In recent years, next-generation sequencing technologies have shed light on epigenetic signatures in malignant tumors (Hussen et al. 2022). Creating an epigenetic landscape of different cancer entities is helpful for revealing the biological mechanisms involved in carcinogenesis (Davies et al. 2020), likely leading to more personalized molecularly targeted therapies in the future (Hussen et al. 2022; Davies et al. 2020). For cancer cells, epigenetic mechanisms seem to be advantageous over genetic alterations since the changes in gene expression are not attributable to mutations in the sequence of DNA (Takeshima and Ushijima 2019). To date, known epigenetic mechanisms involved in carcinogenesis include DNA methylation, posttranslational histone modification, and the regulation of noncoding RNAs (ncRNAs) (Perri et al. 2017). In 1993, Victor Ambros and Gary Ruvkun discovered small ncRNA types that are obviously involved in the posttranscriptional silencing of target genes while working on Caenorhabditis elegans (Lee et al. 1993, Reinhart et al. 2000). Moreover, it is widely accepted that microRNA (miRNAs) are crucial for the regulation of gene expression and participate in the control of almost all biological processes (BP) regulating the maintenance of cellular integrity (Galagali and Kim 2020). Furthermore, during the last two decades, it has become evident that pathophysiological conditions, diseases, and even viral infections can be related to specific miRNA expression patterns (Vaghf et al. 2022). Genes encoding small RNAs often reside within fragile chromosome sites; thus, the loss of heterozygosity concerning miRNAs acting as tumor suppressors may promote carcinogenesis (Calin et al. 2004). In fact, more than 50% of miRNA genes are in genomic regions associated with the emergence of cancer (Calin et al. 2004). Moreover, copy number variations, epigenetic mechanisms, transcriptional regulation, impaired processing, effects on miRNA binding sites, and the expression of competing endogenous RNAs can alter the expression and function of miRNAs and thus may contribute to carcinogenesis (Misiewicz-Krzeminska et al. 2019).
Recently, we and others found that cancer cell lines treated with AdoMet, the second most extensively used enzyme cofactor after ATP, exhibited significant decreases in proliferation, migration, and invasion (Schmidt et al. 2016). Transcriptome data from PC-3 cells, a prostate cancer cell line that was treated with AdoMet S-adenosylmethionine (SAM), revealed the upregulation of numerous tumor suppressor genes and the downregulation of proto-oncogenes. We determined interconnections between AdoMet and alterations in histone methylation (Mathes et al. 2024) as well as promoter methylation (Schmidt et al. 2016). In recent literature, links between SAM and ncRNAs have been reported and discussed (Mosca et al. 2021). For example, Chu et al. (2019) showed changes in the promoter methylation status of cancer-related long ncRNAs and miRNAs in hepatocellular carcinoma cells, which displayed an increase in the intracellular concentration of AdoMet. Furthermore, the regulation of miRNA expression profiles by SAM has been shown in head and neck cancer cells as well as in breast cancer cells (Chu et al. 2019; Pagano et al. 2020).
The aim of this work was to investigate the regulation of the miRNA expression profile through AdoMet in prostate cancer cells (PC-3).
PC-3 cells were grown in a RPMI 1640 medium supplemented with 10% fetal calf serum and 1.2% penicillin/streptomycin as described previously (Schmidt et al. 2016) (PAN-Biotech GmbH, Aidenbach, Germany). For RNA analysis, cells were grown to a subconfluence of 70–80%. The cells were treated with vehicle (0.005 M H2SO4 and 10% Ethanol) or 200 μM of SAM (32 mM prepared in 5 mM sulfuric acid and 10% ethanol; New England Biolabs, Ipswich, MA, USA) for 120 h. Under both conditions, the medium was changed once per 24 h. Cells were trypsinized and counted. A number of 1 Mio cells was used for the isolation of RNA.
The total RNA was isolated from PC-3 cells using a NucleoSpin miRNA Kit Macherey Nagel (Düren, Germany) according to the manufacturer’s instructions. By following ligation of the RNA to modified 3′ and 5′ adaptors, the products were reverse transcribed (Super Script III; Thermo Fisher Scientific, Waltham, MA, USA), purified (SPRIselect reagent kit, Beckman Coulter, Brea CA, USA), and PCR amplified for 14 cycles (KAPA HiFi HotStart PCR Kit, KAPA Biosystems, Wilmington, MA, USA). After size selection by polyacrylamide gel electrophoresis, miRNAs containing flanking p5 and p7 adapters were sequenced on a HiSeq 2000 instrument (Illumina; library preparation and sequencing were performed by GenXPro GmbH, Frankfurt, Germany). The results were uploaded to EMBL Array Express. The accession number is E-MTAB-14388.
The total RNA was reverse transcribed into cDNA using Super Script III (Thermo Fisher Scientific) according to the manufacturer’s instructions. qPCR was subsequently performed using a TaqMan miRNA assay kit Applied Biosystems (Waltham MA, USA); (Thermo Fisher Scientific) according to the manufacturer’s protocol to amplify miRNAs on an ABI 7500 Real-Time PCR system (Applied Biosystems; Thermo Fisher Scientific). The thermocycling reactions were performed in the following three steps: initial denaturation at 95°C for 5 min, 40 cycles of 95°C for 10 s, and 60°C for 30 s; and the following primer pairs for qPCR are as follows: miR-192-5p forward, 5′-GGACTTTCTTCATTCACA CCG-3′; reverse, 5′-GACCACTGAGGTTAGAGCCA-3′; and U6 forward, 5′-TCGCTTCGGCAGCACATATACT-3′ and reverse, 5′-ACGCTTCGGGAATTTGCGTGTC-3′. StarD13 forward, 5′-ACTGTCTGTGGTGGGAAACA-3′; StarD13 reverse, TGAGGCACACTTGTTCAACG-3′; GAPDH forward, 5′-TCAAGAAGGTGGTGAAGCAGG-3′; and GAPDH reverse, 5′-TCAAAGGTGGAGGAGTGGGT-3′. The expression levels were quantified using the 2−ΔΔCt method, miRNA expression was normalized to that of U6, and mRNA expression was normalized to that of GAPDH. The reactions were performed in triplicate for each sample, with at least three independent runs.
PC-3 cells were seeded in 6-well plates. Transfection of the miR-192-5p antagomir (CUGACCUAUGAAUUGACAGCC) and the corresponding negative control miRNA (miR-NC, UUUGUACUACACAAAAGUACUG) were purchased from Thermo Fisher Scientific Company. Transfection into PC-3 cells was performed using Lipofectamine 3000 in 250 mL of Opti-MeM (Reduced Serum Medium) (Invitrogen, Waltham MA, USA) according to the manufacturer’s instructions. Then, the cells were trypsinized and counted for subsequent analyses of hsa-microRNA-192-5p expression or for use in cell proliferation assays.
To assess the proliferation of transfected or treated PC-3 cells, the CellTiter 96® AQueous Nonradioactive Cell Proliferation Assay (MTS) was used (Promega, Mannheim, Germany). Transfected or treated PC-3 cells were seeded in 96-well plates (2000 cells per well), after which MTS was added, and the absorbance was measured after 24 h, 48 h, and 120 h. At least three independent experiments were performed in triplicate. The data are expressed as percentages of proliferation, where the proliferation rates for the control were arbitrarily set to 100%.
PC-3 cells were washed with phosphate buffered saline. Cell lysis buffer was added (#70108, Cell Signaling Technology, Danvers, MA, USA), and cell lysates were collected in Eppendorf cups. Subsequently, the caspase activity assay (Caspase-3 Activity Assay Kit #5723, Cell Signaling Technology) was performed according to the manufacturer’s instructions. Fluorescence was measured using a plate reader (excitation wavelength: 490 nm; emission wavelength: 525 nm) and expressed in relative fluorescence units.
The analysis of the miRNA-seq libraries was conducted using omiRas (Müller et al. 2013). After removing adapters and low-quality reads, the remaining reads were summarized as tags, and singletons were excluded from the dataset. Subsequently, the tags were mapped to the human genome (hg19) with bowtie (Li and Durbin 2009). The annotation of tags and mapping to the hg19 were performed using various databases of coding and ncRNAs retrieved from the UCSC Table Browser. ncRNAs that are not mapped to exonic regions of coding genes were quantified in each library. By following normalization of the read number for each tag to the number of mapping loci, the differential expression analysis was performed between treated samples and controls for each miRNA using the DEGseq bioconductor package (Wang et al. 2010). A clustered heatmap was generated using the heatmap package of R software (version 3.5.0). A volcano plot was created using the R package ggplot.
To identify miRNA-correlated genes, a two-step approach was used. First, three miRNA databases were used to predict potential mRNA targets of miRNAs (miRDB, TargetScan, and miRanda). Second, Pearson correlation coefficients of the intersecting miRNA–mRNA pairs were calculated. (R < ‒0.7 and p < 0.05) for the downstream analysis. The results intersected with previously published RNA-seq data (GSE71070).
GO and KEGG pathway enrichment analyses of the DEGs were performed using the DAVID bioinformatics resource (Dennis et al. 2003). We used a false discovery rate (FDR) <0.01 as screening threshold to determine the significance of the functions and pathways, and the “GO KEGG bar dot” function of the bioinformatics analysis software “SR plot” (Tang et al. 2023) was used to visualize the GO terms and KEGG enrichment.
We analyzed the miRNA-seq dataset of PC-3 cell samples treated with SAM and untreated controls by means of omiRas. We detected 17 differentially expressed mature miRNAs that satisfied the criterion of an FDR-corrected P < 0.005. The set of miRNAs included a total of seven upregulated miRNAs in the treated samples (Figure 1a), many of them with tumor suppressor function, for example, hsa-miR-4454 and hsa-miR-503. In contrast, we detected 10 downregulated miRNAs, displaying mainly oncogenic functions (Figure 1a), such as hsa-miR-429 and also hsa-miR-9-5p, which is highly expressed in prostate cancer cells under normal conditions. The volcano plot in Figure 1b displays the differential expression of upregulated and downregulated miRNAs between SAM-treated samples and controls, whereas the heatmap in Figure 1a clearly shows that the hierarchical clustering analysis performed with omiRas completely discriminated the two groups (SAM-treated and control samples) as well as those upregulated from downregulated samples. Target identification of the respective miRNAs was performed using three different miRNA databases (miRDB, TargetScan, and miRanda) to predict potential mRNA targets of miRNAs. We selected only those miRNAs that were found to be predicted in at least two of the three databases. In total, we found 2775 predicted mRNA targets for upregulated miRNAs (Figure 2a) and 5384 predicted target genes for downregulated miRNAs (Figure 2b).
Detection of differentially expressed miRNAs in PC-3 cells treated with SAM and controls. (A) Heatmap of differentially expressed miRNAs in PC-3 cells treated with SAM and untreated controls. Seventeen differentially expressed mature miRNAs (7 upregulated and 10 downregulated) were detected (P < 0.005). Clustering clearly distinguished between the treated samples and controls as well as between the upregulated and downregulated groups. (B) A volcano plot displaying the differentially expressed miRNAs. miRNAs, microRNA; SAM, S-adenosylmethionine.
Combined analyses of miRNA expression and RNA-seq data. The differentially expressed genes of the transcriptome that were matched to target genes were detected via three different miRNA databases (miRDB, TargetScan, and miRanda). (A) A total of 92 downregulated genes were associated with upregulated miRNAs, and (B) 41 upregulated genes were associated with downregulated miRNAs. The genes are shown in the boxes. (C) Gene expression levels of StarD13 following treatment with 200 μm SAM. A significant increase in the expression of StarD13 was observed (211% of the control). miRNA, microRNA; SAM, S-adenosylmethionine; StarD13, StAR-related lipid transfer domain containing 13.
Based on the results of the target identification, we concentrated especially on those potential transcript-miRNA interactions in which the differential expression of transcripts and miRNAs among the treated samples and controls exhibited the opposite trend, i.e., when transcripts were upregulated in a particular comparison, the miRNA was downregulated, and this was the opposite. Hence, 92 target transcripts were downregulated (Figure 2a), 32 of which exhibited oncogenic functions, while the expression of the mature miRNA was upregulated in the treated samples compared with the control samples. Among those downregulated transcripts, we identified, for example, transforming growth factor beta 2 (TGFB2) (Figure 2a), which is known to promote cancer progression in later stages of prostate cancer. In contrast, 41 targets were upregulated (Figure 2b) in the treated samples compared with the control samples, while the mature miRNAs were downregulated. Eleven of those upregulated genes are known to act as tumor suppressor genes. Among the upregulated genes, SERPINE1 (Figure 2b), which encodes plasminogen activator inhibitor-1 (PAI-1) and is known to be increased in prostate cancer cell lines as well as in tumor samples of patients (Kubala and De Clerck 2019), was detected. PAI-1 usually inhibits the urokinase plasminogen activator (uPA), which has been implicated in tumor cell invasion, survival, and metastasis in a variety of cancer entities, including prostate cancer (Kubala and De Clerck 2019). Since an increase in StarD13 gene expression in the transcriptome could not be detected despite the significant upregulation of hsa-miR-9-5p, we performed real-time PCR for StarD13 in SAM-treated PC-3 cells and ultimately found significant upregulation of its expression (211% of the untreated control) (Figure 2c).
To confirm the impact of SAM on the differential expression of miRNAs in PC-3 cells, we carried out quantitative PCR on PC-3 cells treated with SAM. We used primers against hsa-miR-192-5p since it is known to be overexpressed and promote cell proliferation in prostate cancer cells (Chen et al. 2019b). SAM treatment caused significant downregulation of hsa-miR-192-5p expression after 48 h and 120 h (Figure 3a). After transfection of cells with an inhibitor against hsa-miR-192-5p, the expression significantly decreased after 24 h, 48 h, and 120 h (Figure 3a). A combination of both treatments even increased the downregulation of hsa-miR-192-5p expression (Figure 3a). We also examined if the downregulation of hsa-miR-192-5p causes downregulation of proliferation. After 48 h and 120 h, the proliferation of the samples transfected with the hsa-miR-192-5p agomir significantly decreased (Figure 3b). A combination of SAM and the hsa-miR-192-5p agomir had the most significant downregulatory effect on PC-3 cell proliferation after 120 h (decrease of 77% compared with that of the untreated controls) (Figure 3b). To clarify if the results of the MTS assay are linked to a loss in cell proliferation or to cell death, a fluorogenic caspase-3 activity assay was performed, wherein we could detect a significantly induced caspase-3 activity in PC-3 cells transfected with the hsa-miR-192-5p agomir after 120 h of cell culture (Figure 3c). Again, a combination of SAM-treatment and transfection with the hsa-miR-192-5p agomir had the strongest effect after 120 h of cell culture (Figure 3c). We conclude that downregulation of hsa-miR-192-5p may induce apoptosis in PC-3 cells.
(A) Differential expression of hsa-miR-192-5p (after 24, 48, and 120 h) in untreated PC-3 cells or mock-transfected PC-3 cells. Since both groups displayed almost no differences, we described them as controls and set them to 100%. The expression of hsa-miR-192-5p was significantly downregulated following treatment with SAM (60% of the control after 48 h and 45% after 120 h), after transfection with the miRNA agomir (70% of the control after 24 h, 29% of the control after 48 h, and 19% after 120 h), or after combination treatment (68% of the control after 24 h, 13% of the control after 48 h, and 9% of the control after 120 h). (B) Transfection with the hsa-miR-192-5p agomir alone or in combination with SAM inhibited the proliferation of PC-3 cells. Twenty-four hours after PC-3 cells were transfected with the hsa-miR-192-5p agomir, and the cells were seeded in 96-well plates and grown for 24 h, 48 h, or 120 h. Proliferation was measured using the 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl tetrazolium bromide assay. Differences between untransfected PC-3 cells (PC-3) and mock-transfected PC-3 cells (miR-NC) were hardly detectable. Transfection with the hsa-miR-192-5p agomir resulted in significantly diminished proliferation of 71% (after 48 h) and 62% (after 120 h) compared to that of the controls. A combination of SAM treatment and transfection with the miRNA agomir decreased the proliferation rate of PC-3 cells even more clearly (70% of the control after 24 h, 40% of the control after 48 h, and 23% of the control after 120 h). (C) A fluorogenic caspase activity assay revealed a significant upregulation of the caspase-3 activity in PC-3 cells after transfection with hsa-miR-192-5p agomir or a combination of SAM treatment and hsa-miR-192-5p agomir transfection for 120 h. The results are expressed as the means ± SDs of three independent experiments: *P < 0.01, **P < 0.001, ***P < 0.0001, and ****P < 0.00001. miRNA, microRNA; SAM, S-adenosylmethionine.
GO term enrichment and KEGG pathway analysis of the differentially expressed genes were performed by comparing the treatment group (treated with SAM) and the control group. Among the significantly enriched GO terms for the downregulated transcripts, “cellular response for bone morphogenic protein (BMP) stimulus” (GO:0071773), “response to BMP” (GO:0071772), “regulation of BMP signaling pathway” (GO:0030510), “BMP signaling pathway” (GO:0030509), “positive regulation of epithelial cell migration” (GO:0010634), “regulation of epithelial cell migration” (GO:0010632), “regulation of cellular response to growth factor stimulus” (GO:0090287), and “negative regulation of cytoskeleton organization” (GO:0051494) were found in the BP category (Figure 4a and Table S2). In terms of molecular functions, “insulin-like growth factor I binding” (GO:0031994), “transcription coregulator activity” (GO:0003712), “fibroblast growth factor binding” (GO:0017134), and “transforming growth factor beta receptor binding” (GO:0005160) were found to be enriched significantly in the set of downregulated mRNAs (Figure 4a and Table S2); whereas, for the cellular component (CC) category, we detected enrichment in the set of downregulated transcripts for “actin-based cell projection” (GO:0098858), “actomyosin” (GO:0042641), and “cell–cell junction” (GO:0005911) (Figure 4a and Table S3). The KEGG pathway analysis revealed particular enrichment of miRNAs in cancer and the TGF-β signaling pathway (Figure 4a and Table S4). For the TGF-β signaling pathway, five downregulated transcripts, i.e., FST, SMAD6, TGFB2, INHBB, and BMPR2, were detected (Figure 4c).
GO term enrichment and KEGG pathway analysis of differentially methylated regions. Differentially expressed transcripts identified in (A) downregulated expression genes and in (B) upregulated expression genes. GO enrichment was used to identify enriched regulatory motifs, molecular functions, BP, and CC, as was KEGG pathway enrichment (C). BP, biological process; CC, cellular component; GO, gene ontology.
Considering upregulated gene expression, we detected, for example, significant enrichment for “cell–cell adhesion via plasma-membrane adhesion molecules” (GO:0098742), “cell killing” (GO:0001906), “negative regulation of cytokine production” (GO:0001818), and “positive regulation of cell killing” (GO:0031343) in the BP category (Figure 4b and Table S5); “growth factor binding” (GO:0031343), “extracellular matrix structural constituent” (GO:0005201), and “cytokine activity” (GO:0005201) in the molecular functions category (Figure 4b and Table S6); and “plasma membrane bounded cell projection cytoplasm” (GO:0032838), “cytoplasmic region” (GO:0099568), and “collagen-containing extracellular matrix” (GO:0062023) in the CC category (Figure 4b and Table S7). The KEGG pathway analysis of the upregulated transcripts revealed enrichment of pathways involved in “cell adhesion molecules” and “cytokine–cytokine receptor interaction” (Figure 4b and Table S8).
It was suggested that AdoMet could be involved in the regulation of ncRNAs (Mosca et al. 2021; Pagano et al. 2020), e.g., a recently published study linked the treatment of cancer cells with SAM or methyladenosine to the combat of metastasis by targeting specific miRNAs (Tomasi et al. 2017). In the present publication, the differential expression of miRNAs in prostate cancer cells following treatment with SAM was investigated for the first time. Treatment of prostate cancer cells (PC-3 cells) with AdoMet resulted in upregulation and downregulation of miRNAs. Prediction of target genes and alignment with a recently performed transcriptome study revealed 92 downregulated and 41 upregulated genes. Thirty-one of the downregulated genes were identified as proto-oncogenes, and 11 of the upregulated transcripts were identified as tumor suppressor genes. Among the differentially expressed miRNAs, hsa-miR-9-5p was detected; under normal conditions, hsa-miR-9-5p is highly expressed in prostate cancer cells and targets StAR-related lipid transfer domain containing 13 (StarD13), thus leading to an increase in the expression of vimentin and N-cadherin, which are the two key factors involved in epithelial–mesenchymal transformation (Chen et al. 2019a). In conjunction with this, a decrease in the expression of StarD13 is usually tightly associated with an increase in the viability and invasion and migration potential of prostate cancer cells (Chen et al. 2019a). In the present study, hsa-miR-9-5p was clearly downregulated following SAM treatment; however, an increase in the expression of StarD13 could not be detected in the associated transcriptome. Real-time quantitative PCR revealed significant upregulation of StarD13, suggesting the presence of a functional system. Additionally, hsa-miR-192-5p, which is usually overexpressed and known to promote cell proliferation in prostate cancer, was found to be downregulated in treated samples (Chen et al. 2019b). One of the predicted targets of hsa-miR-192-5p is SERPINE1 (urokinase PAI-1), which was simultaneously upregulated in the transcriptome of SAM-treated samples in this study. SERPINE1 may generally be considered a prognostic factor for disease progression and relapse in several cancer types (Kubala and De Clerck 2019). However, in prostate cancer, the upregulation of SERPINE1 seems to be favorable since the uPA/PAI-1 ratio and urokinase-type plasminogen activator receptor (uPAR) were found to be higher in prostate carcinoma samples than in benign prostatic hyperplasia samples. Generally, uPAR binds the proactive form of uPA, which subsequently cleaves plasminogen into plasmin, converting growth factors and matrix metalloproteinases to their active forms, leading to the degradation of components of the extracellular matrix (ECM) and the basement membrane. In this way, metastasis could be promoted (Kubala and De Clerck 2019). PAI-1 inhibits the catalytic activity of uPA and plasmin, preventing the degradation of ECM and basement membrane constituents, and thus may impede metastasis (Kubala and De Clerck 2019). Additionally, uPAR can be cleaved between catalytic domains, allowing it to interact with G protein-coupled receptors and activating different signaling pathways involved in cancer progression (Kubala and De Clerck 2019). To obtain additional information about the effects of hsa-miR-192-5p on prostate cancer cells (PC-3 cells), a knockdown of the miRNA was performed by the transfection with the respective agomir, followed by MTS assays, which revealed a significant decrease of viable cancer cells, clearly indicating its anticancer effects. To clarify, if this decrease was linked to a loss in cell proliferation or the upregulation of apoptosis, we performed fluorometric caspase-3 assays that showed a significant increase of the caspase-3 activity in transfected cells. Recently, it was reported that the downregulation of miRNA-888-5p induced apoptosis in laryngeal squamous cancer cells (Pagano et al. 2020). Upregulated miRNAs, e.g., hsa-miR-503, which is known to suppress tumor cell proliferation and metastasis in prostate cancer cells (Hu et al. 2023), were also found in this study.
To further clarify the biological impact of the predicted target genes aligned to the previously conducted transcriptome, GO term enrichment and KEGG pathway analyses were performed for the 133 upregulated and downregulated genes. In the BP category, downregulated genes associated with cellular responses to BMP stimulus were detected. BMP ligand dimers bind to type I and type II serine/threonine receptor monomers, resulting in the formation of a heterotetrameric kinase complex. Subsequently, the active type I kinase, in turn, activates the receptor-mediated Smad protein via phosphorylation followed by translocation of the complex to the nucleus, where it affects the transcription of BMP target genes involved in proliferation (Provera et al. 2023). Further significantly downregulated BP terms are involved, for example, in the control of epithelial cell migration and cellular response to growth factor stimulus, all of which are crucial features for cancer initiation, progression, and metastasis (Grant and Kyprianou 2013; Joshi et al. 2015). The KEGG pathway analysis revealed that downregulation of the TGF-β pathway, which is well known to act in tumor suppression in early-stage prostate cancer cells and in tumor promotion in later stages of the disease (Thompson-Elliott et al. 2021), occurred. The turning point is likely the development of resistance to the inhibitory effects of TGF-β signaling on cell proliferation (Thompson-Elliott et al. 2021). The cell line we used (PC-3) represents a model for later stages of prostate cancer; therefore, downregulation of the TGF-β pathway seems to be favorable in this respect. Among the downregulated members of the pathway, we found, e.g., SMAD6, leading to a decrease in SMAD2/SMAD3 activity, which is usually necessary for the regulation of target genes in the nucleus (among others involved in proliferation, migration, and invasion) (Thompson-Elliott et al. 2021). As expected, the KEGG pathway analysis also revealed the downregulation of miRNAs in cancer. Upregulated gene targets corresponding to downregulated miRNAs in the BP category were involved in the positive regulation of cell killing and the negative production of cytokine factors, which, under normal conditions, may contribute to the proliferation of cancer cells (Joshi et al. 2015; Pejčić et al. 2023). In the molecular function category, upregulation of ECM constituents was associated with upregulation of collagen containing ECM in the CC category. This seems to be important since the integrity of the ECM, as mentioned above, is crucial for the origination of cancer and metastasis (Luthold et al. 2022).
A clear limitation of the study is the lack of information concerning the mechanisms by which AdoMet may affect the regulation of differentially expressed miRNAs in PC-3 cells, for which alterations in promoter or histone methylation may be the cause (Schmidt et al. 2016; Mathes et al. 2024). However, in previous studies focused on promoter and histone methylation (H3K4me3/H3K27me3), we could not detect any of the differentially expressed miRNAs found in this study (Schmidt et al. 2016; Mathes et al. 2024). Extending the experiments to further histone marks and the analysis of different genomic elements other than promoter regions may be helpful in the future (Del Valle-Morales et al. 2022). Recently, it was reported that differentially methylated intronic regions could be involved in the expression of intragenic miRNAs (Del Valle-Morales et al. 2022). Moreover, during the last decade, it became obvious that N6-methyladenosine (m6A) methylation, one of the most common RNA modifications, may regulate the generation and degradation of ncRNAs, thus being especially crucial for cancer initiation and progression (Mosca et al. 2021). These points should be considered in future studies.
In conclusion, treatment with SAM leads to differential expression of miRNAs in castration-resistant prostate cancer cells and subsequent gene-to-peak annotation alignment with the results of a transcriptome study as well as GO term analysis and knockdown experiments revealed the biological relevance of the SAM.