SRSF9 promotes colorectal cancer progression via stabilizing DSN1 mRNA in an m6A-related manner

Sixty-three paraffin-embedded human CRC tissues and corresponding normal mucosal tissues and sixteen fresh human CRC tissues and matched normal mucosal tissues were collected for this study. All the samples were obtained from the Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China (from March 2018 to June 2019). All samples were obtained from patients who had been diagnosed with primary CRC by two pathologists based on microscopic analysis of tumor tissue, and none of the patients received radiotherapy or chemotherapy prior to surgical resection. Sixty-three cases, including 38 (60.3%) males and 25 (39.7%) females, were recruited for this study. The use of human materials was approved by the Medical Ethics Committee of Nanfang Hospital, Southern Medical University. Seven human CRC cell lines (HCT8, SW620, Caco2, HT29, HCT15, HCT116, and LOVO) and an immortal human intestinal epithelial cell line (NCM460) were donated by Guangdong Province Key Laboratory of Molecular Tumor Pathology, Southern Medical University and validated by short tandem repeat sequence analysis prior to the start of this project and cultured in Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS, Gibco). All cells were maintained in a cell incubator under 5% CO₂ at 37 °C.

IHC was performed according to standard protocols as previously described [17]. After deparaffinization and rehydration, 4-μm-thick tissue slides were blocked with 3% H₂O₂ and boiled in ethylenediaminetetraacetic acid (EDTA) (0.01 M, pH 8.0) for antigen retrieval. After blocking with 10% normal goat serum PBS, the slides were incubated with primary antibody at 4 °C, followed by treatment with secondary antibody and diaminobenzidine (DAB). Information on the primary and secondary antibodies used in this study is provided in Additional file 1: Table S1. The staining intensity of each tissue section was determined by two experienced pathologists in a double-blind manner. We referred to published standards to define “low” and “high” expression of SRSF9 [17]. Tumor cells were graded according to the following criteria: ① Staining intensity score: 0, no staining; 1, poor staining; 2, moderate staining; and 3, strong staining; ② Positive staining score: 0, < 10% positive; 1, 10–30% positive; 2, 30–50% positive; and 3, > 50% positive. The total score was calculated as ① Staining intensity score × ② Positive staining score. Samples with total scores ≥ 5 were defined as showing high expression; those with scores < 5 were defined as showing low expression.

Total RNA was extracted from human CRC cells and tissues, and mRNA was reverse-transcribed into complementary DNA using the PrimeScript™ RT reagent kit (TaKaRa, Shiga, Japan) in accordance with the manufacturer’s recommendations. qRT-PCR was performed in a 7500 Fast Real-Time PCR System with SYBR Green qRT-PCR master mix (TaKaRa). Information on the primer sequences used in this study is provided in Additional file 2: Table S2. Each experiment was performed in triplicate, and expression of all genes was normalized to that of β-actin.

Protein samples were separated by electrophoresis on 10% sodium dodecyl sulfate gels and transferred to polyvinylidene fluoride membranes (Millipore, MA, USA). The membranes were then blocked in 5% skim milk for 1 h at room temperature, followed by incubation with primary antibodies for 8 h at 4 °C. The membranes were incubated with HRP-conjugated secondary antibodies for 1 h at room temperature. Finally, the signal density was detected by chemiluminescence using an ECL reagent kit (FDbio Science, Hangzhou, China). Information on the primary and secondary antibodies used in this study is provided in Additional file 1: Table S1.

For transient transfection, small interfering RNAs (siRNAs) directed against SRSF9 (#SIGS0008682-4, RIBOBIO, Guangzhou, China), DSN1 (#SIGS0013609-1, RIBOBIO), METTL3 (#SIGS00056532-1, RIBOBIO) and negative control RNAs (si-ctrl) were synthesized by RIBOBIO Company (Guangzhou, China). Transient transfection was performed using a Hieff Trans™ Liposomal Transfection Reagent kit (#40802, Yeasen, Shanghai, China) in accordance with the standard protocol. Cells were collected after 24 h for qRT-PCR and after 48 h for Western blotting and functional studies.

For lentiviral transfection, Flag-SRSF9 (ov-SRSF9), empty vector (Vector), shSRSF9 (sh1, sh2), and shNC were purchased from GeneChem Company (Shanghai, China). Caco2 cells and HT29 cells were used to establish stable SRSF9 overexpression models, and HCT116 cells and LOVO cells were used in the stable SRSF9 knockdown experiments. According to the manufacturer’s instructions, 4 × 10⁴ cells per well were seeded and transfected with the indicated lentiviruses. The infected cells were screened using 5 μg/mL puromycin (Solarbio, Beijing, China) for 1 week or longer, and transfection efficiency was determined by qRT-PCR and Western blotting analysis.

For CCK-8 assays, treated cells were placed in 96-well plates at 1 × 10³ cells/well in quintuplicate and cultured in RPMI-1640 medium containing 10% FBS for 4 h, 8 h, 16 h, 32 h, and 64 h at 37 °C in 5% CO₂. CCK-8 detection was performed using a Cell Counting Kit-8 (CCK-8, Yeasen) according to the manufacturer’s instructions. The cells were cultured in the presence of 100 μL of CCK-8 (1:10 dilution) for 2 h at 37 °C, and the optical densities (ODs) at 450 nm of the cultures were then measured. Each sample was analyzed in triplicate.

For the colony-forming assay, treated cells were seeded in 6-well plates at 200 cells/well and incubated in RPMI-1640 medium containing 10% FBS for 2 weeks at 37 °C in 5% CO₂. The cells were then washed twice with PBS and fixed in 4% paraformaldehyde (Leagene, Beijing, China) for 20 min at 4 °C. Staining with 0.1% crystal violet (Sigma, St. Louis, MO, USA) was then performed within 20 min. The stained cells were counted using a scanner (Bio-Rad, Hercules, CA, USA). Each sample was analyzed in triplicate.

Transwell chambers (Corning, NY, USA) were used to observe cell migration and invasion. For migration, cells were collected and resuspended in serum-free medium. A total of 200 µL of cell suspension at 5 × 10⁵ cells/mL was placed in the upper chamber, and 600 µL of RPMI-1640 medium containing 10% FBS was added to the lower chamber. For invasion, the upper chamber was covered with 2 mg/mL Matrigel (Corning) prior to cell seeding. After allowing time for migration and invasion, the cells were fixed in 4% paraformaldehyde (Leagene) for 20 min at 4 °C and stained with 0.1% crystal violet (Sigma) for 30 min. The cells in three randomly selected visual fields of each sample were photographed and counted under a light microscope at 200× magnification. Each sample was analyzed in triplicate.

Wound-healing assay was also performed to observe cell migration. Monolayers of cells were uniformly scratched using the narrow edge of a 10-μL pipette tip and cultured in serum-free RPMI-1640 medium. At 24 h and 48 h thereafter, the extent of healing of the scratches was observed under a light microscope at 200× magnification. Each sample was analyzed in triplicate.

Treated cells were washed three times with PBS and fixed in 70% cold ethanol overnight at 4 °C. The cells were then washed again, centrifuged, suspended and stained with 50 μg/mL propidium iodide and 1 mg/mL RNase in PBS. Cell cycle analysis was performed using a flow cytometer (BD Biosciences, NJ, USA) and analyzed by ModFit software (BD Biosciences). Each sample was analyzed in triplicate.

Animal experiments were approved by the Use Committee for Animal Care and performed in accordance with institutional ethical guidelines for animal experiments. Stable Caco2 and HCT116 cells (5 × 10⁶ cells/injection) were resuspended in 200 μL of PBS and subcutaneously injected into 4-week-old female athymic BALB/c nude mice purchased from the Guangdong Medical Laboratory Animal Center. The resulting tumors were measured every 3–5 days, and their size was calculated according to the formula: volume = 1/2 * (width² × length). After 4 weeks, the mice were sacrificed, and the xenograft tumors were excised, photographed, and fixed in formalin for histological analysis.

Downstream gene data for the RNA-binding protein SRSF9 were obtained from the web tool POSTAR2 (http://​lulab.​life.​tsinghua.​edu.​cn/​postar), which is a comprehensive database for exploring posttranscriptional regulation based on high-throughput sequencing data and provides information on a large number of binding sites for RNA-binding proteins [18]. Data on the differentially expressed genes (DEGs) between 473 human CRC tissue sample data sets and 41 normal tissue sample data sets downloaded from The Cancer Genome Atlas (TCGA) database (https://​www.​cancer.​gov/​about-nci/​organization/​ccg/​research/​structural-genomics/​tcga) were analyzed using the edge R package [19] of R software (Vienna, Austria). The DEGs between 17 human CRC tissue sample data sets and 17 normal tissue sample data sets obtained from the Gene Expression Omnibus (GEO) database (https://​www.​ncbi.​nlm.​nih.​gov/​geo/​) (GSE32323) were analyzed by the limma package [20] of R software. Software default settings were utilized in the analyses. Downstream genes of SRSF9 identified in the POSTAR2 database and the DEGs from TCGA and GEO were screened using Venn diagrams to identify the shared genes. Potential downstream target genes had to meet the following requirements: (1) their mRNAs were binding targets of the human RNA-binding protein SRSF9; and (2) the differences in their expression between human CRC tissue samples and normal tissue samples had P values < 0.05 by statistical analysis and log₂(fold change) > 1.

The association of SRSF9 and DSN1 expression with overall survival was analyzed using the web tool GenomicScape (http://​www.​genomicscape.​com/​microarray/​survival.​php), which was established based on data obtained from GEO [21, 22]. Fifty-five CRC individuals in the GEO database (GSE17538) were sorted according to the expression of SRSF9 and DSN1. All cases from GenomicScape were provided to the algorithm for survival analysis. Kaplan–Meier survival plots with hazard ratios (HRs) and log-rank P values were obtained using the webpage. P values < 0.05 were considered to indicate significant differences.

Single-stranded RNA oligonucleotide probes containing the m6A-binding consensus sequence GGACU with methylated (ss-m6A, 5′-biotin-CGUCUCGG(m6A) CUCGG(m6A)CUGCU-3′) or unmethylated (ss-A, 5′-biotin-CGUCUCGGACUC GGACUGCU-3′) adenosine were synthesized by RIBOBIO Company (Guangzhou, China) and validated by mass spectrometry. According to the standardized procedure used with the RNA pull-down kit (gzscbio, Guangzhou, China), each single-stranded RNA oligonucleotide probe was immobilized on streptavidin magnetic beads and coincubated with total proteins extracted from LOVO cells for 8 h at 4 °C. After two washes, the proteins combined with the single-stranded RNA oligonucleotide probe (methylated or unmethylated) were separated by electrophoresis on 10% sodium dodecyl sulfate gels and detected by silver staining and immunoblotting analysis.

Proteins that had been separated on gels were stained using a Protein Fast Silver Stain Kit (BBproExtra, Guangzhou, China) according to the manufacturer’s recommendations, and the silver signal density was analyzed using a scanner (Bio-Rad).

Total RNA was isolated from LOVO cells. According to the standard operating protocol for the EpiQuik™ CUT&RUN m6A RNA Enrichment Kit (Epigentek, NY, USA), RNA samples were fragmented and incubated with anti-m6A antibody (#A-1801, Epigentek) for 90 min at room temperature. A nonimmune IgG was used as a negative control. RT-qPCR assays with DSN1 primers were performed to quantify the enrichment of m6A-containing RNA. Information on the primer sequences used in this study is summarized in Additional file 2: Table S2. Each experiment was performed in triplicate, and all samples were normalized to β-actin.

cDNAs containing the SRSF9-binding region of DSN1 mRNA sequences (chr20:36773597–36773736) were cloned into a GV361 control reporter plasmid consisting of firefly luciferase (F-luc) and verified by DNA sequencing (GeneChem, Shanghai, China). In the mutant reporter plasmid, adenosine (A) on the m6A motif was replaced by thymine (T). The GV219 vector (GeneChem) formed the backbone of the SRSF9 expression plasmid, and DNA sequencing was performed for verification. 293T cells were seeded into 24-well plates followed by cotransfection with 500 ng of DSN1 luciferase reporter plasmid (GV361-DSN1-WT and GV361-DSN1-MUT) and 0 µg, 0.25 µg, or 0.5 µg of SRSF9 expression plasmid (GV219-SRSF9) using a Dual-Luciferase Assay kit (Promega, WI, USA). After 48 h, the cells were harvested, and luciferase activity was measured according to the recommended protocols. Each group was assayed in triplicate.

Stable LOVO cells and stable Caco2 cells were incubated with 5 μg/mL actinomycin D (APExBIO, TX, USA) for 0 h, 2 h, 4 h, 6 h, or 8 h, and RNA was then extracted from the cells. Analysis of the half-life of DSN1 mRNA was performed using qRT-PCR as described earlier [9].

The results are presented as the mean ± SD. Student’s t test (two-tailed) and one-way or two-way ANOVA were used to evaluate quantitative data. The chi-squared test was used to analyze qualitative data. The Wilcoxon test was used to analyze rank data. The log-rank test was used to assess significant differences in overall survival. All of the analyses were performed in SPSS 24.0 (SPSS, Inc., IL, USA) and GraphPad Prism 6.0 (GraphPad, Inc., CA, USA). The level of statistical significance was defined as P < 0.05 (*P < 0.05; *P < 0.01; ***P < 0.001; ****P < 0.0001; NS: not significant).

We first measured the expression of SRSF9 in human CRC cells. Our results showed that SRSF9 expression was increased at both the mRNA (Fig. 1A) and protein levels (Fig. 1B) in CRC cells compared to NCM460 cells. We then analyzed the expression of SRSF9 in CRC as reported in TCGA. The results showed that SRSF9 expression was noticeably upregulated in tumor tissues compared with normal tissues (P = 3.491e−13) (Fig. 1C). Furthermore, we analyzed the association between SRSF9 expression and clinicopathological features in 63 CRC patients. IHC assays indicated that SRSF9 protein was mainly localized in the nucleus (Fig. 1D), and the immunoreactivity score revealed that the expression of SRSF9 protein was higher in CRC tissues than in matched normal mucosa tissues (P < 0.001) (Fig. 1E). Overexpression of SRSF9 was significantly associated with tumor lymph node metastasis and high Dukes stage (P < 0.05; Table 1). However, no relationship between SRSF9 expression and other clinicopathological parameters such as sex, age and differentiation grade was found (P > 0.05; Table 1). High SRSF9 expression was more frequently observed in Dukes stage C–D tumors and tumors accompanied by lymph node metastasis than in Dukes stage A–B tumors and tumors not accompanied by lymph node metastasis (Fig. 1F, G). Survival analysis showed that CRC patients (GSE17538) with higher SRSF9 expression exhibited significantly poorer overall survival (OS) (P = 0.0052) and had higher risk of death (HR = 3.3) (Fig. 1H), indicating that CRC patients with elevated expression of SRSF9 generally had poor prognoses. Taken together, our data suggest that SRSF9 expression is elevated in CRC and that it is strongly associated with lymph node metastasis and high Dukes stage. High SRSF9 expression suggests poor prognosis and might be used as an indicator of CRC progression.

Overexpression and knockdown of SRSF9 were conducted in Caco2/HT29 and LOVO/HCT-116 cells, respectively (Figs. 2A, B and 3A, B). The results of CCK-8 assays (Fig. 2C) and clone formation assays (Fig. 2D) showed that the proliferation ability of CRC cells was increased in the SRSF9-overexpression group compared with the control group, indicating that SRSF9 contributes to the proliferation ability of CRC cells. Flow cytometry showed that compared to that in the control group, the proportion of cells in G0/G1 phase decreased significantly and the proportion of cells in S phase increased in the SRSF9-overexpression group in both cell lines (Fig. 2E), indicating that SRSF9 promotes CRC cell cycle progression. In addition, transwell assays and wound healing assays demonstrated that the migration capability of Caco2 and HT29 cells in the SRSF9-overexpression group was markedly increased (Fig. 2F, H, I), suggesting that SRSF9 enhances the migration ability of CRC cells. Alterations in invasion capability, a characteristic that is related to migration ability, were observed in Caco2 cells but not in HT29 cells (Fig. 2G). This result is probably due to the distinct genetic context that mediates the function of SRSF9 during invasion. Furthermore, as shown in Fig. 2J–L, xenograft tumor burdens were significantly elevated in mice that received subcutaneously transplanted SRSF9-overexpressing Caco2 cells compared to the control group, suggesting that SRSF9 performs a tumor-promoting function in CRC. Taken together, the results of the above gain-of-function assays indicate that overexpression of SRSF9 enhances the growth, migration, and invasion ability of CRC cells in vitro and in vivo.

We also performed loss-of-function assays. The results showed that the growth of LOVO cells and HCT-116 cells in the SRSF9 knockdown groups was noticeably impaired compared with that in the control groups, according to the results of CCK-8 assays (Fig. 3C) and clone formation assays (Fig. 3D). This finding implies that SRSF9 deficiency inhibits CRC cell proliferation. Flow cytometry showed that cell cycle arrest at S phase was induced by SRSF9 knockdown in LOVO and HCT-116 cells (Fig. 3E), suggesting that silencing of SRSF9 can arrest the cell cycle of CRC cells. Cell migration and invasion assays demonstrated that the migration and invasion abilities of LOVO and HCT-116 cells in the SRSF9-inhibited groups were reduced compared to those of the cells in the control groups (Fig. 3F–I), indicating that SRSF9 is necessary for cell migration and invasion in CRC. In addition, tumor xenograft models were constructed by injecting HCT-116 cells with stable knockdown (shSRSF9 sh1, sh2) of SRSF9 into nude mice (Fig. 3J). We found that SRSF9 depletion repressed tumorigenesis (Fig. 3K) and resulted in tumor volumes that were noticeably lower than those in the negative control group (Fig. 3L), suggesting that SRSF9 exerts its oncogenic role in CRC by regulating cell growth in vivo. All the results of the above loss-of-function assays imply that knockdown of SRSF9 represses the proliferation and migration and reduces the invasion capability of CRC cells in vitro and in vivo.

To understand the mechanism through which SRSF9 exerts its tumor-promoting effects in CRC, we mined POSTAR2 data, GEO data and TCGA data to screen for potential targets of SRSF9 in CRC. The DEGs of CRC tissues and normal tissues obtained from GEO and TCGA are shown in Fig. 4A, B. On systematic screening of the POSTAR2 data, a total of 1021 target mRNAs of the SRSF9 protein were identified (Additional file 3: Table S3). Of these a, 23 were found in both the POSTAR2 data and TCGA data (Additional file 4: Table S4), and 22 were found in both the POSTAR2 data and GEO data (GSE32323) (Additional file 5: Table S5). All these genes are promising targets for SRSF9; we selected DSN1, which is shared by all three profiles, for further study (Fig. 4C).

We first analyzed the expression of DSN1 in CRC according to the TCGA data. The results showed that DSN1 expression was significantly upregulated in CRC tissues compared with normal tissues (P = 2.303e−21) (Fig. 4D). We then performed qRT-PCR and Western blot assays to measure the expression of DSN1 in CRC cell lines and human clinical CRC fresh tissues. The results showed that the level of DSN1 mRNA was upregulated in most CRC cell lines compared with that in NCM460 cell lines (Fig. 4E). Our data for 16 CRC clinical tissue samples also showed that the mRNA (Fig. 4F) and protein (Fig. 4G) levels of DSN1 were noticeably elevated in tumor tissues. Moreover, we performed survival analyses and found that CRC patients with higher DSN1 expression exhibited significantly lower overall survival (P = 0.049) and higher risk of death (HR = 2.7) (Fig. 4H). Thus, the expression of DSN1 is upregulated in CRC patients with a poor prognosis.

The GEPIA database [23] (http://​gepia.​cancer-pku.​cn/​) was also used to explore the correlation of the expression of SRSF9 and DSN1 in CRC tissues according to the TCGA data. There was a marked positive correlation between the expression levels of SRSF9 and DSN1 in CRC tissues (R = 0.67, P < 0.001) (Fig. 4I). DSN1 was upregulated in SRSF9-overexpressing CRC cell lines (Fig. 4J, K) but downregulated in SRSF9-inhibited cell lines (Fig. 4L, M) in vitro and in vivo (Fig. 4N, O). In clinical CRC fresh tissue samples, the expression levels of DSN1 and SRSF9 were also positively correlated (Fig. 4P–R). Collectively, our results indicate that there is a positive relationship between the expression of SRSF9 and that of DSN1 in CRC and that alteration of SRSF9 expression induces corresponding changes in DSN1 expression, suggesting that DSN1 is a downstream gene of SRSF9.

We interfered with DSN1 expression in CRC cells with stable overexpression of SRSF9, which was validated by Western blotting assays before functional experiments were performed (Fig. 5A, B). Notably, SRSF9 overexpression promoted cell growth, colony formation and cell cycle progression in both Caco2 and HT29 cells, whereas knockdown of DSN1 impaired this effect (Fig. 5C–F). DSN1 knockdown also suppressed SRSF9 overexpression-induced cell migration and invasion by CRC cells (Fig. 5G–J). These results suggest that DSN1 is a critical downstream target of SRSF9 that facilitates CRC progression.

To identify m6A-binding proteins, we initially used methylated single-stranded RNA bait (ss-m6A) with the consensus sequence GG(m6A)CU or unmethylated control RNA (ss-A) for RNA pull-down to enrich m6A-binding proteins in LOVO cells, followed by immunoblotting and silver staining assays to identify SRSF9 among the m6A-binding proteins. The results showed that the SRSF9 protein binds selectively to the methylated bait (ss-m6A) with an affinity four to fivefold higher than that with which it binds to the unmethylated control (ss-A) (Fig. 6A), suggesting that the SRSF9 protein is an m6A-binding protein. To search for m6A-containing RNA in cells, we performed gene-specific m6A qRT-PCR in LOVO cells using DSN1 mRNA primers. The results showed a significant enrichment of m6A-modified DSN1 mRNA in cellulo (Fig. 6B), indicating that DSN1 mRNA is an m6A-modified transcript.

To explore whether the relationship between SRSF9 and DSN1 is related to m6A modification and recognition, we analyzed the distribution of m6A-enriched regions of DSN1 mRNA across the eCLIP data in POSTAR2. The results showed that DSN1 mRNA is the binding target gene of SRSF9 protein (Fig. 6C upper) and the specific region of DSN1 mRNA to which SRSF9 protein binds contains m6A modifications at two sites (Fig. 6C below). Therefore, we postulated that the association of SRSF9 with DSN1 is correlated with m6A modification. According to the sequence of the SRSF9-DSN1-binding region, dual-luciferase reporter assays with wild-type (WT) and mutant (MUT) plasmids were then conducted. For the MUT reporter, two adenosine bases (A) in the m6A consensus sequences were replaced by thymine bases (T) to eliminate the effect of m6A motifs, while the WT reporter contained the intact m6A sites (Fig. 6D). The results showed that ectopic SRSF9 induced a significant increase in the luciferase activity of the WT reporter (Fig. 6E). Such increases were largely impaired by mutations in the m6A consensus sites (Fig. 6E). Moreover, ectopic SRSF9 induced these increases in a dose-dependent manner (Fig. 6F). Our results demonstrate that the binding of SRSF9 protein and DSN1 mRNA relies on the presence of wide-type m6A motifs within SRSF9-DSN1-binding region. Moreover, TCGA data in GEPIA database showed that there was a marked positive correlation between the expression levels of SRSF9 and methyltransferase METTL3 (R = 0.7, P < 0.001) (Fig. 6G), as well as DSN1 and METTL3 (R = 0.56, P < 0.001) (Fig. 6H) in CRC tissues. The level of DSN1 expression was up-regulated in SRSF9-overexpressing CRC cell lines (Fig. 6I, J). Interference with METTL3 expression in SRSF9-overexpressing cells significantly down-regulated the expression of DSN1 while the level of SRSF9 expression was unchanged (Fig. 6I, J), suggesting that both SRSF9 and METTL3 regulate the expression of DSN1. Given that METTL3 is a typical m6A methyltransferase (termed “writer”) [9], our data indicated that DSN1 is the downstream target of SRSF9 and the regulation of DSN1 by SRSF9 is likely to be related to its RNA m6A modification. Furthermore, we performed mRNA stability assays using SRSF9-knockdown cells (Fig. 6K), SRSF9-overexpressing cells (Fig. 6M) and SRSF9-overexpressing with METTL3 interfered cells (Fig. 6O). Our results showed that the turnover of DSN1 mRNA was markedly accelerated in SRSF9-inhibited cell lines (Fig. 6L), while the half-life of DSN1 mRNA was markedly prolonged in SRSF9-overexpressing cell lines (Fig. 6N) and such stabilizing was weakened upon m6A writer knockdown (Fig. 6P), indicating that SRSF9 promotes the stability of DSN1 mRNA and such regulation is correlated with its RNA m6A modification in CRC cells.

Taken together, our results suggest that SRSF9 acts as an m6A-binding protein by recognizing and binding the m6A modification sites of DSN1 transcripts to enhance the stability of DSN1 mRNA in CRC cells.