Modulating cancer stem cell characteristics in CD133+ melanoma cells through HIF1α, KLF4, and SHH silencing

Modulating cancer stem cell characteristics in CD133+ melanoma cells through HIF1α, KLF4, and SHH silencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Modulating cancer stem cell characteristics in CD133+ melanoma cells through HIF1α, KLF4, and SHH silencing Huseyin Aktug, Berrin Ozdil, Cigir Biray Avci, Duygu Calik Kocaturk, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4808028/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Malignant melanoma, an aggressive skin cancer derived from melanocytes, contains a subpopulation known as cancer stem cells (CSCs), with distinct self-renewal and differentiation abilities, setting them apart from non-cancer stem cells (NCSCs). This study aims to examine how CSCs respond to the suppression of their stem cell characteristics through targeted gene silencing of HIF1α, KLF4, and SHH within the context of the extracellular matrix, with a particular focus on Matrigel. Silencing targeted genes individually induced distinct changes in CSCs behavior, revealing novel therapeutic targets through analysis of gene expression, protein levels, and cell cycle dynamics. A comparison between melanoma CSCs and NCSCs revealed significant shifts in SHH signaling, epigenetic markers, differentiation, migration, and vascularization genes. Specifically, CSCs exhibited elevated levels of SHH, Gli1, and HDAC9, while NCSCs showed increased expression of Mif. Our findings highlight the emergence of a unique cellular phenotype following gene silencing, distinct from both CSCs and NCSCs. Diverse signaling pathways underlie this phenomenon, offering valuable insights for development of melanoma therapies. Melanoma Cancer stem cell HIF1α KLF4 SHH Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Malignant melanoma, a highly aggressive skin tumor originated from melanocytes, displays a complex genetic profile influenced by both genetic and environmental factors 1 . The abundance and activity of CSCs is associated with the aggressiveness of malignant melanoma as in other cancer types 2 . The detection of tumor heterogeneity, undifferentiated molecular markers, and increased tumor identity of melanoma subtypes with embryonic-like developmental plasticity strongly suggest the presence and involvement of malignant melanoma stem cells in the initiation and progression of this malignancy 3 . Most physiological functions in cellular processes are regulated by oxygen concentration. Cancer cells can adapt to low oxygen level by regulating the hypoxia signaling, where the key regulator is HIF1α 4,5 . In cancer progression, hypoxia, which activates angiogenesis, thereby increases the risk of invasion and metastasis 5,6 . This increases cell survival within the tumor as well as suppresses anti-tumor immunity and inhibits the therapeutic response. A study conducted on hypoxia in the GBM cell line demonstrated that the upregulation of HIF1 and HIF2 resulted in elevated levels of SOX2 and KLF4, thereby affecting the stemness and cell cycle of the cells through this signaling pathway 7 . Supporting the hypoxia mechanism, here, we focus on KLF4 and SHH signaling for the changes in stemness regulators. The role of KLF4 in melanoma remains unknown, despite its strong expression in post-mitotic epidermal cells and terminally differentiated skin and gut cells 8 . A 2017 study investigating KLF4’s function in melanoma cell lines (SK-Mel-2, SK-Mel-5, SK-Mel-28, SSM2c, M26c, M33x and M51) and its interaction with the MAPK signaling system found strong expression of KLF4 in human melanomas. In addition, knockdown of KLF4 decreased patient melanoma cell proliferation and promoted cell death, while ectopic expression of KLF4 increased melanoma cell growth by lowering apoptosis 8 . Although KLF4 has been extensively studied in melanoma, research on melanoma CSCs remains limited. Understanding the role of the KLF4 protein is crucial for studying and characterizing CSCs. The SHH protein, a key player in the Hedgehog signaling pathway, contributes significantly to various developmental processes, especially in nervous system development, where it acts as a morphogen during early developmental stages. Besides its crucial role in embryonic development, Hedgehog signaling has been shown to be involved in vasculogenesis and angiogenesis 9 . Despite its prominence in the developmental process, the SHH signaling pathway is also actively involved in studies on CSC. Hypoxia-induced activation of the SHH signaling pathway in different tissues 10 , highlights its importance in CSC biology 11 . However, research on SHH signaling in melanoma cell lines is limited, with most studies focusing on PI3K rather than Gli1 12,13 . In a thesis study that has not yet been published, malignant vector transfection of the SHH protein into CHL-1 melanoma cells, followed by examination of the PI3K signaling pathway and Gli1/Gli3 expression 14 . Here, SHH gene expression was directed using siRNA technology and cells were classified as either CSCs or NCSCs. Tumor microenvironment is another factor affecting tumor character; extracellular matrix molecules are responsible for tumor progression. Therefore, it is extremely important to establish the tumor microenvironment to create specific target therapies. In order to increase the metastatic capacity, melanoma cells manipulate the extracellular matrix and secrete extracellular factors for this 15 , and each change or modification on the extracellular matrix is effective on the cancer biological behavior and has to be examined 16–18 . The main aim in this study to demonstrate the differential signaling in CSC and NCSC in the presence of extracellular matrix components, Matrigel, with or without silencing the stem cell character by targeting the HIF1α, KLF4 and SHH genes in CSCs. The results indicate that silencing any of these genes did not alter the characteristics between these cells, but instead led to the emergence of new ones. METHODS Cell Lines and Culture Nonpigmented human melanoma cell lines CHL-1 (ATCC® CRL-9446TM) were cultured in EMEM (Eagle's Minimum Essential Medium) (Biowest L0416) supplemented with 10% fetal bovine serum (Biowest, S1810) at 5% CO 2 at 37°C. Regular authentication and mycoplasma infection checks were conducted on the cell lines. For experimental consistency and reproducibility, cells within passages 6–8 were selected for flow cytometry sorting. Fluorescence-activated cell sorting (FACS) The cells were detached from the surface of the flask by trypsinization, a standard method for enzymatically releasing adherent cells. Following detachment, the cells were washed twice with cold 1X PBS to remove any residual trypsin and cellular debris. The collected cells were then diluted to a concentration of 10 6 cell/ml in 10 ml cold 1X PBS. Following this, they were incubated with 10 µL CD133 phycoerythrin (PE)-labelled antibody (Miltenyi Biotec Ltd. 130-113-186) and 10 µL DAPI for 15 minutes at + 4 o C. After the incubation period, the cells were washed with 1X PBS supplemented with 1% dialyzed fetal bovine serum (FBS). Control cells were subjected to staining with DAPI only, without the addition of any antibodies. Subsequently, cell sorting was performed using BD FACS Diva 8.0. The collection tubes labeled CD133 + were designated as CSCs, while CD133- cells were categorized as NCSCs. Within the malignant melanoma cell population, a CD133 + cell subset was ranging from 0.1–0.4%. CD133 + cell subset was used as passage 2 to passage 4, were utilized in subsequent experimental procedures (see Supp Fig. 1A). Cell counting was conducted after sorting and experimental procedures using Muse® Cell Analyzer, a specialized instrument for automated cell counting and analysis. Matrigel coating Before seeding cells, the surface was coated with Matrigel (Corning® Matrigel® Basement Membrane Matrix, 354234). To ensure optimal coating, all materials required for this process were pre-cooled to + 4 o C and handled accordingly. The Matrigel used for surface coating was prepared by mixing it with serum-free medium at a ratio of 1:4 while it was in liquid form, ensuring no bubbles formed. On a 15 mm coverslip (with a surface area of 1.77 cm 2 ), 100 microliters of Matrigel solution were applied, while on the surface of a 6-well plate (with a surface area of 9.6 cm 2 ), 542 microliters of Matrigel solution were distributed on ice. Coverslips measuring 15 mm were utilized for immunofluorescence, AFM, SEM and XPS analysis, while the 6 well plates were used for RT-PCR. The matrigel, which remained in state at + 4 o C, was uniformly distributed across the surface during matrigel coating. To achieve even distribution, the surface containing the liquid Matrigel and in contact with a cold surface was placed on a shaker. Following the coating of Matrigel, the surface was then transferred to a standard cell culture incubator set at 37 o C. Through observation, it was determined that a 20-minute incubation period was optimal for complete polymerization of the Matrigel coating, ensuring a stable and supportive environment for subsequent cell seeding. The surface coating of Matrigel was further characterized using atomic force microscopy (Bruker Dimension Edge with ScanAsyst AFM (Bruker, Germany)) 19,20 and scanning electron microscopy (Thermo Scientific Apreo S LoVac SEM (ThermoFisher Scientific, US)) 20,21 imaging techniques (Supp. Figure 1B). Small interfering RNA (siRNA) transfection To silence the HIF1α, KLF4, and SHH genes, CD133 + malignant melanoma CSCs were transfected with 0-200 nM siRNA (On-Targetplus Human siRNA, Smartpool, L-005089-00-0005, L-004018-00-0005, L-006036-00-0005 Horizon). The transfection was performed to determine the optimal siRNA dosage, and gene expression levels were subsequently assessed using RT-PCR for control. The fold-change cut-off limit was set at 2, with changes between 0 and 2 were considered insignificant. In the experiment, siRNA concentrations ranging from 0-200 nM were used. Specifically, the use of 5nM HIF1α siRNA resulted in a fold change of -56.44x. For KLF4, the highest fold change (-4.56x fold) was observed at 5 nM, while SHH gene silencing was achieved at 25 nM. (-3.84 x fold). Subsequently, 5 nM HIF1α, and KLF4; 25 nM negative control siRNA and SHH were used for further experiments. Cells were fixed after 24 hrs of incubation following siRNA transfection for analysis. Real-Time Polymerase Chain Reaction (RT-PCR ) Cells were incubated for 24 hrs following siRNA transfection after seeding a concentration at 10 5 cells/ml. The cells were rinsed with 1X Phosphate-buffered saline (PBS), detached from the surface with StemProTM AccutaseTM Cell Dissociation Reagent (Thermo Fisher) and dissolved in RNA buffer (Roche). The subsequent steps were conducted in accordance with the Roche isolation kit and customized Roche panel used for the gene expression profiling. Sampling was performed three times. The heatmap with hierarchical trees was generated using Clustvis 22 . Immunofluorescence Staining After 24 hours of siRNA transfection, cells were fixed using 4% paraformaldehyde (PFA) (Sigma P-6148) and permeabilized with 0.25% TritonX-100 (Biotech, C34H62O11). Subsequently, cells were subjected to the blocking with 3% bovine serum albumin (Chem Cruz, sc-2323) to minimize non-spesific binding. Following blocking, cells were incubated overnight at + 4 o C with primary antibodies (HIF1α, Bioss bs-0737R; KLF4, Thermo PA1-095; SHH, Santa cruz, sc-373779; Gli1, Bioss AI05040; Smo, Bioss AA062588; VEGF, bs-1665R; Mif, Santa Cruz sc-271631; MMP9, Bioss bs-4593R; P300, Bioss, bs-5339R; HDAC9, Bioss, bsm-54186R;) diluted 1:200 in 1%BSA. Following primary antibody incubation, cells were washed and incubated with secondary antibodies (Alexa Fluor® 488-AffiniPure Goat Anti-Rabbit Jackson Immuno Research, 111-545-003) at room temperature for one hour. Finally, cells were mounted with Fluoroshield Mounting Medium with DAPI (abcam, ab104139) for nuclear counterstaining. Microscopic images were acquired using an Olympus BX-51 microscope (Olympus Optical Co., Tokyo, Japan). The Enzyme Linked Immunosorbent Assay (ELISA) Based on the results obtained from RT-PCR analysis, Gli1, HDAC9, and MMP9 were identified as the most significantly differentially expressed genes. To further investigate their potential role in paracrine signalling Enzyme Linked Immunosorbent Assay (ELISA) was performed. Prior to assay, all reagents were equilibrated at room temperature before use. The supernatants collected from the cells were processed following by the ELISA kit protocol (BT-Lab E6417Hu, E5411Hu and E0936Hu), as previously described in our earlier study 23 . The amount of secreted protein was quantified using a microplate reader set to measure absorbance at 450 nm. Cell Cycle Analysis The cell cycle phases were assessed using a Muse® cell analyzer 19 . The cells were subjected to a standard passage protocol, and cells were harvested and cell pellet was collected. The cells were then fixed in 70% ice-cold ethanol and stored at -20 o C overnight to ensure proper fixation. The following day, the ethanol was carefully removed from the samples, and cell cycle kit solution (Millipore lot:2941162) was added to each sample. The samples were incubated in the dark for 30 minutes. The Muse® cell analyzer was used to read the samples in the cell cycle analysis mode. Statistical Analysis The 'Multiple Plate Analysis' tool was used to examine gene expression profiles. For relative quantification, the expression of reference housekeeping genes (actin, glyceraldehyde 3phosphate dehydrogenase) was measured. The fold change was estimated using 2 −ΔΔCt . The experimental groups were compared using the students’ t-test statistical analysis, and fold change values were analyzed to see if they were less than or greater than two-fold. For all RT-PCR, protein intensity, image processing and analyses were carried out entirely blinded. Cell cycle analysis was evaluated by IBM SPSS Statistics 25.0. ImageJ/Fiji was used to measure the intensity of proteins (Image analysis software, National Institutes of Health, Bethesda, MD) 24 , with a minimum of 100 cells analyzed from at least ten photographs obtained from at least three different experiments. The RGB images obtained as raw data were saved as 8-bit black and white images in the channel containing the protein image using the split channels function before the analysis stage. The boundaries of the cells were drawn using the 'freehand selection' tool, and the intensities from this area were statistically compared between groups. The intensity values of the groups were normalized using the corrected total cell fluorescence (CTCF) method 25,26 . The normality of the data was assessed using the Shapiro-Wilk test, while the homogeneity of variance was evaluated through Levene's test. Except for the RT-PCR data, ANOVA and Bonferroni post-hoc test was used to analyze samples with normal distribution, Kruskal-Wallis and Pairwise comparison were used to examine samples without normal distribution. Unless otherwise specified, results were presented as mean standard deviation (SD). Statistically significant difference was defined as *p < 0.05, **p < 0.01, ***p < 0.001. RESULTS Gene Expression Profiling and siRNA Modulation in Malignant Melanoma The expression levels of 27 genes, including four housekeeping genes, were analyzed for comprehensive gene expression profiling (Table 1 , supp Fig. 2). Gene expression analysis was centered on the CD133 + group, with a 2-fold difference in expression was considered significant. Here, we examined genes associated with hypoxia, the SHH pathway, epigenetics, Table 1 Gene Expression Profile and Differential Regulation in Cells Groups. RT-PCR results normalized with CD133 + cells and gene expression fold changes were listed. gene CD133+ CD133- CD133+/Hif1a- CD133+/KLF4- CD133+/SHH- CD133+/negc HOUSEKEEPING GENES ACTB 1 1.6 -1.45 -1.78 -2.1 -1.35 GAPDH 1 -1.6 1.45 1.78 2.1 1.35 B2M 1 -2.37 -45.31 -20 -14.17 -6.65 YWHAZ 1 2.27 -14.07 -12.95 -2.64 -4.31 HYPOXIA HIF1A 1 -1.78 -52.89 -5.32 -6.3 -4.82 VHL 1 -1.45 -13.16 -7.39 -13.63 -3 HIF2A 1 -1.67 -4.83 -11.2 -6.04 -3.98 SHH PATHWAY SHH 1 -1.8 -5.77 -3.47 -18.32 -2.35 GLI1 1 -3.36 -41.98 -104.09 -88.34 -15.21 SMO 1 -1.88 -15.26 -3.99 -3.5 -3.85 PTCH2 1 -3.4 -435.04 -53.51 -104.09 -15.17 EPIGENETIC MARKERS HDAC9 1 -9.56 -144.17 -82.62 -138.3 -19.34 CREBBP 1 -3.41 -18.23 -14.6 -19.45 -4.37 HDAC1 1 -1.5 -1.59 -1.05 -1.09 -1.62 EP300 1 -7.34 -9.95 -6.54 -22.09 -3.36 DIFFERENTIATION CD133 1 -23.1 -3.85 -2.85 -3.13 -1.72 KLF4 1 -4.29 -5.2 -61.46 -7.37 -4.65 NANOG 1 1.06 -1.02 1.42 -3.31 1.14 MYC 1 1.14 -2.51 1.03 1.09 -2.11 SMAD2 1 -4.87 -19.14 -54.25 -27.89 -3.36 MAPK1 1 -2.02 -14.91 -7.7 -7.22 -3.28 SOX2 1 -1.36 -17.86 -23.89 -13.59 -8.75 MIGRATION MMP2 1 -2.82 -19.58 -16.17 -14.71 -4.05 MMP9 1 -3.99 -38.63 -34.66 -25.02 -8.46 VASCULARIZATION VEGFR2 1 -1.1 -46.05 -22.92 -10.42 -7.36 FLT1 1 -5.83 -19.95 -32.33 -13.25 -4.41 VEGFA 1 1.27 -11.7 -5.31 -5.61 -2.58 cellular differentiation, migration, and vascularization signaling pathways. Comparative analysis of CSCs and NCSCs at transcription level A comparison between cancer stem cells (CSCs) and non-cancer stem cells (NCSCs) is crucial for addressing the critiques on signaling pathways. In this comparison, CD133 + and CD133- cells exhibit distinct patterns in Gli1 and Ptch2 expression, which are key molecules in the Sonic Hedgehog (SHH) signaling pathway that play significant roles in cancer and stem cell biology 27,28 . Moreover, transcripts of epigenetic markers such as HDAC9, CREBBP, and EP300 are found to be elevated in CSCs compared to NCSCs, suggesting the involvement of epigenetic mechanisms in the resistance of CSCs. markers related to differentiation and migration were more abundant in CSCs compared to NCSCs. Interestingly, the transcript levels of FLT1 were higher in CSCs than in NCSCs, but this was not observed for VEGFR2. Gene silencing resulted in a decrease in hypoxia-related genes at the RNA level. HIF1α, Vhl, and Epsa1 (HIF2α) genes were targeted as hypoxia-related genes. While both CD133 + and CD133- cell groups exhibited similar patterns, for each gene, all three genes showed decreased expression in siRNA-treated groups. siRNA treatment was observed to be effective in modulating the hypoxia mechanism based on the expression of hypoxia related genes. The most affected gene expressions were Gli1 and Ptch2 after gene silencing in SHH pathway. The examination extended to the SHH pathway genes, which encompassed Shh, Gli1, Smo, and Ptch2. Except for the Gli1 gene, which exhibited lower expression in the CD133- cell group compared to the CD133 + group, no significant difference was observed between the two cell groups. All three genes showed decreased expression levels in the siRNA-treated groups. Notably, the siRNA-treated groups showed negative regulation, particularly affecting the expression of the Gli1 and Ptch2 genes. Consequently, siRNA treatments were found to be effective in modulating the SHH signaling pathway mechanism based on gene expression. HDAC9 and EP300 emerge as key molecules in the epigenetic marker panel. Expanding the investigation, attention turned to examining the histone acetylation mechanism through the analysis of HDAC9, Crebbp, HDAC1, and Ep300 genes. Except for HDAC1 (-1.5x fold change), other histone acetylation related genes exhibited negative regulation in the CD133- cell group. This depicts the genes considered as hypoxia targets in CSC resistance. Similarly, in the siRNA treated groups, HDAC1 expression was similar to that of the CD133 + cell group, while the other three genes showed a decrease. It showed negative regulation, particularly on the HDAC9 gene, in the siRNA treated groups. As a result, siRNA treatment may be effective on genes involved in histone acetylation. SMAD2 has the potential to participate in melanoma stemness in the CHL-1 cell line. We explored the regulation of differentiation by assessing the expression of CD133, KLF4, NANOG, MYC, SMAD2, MAPK1, and SOX2 genes. The CD133 marker serves as a label in the differentiation of CSCs. Notably, the CD133- cell group exhibited a significant difference in the experimental stage (-23.1x) compared to the CD133 + cell group, indicating the presence of our CSC-like population. While the expression levels of NANOG, MYC and SOX2 genes were comparable between CD133 + and CD133- cell groups (1.06x, 1.14x and − 1.36x, respectively), MAPK1 gene expression was similar (-2.02x) in both cell groups. Conversely, KLF4 and SMAD2 genes are negatively regulated in CD133- cell group (-4.29x and − 4.87x, respectively). Interestingly, while siRNA treatment reduced the CD133 + trait, resulting in lower CD133 gene expression compared to CD133 + cell group, CD133 + cell group treated with negative control siRNA exhibited similar CD133 gene expression levels. Additionally, while NANOG showed comparable gene expression levels in the KLF4 and HIF1α siRNA groups, the SHH siRNA group (CD133+/SHH-) showed negative regulation. MYC gene expression also exhibited similarity between these two cell groups. The siRNA-treated groups showed negative regulation, particularly affecting the expression of SMAD2, MAPK1 and SOX2 genes. Furthermore, the KLF4 gene also showed negative regulation in the siRNA-treated groups. Among MMP2 and MMP9, MMP9 was more affected by gene silencing. Migration related genes were examined by focusing on MMP2 and MMP9 gene expression. Both MMP2 and MMP9 gene expression levels were found to be higher in CD133 + cells (-2.82x, -3.99x, respectively). Conversely, siRNA-treated groups showed decreased MMP2 and MMP9 gene expression. While FLT1 is influenced by melanoma stemness, gene silencing negatively regulates three vascularization-related gene expressions. Genes associated with vascularization, specifically VEGFR2, FLT1, and VEGFA, were analyzed between the groups. It was observed that FLT1 and VEGFA gene expression levels were comparable between CD133 + and CD133- cell groups (1.85x, -1.79x, respectively), while VEGF gene expression was found to be negatively regulated in the CD133- cell group. Notably, the group treated with HIF1α siRNA exhibited similar FLT1 gene expression to the CD133 + cell group, whereas the groups treated with KLF4 and SHH siRNA showed negative regulation. Additionally, a decrease in VEGFR2 and VEGFA gene expression levels was noted in the siRNA-treated groups. Analysis of Protein Expression in Cellular Response to siRNA Applications At least 100 cells were examined for immunofluorescence staining, with comparisons in cell area entirely based on fluorescence intensity. The protein expression of the cells was statistically compared based on the intensity signals. HIF1α, SHH, Gli, Smo, KLF4, VEGF, MMP9, P300, HDAC9, and Mif proteins were monitored and evaluated in the experimental groups as part of this study. Protein intensity values were graphically presented in Fig. 1 corresponding to HIF1α siRNA, KLF4 siRNA, and SHH siRNA applications. Immunofluorescent images of the cells are provided in the supplementary figures. Silencing SHH Reduces HIF1α Expression in CD133 + Melanoma Cells CD133 + cells without any silencing showed high HIF1α intensity, suggesting that CD133 + cells naturally have elevated HIF1α expression. The group with HIF1α silencing still showed relatively high intensity. This might be due to feedback mechanisms or partial silencing effects in 24 hours, but have tendency to decrease. Silencing KLF4 in CD133 + cells resulted in higher HIF1α intensity than both the CD133+/SHH- and CD133- groups, indicating that KLF4 silencing has a less suppressive effect on HIF1a expression compared to SHH silencing. Silencing SHH in CD133 + cells appear to significantly reduce HIF1a expression compared to other groups (p < 0.01)(supp Fig. 3). Silencing SHH and HIF1α tends to reduce KLF4 Expression in CD133 + Melanoma Cells CD133 + cells exhibited the highest KLF4 expression, highlighting the strong association between CD133 character and KLF4 levels, however there was no statistical difference between CD133 + and CD133- cell groups. Silencing KLF4 in CD133 + cells decreased KLF4 protein intensity, confirming the effectiveness of the silencing. Silencing SHH and HIF1α in CD133 + cells also reduced KLF4 intensity, with HIF1α silencing having a more pronounced effect. CD133- cells had the lowest KLF4 expression, suggesting the importance of CSC character in maintaining high KLF4 levels. However, KLF4 expression was not statistically different between CD133+, CD133- and siRNA treated groups (supp Fig. 4). Silencing KLF4 and HIF1α impacts SHH expression in CD133 + melanoma cells, while CD133 + cells naturally exhibit higher SHH intensity compared to CD133- cells. CD133 + cells exhibited the highest SHH expression, indicating a strong association between CD133 and SHH levels. CD133- cells had the lowest SHH intensity, highlighting the CSC character in maintaining high SHH expression (p < 0.001). Silencing KLF4 in CD133 + cells resulted in high SHH intensity and protein level was comparable to CD133+. Silencing HIF1α in CD133 + cells moderately reduced SHH intensity (p < 0.05), implying HIF1α's role in positively regulating SHH expression. Silencing SHH in CD133 + cells showed a decrease in SHH intensity compared to CD133 + cells (p < 0.001) (supp Fig. 5). Gli1 protein expression was higher in CD133 + cells compared to CD133- cells, even following siRNA applications. CD133 + cells exhibited the highest Gli1 expression, highlighting the association between CD133 and elevated Gli1 levels. CD133- cells showed the lowest Gli1 intensity, indicating minimal Gli1 expression (p < 0.001). Silencing HIF1α in CD133 + cells decreased Gli1 intensity, suggesting roles for HIF1α in positively regulating Gli1 expression (supp Fig. 6). Smo protein expression tended to increase after siRNA applications; especially after SHH siRNA treatments. Following siRNA treatment, an increase in Smo protein expression was observed, with the CD133+/SHH- group exhibiting significantly different Smo protein levels (p < 0.001) (supp Fig. 7). VEGF protein expression responded differently after siRNA treatment. There was no significant difference in VEGF protein expression between the CD133 + and CD133- groups. However, both CD133 + and CD133- groups exhibited higher VEGF expression levels compared to the CD133+/SHH- group (p < 0.01 and p < 0.001, respectively). Silencing HIF1α in CD133 + cells (CD133+/HIF1α-) resulted in a significant increase in VEGF protein expression relative to the CD133+ (p < 0.001) and CD133- (p < 0.01) groups. Additionally, silencing KLF4 in CD133 + cells led to a moderate increase in VEGF intensity. VEGF protein expression exhibited differential responses following siRNA treatments; specifically, silencing SHH in CD133 + cells resulted in decreased VEGF expression when compared to both the CD133 + and CD133- groups (p < 0.01 and p < 0.001, respectively) (supp Fig. 8). Mif protein expression increased after HIF1α and SHH siRNA application. MIF protein levels were significantly higher in the CD133- group compared to the CD133 + group (p < 0.01). The silencing of HIF1α in CD133 + cells led to a notable increase in MIF intensity, emphasizing a strong regulatory influence of HIF1α on MIF expression (p < 0.001 for CD133+/HIF1α- versus CD133 + and p < 0.01 for CD133+/HIF1α- versus CD133+/neg). Additionally, MIF protein expression was elevated following SHH siRNA application compared to the CD133- group (p < 0.01) (supp Fig. 9). KLF4, SHH, and HIF1α silencing does not directly affect MMP9 expression in 24 hours. The expression level of MMP9 protein was similar between the CD133 + and CD133- groups as well as the siRNA-treated groups. However, there was a slight increase in MMP9 expression following KLF4 silencing (supp Fig. 10). P300 protein expression increased after all three siRNA applications. The CD133- group showed moderate P300 intensity, comparable to CD133 + cells. Silencing KLF4, SHH, and HIF1α, as well as other siRNA treatments, led to increased P300 protein expression, with significant differences observed between the CD133 + and CD133- groups. P300 protein expression was elevated following all three siRNA applications (supp Fig. 11). HDAC9 protein expression tends to decrease after siRNA applications. HDAC9 protein levels were significantly higher in the CD133 + group compared to the CD133- cell group (p < 0.001). The CD133+/KLF4- group showed HDAC9 levels comparable to the CD133- group but was statistically distinct from both the CD133+/HIF1α and CD133+/SHH- groups (p < 0.001). HDAC9 protein expression tended to decrease following siRNA applications (supp Fig. 12). GLI1, HDAC9 and MMP9 secretion did not differ after silencing HIF1α, KLF4 and SHH To determine whether the cell groups released the relevant proteins from the cell, ELISA was conducted. Gli1 secretion was highest in the CD133+/KLF4- (770.54 pg/ml) and CD133+/HIF1α- cell groups (763.403 pg/ml). A statistical difference was observed between CD133+/ HIF1α- and CD133- (607.035 pg/ml) (p < 0.01). Following CD133+/KLF4- and CD133+/HIF1α-, CD133+/neg showed the highest secretion (724.645 pg/ml). KLF4, SHH, and HIF1α siRNA treatment increased Gli1 secretion (Fig. 2A). HDAC9 secretion was the highest in the CD133 + group. It was demonstrated that CD133 + and CD133- cells were not distinct from one another (4.65 and 4.355 pg/ml). After siRNA applications, the level of HDAC9 secretion was similar (Fig. 2B). The maximum MMP secretion was seen in the CD133+/neg cells (8.09 pg/ml). Between the CD133+/SHH- group and the CD133+/neg group, there was a statistically significant difference (p < 0.01). After siRNA treatment, MMP9 secretion was consistent (Fig. 2C). GLI1, HDAC9 and MMP9 secretion did not statistically differ after silencing HIF1α, KLF4 and SHH. Cell cycle analysis revealed the critical roles of HIF1α and KLF4 in the G0/G1 and S phases, while SHH was found to be crucial in the G2/M phase of melanoma CSCs. In this study, we evaluated the cell cycle by analyzing the distribution of cells across the G0/G1, S, and G2/M phases (Fig. 3,A-F). In the G0/G1 phase, there was no difference between CD133 + and CD133- cells (2.9%, 3.4%); however, silencing HIF1α (CD133+/HIF1α-) resulted in significant increase (16.2%; p < 0.05) and CD133+/HIF1α- cell group was statistically different from CD133 + and CD133- cell groups (p < 0.05) (Fig. 3, G). Similarly, KLF4 silencing led to an increase in the G0/G1 phase (18.6%) (p < 0.01) (Fig. 3, H), while SHH silencing resulted in a percentage increase 10.4% (Fig. 3, I). Negative siRNA-treated group (CD133+/neg, 7.03%) was statistically different from CD133 + and CD133- cell groups (p < 0.05). Comparatively, in the S phase, there was no difference between CD133 + and CD133- cells (54.3%, 60.1%), while CD133+/ HIF1α- cells exhibited a significant decrease (48.2%) compared to CD133- cells (p < 0.01) (Fig. 3). Furthermore, while CD133+/KLF4 cells showed an increase (61.3%) compared to CD133+, CD133+/SHH (49.4%) cells remained similar to CD133 + cell group. Conversely, no differences were observed between cell groups in the G2/M phase with HIF1α and KLF4 silencing (Fig. 3, G, H), whereas SHH silencing leads to decrease (p < 0.05) (Fig. 3, I). DISCUSSION Melanoma, a highly aggressive type of skin cancer, presents significant treatment challenges due to its low survival rate and resistance to multiple drugs 29,30 . Within the tumor cell hierarchy, CSCs are a distinct population of undifferentiated cells with heightened tumorigenicity, metastatic ability, self-renewal capabilities, and therapy resistance 31 . Here, we aim to identify differences between CSCs and NCSCs regarding their interaction with extracellular matrix and determine if manipulating genes associated with hypoxia and differentiation alters the characteristics of CSCs. In this study, CD133 marker was considered to represent the CSC. The cells obtained by flow cytometry maintained the stem cell characteristics on the matrigel over the CD133 marker at gene expression level. Additionally, the stemness feature decreased after siRNA treatments. This outcome indicates that specific signaling pathways have the potential to reduce CSC character, highlighting the potential of transitioning cells into NCSCs through alterations in stem cell characteristics. CSCs’ characteristics have been manipulating using various extracellular matrices to mimic their in vivo environment. Bonturi et al. investigated laminin, fibronectin, vitronectin and other intercellular agents separately in culture mediums and evaluated their protease efficiency 32 . Matrigel, particularly in studies involving the melanoma CHL-1 cell line, has shown significant effects on cell invasion potential, both on culture surfaces and in invasion studies. These findings are commonly used to explore cellular differentiation potential 32–34 . The primary objective of our study is to examine alterations in cancer-related signaling molecules by silencing the HIF1α, KLF4, and SHH genes. Accurate interpretation of gene expression analyses relies on stable housekeeping genes 35 . In our study, ACTB and GAPDH showed consistent expression levels, making them reliable options for data normalization. Significant variations were observed in B2M and YWHAZ expression across groups, with B2M decreasing significantly (-45.31x) following HIF1α gene silencing, suggesting its potential role in malignancy 36–39 . Similarly, YWHAZ expression decreased (-12.95x and − 14.07x) following KLF4 and HIF1α silencing, likely impacting cellular processes like cell development, cell cycle regulation, and apoptosis 40–44 . CSCs are more prominent in tumor masses, particularly in cell lines and spheroid models. Based on the hypothesis of maintaining stem cell capacity, the HIF1α gene of CSCs has been silenced in previous studies 45,46 . Here, in hypoxia panel, the most prominent finding revealed that CSCs showed a decreased expression of the VHL gene upon gene silencing (Table 1 ). This aligns with HIF1α and VHL relation, wherein VHL stabilizes HIF1α under normoxic conditions. Similar pattern were observed in VHL-deficient renal cancer cells, suggesting the impact on the downstream signalling of HIF1α 47 . HIF1α protein interacts with CD133 gene promoter, increasing the frequency of CD133 + glioma, colon, and pancreatic cells CSCs, via OCT4 and SOX2 48–53 . Additionally, a cytoplasmic correlation between HIF1α and CD133 was observed 54 , where CD133 can influence HIF1α expression and facilitate its nuclear translocation during hypoxia 55 . Previous research has demonstrated a correlation between NANOG and OCT4 expression and HIF1α levels in prostate cancer cells 56 . Although HIF1α and NANOG showed similar trends in the prostate cancer study, decreased HIF1α gene expression did not significantly change NANOG gene expression here in melanoma CSC. SHH plays a crucial role in cell differentiation and tissue polarity during embryonic development with mutations in SHH pathway genes observed in melanoma patients 57 . Our study highlighted Gli1 as a potential target, particularly evident with KLF4 silencing, leading to significant decrease in Gli1 expression. This indicates that there may be Gli1 and KLF4 interaction specific to melanoma or melanoma stem cells. Furthermore, PTCH2 expression was significantly affected by gene silencing, particularly with HIF1α silencing, suggesting potential interaction of HIF1α and PTCH2. Protein expression analysis revealed higher SHH and Gli1 levels in CD133 + cells compared to CD133- cells, while Smo expression was comparable. After siRNA treatments, various differentiation-related genes responded differently, notably, SHH siRNA altered NANOG expression uniquely compared to other siRNA applications, indicating potential interaction between NANOG and hedgehog signaling proteins Gli1 and Gli3 reported in a 2016 embryonic stem cell study 58 . As an oncoprotein, MYC orchestrates various cellular processes, including cell division, differentiation, angiogenesis, DNA replication, RNA processing 59–62 by regulating gene expression, often linked to tumor markes, interacting with pathways like Wnt, Notch, Hedgehog signaling 60,63,64 . While other siRNA treatments showed no difference in CD133 + and CD133- cells, HIF1α gene silencing led to a slight decrease in MYC gene expression, possibly indicating an interaction between MYC and HIF1α, both are critical in development and homeostasis 62 . In hypoxia, HIF enhances MYC proteasomal degradation 65,66 and interacts physically, stimulated p21 expression for cell cycle arrest 67 . While MYC enhances the abundance of pVHL complex constituents, it diminishes the binding of HIF1α to the pVHL complex, thereby impeding the degradation of HIF1α 62,68 . The support of this literatures, silencing of the HIF1α gene supports the decreasing trend of MYC and VHL gene expression. Morever, MYC enhances HIF1α activity at the chromatin level, potentially by facilitating histone acetylation 62,69–71 . In our study, HIF1α silencing correlated with reduced expression of EP300, involved in histone acetylation, and HDAC9, implicated in angiogenesis and cancer 72–75 . P300 protein levels increased with all three siRNA applications, while HDAC9 tended to decrease. Furthermore, all three siRNA treatments resulted in lower levels of HDAC9 protein secretion compared to the CD133 + cell group. Specifically, the KLF4 siRNA application showed the lowest amount of secreted HDAC9. MMP2 and MMP9 are key enzymes involved in breaking down the extracellular matrix under physiological conditions, and studies have identified them as a potential markers for breast 76 and melanoma 77,78 cancers. In our study, comparing CSCs and NCSCs from the CHL-1 cell line, we observed lower MMP expression in the NCSC, with siRNA treatments leading to decrease MMP2 and MMP9 gene expression. This reduction aligns with disruption in KLF4, SHH, and HIF1α genes, indicating their efficacy against on cancer cells. Previous studies have shown a decrease in MMP proteins after SHH siRNA treatment in gastric and liver cancer cells 79,80 . Similarly, in glioblastoma research, HIF1α, MMP9 and VEGF proteins displayed similar trends, potentially explaining the decrease in VEGF and MMP9 gene expression with HIF1α is silencing 81 . Interestingly, our protein-level analysis revealed similar protein expression pattern after 24 hours incubation with siRNA. As a hallmark of cancers, a cancer-specific network of blood vessels is required for melanoma to survive and grow 82 . Research on melanoma cell lines has highlighted the significance of VEGFR2 and VEGFA in cell metastasis, with VEGFR2 playing a dominant role in invasion due to its higher expression level 83 . Within our study, VEGFR2 gene expression exhibited lower levels in NCSCs compared to CSCs, while VEGFA gene expression demonstrated similarity between the two groups. Moreover, following the application of siRNA, both gene expression underwent a reduction, with VEGFR2 experiencing a more notable decrease, which was corroborated by MMP gene expression, further signifying a decline in invasion upon siRNA treatment. In melanoma cells, autocrine or paracrine VEGFR-1 (FLT1) activation increases cancer cell survival, cell migration, invasion, and chemotherapy resistance. In a study performed in A375 and M14 melanoma cell lines, it has been shown to reduce invasion capacity following with VEGFR-1 inhibition 84 . VEGFR1 gene expression decreased in after SHH siRNA, but increased KLF4 and HIF1α silencing. VEGF protein intensity showed a decrease with SHH silencing with correlated with transcriptional level, which indicated direct relation with VEGF and SHH in CD133 + melanoma cells. Cell cycle regulation is one of the important hallmarks of tumor resistance. Several studies show that cell cycle arrest were operated via HIF1α 85–88 . Our study observed G0/G1 phase arrest after HIF1α silencing, consistent with the literature. Additionally, HIF1α and KLF4 siRNA-treatment reduces the S phase compared to the CSCs and NCSC, indicating HIF1α’s specific targeting potential. SHH silencing impacted the G2/M phase, aligning with the literature on SHH signalling 89 . Further studies are required for exploring vascularization, glycolytic pathways and mitochondrial processes in the tumor microenvironment. CONCLUSIONS In this study, we utilized a malignant melanoma model to comprehensively investigate the characteristics of CSCs and NCSCs, aiming to discern the essential distinctions between these two cellular populations. Through a comparative analysis of cellular responses following the targeted silencing of three distinct genes, we uncovered potential therapeutic targets within malignant melanoma stem cells. Our findings underscore the dynamic influences of HIF1α, KLF4, and SHH in modulating CSC behavior, positioning them as pivotal modulators and suggesting the conceptualization of an siRNA-based therapeutic strategy in pathological states. The study also highlighted the significant effect of the interaction between cells and the extracellular matrix, emphasizing the importance of mimicking an in vivo-like layout for the study. Notable differences were observed at both the transcriptome and protein levels across all three siRNA treatments, with the interplay between SHH and HIF1α presenting an additional opportunity for targeted therapeutic interventions, potentially linked with the SHH pathway. As a result of siRNA applications here, Gli1 and PTCH2, the main proteins in the SHH pathway, came to the fore as target molecules for further studies. Our study demonstrates that certain cancer subtypes could potentially undergo reprogramming by silencing, resulting in the generation of a distinct cancer cell subtype, which does not exhibit intermediary characteristics between CSCs and non-stem cancer cells. Abbreviations CSCs: Cancer stem cells NCSCs: Non-cancer stem cells siRNA: short interfering RNA HIF1 a : Hypoxia-inducible factor 1 a KLF4: Kruppel-like factor 4 SHH: Sonic Hedgehog Mif: macrophage migration inhibitory factor HDAC9: Histone deacetylase 9 CD: Cluster of Differentiation SMO: Smoothened, Frizzled Class Receptor SOX2: Sex determining region Y-box 2 PI3K: Phosphoinositide 3-kinase EMEM: Eagle's Minimal Essential Medium PE: Phycoerythrin PBS: Phosphate Buffered Saline FBS: Fetal Bovine Serum nM: nano molar hrs: hours RT-PCR: Real‐Time Polymerase Chain Reaction ELISA: Enzyme Linked Immunosorbent Assay MMP9: Matrix metalloproteinase 9 DAPI: 4',6-diamidino-2-phenylindole ACTB: Beta-actin GAPDH: Glyceraldehyde-3-Phosphate Dehydrogenase B2M: Beta-2-Microglobulin YWHAZ: Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Zeta VHL: von Hippel-Lindau HIF2 a : Hypoxia-inducible factor 2 a PTCH2: Protein patched homolog 2 CREBBP: Cyclic adenosine monophosphate Response Element Binding protein HDAC1: Histone deacetylase 1 EP300: E1A-associated protein p300 MAPK1: mitogen-activated protein kinase 1 MMP2: Matrix metalloproteinase 2 VEGF2: vascular endothelial growth factor 2 FLT1: Fms Related Receptor Tyrosine Kinase 1 VEGFA: vascular endothelial growth factor A MHC: major histocompatibility complex Declarations Ethics approval and consent to participate Not applicable. There are no humans or animals directly involved in this study. Ethics approval and consent to participate Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Completing interests The author declares that there is none of the conflicts. Funding This study was supported by Ege University Scientific Research Projects Coordination Unit. Project Number: 20785 Authors’ contributions HA, BO and CBA designed the study.BO and DCK performed the experiments. BO wrote the manuscript. BO analyzed the data and organized the final manuscript. NUKY, VG, AU and GG revised the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank Ege University Scientific Research Projects Coordination Unit to financial support. 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University","correspondingAuthor":false,"prefix":"","firstName":"Berrin","middleName":"","lastName":"Ozdil","suffix":""},{"id":345942792,"identity":"15d0db9b-1af1-499d-af9f-72f72a040cb6","order_by":2,"name":"Cigir Biray Avci","email":"","orcid":"","institution":"Ege University","correspondingAuthor":false,"prefix":"","firstName":"Cigir","middleName":"Biray","lastName":"Avci","suffix":""},{"id":345942793,"identity":"2ba0a7b3-f144-4496-bff9-14e879cfe5cc","order_by":3,"name":"Duygu Calik Kocaturk","email":"","orcid":"","institution":"Dr. İsmail Fehmi Cumalioglu City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Duygu","middleName":"Calik","lastName":"Kocaturk","suffix":""},{"id":345942794,"identity":"e4ca9271-02dd-47b0-b94a-cf0f354fc89d","order_by":4,"name":"Volkan Gorgulu","email":"","orcid":"","institution":"Ege University","correspondingAuthor":false,"prefix":"","firstName":"Volkan","middleName":"","lastName":"Gorgulu","suffix":""},{"id":345942795,"identity":"86e34765-cc53-49fa-b3ad-c533afe26f9a","order_by":5,"name":"Aysegul Uysal","email":"","orcid":"","institution":"Ege University","correspondingAuthor":false,"prefix":"","firstName":"Aysegul","middleName":"","lastName":"Uysal","suffix":""},{"id":345942796,"identity":"2d4ebfa9-11d3-4fb2-8a37-fd3500010254","order_by":6,"name":"Gunnur Guler","email":"","orcid":"","institution":"Izmir Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Gunnur","middleName":"","lastName":"Guler","suffix":""},{"id":345942797,"identity":"e009fdcd-131a-43c2-abca-0671726cb450","order_by":7,"name":"Nefise Ulku Karabay Yavasoglu","email":"","orcid":"","institution":"Ege University","correspondingAuthor":false,"prefix":"","firstName":"Nefise","middleName":"Ulku Karabay","lastName":"Yavasoglu","suffix":""}],"badges":[],"createdAt":"2024-07-26 12:24:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4808028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4808028/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63660744,"identity":"321e01c8-a9b7-4544-9fe1-2bc995065213","added_by":"auto","created_at":"2024-08-30 17:44:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":664294,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"figure1copy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4808028/v1/41571d981e9844e49bc6f6ad.jpg"},{"id":63660981,"identity":"22bab95c-cf00-45f5-b312-79be9a48b910","added_by":"auto","created_at":"2024-08-30 17:52:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":322737,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"figure2ELISAcopy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4808028/v1/1e20741eb0b6d91036bb32a2.jpg"},{"id":63660743,"identity":"b89f77d3-0437-423d-b3d0-8fccd47142a2","added_by":"auto","created_at":"2024-08-30 17:44:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":767697,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"figure3cellcyclecopy.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4808028/v1/54478fbf57e1bcf58ffa7268.jpg"},{"id":67755056,"identity":"d2cabe58-6f21-45db-8b47-af7bf22a77d7","added_by":"auto","created_at":"2024-10-29 11:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3140019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4808028/v1/28a5f674-a53d-461f-a745-8a51aeb7970e.pdf"},{"id":63660747,"identity":"adfde044-3f4b-4d28-acb8-fedfc5757979","added_by":"auto","created_at":"2024-08-30 17:44:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58387793,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresandlegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-4808028/v1/e456827e0a943a71b84be24d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modulating cancer stem cell characteristics in CD133+ melanoma cells through HIF1α, KLF4, and SHH silencing","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMalignant melanoma, a highly aggressive skin tumor originated from melanocytes, displays a complex genetic profile influenced by both genetic and environmental factors \u003csup\u003e1\u003c/sup\u003e. The abundance and activity of CSCs is associated with the aggressiveness of malignant melanoma as in other cancer types \u003csup\u003e2\u003c/sup\u003e. The detection of tumor heterogeneity, undifferentiated molecular markers, and increased tumor identity of melanoma subtypes with embryonic-like developmental plasticity strongly suggest the presence and involvement of malignant melanoma stem cells in the initiation and progression of this malignancy \u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMost physiological functions in cellular processes are regulated by oxygen concentration. Cancer cells can adapt to low oxygen level by regulating the hypoxia signaling, where the key regulator is HIF1α \u003csup\u003e4,5\u003c/sup\u003e. In cancer progression, hypoxia, which activates angiogenesis, thereby increases the risk of invasion and metastasis \u003csup\u003e5,6\u003c/sup\u003e. This increases cell survival within the tumor as well as suppresses anti-tumor immunity and inhibits the therapeutic response. A study conducted on hypoxia in the GBM cell line demonstrated that the upregulation of HIF1 and HIF2 resulted in elevated levels of SOX2 and KLF4, thereby affecting the stemness and cell cycle of the cells through this signaling pathway \u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSupporting the hypoxia mechanism, here, we focus on KLF4 and SHH signaling for the changes in stemness regulators. The role of KLF4 in melanoma remains unknown, despite its strong expression in post-mitotic epidermal cells and terminally differentiated skin and gut cells\u003csup\u003e8\u003c/sup\u003e. A 2017 study investigating KLF4\u0026rsquo;s function in melanoma cell lines (SK-Mel-2, SK-Mel-5, SK-Mel-28, SSM2c, M26c, M33x and M51) and its interaction with the MAPK signaling system found strong expression of KLF4 in human melanomas. In addition, knockdown of KLF4 decreased patient melanoma cell proliferation and promoted cell death, while ectopic expression of KLF4 increased melanoma cell growth by lowering apoptosis \u003csup\u003e8\u003c/sup\u003e. Although KLF4 has been extensively studied in melanoma, research on melanoma CSCs remains limited. Understanding the role of the KLF4 protein is crucial for studying and characterizing CSCs.\u003c/p\u003e \u003cp\u003eThe SHH protein, a key player in the Hedgehog signaling pathway, contributes significantly to various developmental processes, especially in nervous system development, where it acts as a morphogen during early developmental stages. Besides its crucial role in embryonic development, Hedgehog signaling has been shown to be involved in vasculogenesis and angiogenesis \u003csup\u003e9\u003c/sup\u003e. Despite its prominence in the developmental process, the SHH signaling pathway is also actively involved in studies on CSC. Hypoxia-induced activation of the SHH signaling pathway in different tissues \u003csup\u003e10\u003c/sup\u003e, highlights its importance in CSC biology \u003csup\u003e11\u003c/sup\u003e. However, research on SHH signaling in melanoma cell lines is limited, with most studies focusing on PI3K rather than Gli1\u003csup\u003e12,13\u003c/sup\u003e. In a thesis study that has not yet been published, malignant vector transfection of the SHH protein into CHL-1 melanoma cells, followed by examination of the PI3K signaling pathway and Gli1/Gli3 expression \u003csup\u003e14\u003c/sup\u003e. Here, SHH gene expression was directed using siRNA technology and cells were classified as either CSCs or NCSCs.\u003c/p\u003e \u003cp\u003eTumor microenvironment is another factor affecting tumor character; extracellular matrix molecules are responsible for tumor progression. Therefore, it is extremely important to establish the tumor microenvironment to create specific target therapies. In order to increase the metastatic capacity, melanoma cells manipulate the extracellular matrix and secrete extracellular factors for this \u003csup\u003e15\u003c/sup\u003e, and each change or modification on the extracellular matrix is effective on the cancer biological behavior and has to be examined \u003csup\u003e16\u0026ndash;18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe main aim in this study to demonstrate the differential signaling in CSC and NCSC in the presence of extracellular matrix components, Matrigel, with or without silencing the stem cell character by targeting the HIF1α, KLF4 and SHH genes in CSCs. The results indicate that silencing any of these genes did not alter the characteristics between these cells, but instead led to the emergence of new ones.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell Lines and Culture\u003c/h2\u003e \u003cp\u003eNonpigmented human melanoma cell lines CHL-1 (ATCC\u0026reg; CRL-9446TM) were cultured in EMEM (Eagle's Minimum Essential Medium) (Biowest L0416) supplemented with 10% fetal bovine serum (Biowest, S1810) at 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C. Regular authentication and mycoplasma infection checks were conducted on the cell lines. For experimental consistency and reproducibility, cells within passages 6\u0026ndash;8 were selected for flow cytometry sorting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFluorescence-activated cell sorting (FACS)\u003c/h2\u003e \u003cp\u003eThe cells were detached from the surface of the flask by trypsinization, a standard method for enzymatically releasing adherent cells. Following detachment, the cells were washed twice with cold 1X PBS to remove any residual trypsin and cellular debris. The collected cells were then diluted to a concentration of 10\u003csup\u003e6\u003c/sup\u003e cell/ml in 10 ml cold 1X PBS. Following this, they were incubated with 10 \u0026micro;L CD133 phycoerythrin (PE)-labelled antibody (Miltenyi Biotec Ltd. 130-113-186) and 10 \u0026micro;L DAPI for 15 minutes at +\u0026thinsp;4\u003csup\u003eo\u003c/sup\u003eC. After the incubation period, the cells were washed with 1X PBS supplemented with 1% dialyzed fetal bovine serum (FBS). Control cells were subjected to staining with DAPI only, without the addition of any antibodies. Subsequently, cell sorting was performed using BD FACS Diva 8.0. The collection tubes labeled CD133\u0026thinsp;+\u0026thinsp;were designated as CSCs, while CD133- cells were categorized as NCSCs. Within the malignant melanoma cell population, a CD133\u0026thinsp;+\u0026thinsp;cell subset was ranging from 0.1\u0026ndash;0.4%. CD133\u0026thinsp;+\u0026thinsp;cell subset was used as passage 2 to passage 4, were utilized in subsequent experimental procedures (see Supp Fig.\u0026nbsp;1A). Cell counting was conducted after sorting and experimental procedures using Muse\u0026reg; Cell Analyzer, a specialized instrument for automated cell counting and analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMatrigel coating\u003c/h2\u003e \u003cp\u003eBefore seeding cells, the surface was coated with Matrigel (Corning\u0026reg; Matrigel\u0026reg; Basement Membrane Matrix, 354234). To ensure optimal coating, all materials required for this process were pre-cooled to +\u0026thinsp;4\u003csup\u003eo\u003c/sup\u003eC and handled accordingly. The Matrigel used for surface coating was prepared by mixing it with serum-free medium at a ratio of 1:4 while it was in liquid form, ensuring no bubbles formed. On a 15 mm coverslip (with a surface area of 1.77 cm\u003csup\u003e2\u003c/sup\u003e), 100 microliters of Matrigel solution were applied, while on the surface of a 6-well plate (with a surface area of 9.6 cm\u003csup\u003e2\u003c/sup\u003e), 542 microliters of Matrigel solution were distributed on ice. Coverslips measuring 15 mm were utilized for immunofluorescence, AFM, SEM and XPS analysis, while the 6 well plates were used for RT-PCR. The matrigel, which remained in state at +\u0026thinsp;4\u003csup\u003eo\u003c/sup\u003eC, was uniformly distributed across the surface during matrigel coating. To achieve even distribution, the surface containing the liquid Matrigel and in contact with a cold surface was placed on a shaker. Following the coating of Matrigel, the surface was then transferred to a standard cell culture incubator set at 37\u003csup\u003eo\u003c/sup\u003eC. Through observation, it was determined that a 20-minute incubation period was optimal for complete polymerization of the Matrigel coating, ensuring a stable and supportive environment for subsequent cell seeding. The surface coating of Matrigel was further characterized using atomic force microscopy (Bruker Dimension Edge with ScanAsyst AFM (Bruker, Germany))\u003csup\u003e19,20\u003c/sup\u003e and scanning electron microscopy (Thermo Scientific Apreo S LoVac SEM (ThermoFisher Scientific, US))\u003csup\u003e20,21\u003c/sup\u003e imaging techniques (Supp. Figure\u0026nbsp;1B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSmall interfering RNA (siRNA) transfection\u003c/h2\u003e \u003cp\u003eTo silence the HIF1α, KLF4, and SHH genes, CD133\u0026thinsp;+\u0026thinsp;malignant melanoma CSCs were transfected with 0-200 nM siRNA (On-Targetplus Human siRNA, Smartpool, L-005089-00-0005, L-004018-00-0005, L-006036-00-0005 Horizon). The transfection was performed to determine the optimal siRNA dosage, and gene expression levels were subsequently assessed using RT-PCR for control. The fold-change cut-off limit was set at 2, with changes between 0 and 2 were considered insignificant. In the experiment, siRNA concentrations ranging from 0-200 nM were used. Specifically, the use of 5nM HIF1α siRNA resulted in a fold change of -56.44x. For KLF4, the highest fold change (-4.56x fold) was observed at 5 nM, while SHH gene silencing was achieved at 25 nM. (-3.84 x fold). Subsequently, 5 nM HIF1α, and KLF4; 25 nM negative control siRNA and SHH were used for further experiments. Cells were fixed after 24 hrs of incubation following siRNA transfection for analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReal-Time Polymerase Chain Reaction (RT-PCR\u003c/b\u003e)\u003c/p\u003e \u003cp\u003eCells were incubated for 24 hrs following siRNA transfection after seeding a concentration at 10\u003csup\u003e5\u003c/sup\u003e cells/ml. The cells were rinsed with 1X Phosphate-buffered saline (PBS), detached from the surface with StemProTM AccutaseTM Cell Dissociation Reagent (Thermo Fisher) and dissolved in RNA buffer (Roche). The subsequent steps were conducted in accordance with the Roche isolation kit and customized Roche panel used for the gene expression profiling. Sampling was performed three times. The heatmap with hierarchical trees was generated using Clustvis \u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence Staining\u003c/h2\u003e \u003cp\u003eAfter 24 hours of siRNA transfection, cells were fixed using 4% paraformaldehyde (PFA) (Sigma P-6148) and permeabilized with 0.25% TritonX-100 (Biotech, C34H62O11). Subsequently, cells were subjected to the blocking with 3% bovine serum albumin (Chem Cruz, sc-2323) to minimize non-spesific binding. Following blocking, cells were incubated overnight at +\u0026thinsp;4\u003csup\u003eo\u003c/sup\u003eC with primary antibodies (HIF1α, Bioss bs-0737R; KLF4, Thermo PA1-095; SHH, Santa cruz, sc-373779; Gli1, Bioss AI05040; Smo, Bioss AA062588; VEGF, bs-1665R; Mif, Santa Cruz sc-271631; MMP9, Bioss bs-4593R; P300, Bioss, bs-5339R; HDAC9, Bioss, bsm-54186R;) diluted 1:200 in 1%BSA. Following primary antibody incubation, cells were washed and incubated with secondary antibodies (Alexa Fluor\u0026reg; 488-AffiniPure Goat Anti-Rabbit Jackson Immuno Research, 111-545-003) at room temperature for one hour. Finally, cells were mounted with Fluoroshield Mounting Medium with DAPI (abcam, ab104139) for nuclear counterstaining. Microscopic images were acquired using an Olympus BX-51 microscope (Olympus Optical Co., Tokyo, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe Enzyme Linked Immunosorbent Assay (ELISA)\u003c/h2\u003e \u003cp\u003eBased on the results obtained from RT-PCR analysis, Gli1, HDAC9, and MMP9 were identified as the most significantly differentially expressed genes. To further investigate their potential role in paracrine signalling Enzyme Linked Immunosorbent Assay (ELISA) was performed. Prior to assay, all reagents were equilibrated at room temperature before use. The supernatants collected from the cells were processed following by the ELISA kit protocol (BT-Lab E6417Hu, E5411Hu and E0936Hu), as previously described in our earlier study \u003csup\u003e23\u003c/sup\u003e. The amount of secreted protein was quantified using a microplate reader set to measure absorbance at 450 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell Cycle Analysis\u003c/h2\u003e \u003cp\u003eThe cell cycle phases were assessed using a Muse\u0026reg; cell analyzer \u003csup\u003e19\u003c/sup\u003e. The cells were subjected to a standard passage protocol, and cells were harvested and cell pellet was collected. The cells were then fixed in 70% ice-cold ethanol and stored at -20\u003csup\u003eo\u003c/sup\u003eC overnight to ensure proper fixation. The following day, the ethanol was carefully removed from the samples, and cell cycle kit solution (Millipore lot:2941162) was added to each sample. The samples were incubated in the dark for 30 minutes. The Muse\u0026reg; cell analyzer was used to read the samples in the cell cycle analysis mode.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe 'Multiple Plate Analysis' tool was used to examine gene expression profiles. For relative quantification, the expression of reference housekeeping genes (actin, glyceraldehyde 3phosphate dehydrogenase) was measured. The fold change was estimated using 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e. The experimental groups were compared using the students\u0026rsquo; t-test statistical analysis, and fold change values were analyzed to see if they were less than or greater than two-fold. For all RT-PCR, protein intensity, image processing and analyses were carried out entirely blinded. Cell cycle analysis was evaluated by IBM SPSS Statistics 25.0. ImageJ/Fiji was used to measure the intensity of proteins (Image analysis software, National Institutes of Health, Bethesda, MD) \u003csup\u003e24\u003c/sup\u003e, with a minimum of 100 cells analyzed from at least ten photographs obtained from at least three different experiments. The RGB images obtained as raw data were saved as 8-bit black and white images in the channel containing the protein image using the split channels function before the analysis stage. The boundaries of the cells were drawn using the 'freehand selection' tool, and the intensities from this area were statistically compared between groups. The intensity values of the groups were normalized using the corrected total cell fluorescence (CTCF) method \u003csup\u003e25,26\u003c/sup\u003e. The normality of the data was assessed using the Shapiro-Wilk test, while the homogeneity of variance was evaluated through Levene's test. Except for the RT-PCR data, ANOVA and Bonferroni post-hoc test was used to analyze samples with normal distribution, Kruskal-Wallis and Pairwise comparison were used to examine samples without normal distribution. Unless otherwise specified, results were presented as mean standard deviation (SD). Statistically significant difference was defined as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGene Expression Profiling and siRNA Modulation in Malignant Melanoma\u003c/h2\u003e \u003cp\u003eThe expression levels of 27 genes, including four housekeeping genes, were analyzed for comprehensive gene expression profiling (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, supp Fig.\u0026nbsp;2). Gene expression analysis was centered on the CD133\u0026thinsp;+\u0026thinsp;group, with a 2-fold difference in expression was considered significant. Here, we examined genes associated with hypoxia, the SHH pathway, epigenetics,\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene Expression Profile and Differential Regulation in Cells Groups. RT-PCR results normalized with CD133\u0026thinsp;+\u0026thinsp;cells and gene expression fold changes were listed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD133+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD133-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCD133+/Hif1a-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD133+/KLF4-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD133+/SHH-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCD133+/negc\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eHOUSEKEEPING GENES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-2.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-45.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-14.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-6.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYWHAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-14.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-12.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-2.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-4.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHYPOXIA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIF1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-52.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-5.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-6.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-4.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-13.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-7.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-13.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIF2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-4.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-11.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-6.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-3.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSHH PATHWAY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-5.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-3.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-18.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-2.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-41.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-104.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-88.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-15.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-15.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-3.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-3.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTCH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-435.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-53.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-104.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-15.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEPIGENETIC MARKERS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDAC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-9.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-144.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-82.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-138.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-19.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCREBBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-18.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-14.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-19.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-4.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEP300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-7.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-9.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-6.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-22.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-3.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eDIFFERENTIATION\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-23.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-3.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-2.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-3.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKLF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-4.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-5.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-61.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-7.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-4.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNANOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-3.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-2.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMAD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-4.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-19.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-54.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-27.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-3.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAPK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-14.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-7.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-7.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-3.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-17.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-23.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-13.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-8.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMIGRATION\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-19.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-16.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-14.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-4.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-38.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-34.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-25.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-8.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eVASCULARIZATION\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVEGFR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-46.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-22.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-10.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-7.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFLT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-5.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-19.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-32.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-13.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-4.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-11.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-5.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-5.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-2.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ecellular differentiation, migration, and vascularization signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparative analysis of CSCs and NCSCs at transcription level\u003c/h2\u003e \u003cp\u003eA comparison between cancer stem cells (CSCs) and non-cancer stem cells (NCSCs) is crucial for addressing the critiques on signaling pathways. In this comparison, CD133\u0026thinsp;+\u0026thinsp;and CD133- cells exhibit distinct patterns in Gli1 and Ptch2 expression, which are key molecules in the Sonic Hedgehog (SHH) signaling pathway that play significant roles in cancer and stem cell biology \u003csup\u003e27,28\u003c/sup\u003e. Moreover, transcripts of epigenetic markers such as HDAC9, CREBBP, and EP300 are found to be elevated in CSCs compared to NCSCs, suggesting the involvement of epigenetic mechanisms in the resistance of CSCs. markers related to differentiation and migration were more abundant in CSCs compared to NCSCs. Interestingly, the transcript levels of FLT1 were higher in CSCs than in NCSCs, but this was not observed for VEGFR2.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene silencing resulted in a decrease in hypoxia-related genes at the RNA level.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHIF1α, Vhl, and Epsa1 (HIF2α) genes were targeted as hypoxia-related genes. While both CD133\u0026thinsp;+\u0026thinsp;and CD133- cell groups exhibited similar patterns, for each gene, all three genes showed decreased expression in siRNA-treated groups. siRNA treatment was observed to be effective in modulating the hypoxia mechanism based on the expression of hypoxia related genes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe most affected gene expressions were Gli1 and Ptch2 after gene silencing in SHH pathway.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe examination extended to the SHH pathway genes, which encompassed Shh, Gli1, Smo, and Ptch2. Except for the Gli1 gene, which exhibited lower expression in the CD133- cell group compared to the CD133\u0026thinsp;+\u0026thinsp;group, no significant difference was observed between the two cell groups. All three genes showed decreased expression levels in the siRNA-treated groups. Notably, the siRNA-treated groups showed negative regulation, particularly affecting the expression of the Gli1 and Ptch2 genes. Consequently, siRNA treatments were found to be effective in modulating the SHH signaling pathway mechanism based on gene expression.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHDAC9 and EP300 emerge as key molecules in the epigenetic marker panel.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExpanding the investigation, attention turned to examining the histone acetylation mechanism through the analysis of HDAC9, Crebbp, HDAC1, and Ep300 genes. Except for HDAC1 (-1.5x fold change), other histone acetylation related genes exhibited negative regulation in the CD133- cell group. This depicts the genes considered as hypoxia targets in CSC resistance. Similarly, in the siRNA treated groups, HDAC1 expression was similar to that of the CD133\u0026thinsp;+\u0026thinsp;cell group, while the other three genes showed a decrease. It showed negative regulation, particularly on the HDAC9 gene, in the siRNA treated groups. As a result, siRNA treatment may be effective on genes involved in histone acetylation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSMAD2 has the potential to participate in melanoma stemness in the CHL-1 cell line.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe explored the regulation of differentiation by assessing the expression of CD133, KLF4, NANOG, MYC, SMAD2, MAPK1, and SOX2 genes. The CD133 marker serves as a label in the differentiation of CSCs. Notably, the CD133- cell group exhibited a significant difference in the experimental stage (-23.1x) compared to the CD133\u0026thinsp;+\u0026thinsp;cell group, indicating the presence of our CSC-like population. While the expression levels of NANOG, MYC and SOX2 genes were comparable between CD133\u0026thinsp;+\u0026thinsp;and CD133- cell groups (1.06x, 1.14x and \u0026minus;\u0026thinsp;1.36x, respectively), MAPK1 gene expression was similar (-2.02x) in both cell groups. Conversely, KLF4 and SMAD2 genes are negatively regulated in CD133- cell group (-4.29x and \u0026minus;\u0026thinsp;4.87x, respectively). Interestingly, while siRNA treatment reduced the CD133\u0026thinsp;+\u0026thinsp;trait, resulting in lower CD133 gene expression compared to CD133\u0026thinsp;+\u0026thinsp;cell group, CD133\u0026thinsp;+\u0026thinsp;cell group treated with negative control siRNA exhibited similar CD133 gene expression levels. Additionally, while NANOG showed comparable gene expression levels in the KLF4 and HIF1α siRNA groups, the SHH siRNA group (CD133+/SHH-) showed negative regulation. MYC gene expression also exhibited similarity between these two cell groups. The siRNA-treated groups showed negative regulation, particularly affecting the expression of SMAD2, MAPK1 and SOX2 genes. Furthermore, the KLF4 gene also showed negative regulation in the siRNA-treated groups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAmong MMP2 and MMP9, MMP9 was more affected by gene silencing.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMigration related genes were examined by focusing on MMP2 and MMP9 gene expression. Both MMP2 and MMP9 gene expression levels were found to be higher in CD133\u0026thinsp;+\u0026thinsp;cells (-2.82x, -3.99x, respectively). Conversely, siRNA-treated groups showed decreased MMP2 and MMP9 gene expression.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWhile FLT1 is influenced by melanoma stemness, gene silencing negatively regulates three vascularization-related gene expressions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGenes associated with vascularization, specifically VEGFR2, FLT1, and VEGFA, were analyzed between the groups. It was observed that FLT1 and VEGFA gene expression levels were comparable between CD133\u0026thinsp;+\u0026thinsp;and CD133- cell groups (1.85x, -1.79x, respectively), while VEGF gene expression was found to be negatively regulated in the CD133- cell group. Notably, the group treated with HIF1α siRNA exhibited similar FLT1 gene expression to the CD133\u0026thinsp;+\u0026thinsp;cell group, whereas the groups treated with KLF4 and SHH siRNA showed negative regulation. Additionally, a decrease in VEGFR2 and VEGFA gene expression levels was noted in the siRNA-treated groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Protein Expression in Cellular Response to siRNA Applications\u003c/h2\u003e \u003cp\u003eAt least 100 cells were examined for immunofluorescence staining, with comparisons in cell area entirely based on fluorescence intensity. The protein expression of the cells was statistically compared based on the intensity signals. HIF1α, SHH, Gli, Smo, KLF4, VEGF, MMP9, P300, HDAC9, and Mif proteins were monitored and evaluated in the experimental groups as part of this study. Protein intensity values were graphically presented in Fig.\u0026nbsp;1 corresponding to HIF1α siRNA, KLF4 siRNA, and SHH siRNA applications. Immunofluorescent images of the cells are provided in the supplementary figures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSilencing SHH Reduces HIF1α Expression in CD133\u0026thinsp;+\u0026thinsp;Melanoma Cells\u003c/h2\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;cells without any silencing showed high HIF1α intensity, suggesting that CD133\u0026thinsp;+\u0026thinsp;cells naturally have elevated HIF1α expression. The group with HIF1α silencing still showed relatively high intensity. This might be due to feedback mechanisms or partial silencing effects in 24 hours, but have tendency to decrease. Silencing KLF4 in CD133\u0026thinsp;+\u0026thinsp;cells resulted in higher HIF1α intensity than both the CD133+/SHH- and CD133- groups, indicating that KLF4 silencing has a less suppressive effect on HIF1a expression compared to SHH silencing. Silencing SHH in CD133\u0026thinsp;+\u0026thinsp;cells appear to significantly reduce HIF1a expression compared to other groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)(supp Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSilencing SHH and HIF1α tends to reduce KLF4 Expression in CD133\u0026thinsp;+\u0026thinsp;Melanoma Cells\u003c/h2\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;cells exhibited the highest KLF4 expression, highlighting the strong association between CD133 character and KLF4 levels, however there was no statistical difference between CD133\u0026thinsp;+\u0026thinsp;and CD133- cell groups. Silencing KLF4 in CD133\u0026thinsp;+\u0026thinsp;cells decreased KLF4 protein intensity, confirming the effectiveness of the silencing. Silencing SHH and HIF1α in CD133\u0026thinsp;+\u0026thinsp;cells also reduced KLF4 intensity, with HIF1α silencing having a more pronounced effect. CD133- cells had the lowest KLF4 expression, suggesting the importance of CSC character in maintaining high KLF4 levels. However, KLF4 expression was not statistically different between CD133+, CD133- and siRNA treated groups (supp Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSilencing KLF4 and HIF1α impacts SHH expression in CD133\u0026thinsp;+\u0026thinsp;melanoma cells, while CD133\u0026thinsp;+\u0026thinsp;cells naturally exhibit higher SHH intensity compared to CD133- cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;cells exhibited the highest SHH expression, indicating a strong association between CD133 and SHH levels. CD133- cells had the lowest SHH intensity, highlighting the CSC character in maintaining high SHH expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Silencing KLF4 in CD133\u0026thinsp;+\u0026thinsp;cells resulted in high SHH intensity and protein level was comparable to CD133+. Silencing HIF1α in CD133\u0026thinsp;+\u0026thinsp;cells moderately reduced SHH intensity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), implying HIF1α's role in positively regulating SHH expression. Silencing SHH in CD133\u0026thinsp;+\u0026thinsp;cells showed a decrease in SHH intensity compared to CD133\u0026thinsp;+\u0026thinsp;cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (supp Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGli1 protein expression was higher in CD133\u0026thinsp;+\u0026thinsp;cells compared to CD133- cells, even following siRNA applications.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCD133\u0026thinsp;+\u0026thinsp;cells exhibited the highest Gli1 expression, highlighting the association between CD133 and elevated Gli1 levels. CD133- cells showed the lowest Gli1 intensity, indicating minimal Gli1 expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Silencing HIF1α in CD133\u0026thinsp;+\u0026thinsp;cells decreased Gli1 intensity, suggesting roles for HIF1α in positively regulating Gli1 expression (supp Fig.\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSmo protein expression tended to increase after siRNA applications; especially after SHH siRNA treatments.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFollowing siRNA treatment, an increase in Smo protein expression was observed, with the CD133+/SHH- group exhibiting significantly different Smo protein levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (supp Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003e \u003cb\u003eVEGF protein expression responded differently after siRNA treatment.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere was no significant difference in VEGF protein expression between the CD133\u0026thinsp;+\u0026thinsp;and CD133- groups. However, both CD133\u0026thinsp;+\u0026thinsp;and CD133- groups exhibited higher VEGF expression levels compared to the CD133+/SHH- group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Silencing HIF1α in CD133\u0026thinsp;+\u0026thinsp;cells (CD133+/HIF1α-) resulted in a significant increase in VEGF protein expression relative to the CD133+ (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD133- (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) groups. Additionally, silencing KLF4 in CD133\u0026thinsp;+\u0026thinsp;cells led to a moderate increase in VEGF intensity. VEGF protein expression exhibited differential responses following siRNA treatments; specifically, silencing SHH in CD133\u0026thinsp;+\u0026thinsp;cells resulted in decreased VEGF expression when compared to both the CD133\u0026thinsp;+\u0026thinsp;and CD133- groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively) (supp Fig.\u0026nbsp;8).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMif protein expression increased after HIF1α and SHH siRNA application.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMIF protein levels were significantly higher in the CD133- group compared to the CD133\u0026thinsp;+\u0026thinsp;group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The silencing of HIF1α in CD133\u0026thinsp;+\u0026thinsp;cells led to a notable increase in MIF intensity, emphasizing a strong regulatory influence of HIF1α on MIF expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for CD133+/HIF1α- versus CD133\u0026thinsp;+\u0026thinsp;and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for CD133+/HIF1α- versus CD133+/neg). Additionally, MIF protein expression was elevated following SHH siRNA application compared to the CD133- group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (supp Fig.\u0026nbsp;9).\u003c/p\u003e \u003cp\u003e \u003cb\u003eKLF4, SHH, and HIF1α silencing does not directly affect MMP9 expression in 24 hours.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe expression level of MMP9 protein was similar between the CD133\u0026thinsp;+\u0026thinsp;and CD133- groups as well as the siRNA-treated groups. However, there was a slight increase in MMP9 expression following KLF4 silencing (supp Fig.\u0026nbsp;10).\u003c/p\u003e \u003cp\u003e \u003cb\u003eP300 protein expression increased after all three siRNA applications.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe CD133- group showed moderate P300 intensity, comparable to CD133\u0026thinsp;+\u0026thinsp;cells. Silencing KLF4, SHH, and HIF1α, as well as other siRNA treatments, led to increased P300 protein expression, with significant differences observed between the CD133\u0026thinsp;+\u0026thinsp;and CD133- groups. P300 protein expression was elevated following all three siRNA applications (supp Fig.\u0026nbsp;11).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHDAC9 protein expression tends to decrease after siRNA applications.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHDAC9 protein levels were significantly higher in the CD133\u0026thinsp;+\u0026thinsp;group compared to the CD133- cell group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The CD133+/KLF4- group showed HDAC9 levels comparable to the CD133- group but was statistically distinct from both the CD133+/HIF1α and CD133+/SHH- groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). HDAC9 protein expression tended to decrease following siRNA applications (supp Fig.\u0026nbsp;12).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGLI1, HDAC9 and MMP9 secretion did not differ after silencing HIF1α, KLF4 and SHH\u003c/b\u003e To determine whether the cell groups released the relevant proteins from the cell, ELISA was conducted.\u003c/p\u003e \u003cp\u003eGli1 secretion was highest in the CD133+/KLF4- (770.54 pg/ml) and CD133+/HIF1α- cell groups (763.403 pg/ml). A statistical difference was observed between CD133+/ HIF1α- and CD133- (607.035 pg/ml) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Following CD133+/KLF4- and CD133+/HIF1α-, CD133+/neg showed the highest secretion (724.645 pg/ml). KLF4, SHH, and HIF1α siRNA treatment increased Gli1 secretion (Fig.\u0026nbsp;2A). HDAC9 secretion was the highest in the CD133\u0026thinsp;+\u0026thinsp;group. It was demonstrated that CD133\u0026thinsp;+\u0026thinsp;and CD133- cells were not distinct from one another (4.65 and 4.355 pg/ml). After siRNA applications, the level of HDAC9 secretion was similar (Fig.\u0026nbsp;2B). The maximum MMP secretion was seen in the CD133+/neg cells (8.09 pg/ml). Between the CD133+/SHH- group and the CD133+/neg group, there was a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). After siRNA treatment, MMP9 secretion was consistent (Fig.\u0026nbsp;2C). GLI1, HDAC9 and MMP9 secretion did not statistically differ after silencing HIF1α, KLF4 and SHH.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCell cycle analysis revealed the critical roles of HIF1α and KLF4 in the G0/G1 and S phases, while SHH was found to be crucial in the G2/M phase of melanoma CSCs.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, we evaluated the cell cycle by analyzing the distribution of cells across the G0/G1, S, and G2/M phases (Fig.\u0026nbsp;3,A-F). In the G0/G1 phase, there was no difference between CD133\u0026thinsp;+\u0026thinsp;and CD133- cells (2.9%, 3.4%); however, silencing HIF1α (CD133+/HIF1α-) resulted in significant increase (16.2%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and CD133+/HIF1α- cell group was statistically different from CD133\u0026thinsp;+\u0026thinsp;and CD133- cell groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;3, G). Similarly, KLF4 silencing led to an increase in the G0/G1 phase (18.6%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;3, H), while SHH silencing resulted in a percentage increase 10.4% (Fig.\u0026nbsp;3, I). Negative siRNA-treated group (CD133+/neg, 7.03%) was statistically different from CD133\u0026thinsp;+\u0026thinsp;and CD133- cell groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Comparatively, in the S phase, there was no difference between CD133\u0026thinsp;+\u0026thinsp;and CD133- cells (54.3%, 60.1%), while CD133+/ HIF1α- cells exhibited a significant decrease (48.2%) compared to CD133- cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;3). Furthermore, while CD133+/KLF4 cells showed an increase (61.3%) compared to CD133+, CD133+/SHH (49.4%) cells remained similar to CD133\u0026thinsp;+\u0026thinsp;cell group. Conversely, no differences were observed between cell groups in the G2/M phase with HIF1α and KLF4 silencing (Fig.\u0026nbsp;3, G, H), whereas SHH silencing leads to decrease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;3, I).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMelanoma, a highly aggressive type of skin cancer, presents significant treatment challenges due to its low survival rate and resistance to multiple drugs \u003csup\u003e29,30\u003c/sup\u003e. Within the tumor cell hierarchy, CSCs are a distinct population of undifferentiated cells with heightened tumorigenicity, metastatic ability, self-renewal capabilities, and therapy resistance \u003csup\u003e31\u003c/sup\u003e. Here, we aim to identify differences between CSCs and NCSCs regarding their interaction with extracellular matrix and determine if manipulating genes associated with hypoxia and differentiation alters the characteristics of CSCs. In this study, CD133 marker was considered to represent the CSC. The cells obtained by flow cytometry maintained the stem cell characteristics on the matrigel over the CD133 marker at gene expression level. Additionally, the stemness feature decreased after siRNA treatments. This outcome indicates that specific signaling pathways have the potential to reduce CSC character, highlighting the potential of transitioning cells into NCSCs through alterations in stem cell characteristics.\u003c/p\u003e \u003cp\u003eCSCs\u0026rsquo; characteristics have been manipulating using various extracellular matrices to mimic their \u003cem\u003ein vivo\u003c/em\u003e environment. Bonturi et al. investigated laminin, fibronectin, vitronectin and other intercellular agents separately in culture mediums and evaluated their protease efficiency \u003csup\u003e32\u003c/sup\u003e. Matrigel, particularly in studies involving the melanoma CHL-1 cell line, has shown significant effects on cell invasion potential, both on culture surfaces and in invasion studies. These findings are commonly used to explore cellular differentiation potential \u003csup\u003e32\u0026ndash;34\u003c/sup\u003e. The primary objective of our study is to examine alterations in cancer-related signaling molecules by silencing the HIF1α, KLF4, and SHH genes.\u003c/p\u003e \u003cp\u003eAccurate interpretation of gene expression analyses relies on stable housekeeping genes\u003csup\u003e35\u003c/sup\u003e. In our study, ACTB and GAPDH showed consistent expression levels, making them reliable options for data normalization. Significant variations were observed in B2M and YWHAZ expression across groups, with B2M decreasing significantly (-45.31x) following HIF1α gene silencing, suggesting its potential role in malignancy\u003csup\u003e36\u0026ndash;39\u003c/sup\u003e. Similarly, YWHAZ expression decreased (-12.95x and \u0026minus;\u0026thinsp;14.07x) following KLF4 and HIF1α silencing, likely impacting cellular processes like cell development, cell cycle regulation, and apoptosis\u003csup\u003e40\u0026ndash;44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCSCs are more prominent in tumor masses, particularly in cell lines and spheroid models. Based on the hypothesis of maintaining stem cell capacity, the HIF1α gene of CSCs has been silenced in previous studies \u003csup\u003e45,46\u003c/sup\u003e. Here, in hypoxia panel, the most prominent finding revealed that CSCs showed a decreased expression of the VHL gene upon gene silencing (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This aligns with HIF1α and VHL relation, wherein VHL stabilizes HIF1α under normoxic conditions. Similar pattern were observed in VHL-deficient renal cancer cells, suggesting the impact on the downstream signalling of HIF1α \u003csup\u003e47\u003c/sup\u003e. HIF1α protein interacts with CD133 gene promoter, increasing the frequency of CD133\u0026thinsp;+\u0026thinsp;glioma, colon, and pancreatic cells CSCs, via OCT4 and SOX2 \u003csup\u003e48\u0026ndash;53\u003c/sup\u003e. Additionally, a cytoplasmic correlation between HIF1α and CD133 was observed \u003csup\u003e54\u003c/sup\u003e, where CD133 can influence HIF1α expression and facilitate its nuclear translocation during hypoxia\u003csup\u003e55\u003c/sup\u003e. Previous research has demonstrated a correlation between NANOG and OCT4 expression and HIF1α levels in prostate cancer cells \u003csup\u003e56\u003c/sup\u003e. Although HIF1α and NANOG showed similar trends in the prostate cancer study, decreased HIF1α gene expression did not significantly change NANOG gene expression here in melanoma CSC.\u003c/p\u003e \u003cp\u003eSHH plays a crucial role in cell differentiation and tissue polarity during embryonic development with mutations in SHH pathway genes observed in melanoma patients \u003csup\u003e57\u003c/sup\u003e. Our study highlighted Gli1 as a potential target, particularly evident with KLF4 silencing, leading to significant decrease in Gli1 expression. This indicates that there may be Gli1 and KLF4 interaction specific to melanoma or melanoma stem cells. Furthermore, PTCH2 expression was significantly affected by gene silencing, particularly with HIF1α silencing, suggesting potential interaction of HIF1α and PTCH2. Protein expression analysis revealed higher SHH and Gli1 levels in CD133\u0026thinsp;+\u0026thinsp;cells compared to CD133- cells, while Smo expression was comparable. After siRNA treatments, various differentiation-related genes responded differently, notably, SHH siRNA altered NANOG expression uniquely compared to other siRNA applications, indicating potential interaction between NANOG and hedgehog signaling proteins Gli1 and Gli3 reported in a 2016 embryonic stem cell study \u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs an oncoprotein, MYC orchestrates various cellular processes, including cell division, differentiation, angiogenesis, DNA replication, RNA processing\u003csup\u003e59\u0026ndash;62\u003c/sup\u003e by regulating gene expression, often linked to tumor markes, interacting with pathways like Wnt, Notch, Hedgehog signaling \u003csup\u003e60,63,64\u003c/sup\u003e. While other siRNA treatments showed no difference in CD133\u0026thinsp;+\u0026thinsp;and CD133- cells, HIF1α gene silencing led to a slight decrease in MYC gene expression, possibly indicating an interaction between MYC and HIF1α, both are critical in development and homeostasis \u003csup\u003e62\u003c/sup\u003e. In hypoxia, HIF enhances MYC proteasomal degradation \u003csup\u003e65,66\u003c/sup\u003e and interacts physically, stimulated p21 expression for cell cycle arrest \u003csup\u003e67\u003c/sup\u003e. While MYC enhances the abundance of pVHL complex constituents, it diminishes the binding of HIF1α to the pVHL complex, thereby impeding the degradation of HIF1α \u003csup\u003e62,68\u003c/sup\u003e. The support of this literatures, silencing of the HIF1α gene supports the decreasing trend of MYC and VHL gene expression. Morever, MYC enhances HIF1α activity at the chromatin level, potentially by facilitating histone acetylation \u003csup\u003e62,69\u0026ndash;71\u003c/sup\u003e. In our study, HIF1α silencing correlated with reduced expression of EP300, involved in histone acetylation, and HDAC9, implicated in angiogenesis and cancer \u003csup\u003e72\u0026ndash;75\u003c/sup\u003e. P300 protein levels increased with all three siRNA applications, while HDAC9 tended to decrease. Furthermore, all three siRNA treatments resulted in lower levels of HDAC9 protein secretion compared to the CD133\u0026thinsp;+\u0026thinsp;cell group. Specifically, the KLF4 siRNA application showed the lowest amount of secreted HDAC9.\u003c/p\u003e \u003cp\u003eMMP2 and MMP9 are key enzymes involved in breaking down the extracellular matrix under physiological conditions, and studies have identified them as a potential markers for breast \u003csup\u003e76\u003c/sup\u003e and melanoma \u003csup\u003e77,78\u003c/sup\u003e cancers. In our study, comparing CSCs and NCSCs from the CHL-1 cell line, we observed lower MMP expression in the NCSC, with siRNA treatments leading to decrease MMP2 and MMP9 gene expression. This reduction aligns with disruption in KLF4, SHH, and HIF1α genes, indicating their efficacy against on cancer cells. Previous studies have shown a decrease in MMP proteins after SHH siRNA treatment in gastric and liver cancer cells \u003csup\u003e79,80\u003c/sup\u003e. Similarly, in glioblastoma research, HIF1α, MMP9 and VEGF proteins displayed similar trends, potentially explaining the decrease in VEGF and MMP9 gene expression with HIF1α is silencing \u003csup\u003e81\u003c/sup\u003e. Interestingly, our protein-level analysis revealed similar protein expression pattern after 24 hours incubation with siRNA.\u003c/p\u003e \u003cp\u003eAs a hallmark of cancers, a cancer-specific network of blood vessels is required for melanoma to survive and grow \u003csup\u003e82\u003c/sup\u003e. Research on melanoma cell lines has highlighted the significance of VEGFR2 and VEGFA in cell metastasis, with VEGFR2 playing a dominant role in invasion due to its higher expression level \u003csup\u003e83\u003c/sup\u003e. Within our study, VEGFR2 gene expression exhibited lower levels in NCSCs compared to CSCs, while VEGFA gene expression demonstrated similarity between the two groups. Moreover, following the application of siRNA, both gene expression underwent a reduction, with VEGFR2 experiencing a more notable decrease, which was corroborated by MMP gene expression, further signifying a decline in invasion upon siRNA treatment. In melanoma cells, autocrine or paracrine VEGFR-1 (FLT1) activation increases cancer cell survival, cell migration, invasion, and chemotherapy resistance. In a study performed in A375 and M14 melanoma cell lines, it has been shown to reduce invasion capacity following with VEGFR-1 inhibition \u003csup\u003e84\u003c/sup\u003e. VEGFR1 gene expression decreased in after SHH siRNA, but increased KLF4 and HIF1α silencing. VEGF protein intensity showed a decrease with SHH silencing with correlated with transcriptional level, which indicated direct relation with VEGF and SHH in CD133\u0026thinsp;+\u0026thinsp;melanoma cells.\u003c/p\u003e \u003cp\u003eCell cycle regulation is one of the important hallmarks of tumor resistance. Several studies show that cell cycle arrest were operated via HIF1α \u003csup\u003e85\u0026ndash;88\u003c/sup\u003e. Our study observed G0/G1 phase arrest after HIF1α silencing, consistent with the literature. Additionally, HIF1α and KLF4 siRNA-treatment reduces the S phase compared to the CSCs and NCSC, indicating HIF1α\u0026rsquo;s specific targeting potential. SHH silencing impacted the G2/M phase, aligning with the literature on SHH signalling \u003csup\u003e89\u003c/sup\u003e. Further studies are required for exploring vascularization, glycolytic pathways and mitochondrial processes in the tumor microenvironment.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn this study, we utilized a malignant melanoma model to comprehensively investigate the characteristics of CSCs and NCSCs, aiming to discern the essential distinctions between these two cellular populations. Through a comparative analysis of cellular responses following the targeted silencing of three distinct genes, we uncovered potential therapeutic targets within malignant melanoma stem cells. Our findings underscore the dynamic influences of HIF1α, KLF4, and SHH in modulating CSC behavior, positioning them as pivotal modulators and suggesting the conceptualization of an siRNA-based therapeutic strategy in pathological states. The study also highlighted the significant effect of the interaction between cells and the extracellular matrix, emphasizing the importance of mimicking an in vivo-like layout for the study. Notable differences were observed at both the transcriptome and protein levels across all three siRNA treatments, with the interplay between SHH and HIF1α presenting an additional opportunity for targeted therapeutic interventions, potentially linked with the SHH pathway. As a result of siRNA applications here, Gli1 and PTCH2, the main proteins in the SHH pathway, came to the fore as target molecules for further studies. Our study demonstrates that certain cancer subtypes could potentially undergo reprogramming by silencing, resulting in the generation of a distinct cancer cell subtype, which does not exhibit intermediary characteristics between CSCs and non-stem cancer cells.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCSCs:\u0026nbsp;\u003c/strong\u003eCancer stem cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCSCs:\u0026nbsp;\u003c/strong\u003eNon-cancer stem cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003esiRNA:\u0026nbsp;\u003c/strong\u003eshort interfering RNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIF1\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eHypoxia-inducible factor 1\u0026nbsp;a\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKLF4:\u0026nbsp;\u003c/strong\u003eKruppel-like factor 4\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSHH:\u0026nbsp;\u003c/strong\u003eSonic Hedgehog\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMif:\u0026nbsp;\u003c/strong\u003emacrophage migration inhibitory factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDAC9:\u0026nbsp;\u003c/strong\u003eHistone deacetylase 9\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCD:\u0026nbsp;\u003c/strong\u003eCluster of Differentiation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSMO:\u0026nbsp;\u003c/strong\u003eSmoothened, Frizzled Class Receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSOX2:\u0026nbsp;\u003c/strong\u003eSex determining region Y-box 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePI3K:\u0026nbsp;\u003c/strong\u003ePhosphoinositide 3-kinase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEMEM:\u0026nbsp;\u003c/strong\u003eEagle\u0026apos;s Minimal Essential Medium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePE:\u0026nbsp;\u003c/strong\u003ePhycoerythrin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePBS:\u0026nbsp;\u003c/strong\u003ePhosphate Buffered Saline\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFBS:\u0026nbsp;\u003c/strong\u003eFetal Bovine Serum\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enM:\u0026nbsp;\u003c/strong\u003enano molar\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehrs:\u0026nbsp;\u003c/strong\u003ehours\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRT-PCR:\u0026nbsp;\u003c/strong\u003eReal‐Time Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eELISA:\u0026nbsp;\u003c/strong\u003eEnzyme Linked Immunosorbent Assay\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMMP9:\u0026nbsp;\u003c/strong\u003eMatrix metalloproteinase 9\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDAPI:\u0026nbsp;\u003c/strong\u003e4\u0026apos;,6-diamidino-2-phenylindole\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACTB:\u0026nbsp;\u003c/strong\u003eBeta-actin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGAPDH:\u0026nbsp;\u003c/strong\u003eGlyceraldehyde-3-Phosphate Dehydrogenase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB2M:\u003c/strong\u003e Beta-2-Microglobulin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYWHAZ:\u0026nbsp;\u003c/strong\u003eTyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Zeta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVHL:\u0026nbsp;\u003c/strong\u003evon Hippel-Lindau\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIF2\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eHypoxia-inducible factor 2\u0026nbsp;a\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTCH2:\u0026nbsp;\u003c/strong\u003eProtein patched homolog 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCREBBP:\u0026nbsp;\u003c/strong\u003eCyclic adenosine monophosphate Response Element Binding protein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDAC1:\u0026nbsp;\u003c/strong\u003eHistone deacetylase 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEP300:\u0026nbsp;\u003c/strong\u003eE1A-associated protein p300\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAPK1:\u0026nbsp;\u003c/strong\u003emitogen-activated protein kinase 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMMP2:\u0026nbsp;\u003c/strong\u003eMatrix metalloproteinase 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVEGF2:\u0026nbsp;\u003c/strong\u003evascular endothelial growth factor 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFLT1:\u0026nbsp;\u003c/strong\u003eFms Related Receptor Tyrosine Kinase 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVEGFA:\u0026nbsp;\u003c/strong\u003evascular endothelial growth factor A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMHC:\u0026nbsp;\u003c/strong\u003emajor histocompatibility complex\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable. There are no humans or animals directly involved in this study.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompleting interests\u003c/p\u003e\n\u003cp\u003eThe author declares that there is none of the conflicts.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by Ege University Scientific Research Projects Coordination Unit. Project Number: 20785\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHA, BO and CBA designed the study.BO and DCK performed the experiments. BO wrote the manuscript. BO analyzed the data and organized the final manuscript. NUKY, VG, AU and GG revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Ege University Scientific Research Projects Coordination Unit to financial support. We would like to express our gratitude to Prof. Dr. Emin İlker Medine for kindly allowing us to use their research facility.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSangalli, A. \u003cem\u003eet al.\u003c/em\u003e Sex-specific effect of RNASEL rs486907 and miR-146a rs2910164 polymorphisms\u0026rsquo; interaction as a susceptibility factor for melanoma skin cancer. \u003cem\u003eMelanoma Res.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 309\u0026ndash;314 (2017).\u003c/li\u003e\n\u003cli\u003eLa Porta, C. A. M. \u0026amp; Zapperi, S. 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Biol.\u003c/em\u003e \u003cstrong\u003e294\u003c/strong\u003e, 133\u0026ndash;147 (2006).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Melanoma, Cancer stem cell, HIF1α, KLF4, SHH","lastPublishedDoi":"10.21203/rs.3.rs-4808028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4808028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalignant melanoma, an aggressive skin cancer derived from melanocytes, contains a subpopulation known as cancer stem cells (CSCs), with distinct self-renewal and differentiation abilities, setting them apart from non-cancer stem cells (NCSCs). This study aims to examine how CSCs respond to the suppression of their stem cell characteristics through targeted gene silencing of HIF1α, KLF4, and SHH within the context of the extracellular matrix, with a particular focus on Matrigel. Silencing targeted genes individually induced distinct changes in CSCs behavior, revealing novel therapeutic targets through analysis of gene expression, protein levels, and cell cycle dynamics. A comparison between melanoma CSCs and NCSCs revealed significant shifts in SHH signaling, epigenetic markers, differentiation, migration, and vascularization genes. Specifically, CSCs exhibited elevated levels of SHH, Gli1, and HDAC9, while NCSCs showed increased expression of Mif. Our findings highlight the emergence of a unique cellular phenotype following gene silencing, distinct from both CSCs and NCSCs. Diverse signaling pathways underlie this phenomenon, offering valuable insights for development of melanoma therapies.\u003c/p\u003e","manuscriptTitle":"Modulating cancer stem cell characteristics in CD133+ melanoma cells through HIF1α, KLF4, and SHH silencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-30 17:44:01","doi":"10.21203/rs.3.rs-4808028/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23795312-62d2-4a4a-812e-5daf44075b9c","owner":[],"postedDate":"August 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-29T11:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-30 17:44:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4808028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4808028","identity":"rs-4808028","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}