thermal difference spectra

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12915-025-02322-8.

Additional file 1: details about 1-the selection of lncRNA using QGRS mapper [61] and G4Hunter [63], 2- the preparation of the oligonucleotides notably primers using primer-BLAST [101], 3- the reagents used in this study, 4- the cell lines and related culture protocols, 5- the ESI-MS protocol [102, 103] and 6- the SRB method [92]. Figure S1: Identification of Putative Quadruplex-forming Sequences (PQS) in long non-coding RNAs (lncRNAs) dysregulated in Colorectal cancer (CRC). Figure S2: Expression of LINC01589, MELTF-AS1, and UXT-AS1 lncRNAs in different cancer cell lines. Figure S3: CD and UV spectra, along with thermal difference signature (TDS) [65] calculated for LINC01589, MELTF-AS1, and UXT-AS1 lncRNAs. Figure S4: in vitro formation of stable G4s in LINC01589, MELTF-AS1, and UXT-AS1 lncRNAs assessed via N-TASQ fluorescence enhancement assay. Figure S5: fluorescence quenching assay (FQA) [74] to determine the affinity of G4-interacting molecules for either DNA G4 (Cy5-Myc) or RNA G4 (Cy5-NRAS). Figure S6: CD-melting experiments performed lncRNA G4s in the presence of ligands. Figure S7: UV-melting experiments performed with lncRNA G4s in the presence of ligands. Figure S8: Fluorescence titrations were performed with RNA G4s and ThT. Figure S9. ESI-MS analysis of folded synthetic RNA G4s. Figure S10: Cytotoxicity of.phpC and BRACO-19 in HT-29 cells using the SRB test. Figure S11. G4RP-RT-qPCR results [57, 73] obtained with BRACO19 and.phpC on LINC01589, MELTF-AS1, and UXT-AS1 lncRNA G4s in HT-29 cells.

Additional file 2: Table S1: Colorectal cancer-LncRNA G4s data.

Additional file 3: Table S2: RT-qPCR primers.

Additional file 4: Table S3: DNA and RNA oligonucleotides.

## Acknowledgements

The authors thank the Department of Biological Sciences and Engineering, Department of Chemistry, Common Research & Technology Development Hub and the Central Instrumentation facility at Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat, India; PACSMUB (Dijon, France) for providing instrumentation facility. The authors also thank Robert H. Hudson (UWO London, CA) for kindly providing.phpC.

## Authors' contributions

D.M. and B.D. managed the project. S.S. and D.M. designed the experiments. S.S. and J.M. conducted the experiments. M.P. helped with the FRET-melting experiments and M.J.P. with the ESI-MS experiments. S.S., J.M., A.P. and D.M. processed the experimental data and analyzed the results. S.S., J.M., A.P. and D.M. wrote the manuscript; B.D. corrected it. All authors read and approved the final manuscript.

## Funding

This work was supported by the Gujarat State Biotechnology Mission [GSBTM/JD(R&D)/626/22-23/00006262] to B.D.; both *Ligue contre le Cancer* [TelOS, AAPEAC2022.LCC/VG] and *Agence Nationale de la Recherche* [INJUNCTION, ANR-22-CE44-0039-01] to D.M.; and European Molecular Biology Organization (EMBO) Scientific Exchange Grant [EMBO/9906] to S.S.

## Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

## Declarations

### Ethics approval and consent to participate

Not applicable.

### Consent for publication

All authors gave their free and informed consent to this publication.

### Competing interests

The CNRS (D.M.) has licensed BioCyTASQ to Merck KGaA for commercialization.

Received: 10 January 2025 Accepted: 4 July 2025

Published online: 08 August 2025

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